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and the resulting need for a drastic increase in

human collective intelligence.


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Human-Level Artificial Intelligence --- And Its Consequences --- Are Near: Why Ai Will Be Created In Roughly A Decade & What That Means

The Time For Powerful Artificial Intelligence Is Rapidly Approaching

The Hardware

The Software

Will It Work?

The Consequences

How Long Till Human-Level Ai? What Do The Experts Say?

More Progress On Generalized Ai Techniques

Other Voices Predicting Ai By 2020

DARPA's Deep Learning Program Could Advance Agi

DARPA s Mind s Eye Project Likely To Advance Ai

DARPA Ipto Projects Likely To Advance Agi

DARPA s 2 Liter, 1kw, 10^14 SyNAPSE Agi Brain



Collective Intelligence --- Our Only Hope For Surviving The Singularity

What Is Collective Intelligence?

Increasing Collective Intelligence *Before* The Singularity Takes Off

Increasing Collective Intelligence *After* The Singularity Takes Off

Some Of The Issues Collective Superintelligence Might Help Us Solve

-1- Once Computers And Robots Can Do Almost Everything Better Than Humans, For Pennies An Hour: --- How Will Most Humans Earn A Living --- What Meaningful Work Will They Have --- And --- How Should Power And Wealth Be Distributed?

-2- What Degree Of Individualism, Privacy, And Competition Should We Have In An Age When We Are All Networked Together --- Ultimately Through Brain Implants.

-3- How Do We Keep Humans In The Loop --- And --- Prevent Superintelligences From Harming Us.

-4- How Much Should We Change Our Values, Bodies, Minds, And Human Nature --- How Much Should We Merge With Machines --- And --- What Aspects Of Humanity Are Most Important To Preserve.

Controlling Agi s By The Collective Will Of Humanity:Goertzel s Theory Of Coherent Aggregated Volition

A Response To Goertzel s Cosmist Manifesto









HUMAN-LEVEL ARTIFICIAL INTELLIGENCE --- AND ITS CONSEQUENCES --- ARE NEAR: Why AI will be created in roughly a decade & what that means






There is a good chance human-level AI will be created within five to fifteen years --- and, almost certainly, within twenty-five.


In ten years, for example, a machine costing one million dollars may well be able to: --- write reliable, complex code faster than a hundred human programmers --- remember every word and concept in a world-class law library, and reason from them hundreds of times faster than a human lawyer --- or ---- contribute more rapidly to the advancement of mathematical physics than all of humanity combined.


A cloud of such systems could represent all the knowledge recorded in books and on the web --- stored in a highly indexed, inter-mapped, semantic deep structure that would allow extremely rapid reasoning from it. Such a cloud would have the power to rapidly search, match, infer, synthesize, and create --- using that world of data --- so as to provide humanity with a font of knowledge, reasoning, technology, and creativity few can currently imagine.


YOU SHOULD BE SKEPTICAL. The AI field has been littered with false promises. But for each of history's long-sought, but long-delayed technical breakthroughs, there has always come a time when that breakthrough --- finally --- DID happen. There are strong reasons to believe that --- for powerful machine intelligence --- that time is fast approaching.


What is the evidence?


It has two major threads.


First, within five to ten years, we are projected, for the first time, to have hardware with the computational power to roughly support human-level intelligence. Within that time, the price for such hardware could be as low as three million dollars, down, by the end, to, perhaps, as little as one hundred thousand. These prices are low enough that virtually every medium to large-size business, educational, and governmental organization would be able to afford them.


Second, due to advances in brain science and in AI, itself, there are starting to be people who have developed reasonable and relatively detailed architectures for how to use such powerful hardware to create near-human and, ultimately, super-human artificial intelligence.






To do computations of the type at which we humans currently out perform computers, you need something within at least several orders of magnitude of the capacity of the human brain, itself. You need such capacity in each of at least four dimensions. These include representational capacity, computational capacity, processor-to-memory bandwidth, and processor-to-processor bandwidth. You can't have the common sense, intuition, natural language capabilities, and context appropriateness of human thought --- unless you can represent, rapidly search, infer between, and make generalizations from, vast portions of human-level world knowledge --- where --- "world knowledge" is the name given to the extremely large body of experientially derived visual, auditory, olfactory, tactile, kinesthetic, emotional, linguistic, semantic, goal-oriented, and behavioral knowledge that most humans have.


Most past AI work has been done on machines that have less than one one millionth the capacity of the human brain in one or more of these four dimensions. That is like trying to do what the human brain does with a brain the size of a spider s. Even many current supercomputers, that cost tens of millions of dollars, have processor-to-processor bandwidths that are three or more orders of magnitude smaller than that of the human brain.


No wonder so many prior attempts at human-level AI have hit a brick wall. Nor is it any surprise that most of the AI establishment does not understand the importance of the correct ---roughly brain-level-hardware --- approach to AI. Such an approach has been impossible to properly study, and experiment with, at prior hardware costs and funding levels --- and, thus, has been impossible to use for advancing one s career in the AI field, or for raising venture capital.


But starting in three to five years it should be possible to make hardware that is much more suited for roughly human-like computing.


Moore s Law is likely to keep going for some time. 22nm node prototypes have already been built. Intel claims it is confident it can take CMOS two generations further, to the 11nm node, by mid to late this decade. But, perhaps even more important, there has been a growing trend toward more AI-capable hardware architectures, and, in particular, toward addressing the bandwidth deficiencies of current computing systems.


This is indicated by the trend toward putting more processor cores, with high speed interconnect, on a chip. Tilera has recently demonstrated a 100 core processor with extremely high internal bandwidth. IBM and Intel both have R&D chips with roughly 64 to 80 mesh-networked processors, and they both plan to provide high bandwidth connections between such processors and memory placed on multiple semiconductor layers above or below them. High bandwidth to such memory will be provided by massive numbers of through-silicon metal vias connected between layers. Intel has said it hoped to have such multi-core, multi-layer modules on the market by 2012. And one of its researchers has said inferencing is one of the major tasks that could make such hardware commercially valuable.


Photonics will enable hundreds of gigabits per second to be communicated on photolithographically produced waveguides at relatively low energy and thermal costs. This, and the through-silicon vias, will substantially break the processor-to-RAM and processor-to-processor bandwidth bottlenecks that are currently the major barriers preventing current clustered systems from being used efficiently for human-like reasoning. These bottlenecks need to be broken because many types of human-like reasoning involve --- massively parallel, highly-non-local, out-of-order, memory accessing --- in huge, sparsely-interconnected, networks of world knowledge. With the rapid advances in integrated photonics --- and in low-cost interconnect between such integrated photonics and optical fibers --- being made by organizations like HP, IBM, Luxtera, and Cornell University, it will become possible to extend massive numbers of extremely high bandwidth optical links across chips, wafers, boards, and multi-board systems --- enabling us to create computers --- and clouds of computers --- having not only more effective representational and computational power than the human brain, but also greater processor-to-memory and processor-to-processor interconnect.


With the highly redundant designs made possible by tiled processors, and their associated memory and network hardware --- wafer-scale and multi-level wafer-scale manufacturing techniques can become practical. Such highly uniform, replicated designs make it easier to provide fault-tolerance and self-test. The conventional wisdom is that wafer-scale integration was proved futile in the 1980s. But that was when the large size of most circuit components made it inefficient to provide redundancy in anything other than highly replicated circuits, such as memories. In the coming decade, however, entire cores will be small enough to be fused out with relatively little functional loss. In addition, redundant vertical and horizontal pathways can be provided in 3D circuitry, so that a defect in part of one layer will not prevent functional access to components above, below, and beside it.


Combined, all these technologies can greatly decrease the cost of manufacturing the massive amounts of memory, processing power, and connectivity demanded for extremely powerful --- roughly brain-level --- artificial intelligence.



For example, if --- 11nm semiconductor lithography --- multilevel circuitry --- and --- integrated-photonic interconnect --- are all in mainstream production in ten years --- as many predict --- then one million dollars should be able to purchase a system with: --- roughly 4 million small processor cores, allowing a theoretical max of 4 thousand trillion instructions per second --- 32 TBytes of 2ns EDRAM, allowing roughly 400 trillion read-modify-writes to EDRAM per second --- over 200 TBytes of sub-20ns-read-access phase-change-memory (PCM), allowing roughly 160 trillion random reads per second --- and a global, sustainable, inter-processor bandwidth of over 20 trillion 64Byte payload messages per second.


The AI community does not know exactly how much representation, computation, processor-to-memory, and processor-to-processor capacity is needed for human-level computing. The estimates vary by four or five orders of magnitude. Some think we will have to match the complexity of the brain almost exactly to get brain-level performance, causing them to think we will have to wait until approximately 2040 to achieve human-level AI. But this fails to take into account the many superiorities electronic hardware has relative to wetware. From my research and calculations, I am relatively confident that the above computational resources --- that could be available for one million dollars by 2020 --- would have more than enough capability to provide something approaching --- or, very possibly, substantially surpassing --- all the useful talents at which a human mind can currently out perform computers.


In addition, a machine with this power could also execute tasks at which computers already substantially out perform humans at speeds, and with exact memory, that exceed that of humans by millions or trillions of times. Combining the types of computing at which humans and machines each currently excel will greatly amplify the power of artificial intelligence. Such a system could have a high-bandwidth, fine-grained interface between these two different types of computation. And it could have the ability to rapidly vary the degree of mixture between them in each of many different concurrent processes or sub-processes --- all under the dynamic control of powerful hierarchical mental behaviors that have been honed by automatic reinforcement learning. This mixture will enable artificial intelligences that are substantially sub-human in some ways, to be hundreds to millions of times more powerful than humans at tasks that now can only be performed by us --- such as --- trying to use on-line tools to find the set of legal cases that are most relevant to a new, complex legal problem --- or trying to find information on the internet in those situations in which Google doesn't seem helpful.



To show why the 2020 system hypothesized above would, most probably, be capable of human-level thinking --- let us assume half of its 200TBytes of PCM memory were used to represent nodes and links in an experientially grounded, self-organizing, semantic-net memory. Assume an average of 100 bytes is required to represent and properly index an occurrence of a pattern, or concept, represented by a node in that net. Assume that roughly another 100 bytes is required to represent one of the relationships of such a concept s occurrence to another pattern or to one or more temporal, spatial, or semantic maps. With these assumptions this 100TBytes could create an experiential record storing an average of 1000 such nodes or links for each of one billion seconds. That s roughly the equivalent of three pages of text to describe a person s experiences for every second in over 31 years. When combined with the type of memory described in the paragraph below, this is almost certainly much, much more world knowledge than a human mind can store.


Continuing this simplified model of memory distribution --- let the remaining 100TByte of PCM store billions of patterns to represent and ground the meaning of the above mentioned nodes and links. This would include an invariant, hierarchical, self-organizing memory representing the composition, generalization, and similarity relationships between such patterns. This semantic net would include --- billions of patterns generalized from activation, or recorded, states in the system's network of sensory and semantic nodes and links --- and --- mappings between such generalized patterns, and their parts, and occurrences of such patterns in perceptions, thoughts, plans, imaginations, and memories. These generalized patterns would include billions of relatively simple sensory and motor patterns. They would also include more complex patterns representing concepts such as objects, persons, actions, emotions, drives, goals, and behaviors. These more complex patterns would include physical and mental behaviors and plans --- and their associated goals and other memories --- including feedback on their value and effectiveness. These patterns would include temporal and spatial relationships, and relationships defined by the relative roles of patterns in larger patterns. They would also include probabilistic statistics on the frequency, long term importance, and relationships between such patterns.


For many applications, such a system would contain many terabytes of information to help them excel at communicating with humans through --- text --- speech --- vision --- gestures --- facial expressions --- tones of voice --- and photorealistic, real-time, audio/video animation. Such systems would record --- tens of millions of compressed photographs, millions of which would be stored in morphable, semantically-labeled photosynths for generating 3D images and animations --- millions of seconds of compressed audio and moving images --- including models of humans communicating --- and --- many millions of mental patterns and behaviors relating to understanding human intentions, communications between humans, and communication with humans.


From the above we can see that the 200TBytes of storage --- provided by the hypothetical 2020 system --- particularly if it uses a context-sensitive, invariant representation scheme (of the type discussed in more detail below) --- is almost certainly enough to represent much more functional world knowledge than a human mind can store --- and to ground the concepts in such knowledge in an extremely rich web of sensory, cognitive, emotional, and behavioral memories and associations. This grounding should be much more than enough to give such a system's symbols --- true meaning.



This hypothetical 2020 system --- not only has enough capacity to more than represent human-level world knowledge --- it also appears to have enough computational and communication capacity to reason from such world knowledge faster than humans. The 2020 system s ability to randomly read its PCM memory 160 trillion times per second, and to perform over a 400 trillion random read-modify-writes to portions of its EDRAM representing dynamic activation values of patterns stored in the PCM, give it tremendous power to reason from its world-knowledge. It would have enough power to perform relatively shallow and most-probable (i.e. subconscious) inferencing simultaneously from billions of somewhat activated patterns --- and --- relatively deep and/or broad (i.e., conscious) inferencing, involving tens of billions of multi-level spreading activations from each of a small number of highly activated patterns that were the focus of attention. This allows rich, deep, grounded, and highly dynamic activation states. Ones that would probably have more useful informational complexity than those in our own minds.


These dynamic activation states --- when combined with mental behaviors for dynamically selecting and focusing attention --- can give rise to a powerful combination of conscious and subconscious thought. In this combination, conscious thought would commonly result from massive activation from a relatively small number of concepts and their relationships. The concepts chosen for such massive conscious activation would be generated, tested, and selected by many billions, or trillions, of computations in the subconscious. These subconscious computations would be made in response to sensations, emotions, desires, goals, memories --- and --- from activations from current and recently consciously activated concepts. In such a system the distribution of activation energy between conscious and subconscious activations, and between various activations within the subconscious, can be rapidly varied. For example, this allows each of many increasingly higher scoring networks of activation in the subconscious to receive increasingly more activation energy to verify which of them actually warrant being thresholded into conscious attention.



In summary, the above numbers give us good reason to believe that within ten years it will be commercially viable to build and sell machines that have the representation, computation, processor-to-memory, and processor-to-processor capacities necessary to support human-level --- and likely superhuman-level --- intelligence.


As one former head of DARPA s AI funding told me, The hardware being there is a given. It s the software that s needed.






Tremendous advances have been made in artificial intelligence in the recent past. This is largely due to the ever increasing rate of progress in brain science. It is also due to the increasing power of the computers that researchers can experiment with.


One example of such recent progress is the paper Learning a Dictionary of Shape-Components in Visual Cortex:... , by Thomas Serre of Prof.Tomasa Poggio s group at MIT. It describes a system that provides human-level performance in one limited, but impressive, type of human visual perception ( ). The Serre-Poggio system learns and uses patterns in a generalization and composition hierarchy. This allows efficient multiple use of representational components, and computations matching against them, in multiple higher level patterns. It allows the system to learn in compositional increments. It also provides surprisingly robust invariant representation. Such invariant representation is extremely important because it allows efficient non-literal matching, pattern recognition, and context appropriate pattern imagining and instantiation. Such non-literal match and instantiation tasks have --- until recently --- been among the major problems in trying to create human-like perception, cognition, imagination, and planning.


Although it is different than the Serre-Poggio system, the system described in Geoff Hinton s Google Tech Talk at demonstrates a character recognition architecture that shares many of these same beneficial characteristics --- including a hierarchical, scalable, and invariant representation/computation scheme that can be efficiently and automatically trained. The Hinton scheme is quite general, and can be applied to many types of learning, recognition, and context sensitive imagining. The architecture described by Jeff Hawkins et al. of Numenta, Inc. in Towards a Mathematical Theory of Cortical Micro-circuits ( ) also shares the concepts of hierarchical memory and invariance, and provides a potentially powerful and general computational model that attempts to describe the functioning of the human cortex in terms of its individual layers.


Similar amazing advances have been made in understanding other brain systems --- including those that control and coordinate the behavior of, and between, multiple areas in the brain --- and those that focus attention and decide which of competing actions to take or consciously consider.


These advances, and many more, provide enough understanding that we can actually start experimenting with designs for powerful artificial minds. It s not as if we have exact blue prints. But we do have a good overview, and good ideas on how to handle every problem I have ever heard mentioned in regard to creating roughly brain-like AI. As Deb Roy, of MIT, once agreed with me after one of his lectures, there are no problems between us and roughly human-level AI that we have no idea how to solve. The major problem that exists is the engineering problem of getting all the pieces to fit and work together well, automatically, and within a commercially-viable computational budget. That will take experimentation.


But we certainly do know enough to design and build general artificial intelligences that could provide useful functions.



The most complete, currently-publicly-available artificial brain architecture of which I am aware, is the OpenCogPrime architecture. It has been created by the open-source AGI initiative headed by Ben Goertzel. There may be other equally complete and impressive brain architectures available to the public. But since I do not know them, let me give a brief -- but hopefully revealing overview of the OpenCog architecture --- as I understand it, in combination with some of my own thinking on the subject. (The OpenCog architecture is described at .)


OpenCog starts with a focus on General Intelligence , which it defines as the ability to achieve complex goals in complex environments. AGI stands for Artificial General Intelligence. It is focused on automatic, interactive learning, experiential grounding, self understanding, and both conscious (focus-of-attention) and unconscious (less attended) thought.


It records its sensory and emotional experiences, finds repeated patterns in such recordings, makes generalizations and compositions out of such patterns --- all through multiple levels of generalization and composition --- based on spatial, temporal, and learned-pattern-defined relationships. It uses Bayesian mathematics --- based on the frequencies of the detection of such patterns and their relationships --- in a way that allows inferences to be drawn from many billions of activated patterns at once.


Patterns -- which can include behaviors (including those that control the operation of the mind itself) -- are recorded, modified, generalized, and deleted all the time. They have to compete for their computational resources, including memory space, and, thus, their own continued existence. Re-enforcement learning and other forms of credit assignment are used to determine which patterns are useful enough to be kept, and for how long. This results in a self-organizing network of similarity, generalization, and composition patterns and relationships, that all must continue to prove their worth in a survival-of-the-fittest, goal-oriented, experiential-knowledge ecology.


Re-enforcement learning is also used to weight patterns for long-term and current short term importance, based on the roles they have played in achieving the system s goals in the past. These indications of importance -- along with a deep memory for past similar experiences, contexts, and goals, and for the relative usefulness of various past inferencing behaviors in such contexts -- significantly narrow and focus attention and spreading activation. This helps avoid the pitfalls of combinatorial explosion, and it tends to result in context-appropriate perception, cognition, and behavior.


OpenCog uses evolutionary program learning --- somewhat like genetic programming --- to increase the system s ability to learn and tune: generalizations of patterns; classifiers; creative ideas; and behaviors --- including physical, attention focusing, inferencing, and learning behaviors. This evolutionary learning is made more powerful by being used by --- and by using --- the rest of the system. This includes the system s composition and generalization hierarchy, its network of probabilistic associations, its inferencing, and its reinforcement learning. Evolutionary programs can be used by the system s experiential probabilistic learning. Such programs can, themselves --- along with experientially learned patterns --- be incorporated --- with or without modification --- into the learning of new evolutionary programs. A compositional and generalization hierarchy including such evolutionarily-learned programs enables complex programs to be learned more efficiently in incremental steps from more simple ones. Experiential memories help guide and evaluate the evolutionary process, including reducing the computation required for estimating the fitness functions for many evolutionary candidates. Experiential memories can also provide information for probabilistically inferring which programs are appropriate to employ, with which parameters, in which contexts.



Taken together, software architectures like those discussed above --- when combined with the hardware likely to be available within a decade --- will allow AGI systems to automatically learn, reason, plan, imagine, and create with a sophistication and power never before possible -- not even for the most intelligent of humans.



Of course, it will take some time for the first such systems to automatically learn roughly the equivalent of human-level world knowledge. After all, it takes over twenty years for most human minds to train up. But there is reason to believe substantial portions of such machine learning could be performed in parallel. And since such machines will be capable of remembering vastly more detail than humans, their learning should be much faster. Such machine learning is likely to be better grounded in physical reality, if such machines can control robotic bodies with human-like senses that enable them to learn by exploring the physical world, as do human children --- or by, at least, having the equivalent in a fairly accurate virtual world. The learning of many concepts would be improved by having human teachers. Once such a system achieves a certain level of world-knowledge --- including a child s level of common-sense physics, basic human behavior, natural-language, and visual scene understanding --- they will be able to rapidly learn by reading and viewing images and diagrams from large libraries of digitally recorded books, and from media on the web.


And once one such system has been fully trained in basic world knowledge --- or in the knowledge relating to a given field of expertise that is linked to a common representation of such world knowledge--- that knowledge can be copied to another similar machine in seconds or minutes.






The answer is most probably, yes, because such systems will: ---


-a- in multiple important ways --- work like the human brain, itself;
-b- have enough representation, computation, and interconnect capacity to make types of AI that were never before even close to possible for most in the AI community --- not only possible --- but commercially practical --- including the ability to represent and rapidly reason from grounded, human-level world knowledge --- and
-c- benefit from the explosion of AI related advances that will occur in this decade.


This explosion of AI-related advances --- in addition to the hardware advances described above --- will occur in: --- brain science --- generalized machine learning and inferencing --- attention and inference control --- large-scale semantic web applications --- learning and reasoning from self-organizing ontologies --- natural language understanding and generation --- common-sense and world-knowledge learning and computing --- evolutionary learning --- machine vision --- multimedia indexing --- command and control --- national security and defense applications --- search --- robotics --- personal assistants --- web agents --- user interfaces --- and more human-like characters for video games and virtual realities. All of this will be in addition to the increasing research that will be performed in AGI, itself.


Within three to seven years, hardware having the effective representation, computation, and inter-connect of small mammal and, then, primate brains will be available --- at sufficiently low costs that thousands of such systems will be used by academic and corporate teams to experiment in such fields. All this research will help identify, refine, and tune various general algorithms that could be put together to create powerful generalized thought robots --- i.e., powerful artificial intelligences that can --- automatically --- or with relatively little handcrafting --- tune their learning and mental behaviors to achieve various goals across a broad range of applications.


AGI is not currently competitive for most applications, because its general algorithms tend to require much more training, memory, and computation than AI systems handcrafted by humans to solve a particular set of problems. But many of the learning, inferencing, and inference control mechanisms deployed in more narrow applications can be generalized to have applicability to AGI. And many early AGI s will have handcrafted parts to make them more competitive for specific applications. As memory, computation, and interconnect costs drop drastically relative to programming costs --- particularly relative to the cost of handcrafting AIs for extremely complex problems --- larger, more general, and more capable AGI will become ever more competitive.


Once created, AGI will be particularly attractive for corporate and cloud computing --- because it can automatically be adapted to the many different uses that different people will want from AI services --- and because it can provide superintelligent user interfaces --- using text, speech, audio, vision, and the real-time generation of animation --- to make it easy for users to instruct, monitor, and learn relevant information from such machines.



So can human-level AGI be built?




The only question is how fast. And it is almost certain that if the right people, got the right money, it could be built within ten years --- that is --- by no later than Twenty-Twenty.


Making this happen should be our nation s "Twenty-Twenty vision" because machine superintelligence is the most transformative technology of all.






It is hard to overstate the economic value and transformative power of the types of machines that will probably be built by 2020 --- and if not by then --- by 2030.


The one-million-dollar 2020 hardware hypothesized above could be rented out on the web, at a profit, for roughly $50 an hour. It --- would have superhuman concentration --- could work close to 24/7 --- could perform many types of reasoning tasks millions of times faster than a human --- and --- if connected to a cloud of similar machines that stored a large percent of human knowledge in instantly-accessible semantic deep structure --- it would, in effect, have photographic memory for almost all of recorded human knowledge. It is not unrealistic to think that for a large number of tasks such a machine could do work at a higher rate than one hundred programmers, lawyers, doctors, or managers.


If such a system were part of a computing cloud --- then an average of --- 64 thousand cores --- 500 GBytes of 2ns EDRAM --- 3 TBytes of 20ns-read-access PCM memory --- and --- over 300 billion global, 64Byte messages per second --- could be provided to serve an individual user of a wireless mobile phone; retinal-scanning, headset computer; or other personal device --- at roughly the same price currently charged for long distance phone calls. This should be enough power to provide users with moderately good --- natural language --- vision --- real-time animation --- intelligent search --- semantic web reasoning --- and machine-mediated collective computing.


Most of the time they were connected, users would not even begin to fully use the 64K processor chunk of hardware described above, but there would frequently be tasks demanding more power --- such as understanding difficult natural language constructions --- performing computationally intensive queries, summaries, and reasoning --- and --- synthesizing creative solutions to complex problems. Larger portions of the cloud could be used in a multiplexed manner for such tasks. Users who utilize more than a certain amount of the cloud's resources in a given time could be notified that they were about to do so, and be billed extra for it at less than one dollar for a the equivalent of using one of the above described one-million-dollar 2020 machine s worth of hardware for one minute.


That one dollar should be enough, for example, to get a reasonably well reasoned legal brief on a moderately complex issue --- something that would cost several hundred to several thousand dollars from most American lawyers.


Even if we make the extremely conservative assumption that our one-million-dollar 2020 machine could only simultaneously do the work of ten human lawyers, doctors, financial experts, or managers --- that would mean it could provide the services of such a professional or manager for $5 dollar per hour --- making most such highly educated professionals or managers unemployable at the current minimum wage.


Furthermore if new areas of electronics --- such as 3D, carbon nanotube,graphene, nanowire, quantum dot, spintronic, quantum entanglement, molecular, neuromorphic, and self-organizing electronics --- keep Moore s law going for several decades past the final density expected for traditional silicon electronics --- in twenty to thirty years a machine of power similar to the one-million-dollar 2020 system might cost less than a current personal computer. Such a continuation of Moore's law is likely. This is because machine superintelligences can be produced at even the 22nm node at sufficiently low prices that they could be commercially useful for greatly increasing the rate of development in electronics and electronic design. If such cost reductions are, in fact, obtained, virtually all human mental jobs could be replaced for one or two pennies an hour in thirty years. If superintelligence is used to speed advances and cost reductions in robotics --- all of humanity --- including in places like China, India, Vietnam, and Haiti --- will cease to be competitive for most current forms of work.


AGI will create a historical singularity of the type Ray Kurzweil has done so much to popularize. That is, a technological change so powerful --- that --- when combined with the massive acceleration it will cause in --- the internet --- electronics --- computing --- robotics --- nanotechnology --- and --- biotechnology --- it will warp the very fabric of human economies, cultures, values, and societies in somewhat the same way the singularity of a black hole warps the fabric of space-time --- and is believed --- by some --- to create an entirely new universe -- one largely disconnected from its past in space and time.


Can we, or should we, stop the advent of superintelligence?


No. It is futile to try.


Too many people already know how much technological, economic, military, cultural, and political advantage can be gained by the nations and corporations that are first to substantially deploy it. It cannot be stopped because electronic technology and our understanding of intelligence are already so advanced that in a decade the development of superintelligence will be well within the economic and intellectual grasp of a most nations, thousands of corporations, and hundreds of universities. It is already within the grasp of the world s leading nations and technological companies. It cannot be stopped by international agreements, because --- compared to the development of nuclear weapons --- in a decade, machine intelligence can be developed for very little money, in very little space, with relatively little electricity. Its development would be very difficult to detect, prove, or stop.



There are many reasons we should want --- rather than oppose --- the development of superintelligence. It --- and the rapid advances in technology and productivity it will bring --- could be a force for tremendous good.


It could create a world of material, medical, mental, and intellectual well being and richness. It could help us develop highly efficient, sustainably, less-polluting, factories, farms, stores, corporations, and transportation. It could teach us how to cure most disease, how to keep our bodies younger longer, and how to make our minds work in more powerful and satisfying ways. It could help us to become a truly enlightened species. It could educate all of us, with virtual tutors more knowledgeable and more capable of explaining things to us than the most brilliant and attentive human teachers. It could learn to know each of us better than we know ourselves, and to provide us with personal counseling superior to that of the best psychologist or friend.


It could help us better simulate and determine the costs, benefits, and risks of personal, corporate, and governmental decisions. It could enable people to communicate, collaborate, and deliberate with an efficiency and fairness never before possible. It could help us better deal with the rapid changes it will produce --- such as the fact it will end most current ways of earning a living in the industrialized world --- by helping us to create a new, fair, and sustainable social contract --- and new types of meaningful work ---- such as sharing more responsibility in much more participatory local, regional, national, and world governments and institutions. It can allow us to have AGI-mediated, real-time virtual conversations, debates, celebrations, songs, dances, games, and prayers --- in which hundreds, thousands, millions, or billions of people take part.


The world is facing many challenges that seem beyond the capacity of our current political institutions to solve. It is arguable we need superintelligence to help us find how to provide food, shelter, clothing, medical services, education, meaningful lives --- and --- most importantly --- peace --- for the projected 9 billion people that will populate earth by 2050. Most of these will come from societies that are brutally poor --- and yet most of them will have access to the extremely powerful --- but, by 2050, inexpensive --- personal information devices of the future --- video devices that will likely teach them to want as much power and material wealth as people in the richest nations. It is arguable that we will need superintelligence to help us deal with such problems without poisoning our planet with pollution --- or by destroying it with war or terrorism.



Butsuperintelligence, and the technologies it will bring, could also cause great harm.


Unless we are careful --- in addition to putting most of us out of work --- superintelligence can be used to create surveillance systems and robotic police or military forces that could enable one class, group, person, or system of machines to create a powerful oligarchy or dictatorship. Unethical people, governments, or machines will almost certainly use superintelligences to constantly try hacking into our networks and intelligent machines --- trying to take control of them for their own selfish purposes.


AGI can create virtual realities, friends, and lovers that are much more attractive, attentive, sensitive, romantic, funny, and seductive than those that are real. These virtual worlds and personalities could weaken the bonds between humans, and could seduce us into increasingly turning over more attention and power to machines, and to the virtual worlds they generate for us --- whether those machines are controlled by businessmen, political leaders, or machines themselves. Low income housing may become stackable 4 x 4 x 8 foot plastic pods with super HD 3D virtual interfaces, in which the elites provide the masses with a bare-minimum physical reality, but an extremely rich --- and much less expensive --- virtual one.


Human laziness may well lead us to turn too much power over to superintelligences --- so much so that we might soon be at their mercy. Ultimately, the machines themselves might well take over. And if they do --- it is not clear whether they would like us enough to let us keep consuming so much of the earth s resources --- which they, themselves, could use for their own purposes --- and their own progeny.



Sometranshumanists say --- the only way in which what we value as "human" can remain competitive in a world bound to be ruled by superintelligences is for us to increasingly merge our values, memories, consciousnesses, and bodies with such machines. They say our seduction by virtual worlds, friends, and lovers is a good thing --- because it will make --- what they view --- as the necessary man-machine merger --- more emotionally acceptable. To make our lives more meaningful --- they say --- we should view the machines as our kin and our posterity.


Sometranshumanists suggest it is necessary for our very survival that we place into, or onto, our brains high bandwidth connections to superintelligences. Preferably such connections will be to a World-Wide-Web of other people similarly connected to such machines. This would make us into Star-Trek-like borgs --- but, let us hope, ones with substantially more individualism, humor, and happiness.


Othertranshumanists suggest uploading our minds to run on such machines so they can live for billions of years.


To most people all this sound like a whipped out science-fiction horror flick. But there are reasons to believe that within only decades --- almost certainly by the end of this century --- much of this could, in fact, come true.


Thetranshumanists may be right. We humans largely rule the earth because our intelligence and knowledge excels that of all other species. By analogy, it only makes sense that --- starting in several decades when there are likely to be networks of many superintelligences --- each thousands of times smarter than humans --- there will ultimately come a time when machines take domination away from us.


That is, perhaps, unless we join them, and make them part us, and us part them. There are already many who look forward to connecting their brains to superintelligences --- and it is almost certain, that once superintelligence arrives, the people who use such implanted, high bandwidth, connections to such machines will be more successful than those who do not.


But even the transhumanist scenario requires that humanity act intelligently and wisely if the transition to humanity+ is to be a happy one.


How we develop --- use --- and control superintelligence is one of the greatest challenges facing mankind. We cannot stop its advent, but we can try to control it --- to reduce its danger, at least to some degree. Great flexibility is possible in the design of AGI s, and we should be careful to learn what types of machines are likely to be more safe and what types are likely to be more dangerous. We should learn how to best use the safer types ofsuperintelligence to protect us against the more dangerous. If you care about humanity --- more important than creating superintelligence per se --- is creating super-intelligence that is well combined with the wisdom, compassion, and voices and concerns of billions of individual human beings.



That is why, ultimately --- from humanity s standpoint --- the most important technology of all is collective intelligence.


It is the technology of using the internet, computers, and, soon, superintelligence, to enable groups, corporations, nations --- and ultimately the world --- to think and act together more intelligently, successfully, and humanely --- as we --- as a species --- have to navigate in an ever more rapidly-changing future.


And that is why --- the single most important use of superintelligence --- is to help give mankind enough collective intelligence that --- for decades , or, perhaps, even centuries --- humanity can safely and happily travel into that rapidly-changing future.



(For a more complete discussion of collective intelligence see . [ on this page at ])






There is a good article entitled How Long Till Human-Level AI? What Do the Experts Say? written by Ben Goertzel, Seth Baum, Ted Goertzel at


To me its most important information is in the figure entitled When Will Milestones of AGI be Achieved without New Funding . It indicates that, of the 21 attendees at the AGI 2009 conference who answered the survey, 42% think AGI s capable of passing the Turning Test will be created within ten to twenty-years.


Oddly that is slightly more than the 38% who think AGI s would achieve the human-like capabilities of a 3rd grader within the same time frame. This might reflect the fact that too many of the attendees have been influenced by the famous Eliza experiment, which was a quasi Turing Test that actually managed to fool some people into thinking they were reading text generated by a human doctor --- using mid-1960s computers.


I have always assumed the Turing test would be administered by humans who understood human psychiatry and brain function, and artificial intelligence sufficiently that they would be able to smoke out a sub-human intelligence relatively quickly in the Turning Test.


In fact, I am the person quoted in that article for giving my reasons why I thought it would be more difficult to make a computer pass the turning test than to posses many of the other useful intellectual capabilities of a powerful human mind --- as quoted in the paragraph that follows:


One observed that making an AGI capable of doing powerful and creative thinking is probably easier than making one that imitates the many, complex behaviors of a human mind many of which would actually be hindrances when it comes to creating Nobel-quality science. He observed humans tend to have minds that bore easily, wander away from a given mental task, and that care about things such as sexual attraction, all which would probably impede scientific ability, rather that promote it. To successfully emulate a human, a computer might have to disguise many of its abilities, masquerading as being less intelligent in certain ways than it actually was. There is no compelling reason to spend time and money developing this capacity in a computer.



I thought the idea --- suggested in one of the survey questioned mentioned in the article --- that AGI might be funded by 100 billion dollars is a little rich. I understand, however, such a large figure was picked to --- in effect --- ask how people how fast they thought AGI would be developed if money was virtually no obstacle.


I think AGI could be developed over ten years for well under 500 million dollars if the right people were administering and working on the project. (This does not count all the other money that is already likely to be invested in electronics, computer science, and more narrow AI in the coming decade.) Unfortunately, it would be hard for the government to know who were the right people, and what were the right approaches, for such a project. But I believe a well designed project, designed to achieve human level AGI, almost certainly could succeed in ten years with only 2 to 4 billion dollars of funding over that period. Such a project would fund multiple teams with say 10 to 30 million dollars to start, and then increasingly allocate funding over time to the teams and approaches that produced the most promising results.


2 to 4 billion dollars over ten years would be totally within the funding capacity of multiple government agencies.


DevelopingAGI in that time frame would be exceptionally valuable to America --- because it would give a tremendous chance to save our economy before its is bled to death --- by our trade imbalance with the rapidly developing world --- and --- by the many tens of trillions of dollars of in health care and other unfunded benefits America owes its seniors and government workers.


Ed Porter





In my first post above on this topic I said:





The answer is most probably, yes, because ...


...many of the learning, inferencing, and inference control mechanisms deployed in more narrow applications can be generalized to have applicability to AGI.


As evidence of the above statement, I am attaching a link to a lecture by Pedro Domingos of the University of Washington on what he views as a highly generalized AI learning and inferencing system using Markov logic networks. . This representation shares many features with the hypergraph representation in OpenCogPrime by Goertzel et al.


There are some other interesting lectures from the same conference at .






In the main post above I stated human level AI could be probably built within roughly a decade, by 2020.


That is much sooner than the conventional wisdom in the AI community. But there are some very knowledgeable people who share my guess of approximately 2020. And some of them have considerable resources to throw at the problem.


In a Google Tech Talk, recorded in May 2006, Doug Lenat, mentioned in passing that Sergey Brin, one of the two founders of Google, had said AI could be built by 2020. Doug Lenat is head of Cycorp, the corporate continuation of one of the largest and longest big-AI projects. Lenat s talk is at . It provides a good overview of Cycorp s Cyc system, and has an amusing introduction of Doug by Peter Norvig, co-author of one of the leading textbooks on AI and Google s director of research.


In response to Lenat s statement about Brin s projection, I did a brief web search to see if I could find exactly what Brin had said about achieving AI by 2020. I was unable to find any other reference to the quote. But I did find the following information relevant to Google s pursuit of AI and to the 2020 estimate.


As was cited on multiple web sites --- including --- Google s Larry Page said at the 2007 conference for the American Association for the Advancement of the Sciences, that researchers at Google were working upon developing Artificial Intelligence. He said human brain algorithms actually weren t all that complicated and could likely be approximated with sufficient computational power. He said, We have some people at Google (who) are really trying to build artificial intelligence and to do it on a large scale. It s not as far off as people think.


According to : Sergey Brin is reported to have said that the perfect search engine would look like the mind of God . Similar ideas, but less extravagantly worded, have come from Marissa Mayer, Google s VP of Search Products and User Experience when she talked about how Google s massive data stores and sophisticated algorithms are acting more and more like intelligence .


In 2008 Nicholas Carr --- who served as executive editor of the Harvard Business Review, and who has written extensively on information technology --- wrote a book entitled The Big Switch: Rewiring the World, From Edison to Google. A review of it, at , says:


the book discussed the future of computing. The main discussion was with Google founders, Larry Page and Sergey Brin, about their dream of what their search engine will do in the coming years. According to Page and Brin, artificial intelligence is the main goal of those behind the future of Google. Google wants to link the human brain with the computer to share its search engine. The author also spoke about advancements Microsoft and other Computer Scientists want for the future of computing. According to Carr, in 2020, Google s dream may come true.


At , Andy Greenberg of interviews Carr about his book. Below is an excerpt:


[AG]Looking further ahead at Google's intentions, you write in The Big Switch that Google's ultimate plan is to create artificial intelligence. How does this follow from what the company's doing today?


[NC] It's pretty clear from what [Google co-founders] Larry Page and Sergey Brin have said in interviews that Google sees search as essentially a basic form of artificial intelligence. A year ago, Google executives said the company had achieved just 5% of its complete vision of search. That means, in order to provide the best possible results, Google's search engine will eventually have to know what people are thinking, how to interpret language, even the way users' brains operate.


Google has lots of experts in artificial intelligence working on these problems, largely from an academic perspective. But from a business perspective, artificial intelligence's effects on search results or advertising would mean huge amounts of money.


[AG] You've also suggested that Google wants to physically integrate search with the human brain.


[NC]This may sound like science fiction, but if you take Google's founders at their word, this is one of their ultimate goals. The idea is that you no longer have to sit down at a keyboard to locate information. It becomes automatic, a sort of machine-mind meld. Larry Page has discussed a scenario where you merely think of a question, and Google whispers the answer into your ear through your cellphone.


[AG] What would an ultra-intelligent Google of the future look like?


[NC]I think it's pretty clear that Google believes that there will eventually be an intelligence greater than what we think of today as human intelligence. Whether that comes out of all the world's computers networked together, or whether it comes from computers integrated with our brains, I don't know, and I'm not sure that Google knows. But the top executives at Google say that the company's goal is to pioneer that new form of intelligence. And the more closely that they can replicate or even expand how peoples' mind works, the more money they make.


[AG] You don't seem very optimistic about a future where Google is smarter than humans.


[NC]I think if Google's users were aware of that intention, they might be less enthusiastic about the prospect than the mathematicians and computer scientists at Google seem to be. A lot of people are worried that a superior intelligence would mean for human beings.


I'm not talking about Google robots walking around and pushing humans into lines. But Google seems intent on creating a machine that's able to do a lot of our thinking for us. When we begin to rely on a machine for memory and decision making, you have to wonder what happens to our free will.


At,1,1010933.story?ctrack=1&cset=true , Google CEO Eric Schmidt is reported to have said in 2007:


By 2012, he wants Google to be able to tell all of us what we want. This technology, what Google co-founder Larry Page calls the "perfect search engine," might not only replace our shrinks but also all those marketing professionals whose livelihoods are based on predicting or guessing consumer desires.


The article also says


iGoogle is growing into a tightly-knit suite of services personalized homepage, search engine, blog, e-mail system, mini-program gadgets, Web-browsing history, etc. that together will create the world's most intimate information database. On iGoogle, we all get to aggregate our lives, consciously or not, so artificially intelligent software can sort out our desires. It will piece together our recent blog posts, where we've been online, our e-commerce history and cultural interests. It will amass so much information about each of us that eventually it will be able to logically determine what we want to do tomorrow and what job we want. is an article about Ian Peterson, chief futurologist at British Telecom. In it he says:


We will probably make conscious machines sometime between 2015 and 2020, I think. But it probably won't be like you and I. It will be conscious and aware of itself and it will be conscious in pretty much the same way as you and I, but it will work in a very different way. It will be an alien. It will be a different way of thinking from us, but nonetheless still thinking


In response to the interviewer pointing out that


as soon as machines become intelligent, according to Moore's Law they will soon surpass humans. By the way, BT's 2006 technology timeline predicts that AI entities will be awarded with Nobel prizes by 2020, and soon after robots will become mentally superior to humans. What comes after that: the super intelligence or God 2.0?


Peterson responds


I think that I would certainly still go along with those time frames for superhuman intelligence, but I won't comment on God 2.0. I think that we still should expect a conscious computer smarter than people by 2020. I still see no reason why that it is not going to happen in that time frame. But I don't think we will understand it. The reason is because we don't even understand how some of the principal functions of consciousness should work.


Of course, Microsoft Research is also putting a lot of effort into artificial intelligence research. A March 2, 2009 New York Times article at , reports on some of Microsoft s efforts in the field. Among other interesting things it says:


CraigMundie, the chief research and strategy officer at Microsoft, expects to see computing systems that are about 50 to 100 times more powerful than today s systems by 2013.


Most important, the new chips will consume about the same power as current chips, making possible things like a virtual assistant with voice- and facial-recognition skills that are embedded into an office door.


We think that in five years time, people will be able to use computers to do things that today are just not accessible to them, Mr. Mundie said during a speech last week. You might find essentially a medical doctor in a box, so to speak, in the form of your computer that could help you with basic, nonacute care, medical problems that today you can get no medical support for.


With such technology in hand, Microsoft predicts a future filled with a vast array of devices much better equipped to deal with speech, artificial intelligence and the processing of huge databases.


So, in sum, there is good reason to believe there will be an explosion in AI in the next ten years.






Below is a link to a DARPA request for a proposal for a program to perform deep learning. It wants a system that can automatically learn patterns of many different types from visual, auditory, and text with little human guidance, using automatically learned hierarchical invariant representations, of the general type described in the first few paragraphs of "THE SOFTWARE" section of the above post.


This is the type of project, which if the right people got the funding could really help advance AGI. It seems like Numenta,Poggio's group, or Hinton, could all submit compelling responses to this proposal. The request says it is interested in sponsoring multiple teams, and in disseminating much of what is learned to the public to advance the computing arts.


Start reading at page four of






TheDARPA Mind s Eye program is another example of an ambitious AI program that is likely to get us closer to human-level AI. This program will be run out of DARPA's TCTO or Transformational Convergence Technology Officee.


The Mind s Eye program --- to reach its goals --- has to be able to:


-have a fairly large invariant ontology of objects, motions, humans, weapons, military behaviors, scenes, and scenarios it recognizes in many different instantiations, forms, views, scale, and lighting;

-do visual scene recognition and understanding;

-understand behaviors of entities it is seeing;

-map such understandings into a larger higher level representation and understanding of what is taking place around it;

-presumably have to combine audio and visual recognition, since sound is an important source of information in a battlefield;

-have to have complex goal pursuit and attention focusing, to decide what to look at, and track, and spend its optical and computational resources on; and

-have natural language communication capabilities, or some other method of creating concise reports for human consumption and for receiving commands from humans


In sum, this project would require quite an advanced set of AI capabilities to function well.


The following is quoted from a short pdf at , to spark interest for people to attend a meeting at which the project will be discussed in more detail. It does not appear the BAA for this project has been posted yet.


The Mind s Eye program seeks to develop in machines a capability that currently exists only in animals: visual intelligence. Humans in particular perform a wide range of visual tasks with ease, which no current artificial intelligence can do in a robust way. Humans have inherently strong spatial judgment and are able to learn new spatiotemporal concepts directly from the visual experience. Humans can visualize scenes and objects, as well as the actions involving those objects. Humans possess a powerful ability to manipulate those imagined scenes mentally to solve problems. A machine‐based implementation of such abilities would be broadly applicable to a wide range of applications.


This program pursues the capability to learn generally applicable and generative representations of action between objects in a scene directly from visual inputs, and then reason over those learned representations. A key distinction between this research and the state of the art in machine vision is that the latter has made continual progress in recognizing a wide range of objects and their properties what might be thought of as the nouns in the description of a scene. The focus of Mind s Eye is to add the perceptual and cognitive underpinnings for recognizing and reasoning about the verbs in those scenes, enabling a more complete narrative of action in the visual experience.


One of the desired military capabilities resulting from this new form of visual intelligence is a smart camera, with sufficient visual intelligence that it can report on activity in an area of observation. A camera with this kind of visual intelligence could be employed as a payload on a broad range of persistent stare surveillance platforms, from fixed surveillance systems, which would conceivably benefit from abundant computing power, to camera‐equipped perch‐and‐stare micro air vehicles, which would impose extreme limitations on payload size and available computing power. For the purpose of this research, employment of this capability on man‐portable unmanned ground vehicles (UGVs) is assumed. This provides a reasonable yet challenging set of development constraints, along with the potential to transition the technology to an objective ground force capability.


Mind s Eye strongly emphasizes fundamental research. It is expected that technology development teams will draw equally from the state of the art in cognitive systems, machine vision, and related fields to develop this new visual intelligence. To guide this transformative research toward operational benefits, the program will also feature flexible and opportunistic systems integration. This integration will leverage proven visual intelligence software to develop prototype smart cameras. Integrators will contribute an economical level of effort during the technology development phase, supporting participation in phase I program events (PI meetings, demonstrations, and evaluations) as well as development of detailed systems integration concepts that will be considered by DARPA at appropriate times for increased effort in phase II systems integration.






Here is a summary of projects of DARPA s IPTO (Information Processing Technique Office) taken from its web site. It shows this office within DARPA is funding a lot of projects that are likely to speed the advance of AI. I have capitalized the portions of text that seem most relevant to the development of AI. (Apologies to those who view all caps as screaming. In the limited word processor offered in this forum, it seems the most efficient way to let one scan highlighted text.). Particularly if it is combined with the type of deep learning DARPA is proposing, described in one of my posts above, or if it combined with DARPA s neuromorphic computing project



Cognitive Systems @


COGNITIVE COMPUTING IS THE DEVELOPMENT OF COMPUTER TECHNIQUES TO EMULATE HUMAN PERCEPTION, INTELLIGENCE AND PROBLEM SOLVING. Cognitive systems offer some important advantages over conventional computing approaches. For example, COGNITIVE SYSTEMS CAN LEARN FROM EVENTS THAT OCCUR IN THE REAL WORLD and so are better suited to applications that require EXTRACTING AND ORGANIZING INFORMATION IN COMPLEX UNSTRUCTURED SCENARIOS than conventional computing systems, which must have the right models built in a priori in order to be effective. Because many of challenges faced by military commanders involve vast amounts of data from sensors, databases, the Web and human sources, IPTO is creating cognitive systems that CAN LEARN AND REASON TO STRUCTURE MASSIVE AMOUNTS OF RAW DATA INTO USEFUL, ORGANIZED KNOWLEDGE WITH A MINIMUM OF HUMAN ASSISTANCE. IPTO is implementing cognitive technology in systems that support warfighters in the decision-making, management, and understanding of complexity in traditional and emergent military missions. These cognitive systems WILL UNDERSTAND WHAT THE USER IS REALLY TRYING TO DO AND PROVIDE PROACTIVE INTELLIGENCE, ASSISTANCE AND ADVICE. Finally, the increasing complexity, rigidity, fragility and vulnerability of modern information technology has led to ever-growing manpower requirements for IT support. The incorporation of COGNITIVE CAPABILITIES IN INFORMATION SYSTEMS WILL ENABLE THEM TO SELF-MONITOR, SELF-CORRECT, AND SELF-DEFEND AS THEY EXPERIENCE SOFTWARE CODING ERRORS, HARDWARE FAULTS AND CYBER-ATTACK.




Advanced Soldier Sensor Information System and Technology (ASSIST)
The main goal of the program is to enhance battlefield awareness via exploitation of soldier-collected information. The program will demonstrate advanced technologies and an integrated system for processing, digitizing and disseminating key data and knowledge captured by and for small squad leaders.
Bootstrapped Learning (BL)
EMBEDDING BL TECHNOLOGY IN COMPUTING SYSTEMS WILL ELIMINATE THE NEED FOR TRAINED PROGRAMMERS IN MANY PRACTICAL SETTINGS, significantly accelerating human-machine instruction, and making possible on-the-fly upgrades by domain experts rather than computer experts. Target applications include a variety of field-trainable military systems, such as human-instructable unmanned aerial vehicles. However, BL technology is being developed and tested against a portfolio of training tasks across very diverse domains, thus it can be applied to any programmable, automated system. As such systems have become ubiquitous, and their operation inaccessible to the layperson, there is also the strong prospect of societal adoption and benefit.
Brood of Spectrum Supremacy (BOSS)
The goal of the Brood of Spectrum Supremacy (BOSS) program is to provide a radio frequency (RF) spectrum analogue to night vision capabilities for the tactical warfighter, with a particular focus on RF-rich urban operations. The program is intended to apply collaborative processing capabilities for software-defined radios to specific military applications.
Cyber Trust (CT)
The Cyber Trust program will create the technology and techniques to enable trustworthy information systems by:
1. Developing hardware, firmware, and microkernel architectures as necessary to provide foundational security for operating systems and applications.
2. Developing tools to find vulnerabilities in complex open source software.
3. Developing scalable formal methods to formally verify complex hardware/software.
Integrated Learning (IL)
Communications are essential to warfighters - they enable warfighters to share situational awareness and to stay coordinated with each other and command. Communications are important for voice and data and the importance for data traffic will only increase in the future. The problem is that urban settings hinder communications. Buildings, walls, vehicles, etc., create obstacles that impact the manner in which radio signals propagate. The net result is unreliable communications in these settings, which can leave warfighters, sensors, etc., without the benefit of reach back to command or each other.
This program will help to solve the urban communications problem by CREATING INTELLIGENT AUTONOMOUS ROBOTIC RADIO RELAY NODES, CALLED LANDROIDS (LOCAL AREA NETWORK DROIDS), WHICH WORK TO ESTABLISH AND MAINTAIN MESH NETWORKS THAT SUPPORT VOICE AND DATA TRAFFIC. Through autonomous movement and intelligent control algorithms, LANdroids can mitigate many of the communications problems present in urban settings, e.g., relaying signals into shadows and making small adjustments to reduce multi-path effects.
LANdroids will be pocket-sized and inexpensive. The concept of operations is that warfighters will carry several LANdroids, which they drop as needed during deployment. TheLANdroids then form the mesh network and work to maintain it - establishing a communications infrastructure that supports the warfighters in that region.
Machine Reading (MR)
Personalized Assistant that Learns (PAL)
Current software systems - in the military and elsewhere - are plagued by brittleness and the inability to deal with changing and novel situations - and must therefore be painstakingly programmed for every contingency. If PAL succeeds it could result in software systems that could learn on their own - that could adapt to changing situations without the need for constant reprogramming. PAL technology could drastically reduce the money spend by DoD on information systems of all kinds.
This is the FIRST BROAD-BASED RESEARCH PROGRAM IN COGNITIVE SYSTEMS SINCE THE STRATEGIC COMPUTING INITIATIVE FUNDED BY DARPA IN THE 1980S. Since then, there have been significant developments in the technologies needed to enable cognitive systems, such as machine learning, reasoning, perception, and, multi-modal interaction. Improvements in processors, memory, sensors and networking have also dramatically changed the context of cognitive systems research. It is now time to encourage the various areas to come together again by focusing on by a common application problem: a Personalized Assistant that Learns.
Developing cognitive systems that learn to adapt to their user could dramatically improve a wide range of military operations. The development and application of intelligent systems to support military decision-making may provide dramatic advances for traditional military roles and missions. The technologies developed under the PAL program are intended to make military decision-making more efficient and more effective at all levels.
For example, today's command centers require hundreds of staff members to support a relatively small number of key decision-makers. If PAL succeeds, and develops a new capability for "cognitive assistants," those assistants could eliminate the need for large command staffs - enabling smaller, more mobile, less vulnerable command centers.
Self-Regenerative Systems (SRS)
The goal of the SRS program is to develop technology for building military computing systems that provide critical functionality at all times, in spite of damage caused by unintentional errors or attacks. All current systems suffer eventual failure due to the accumulated effects of errors or attacks. The SRS program aims to develop technologies enabling military systems to learn, regenerate themselves, and automatically improve their ability to deliver critical services. If successful, self-regenerative systems will show a positive trend in reliability, actually exceeding initial operating capability and approaching a theoretical optimal performance level over long time intervals.
Situation Aware Protocols in Edge Network Technologies (SAPIENT)
The mission of the Situation Aware Protocols in Edge Network Technologies (SAPIENT) program is to create a new generation of adaptive communication systems that achieve new levels of functionality through situation-awareness.
Transfer Learning (TL)
The TRANSFER LEARNING PROGRAM SEEKS TO SOLVE THE PROBLEM OF REUSING KNOWLEDGE DERIVED IN ONE DOMAIN TO HELP EFFECT SOLUTIONS IN ANOTHER DOMAIN. Adaptive systems, systems that respond to changes in their environment, stand to benefit significantly from the application of TL technology. Today's adaptive systems need to be trained for every new situation they encounter. This requires building new training data, which is the most expensive and most limiting aspect of deploying such systems. The TL PROGRAM ADDRESSES THIS SHORTCOMING BY IMBUING ADAPTIVE SYSTEMS WITH THE ABILITY TO ENCAPSULATE WHAT THEY HAVE LEARNED AND APPLY THIS KNOWLEDGE TO NEW SITUATIONS. Thus, rather than having to be retrained for each new context, TL enables systems to leverage what they have already learned in order to be effective much sooner and with less effort spent on training. Early applications of TL technology include adaptive ISR systems, robotic vision and manipulation, and automated population of databases from unstructured text.

Command & Control @


Command and control is the exercise of authority and direction by a properly designated commander over assigned and attached forces in the accomplishment of a mission. Without question the missions faced by our warfighters today (such as counter-insurgency) and the operational environments (such as cities) are more complex and dangerous than ever before. While following their rules of engagement, warfighters must make rapid decisions based on limited observables interpreted in the context of the evolving situation. Command and control systems must augment the observables within constrained timelines and present actionable results to the warfighter. IPTO ENABLES WARFIGTER SUCCESS BY CREATING TECHNOLOGIES AND SYSTEMS THAT PROVIDE TAILORED, CONSISTENT, PREDICTIVE SITUATION AWARENESS ACROSS ALL COMMAND ELEMENTS, AND CONTINUOUS SYNCHRONIZATION OF SENSING, STRIKE, COMMUNICATIONS, AND LOGISTICS TO MAXIMIZE THE EFFECTIVENESS OF MILITARY OPERATIONS WHILE MINIMIZING UNDESIRABLE SIDE EFFECTS. In counter-insurgency operations, targets of interest are often not known until a significant event (e.g. detonation of IED) occurs. In those instances, reliably and quickly determining the origin of the devices/vehicles becomes the key to preventing subsequent attacks. IPTO is creating systems that collect wide area observables in the absence of any strong a priori cues, analyze the prior time history of events and track insurgent activities to their point of origin.



Conflict Modeling, Planning, and Outcomes Experimentation (COMPOEX)
DARPA's Conflict Modeling, Planning, and Outcomes Experimentation (COMPOEX) program is developing a suite of tools that will help military commanders and their civilian counterparts to plan, analyze and conduct complex campaigns. "Complex" here refers to those operations - often of long duration and large scale - that require careful consideration of not only traditional military, but also political, social, economic actions and ramifications.
Deep Green (DG)
The Deep Green concept is an innovative approach to using simulation to support ongoing military operations while they are being conducted. The basic approach is to MAINTAIN A STATE-SPACE GRAPH OF POSSIBLE FUTURE STATES. SOFTWARE AGENTS USE INFORMATION ON THE TRAJECTORY OF THE ONGOING OPERATION, VICE A PRIORI STAFF ESTIMATES AS TO HOW THE BATTLE MIGHT UNFOLD, AS WELL AS SIMULATION TECHNOLOGIES, TO ASSESS THE LIKELIHOOD OF REACHING SOME SET OF POSSIBLE FUTURE STATES. THE LIKELIHOOD, UTILITY, AND FLEXIBILITY OF POSSIBLE FUTURE NODES IN THE STATE SPACE GRAPH ARE COMPUTED AND EVALUATED TO FOCUS THE PLANNING EFFORTS. This notion is called anticipatory planning and involves the generation of options (either manual or semi-automated) ahead of "real time," before the options are needed. In addition, the Deep Green concept provides mechanisms for adaptive execution, which can be described as "late binding," or choosing a branch in the state space graph at the last moment to maintain flexibility. By using information acquired from the ongoing operation, rather than assumptions made during the planning phase, commanders and staffs can make more informed choices and focus on building options for futures that are becoming more likely.
Heterogeneous Airborne Reconnaissance Team (HART)
The complexity of counter-insurgency operations especially in the urban combat environment demands multiple sensing modes for agility and for persistent, ubiquitous coverage. The HART system implements collaborative control of reconnaissance, surveillance and target acquisition (RSTA) assets, so that the information can be made available to warfighters at every echelon.
Persistent Operational Surface Surveillance and Engagement (POSSE)
The POSSE program is building a REAL-TIME, ALL-SOURCE EXPLOITATION SYSTEM TO PROVIDE INDICATIONS AND WARNINGS OF INSURGENT ACTIVITY DERIVED FROM AIRBORNE AND GROUND-BASED SENSORS. Envisioning a day when our sensors can be integrated into a cohesive "ISR Force", it's building AN INTEGRATED SUITE OF SIGNAL PROCESSING, PATTERN ANALYSIS, AND COLLECTION MANAGEMENT SOFTWARE that will increase reliability, reduce manpower, and speed up responses.
Predictive Analysis for Naval Deployment Activities (PANDA)
The current CONOPS for achieving situation awareness in the maritime domain calls for close monitoring of those entities that we already have reason to be concerned about (i.e., we already suspect are threats or which carry cargos that could be dangerous in the hands of the wrong people). PANDA will ADVANCE TECHNOLOGIES AND DEVELOP AN ARCHITECTURE THAT WILL ALERT WATCHSTANDERS TO ANOMALOUS SHIP behavior AS IT OCCURS, allowing them to detect potentially dangerous behavior before it causes harm. These technologies and systems will be transitioned to various partners and customers throughout the development process, ensuring that the end product meets the needs of the services and watchstanders. Participants will work closely with the transition partners to aid in this process.
Urban Leader Tactical Response, Awareness & Visualization (ULTRA-Vis)
Current military operations are focusing efforts on urban and asymmetric warfare, as well as distributed operations, but small unit leaders lack the capability to issue commands and share mission-relevant information in an urban environment non-line-of-sight. Various factors that can impact mission effectiveness and tempo of operations are:
1. Leaders communicate by shouting and hand signals;
2. Teams operate within earshot and line-of-sight;
3. Intra-squad radios are hard to hear; and
4. Leaders must stop to use handheld displays.
Military operations in the urban terrain (extensive areas with hostile forces, non-combatant populations, and complex infrastructure) require special capabilities and agility to conduct close-combat operations under highly dynamic, adverse conditions. In short, tactical leaders need the ability to adapt on the move, coordinate small unit actions and execute commands across a wider area of engagement. SIGNIFICANT TACTICAL ADVANTAGES COULD BE REALIZED THROUGH THE SMALL UNIT LEADER'S ABILITY TO INTUITIVELY GENERATE/ROUTE COMMANDS AND TIMELY ACTIONABLE COMBAT INFORMATION TO THE APPROPRIATE TEAM OR INDIVIDUAL WARFIGHTER IN A READILY UNDERSTOOD FORMAT THAT AVOIDS INFORMATION OVERLOAD.

High Productivity Computing @




Programs [there was currently no available description for these programs]

Architecture-Aware Compiler Environment (AACE)
Disruptive Manufacturing Technology, Software Producibility (DMT-SWP)
High Productivity Computing Systems (HPCS)

Language Processing @


At present, the exploitation of foreign language speech and text is slow and labor intensive and as a result, the availability, quantity and timeliness of information from foreign-language sources is limited. IPTO is creating NEW TECHNOLOGIES AND SYSTEMS FOR AUTOMATING THE TRANSCRIPTION AND TRANSLATION OF FOREIGN LANGUAGES. These language processing capabilities will enable our military to exploit large volumes of speech and text in multiple languages, thereby increasing situational awareness at all levels of command. In particular, IPTO is AUTOMATING THE CAPABILITY TO MONITOR FOREIGN LANGUAGE MEDIA AND TO EXPLOIT FOREIGN LANGUAGE NEWS BROADCASTS with one-way (foreign-language-to-English) translation technologies. IPTO is also DEVELOPING HAND-HELD, TWO-WAY (FOREIGN-LANGUAGE-TO-ENGLISH AND ENGLISH-TO-FOREIGN-LANGUAGE) SPEECH-TO-SPEECH TRANSLATION SYSTEMS that enable the warfighter on the ground to communicate directly with local populations in their native language. Finally, IPTO is creating TECHNOLOGIES TO EXPLOIT THE INFORMATION CONTAINED IN HARD-COPY DOCUMENTS AND DOCUMENT IMAGES RESIDENT ON MAGNETIC AND OPTICAL MEDIA CAPTURED IN THE FIELd. Making full use of all of the information extracted from foreign-language sources REQUIRES THE CAPABILITY TO AUTOMATICALLY COLLATE, FILTER, SYNTHESIZE, SUMMARIZE, AND PRESENT RELEVANT INFORMATION IN TIMELY AND RELEVANT FORMS. IPTO is DEVELOPING NATURAL LANGUAGE PROCESSING SYSTEMS TO ENHANCE LOCAL, REGIONAL AND GLOBAL SITUATIONAL AWARENESS AND ELIMINATE THE NEED FOR TRANSLATORS AND SUBJECT MATTER EXPERTS AT EVERY MILITARY SITE WHERE FOREIGN-LANGUAGE INFORMATION IS OBTAINED.




Global Autonomous Language Exploitation (GALE)
The goal of the GALE (Global Autonomous Language Exploitation) program is to DEVELOP AND APPLY COMPUTER SOFTWARE TECHNOLOGIES TO ABSORB, TRANSLATE, ANALYZE, AND INTERPRET HUGE VOLUMES OF SPEECH AND TEXT IN MULTIPLE LANGUAGES, eliminating the need for linguists and analysts, and automatically providing relevant, concise, actionable information to military command and personnel in a timely fashion. Automatic processing "engines" will convert and distill the data, delivering pertinent, consolidated information in easy-to-understand forms to military personnel and monolingual English-speaking analysts in response to direct or implicit requests.
Multilingual Automatic Document Classification Analysis and Translation (MADCAT)
The United States has a compelling need for reliable information affecting military command, soldiers in the field, and national security. Currently, our warfighters encounter foreign language images in many forms, including, but not limited to graffiti, road signs, printed media, and captured records in the form of paper and computer files. Given the quantity of foreign language material, it is difficult to interpret the salient pieces of information, much of which is either ignored or analyzed too late to be of any use. The mission of the Multilingual Automatic Document Classification Analysis and Translation (MADCAT) Program is to AUTOMATICALLY CONVERT FOREIGN LANGUAGE TEXT IMAGES INTO ENGLISH TRANSCRIPTS, thus eliminating the need for linguists and analysts while automatically providing relevant, distilled actionable information to military command and personnel in a timely fashion.
Spoken Language Communication and Translation System for Tactical Use (TRANSTAC)
Today, phrase-based translation devices are being tactically deployed. These one-way devices translate English input into pre-recorded phrases in target languages. While such systems are useful in many operational settings, the inability to translate foreign speech into English is a significant limitation. The mission of the Spoken Language Communication and Translation System for Tactical Use (TRANSTAC) program is to demonstrate capabilities to rapidly develop and field TWO-WAY TRANSLATION SYSTEMS THAT ENABLE SPEAKERS OF DIFFERENT LANGUAGES TO SPONTANEOUSLY COMMUNICATE WITH ONE ANOTHER IN REAL-WORLD TACTICAL SITUATIONS.

Sensors & Processing @


U.S. forces and sensors are increasingly networked across service, location, domain (land, sea and air), echelon, and platform. This trend increases responsiveness, flexibility and combat effectiveness, but also increases the inherent complexity of sensor and information management. IPTO is CREATING SYSTEMS THAT CAN DERIVE HIGH-LEVEL INFORMATION FROM SENSOR DATA STREAMS (FROM BOTH MANNED AND UNMANNED SYSTEMS), PRODUCE MEANINGFUL SUMMARIES OF COMPLEX DYNAMIC SITUATIONS, AND SCALE TO THOUSANDS OF SOURCES. Future battlefields will continue to be populated with targets that use mobility and concealment as key survival tactics, and high-value targets will range from quiet submarines, to mobile missile/artillery, to specific individual insurgents. IPTO develops and demonstrates system CONCEPTS THAT COMBINE NOVEL APPROACHES TO SENSING, SENSOR PROCESSING, SENSOR FUSION, AND INFORMATION MANAGEMENT TO ENABLE PERVASIVE AND PERSISTENT SURVEILLANCE OF THE BATTLESPACE AND DETECTION, IDENTIFICATION, TRACKING, ENGAGEMENT AND BATTLE DAMAGE ASSESSMENT FOR HIGH-VALUE TARGETS IN ALL WEATHER CONDITIONS AND IN ALL POSSIBLE COMBAT ENVIRONMENTS. Finally, warfighters in the field must concentrate on observing their immediate environment but at the same time must maintain awareness of the larger battlespace picture, and as a result they are susceptible to being swamped by too much detail. IPTO is creating system approaches that can exploit context and advanced information display/presentation techniques to overcome these challenges.



Autonomous Real-time Ground Ubiquitous Surveillance - Imaging System (ARGUS-IS)
The mission of the Autonomous Real-time Ground Ubiquitous Surveillance - Imaging System (ARGUS-IS) program is to provide military users a flexible and responsive capability to find, track and monitor events and activities of interest on a continuous basis in areas of interest.
The overall objective is to increase situational awareness and understanding enabling an ability to find and fix critical events in a large area in enough time to influence events. ARGUS - IS provides military users an "eyes-on" persistent wide area surveillance capability to support tactical users in a dynamic battlespace or urban environment.
FOPEN Reconnaissance, Surveillance, Tracking and Engagement Radar (FORESTER)
The Foliage Penetration Reconnaissance, Surveillance, Tracking and Engagement Radar (FORESTER) is a joint DARPA/Army program to develop and demonstrate an advanced airborne UHF radar capable of detecting people and vehicles moving under foliage. FORESTER will provide robust, wide-area, all-weather, persistent stand-off coverage of moving vehicles and dismounted troops under foliage, filling the surveillance gap that currently exists.
Multispectral Adaptive Networked Tactical Imaging System (MANTIS)
The MANTIS program will develop, integrate and demonstrate A SOLDIER-WORN VISUALIZATION SYSTEM, CONSISTING OF A HEAD-MOUNTED MULTISPECTRAL SENSOR SUITE WITH A HIGH RESOLUTION DISPLAY AND A HIGH PERFORMANCE VISION PROCESSOR (ASIC), CONNECTED TO A SOLDIER-WORN POWER SUPPLY AND RADIO. The helmet-mounted MANTIS Vision Processor will provide the soldier with digitally fused, multispectral video imagery in real time from the Visible/Near Infrared (VNIR), the Short Wave Infrared (SWIR) and the Long Wave Infrared (LWIR) helmet-mounted sensors via the high resolution visor display. The processor adaptively fuses the digital imagery from the multispectral sensors providing the highest context, best nighttime imagery in real-time under varying battlefield conditions. The system also ALLOWS THE VIDEO IMAGERY TO BE RECORDED AND PLAYED BACK ON DEMAND AND ALLOWS THE OVERLAY OF BATTLEFIELD INFORMATION. MANTIS will exploit the existing soldier radio network and PROVIDE SOLDIER-TO-SOLDIER SHARING OF VIDEO CLIPS VIEWED AS PICTURE-IN-PICTURE ON THEIR HELMET MOUNTED DISPLAYS. MANTIS WILL "regain the nighttime advantage" and "EXPLOIT THE NET" TO PROVIDE THE INDIVIDUAL SOLDIER WITH UNPRECEDENTED SITUATIONAL AWARENESS.
NetTrack (NT)


Quint Networking Technology (QNT)
In a network centric battle space, U.S. Forces must exploit distributed sensor platforms to rapidly and precisely find, fix, track, and engage static and moving targets in real time. There are several relevant thrusts to time critical targeting and strike areas within the Services. One aspect of these thrusts is to use data links to fully integrate tactical UAVs, dismounted ground forces and weapon control into the future network centric warfare environment.
TheQuint Networking Technology (QNT) is a modular network data link program focused on providing a multi-band modular capability to close the seams between five nodes - Aircraft, UCAV, Weapons, tactical UAV and dismounted ground forces. The specific intended QNT hardware users are weapons, air control forces on the ground (dismounted) and tactical UAV's. These three are the focal points of the QNT effort with the other two elements using hardware and waveforms from established programs. The assumption is these other two types of platforms provide a starting point for building capability for the other three elements.
Standoff Precision ID in 3-D (SPI-3D)
The SPI-3D program will develop and demonstrate the ability to provide precision geolocation of ground targets combined with high-resolution 3D imagery at useful standoff ranges. These dual capabilities will be provided using a sensor package composed of commercially available components. It will be capable of providing "optical quality precision at radar standoff ranges" and have the ability to overcome limited weapons effects obscuration, and penetrate moderate foliage. The figure below shows the operational concept of the SPI-3D system.
Urban Reasoning and Geospatial Exploitation Technology (URGENT)
The recognition of targets in urban environments poses unique operational challenges for the warfighter. Historically, target recognition has focused on conventional military objects, with particular emphasis on military vehicles such as tanks and armored personnel carriers. In many cases, these threats exhibit unique signatures and are relatively geographically isolated from densely populated areas. The same cannot be said of today's asymmetric threats, which are embedded in urban areas, thereby forcing U.S. Forces to engage enemy combatants in cities with large civilian populations. Under these conditions, even the most common urban objects can have tactical significance: trash cans can contain improvised explosive devices, doors can conceal snipers, jersey barriers can block troop ingress, roof tops can become landing zones, and so on. Today's urban missions involve analyzing a multitude of urban objects in the area of regard. As military operations in urban regions have grown, the need to identify urban objects has become an important requirement for the military. URGENT WILL ENABLE UNDERSTANDING THE LOCATIONS, SHAPES, AND CLASSIFICATIONS OF OBJECTS FOR A BROAD RANGE OF PRESSING URBAN MISSION PLANNING ANALYTICAL QUERIES (E.G., FINDING ALL ROOF TOP LANDING ZONES ON THREE STORY BUILDINGS CLEAR OF VERTICAL OBSTRUCTIONS AND VERIFYING INGRESS ROUTES WITH MAXIMUM COVER FOR GROUND TROOPS). IN ADDITION, URGENT WILL ENABLE AUTOMATED TIME-SENSITIVE SITUATION ANALYSIS (E.G., ALERTING FOR VEHICLES FOUND ON A ROAD SHOULDER AFTER DARK AND ESTIMATING DAMAGE TO A BUILDING EXTERIOR AFTER AN EXPLOSION) THAT WILL MAKE A SIGNIFICANT POSITIVE IMPACT ON URBAN OPERATIONS.
Vehicle and Dismount Exploitation Radar (VADER)
Video and Image Retrieval and Analysis Tool (VIRAT)

Emerging Technologies @





Advanced Speech Encoding (ASE)
Speech is the most natural form of human-to-human communications. However, THE MILITARY IS OFTEN FORCED TO OPERATE IN ENVIRONMENTS WHERE SPEECH IS DIFFICULT. For example, the quality and intelligibility of the acoustic signal can be severely degraded by HARSH ACOUSTIC NOISE BACKGROUNDS that are common in military environments. In addition, many situations also require war fighters to operate in silence and in a stealth mode so that their presence and intent are not compromised. THE ADVANCED SPEECH ENCODING (ASE) PROGRAM WILL DEVELOP TECHNOLOGY THAT WILL ENABLE COMMUNICATION IN THESE CHALLENGING MILITARY ENVIRONMENTS.
Information Theory for Mobile Ad-Hoc Networks (ITMANET)
The mission of the Information Theory for Mobile Ad-Hoc Networks (ITMANET) program is TO DEVELOP AND EXPLOIT MORE POWERFUL INFORMATION THEORY CONCERNING MOBILE WIRELESS NETWORKS. The hypothesis of this program is that a specific challenge problem --- better understanding of MANET capacity limits --- will lead to actionable implications for network design and deployment. The anticipated byproducts of a more evolved theory include new separation theorems to inform wireless network "layering" as well as new protocol ideas.
Integrated Crisis Early Warning System (ICEWS)
The Integrated Crisis Early Warning System (ICEWS) program seeks to DEVELOP A COMPREHENSIVE, INTEGRATED, AUTOMATED, GENERALIZABLE, AND VALIDATED SYSTEM TO MONITOR, ASSESS, AND FORECAST NATIONAL, SUB-NATIONAL, AND INTERNATIONAL CRISES IN A WAY THAT SUPPORTS DECISIONS ON HOW TO ALLOCATE RESOURCES TO MITIGATE THEM. ICEWS will provide Combatant Commanders (COCOMs) with a powerful, systematic capability to anticipate and respond to stability challenges in the Area of Responsibility (AOR); allocate resources efficiently in accordance to the risks they are designed to mitigate; and track and measure the effectiveness of resource allocations toward end-state stability objectives, in near-real time.
TheRealWorld program exploits technology innovation to PROVIDE EVERY WARFIGHTER WITH THE ABILITY TO OPEN A LAPTOP COMPUTER AND RAPIDLY CREATE A MISSION-SPECIFIC SIMULATION IN A RELEVANT GEO-SPECIFIC 3-D WORLD. currently, major simulation programs are time consuming, expensive, and require graduate-level expertise in computer programming. realworld will remove these barriers and, for the first time, PUT THE TACTICAL ADVANTAGE OF REAL-TIME SIMULATION DIRECTLY INTO THE HANDS OF THE WARFIGHTER.





DARPA s Defense Sciences Office (DSO) is supporting the Systems of Neuromorphic Adaptive Plastic Scalable Electronics, or SyNAPSE, project. It s goal, according to its April 8, 2008 BAA (Broad Agency Announcement) is to create a system with roughly: the same number of neurons (they want 10^10); same number of synapses (they want 10^14); and same power as the human brain --- that will fit in a volume of 2 liters or less, and will draw less than one kilowatt of electric power..


TheSyNAPSE BAA says:


The vision for the anticipated DARPA SyNAPSE program is the enabling of electronic neuromorphic machine technology that is scalable to biological levels. Programmable machines are limited not only by their computational capacity, but also by an architecture requiring (human-derived) algorithms to both describe and process information from their environment. In contrast, biological neural systems (e.g., brains) autonomously process information in complex environments by automatically learning relevant and probabilistically stable features and associations .




Architectures will support critical structures and functions observed in biological systems such as connectivity, hierarchical organization, core component circuitry, competitive self-organization, and modulatory/reinforcement systems. As in biological systems, processing will necessarily be maximally distributed, nonlinear, and inherently noise- and defect-tolerant.


Guilio Tononi, who has developed An information integration theory of consciousness (described at ), is working on theSyNAPSE project. As is stated in Cognitive computing: Building a machine that can learn from experience (at ), Tononi is part of a team that will be developing a prototype, small-mammal-brain-powered, neuromorphic AGI for the SyNAPSE project.


Tononi, professor of psychiatry at the UW-Madison School of Medicine and Public Health and an internationally known expert on consciousness, is part of a team of collaborators from top institutions who have been awarded a $4.9 million grant from the Defense Advanced Research Projects Agency (DARPA) for the first phase of DARPA's Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project.


Tononi and scientists from Columbia University and IBM will work on the "software" for the thinking computer, while nanotechnology and supercomputing experts from Cornell, Stanford and the University of California-Merced will create the "hardware." Dharmendra Modha of IBM is the principal investigator.


'The idea is to create a computer capable of sorting through multiple streams of changing data, to look for patterns and make logical decisions.


There's another requirement: The finished cognitive computer should be as small as a the brain of a small mammal and use as little power as a 100-watt light bulb. It's a major challenge. But it's what our brains do every day.


One of the keys to making the types of compact, low-power, extremely powerful supercomputers SyNAPSE envisions within in this coming decade is the memsistor.


This is because memristors enable a synapse to be modeled much more compactly than ever before possible. Memristors are a type of resistor in which the resistance can be varied by changing the magnitude or direction of current passed through it, and can be remembered until the next time it is changed. Hewlet-Packard is currently the world s leading developer of memsistor technology and is an important part of the DARPA s SyNAPSE program.


An article at states the following about the role of memristors in the SyNAPSE project:


So now we've found [memristors], might a new era in artificial intelligence be at hand? The Defense Advanced Research Projects Agency certainly thinks so. DARPA is a US Department of Defense outfit with a strong record in backing high-risk, high-pay-off projects - things like the internet. In April last year, it announced the Systems of Neuromorphic Adaptive Plastic Scalable Electronics Program,SyNAPSE for short, to create "electronic neuromorphic machine technology that is scalable to biological levels".


Williams's team from Hewlett-Packard is heavily involved. Late last year, in an obscure US Department of Energy publication called SciDAC Review, his colleague Greg Snider set out how a memristor-based chip might be wired up to test more complex models of synapses. He points out that in the human cortex synapses are packed at a density of about 10^10 per square centimetre, whereas today's microprocessors only manage densities 10 times less. "That is one important reason intelligent machines are not yet walking around on the street," he says.


'Snider's dream is of a field he calls "cortical computing" that harnesses the possibilities of memristors to mimic how the brain's neurons interact. It's an entirely new idea. "People confuse these kinds of networks with neural networks," says Williams. But neural networks - the previous best hope for creating an artificial brain - are software working on standard computing hardware. "What we're aiming for is actually a change in architecture," he says.


'The first steps are already being taken. Williams and Snider have teamed up with Gail Carpenter and Stephen Grossberg at Boston University, who are pioneers in reducing neural behaviours to systems of differential equations, to create hybrid transitor-memristor chips designed to reproduce some of the brain's thought processes. Di Ventra and his colleague Yuriy Pershin have gone further and built a memristive synapse that they claim behaves like the real thing(


'The electronic brain will be a time coming. "We're still getting to grips with this chip," says Williams. Part of the problem is that the chip is just too intelligent - rather than a standard digital pulse it produces an analogue output that flummoxes the standard software used to test chips. So Williams and his colleagues have had to develop their own test software. "All that takes time," he says.



Two recent articles point out successes HP is making in developing memristors. This progress is so impressive that memristors may well become the major form of long anticipated universal memories (i.e., memory that can be used substantially like SRAM, DRAM, and flash are today. But first ways will have to be found to substantially increase how many times memsistor can have their values changed far beyond the number of times flash memory can be changed. People at HP currently claim to be confidient they can achieve such increases.



An April 7, 2010 NYTimes article (at ) reported Hewlett-Packard has been making significant progress on memsistor technology. In part it said:


they had devised a new method for storing and retrieving information from a vast three-dimensional array of memristors. The scheme could potentially free designers to stack thousands of switches in a high-rise fashion, permitting a new class of ultradense computing devices even after two-dimensional scaling reaches fundamental limits


The most advanced transistor technology today is based on minimum feature sizes of 30 to 40 nanometers and Dr. Williams said that H.P. now has working 3-nanometer memristors that can switch on and off in about a nanosecond, or a billionth of a second.


'He said the company could have a competitor to flash memory in three years that would have a capacity of 20 gigabytes a square centimeter.


An April 9, 3010 article from EEtimes (at ) stated


Hewlett-Packard has demonstrated memristors ("memory resistors") cast in an architecture that can be dynamically changed between logic operations and memory storage. The configurable architecture demonstrates "stateful logic" that HP claims could someday obsolete the dedicated central-processing unit (CPU) by enabling dynamically changing circuits to maintain a constant memory of their state


HP showed that memristive devices could use stateful logic to perform material implication a "complete" operator that can be interconnected to create any logical operation, much as early supercomputers were made from NAND gates. Bertrand Russell espoused material implication in Principia Mathematica, the seminal primer on logic he co-authored with Alfred Whitehead, but until now engineers have largely ignored the concept.


HP realized the material implication gate with one regular resistor connected to two memristive devices used as digital switches (low resistance for "on" and high resistance for "off"). By using three memristors, HP could have realized a NAND gate and thus re-created the conditions under which earlier supercomputers were conceived. But HP claims that material implication is better than NAND for memristive devices, because material implication gates can be cast in an architecture that uses them as either memory or logic, enabling a device whose function can be dynamically changed.


All these article indicate advances in memristors might well hasten the day when human-level AGI s are created.



For more information on the SyNAPSE project look at the following two links


IBM also has part of the SyNAPSE contract as is discussed in the last half of


For DSO s current brief summary of the project see










To deal with the Singularity, and the concentrations in power it can bring, a society s collective intelligence should be able to discuss controversial allegations, and marshal the best evidence available to indicate which of them seem fair or false. This is FIG. 21 from U.S. Patent Application Publication US 2002/0059272, now in the public domain. It describes, in patentese, an intelligent public forum that is a free-form, selectively-collapsible outline of text and media ordered by collaborative ranking and editing. It is viewed through user-selectable combinations of one or more individual or collaborative filterers. (In this hypothetical screen, the view is filtered by all users equally.) More advanced forms of such an intelligent forum are described below under the heading Increasing Collective Intelligence *Before* The Singularity Takes Off .






Over the next several decades there will be an explosion in the rate of technical development. The change is expected to be so great --- many call it the Singularity. It will drastically transform our --- economy --- society --- values --- bodies --- and minds --- in ways that could be very good --- or very bad.


This explosion will be fueled by the ever increasing power of computers. Within two decades machine intelligence is likely to vastly surpass all the powers of the human brain. This will produce superintelligences that can perform --- learning --- understanding --- mathematical --- scientific --- programming --- engineering --- robotic --- manufacturing --- and --- human interfacing --- related mental tasks much faster, better, and less expensively than humans. (For reasons why superintelligence will probably happen so soon, see[ on this page at ] )


Thissuperintelligence will enable breathtaking advances in many other technologies, including: --- nano-electronics --- quantum computing --- networking --- brain science --- brain manipulation, interfacing, & augmentation --- medicine --- life extension --- biotechnology --- genetic engineering --- synthetic biology --- nanotechnology --- molecular & self-organizing manufacturing --- robotics --- nano-robotics --- energy --- sensor networks --- surveillance --- weaponry --- cyber crime & warfare --- and --- interactive virtual worlds, friends, and lovers --- ones more detailed, interactive, and exciting than those that are real.


The Singularity will NOT occur in a vacuum.


It will NOT occur in a realm of pure science, engineering, or philosophy. It will NOT occur in one instant, one year, or one decade.


Instead, it WILL occur --- over multiple decades --- in the real world --- one dominated by struggles for --- personal --- corporate --- political --- and national --- survival, money, and power. How the Singularity s wildly transformative technologies will be developed and deployed will be decided largely by collective entities --- by corporations --- governments --- political parties --- militaries --- bureaucracies --- interest groups --- criminal gangs --- the media --- and public opinion.


The increasing rate and degree of change made possible by the Singularity --- and the power it could give a very few to benefit --- or harm --- very many --- will tend to make the world much less stable --- and much more difficult for human institutions to govern.


In three to five decades the Singularity could drastically increase the world s production of food and necessities ---and/or--- replace almost all human work for pennies an hour in a way that would prevent most people from earning a living. It could create a relatively evenly shared plenty --- or --- extremely concentrated wealth and power. It could enable a plurality of free, networked voices and collaboration --- or --- enable machines to watch everything people do, say, and think --- and punish those who disobey. It could greatly lengthen life and health --- or --- create synthetic life forms that accidentally wreack global havoc and death. It could create machines that greatly empower individual humans and their minds --- or --- enable superintelligences --- controlled by one group, one person, or one system of machines --- to hack into --- and take control of --- virtually all the machines upon which humans depend --- so as to enslave or kill most, or all, of humanity.


We cannot stop the advent of superintelligence. Too many people already know how much --- technological --- economic --- political --- and --- military --- advantage can be gained by the nations and corporations that are first to substantially deploy it. It cannot be stopped because computer technology and our understanding of intelligence are already so advanced --- that most of the world s leading nations and technology companies could develop it --- within roughly a decade --- if they tried.


It is arguable that we will actually --- need --- superintelligence. It is possible that without it we might not be able to deal with many of the problems the world already is facing. It is also possible that if we were smart enough --- as a species --- we could learn how to use it relatively safely to create tremendous benefits for mankind.


Given the complex, and rapidly-changing mix of choices, promises, and threats the Singularity will present --- if humanity is to have any chance of surviving well through this century --- we must harness the coming explosion of technology --- itself --- to vastly increase our collective intelligence, wisdom, and responsibility.


If we --- as a species --- are intelligent enough to design machines that think much more efficiently than we do --- then why can t we also design technology to enable groups of humans --- when connected by the internet --- and augmented by machine superintelligences --- to think together much, much more intelligently, fairly, and responsibly?


Current computer and internet hardware is already powerful enough to substantially increase humanity s collective intelligence. And with the technology of the coming decades we will be able to increase our collective intelligence much, much more.


The major barriers will not be technological. They will come --- from human nature --- from religious, cultural, and national values --- and from selfish interests.



Seen from a system-wide viewpoint --- the current collective intelligence of many human institutions is stunningly stupid. Here are just a few examples:


-The 2008 collapse of the world s financial markets was caused by: --- the false meme that average American residential real estate values never drop (although they had done so twice within the previous eighty years) --- a system of short-term incentives that rewarded people for taking breathtakingly irresponsible risks with other people s money --- and --- by financial rating agencies and legislators that were --- in effect --- bribed to ignore such dangerous risks.


- America s Social Security Trust Fund has been an obvious, worthless, sham for decades. It has no net worth. It is nothing but IOU s from the federal government to itself. The trillions of dollars of Treasury bonds the fund holds have no more value to the federal government than the blank paper it is free --- at any time --- to print into new notes for equal amounts and try to sell --- (i.e. borrow with). And yet our public forum is so dysfunctional that most politicians, media voices, and citizens have acted for decades as if the trust fund had many trillions of dollars of --- actual --- worth. The second President Bush hinted the trust fund s bonds had little worth --- during his ill-fated attempt to reform Social Security --- but, for political reasons, he was not willing to drive home just what an obvious, and harmful, lie both political parties had been telling the American people for decades.


-America s federal government has been so short sighted it has run up --- over many decades and under both political parties --- fifty to eighty trillion dollars of unfunded obligations that will come due in the next few decades. These obligations are highly likely --- given most current economic predictions --- to throw our country into an economic crisis much, much deeper than that we are currently in. That is, unless the federal government has the political will to substantially raise taxes or reduce the benefits it has promised under many entitlement programs and pension agreements. Most political observers believe our government will make such difficult changes --- but only after our economy has been so drastically harmed by this expected debt crisis that our government will be absolutely forced to do so.


As these examples --- and thousands of others that could be listed --- indicate --- many of society's current collective systems are not intelligent enough to deal with many of our current problems.


If this is true --- it is almost certain that such systems will not be smart and wise enough to deal well with the much more disruptive choices, changes, and challenges the Singularity will bring.





This is the subject I would like to see discussed under this topic.


I will start by adding some of my own thoughts below. But I look forward to hearing yours.

[Currently comment can be placed at under corresponding text at and ]




For some thinking on how to make democratic government more intelligent with today s technology, go to:


MIT's Center for Collective Intelligence at , including their experiment in collective intelligence using climate change as the test subject at


the Personal Democracy Forum at , and in particular to their anthology of essays on the subject at It's Time to Reboot America at


For a video describing one currently proposed collective intelligence system see (Thanks to TransAlchemy for pointing this out under the Sousveillance topic)


For a more futurist discussion that relates to how the Singularity might effect human governance, read Goertzel and Bugaj s article on Sousveilance at





Much of important human behavior is performed by groups of people --- such as businesses, militaries, governments, and the voting public. These collections are capable of acting like thinking entities --- by obtaining information, making decisions, and taking actions.


Collective intelligence is the art and science of increasing the intelligence and effectiveness of human collective entities. Legal systems, military hierarchies, corporate management systems, and Robert s Rules of order, are just a few examples of forms of collective intelligence that were started long before the computer/internet era.


In one or two decades an explosion in the rate of technology --- called the Singularity --- will start. It will present humanity with a rapidly growing set of extremely powerful and transformative technologies --- ones that can be used to greatly change --- benefit --- and/or --- harm mankind. For the world's governments and human institutions to deal well with these extremely rapid and profound changes we need much more enlightened and powerful collective intelligence.


If democratic and reasonably egalitarian human values are to survive into the Singularity --- humanity will need a much more intelligent public forum. This forum should be fully empowered by the potential of the internet, computers, artificial intelligence, and --- within two decades --- by machine superintelligence. It should be designed to increasingly enlighten public opinion and debate --- and --- to increasingly transfer power --- from the narrow interests of politicians and political parties in winning elections and of individual corporations in making money --- to the broader interests of the people as a whole.


We will still want political office holders and corporations for many years to come. But we should be intelligent enough to design feedback loops --- including that of voters and consumers enlightened by an intelligent public forum --- that make politicians and corporations better serve society as a whole.


As we progress more deeply into the Singularity --- technology will give us tremendous options to change human existence. Human culture, human thinking, and human communication will increasingly be dominated by --- machine intelligence --- electronically or chemically altered states of mind --- and increased communications between minds and between minds and machines. These changes can be so great it might become difficult to distinguish what human does --- or should -- mean. It will increasingly become less clear what makes an unaltered, flesh-and-blood, person more human than, say --- a superintelligence that similates one or more uploaded human minds -- or --- a whale with a brain implant that gives it the added mental capabilities to converse in spoken human natural language on the web.


Since the only purpose of intelligence is to serve goals and values --- to be collectively intelligent --- humanity will have to decide what are its most important goals and values --- and to create feedback loops that encourage and reward human intelligence and behaviors that serve such purposes.


It is important that we humans have the collective wisdom to shape reality in ways that makes the transition into the future as happy, and meaningful, as possible --- for as many people as possible.





Current computer and internet technology already has the power to greatly improve the intelligence, effectiveness, and fairness of our society. For example, Wikipedia has demonstrated the tremendous potential of collective intelligence. I never cease to be amazed at what wonderfully concise and relatively understandable descriptions it provides on many extremely complex scientific subjects at which I am a relative novice--- such as string theory or relativistic space-time curvature --- and how often its coverage on many controversial social or historical subjects is more balanced than most articles on similar subjects in the mass media.


We should try to develop something that applies the virtues of Wikipedia and other forms of human networked collaboration to making democracy s free market place of ideas --- and its control of our government through public opinion --- much more intelligent, fair, and responsible.


To do so, we need an intelligent, collaborative, public forum. This forum should be a marketplace of ideas where everybody has an equal voice, but collaborative filtering and editing causes the most attention to be given to the most persuasive voices --- and causes those voices to be as articulate as possible.


On many issues, there is often no commonly agreed to notion of what is right or wrong --- important or unimportant. Thus, participants in the forum should be able to rapidly select between sets of human or algorithmic filters to rank what a given view of the forum presents --- as the most important, most articulate points for, against, and about policies and issues of interest to the user. A view that weights the rankings and votes of all participant s equally should be the default view, and will help people get an overview of how their city, state, nation, or the world --- as a whole --- views a given set of issues.


The intelligent forum should --- like Wikipedia --- build a permanent, but evolving, record. This record should be a collaboratively ranked, edited, commented, and voted-on, selection of media, arguments, and discussions on topics, issues and policies. Any filtered view of it will select which, and how many, of those entries are shown, and the ranked order and format in which they are shown. It should include the most important reporting and interpretations of news events --- as they occur --- and over time. It should also create a trace of the evolution of the forum, its entries, and its various rankings and votes over time.


The increasingly sophisticated natural language understanding tools that are starting to appear --- and that will get more sophisticated every year --- should be employed to enable a user to quickly find discussions, opinions, and facts of interest, including those that most relate to something a user wants to read, or register an opinion about. It should enable them to see what, if any posts, are closest to the opinions they want to express. It should enable them to view, edit, comment, vote upon, and/or increase their ranking of such prior posts. And it should enable them to enter new posts, if they think that would do a better job of promoting the opinions they want to express.


This public forum should be designed to be the electronic brain of society. It should be the major forum for communicating media related to entertainment, art, social connections, science, philosophy, religion, and --- (most relevant to this discussion) --- news, politics, and public policy. This way, almost all people will become fluent in its use. It should be designed to handle, mix, and index virtually all major media types. It should be created --- much as Google Wave is --- to provide a standard interface to such media and commenting --- so multiple cloud-service providers can display data in the forum --- even if it was entered using a different cloud provider.


It is vitally important the forum be built to be as resistant as possible to corruption by service providers, people attempting to bribe them, or hackers --- because when major issues are at stake, it is certain some powerful vested interests would --- if they could --- use unethical means to distort the forum in their favor. One of the most important tasks of collective intelligence --- going forward --- should be to highly motivate many of the best minds to understand how to keep our computers, the web, and the public forum as free from the malicious influence of hackers, and people trying to cheat such systems, as possible.


All news, political, or advertising media should remain permanently accessible (with continuing advertising-per-view rights belonging to the owners of such media). Such media should be subject to collaboratively edited and filtered commenting, to keep it more honest. Whenever political opinions or political and commercial ads are shown --- a viewer should be able to instantly view, comment, edit, rank, or vote on the collaboratively commented version of the ad --- as viewed through any one of a selection of filters. If this is done, whenever a news report, opinion piece, or advertisement pushes bad facts, products, policies, or politicians --- chances are users will be able to find evidence on how misleading the opinions or ads really are. This will decrease the value of opinions and advertising that are not honest, and increase the value of those that are --- something that would be very valuable in commerce, as well as in politics.


Such a forum should have a social networking component, to enable groups to form collective filters to represent a given viewpoint, constituency, or attempt at objectivity. It should be designed to help develop teams to --- review, rank, comment, and create edited versions of posts on given topic --- review complex issues or legislation --- and perform investigative journalism or research. Such teams should have their own intelligent forums --- in which only those allowed by the team can participate --- to enable them to coordinate their action. Those who create or manage such teams should be free to allocate ranking and voting power within their own forums. They should be able to use collaborative feedback within their own intelligent forum to provide social network credibility, status, and special privileges for the people whom others in the team think have done the most good work on such collective projects. Team members, as well as all other users of the network, should be allowed to temporarily delegate portions of their ranking and voting power to one or more teams whose work they like.


In such a system, users or teams should be able to collaboratively suggest, refine, rank, and finance requests for real world reporting, research, polls, or investigation on subjects of interest. The forum should have mechanisms for collaboratively awarding contracts, and paying, for such work. In general, it is important that methods be found to fund good reporting and investigative journalism at city, state, national, and global levels in the internet era. It is also important the public forum place an emphasis on reputation and evidence, and provide tools for helping people determine information regarding each.


Yes, most of the time spent in such a public forum will be dedicated to things like sports, celebrities, movies, and TV shows. But many of those who have the largest voices and contribute the most to decision making in the worlds of business, academia, the military, government, and media would be likely to use it in a more serious way --- as would many more ordinary citizens who are truly concerned about the future.


Yes,their would be fanatical and dishonest voices in such a forum --- such as those who say the earth-is-less-than-ten-thousand-years-old. But collaborative filtering could point out to any who were interested in knowing the truth, the best arguments and evidence for and against such voices --- and this would tend to greatly narrow the appeal of ideas that the substantial weight of evidence suggest are unreasonable.


If such an intelligent public forum can be designed to resist corruption and hacking --- good ideas will be much more likely to win out over bad ones --- no matter how much money is spent pushing the bad ones. In such a system, speech will be truly free, and the speech that is most heard or seen will be that ranked most important by people users trust. This will greatly reduce the current need for politicians in Washington to sell their souls for money to run substantially meaningless TV ads.


All of this could be done with current technology. It could involve combining features from Wikipedia, Google, YouTube, Google Wave, Digg, Facebook, Wolfram s Alpha, and other current web sites. It is important that network guru s, network companies, people who are interested in good governance, media producers, and public-minded copyright attorneys work to design such a largely open-source, unified, web system --- one that can be hosted by multiple companies to make it less subject to corruption or bias --- and one that will provide the money necessary for proper reporting and investigating of issues of importance..


It is important that humanity get its collective act together --- in an intelligent --- relatively democratic --- relatively egalitarian way --- before our human institutions have to take the reins of the --- wild --- strong --- fast --- pull into the strange new future --- of the Singularity.





Vernor Vinge said it is hard, or impossible, to predict what the world be like after the Singularity. But we can imagine the powers of some of the technologies that are likely to be available once the singularity does, in fact, take off. And we can try to think how thosetechologies could be used to make humanity more intelligent --- and better able to cope with the Singularity, itself.


The Singularity will probably produce clouds of machine superintelligence powerful enough to store --- in semantic deeps structure --- much of the data in all of the world s books, in all of the internet, and in all publicly available movies, video, audio, still pictures, and diagrams --- and to reason from that data thousands or millions of times faster than humans.


This will enable us to use speech, natural language, and vision interfaces to ask such superintelligences complex questions. Users will be able to ask, and see the answers to, such questions through a netbook, smart phone, head-mounted retinal-scanning computer, or, ultimately a wireless brain implant. The cloud will be able to clarify and answer such questions --- in a conversationally interactive manner --- with real-time, articulate text, speech, video, and animation. This will include providing the evidentiary support for any of the answer s assertions, if desired by the user. By roughly mid-century it is likely a query of the complexity of a legal opinion on a specific, novel problem --- one that would currently take a good human lawyer several days to prepare --- could be produced --- within seconds --- for the current advertising value of a Google search.


Such a cloud could similarly provide arguments and evidence for and against various proposals, and cite the reasons for trusting and distrusting various sources of evidence. And it could make models, simulations, and projections regarding complex economic, political, ecological, or scientific issues and proposals, and explain all the evidence and assumptions behind such models. In addition, such superintelligence could provide educations --- much better than any currently available --- to all of society . And it could also let people collaboratively think and act together in ways never before possible.


For example, people can use such intelligence to help them search, navigate, post to, edit, comment, and rank entries in a collaborative intelligent forum, like that described in the section above. Superintelligences could help humans to: --- instantly find the best information and arguments regarding a given topic, as ranked by people or machines they trust --- see the viewpoints on such issues that are held by other groups of people --- more rapidly research and compose articulate, well-supported entries in such a forum --- and --- more accurately check the validity or reputation of arguments, contributors, filterers, or evidence --- including providing metrics on the accuracy or efficacy of predictions or actions taken by individuals, groups, orsuperintelligences in the past.


Within one to four decades it is likely many people will have wireless, high bandwidth network interfaces in or on their brains. This will enable them to send information to, and receive it from, specific superintelligences, the intelligent cloud, and other humans similarly connected. This will let us communicate with much greater speed and fluidity with machines, other human minds, navigable representational spaces, and virtual realities. Machine superintelligence can be used to enable the thoughts of thousand, millions, or billions of people to be compared, summarized, filtered, and broadcast in real time --- or across time --- enabling people to --- in fact --- think together as one collective mind.


The Singularity may well enable what Goertzel and Bugaj call Sousveillance in their interesting article at . Sousveillance is a form of pervasive surveillance that is done by everyone and which watches everyone. It is not Big Brother watching us --- it is all of us watching each other. Ultimately it will be able to monitor people's thoughts as well as their actions. Although this would grossly violate most current notions of privacy --- something like this may well be necessary in the future. This is because --- as the Singularity s technical advances make it possible for fewer and fewer people --- and fewer and fewer machines --- to harm more and more other people --- it may well become necessary for everyone --- and all machines --- to be watched by other people and by machines people trust. As Goertzel and Bugaj discuss --- having the intimate closeness sousveilance could bring might well engender a much deeper sense of shared interest between humans, and between humans and the machines that interconnect them.


If we --- as a species --- have the wisdom to use the technology of the Singularity well --- humanity could achieve collective superintelligence --- and --- with that collective power and understanding --- humanity might well thrive --- for many decades ---and, perhaps, many centuries --- into the rapidly changing future.





The Singularity will create many questions that collective superintelligence can help humanity better answer. Among many others --- these include the following:





In 1976 James Albus --- an early leading thinker in robotics and artificial intelligence who has held important positions in NASA and the National Institute of Standards and Technology --- published a book called People s Capitalism: The Economics of the Robot Revolution. It envisioned that one day machines would put most people out of well paying work. To counter this, he proposed that virtually all people be granted ownership of enough corporate stock that most could earn a middle-class income from their ownership of the companies that owned the machines.


Something of this scale will probably have to be done to prevent the age of superintelligent machines --- and the robots they control --- from concentrating almost all wealth and income into a relatively small ownership class. We will need collective wisdom to know how to best do this. We need to develop a new, intelligent, realistic, social contract to deal with such issues.


If most people are going to have to be subsidized, I think it is important that they work for their subsidies. I believe it is important that most humans continue to work --- even if machines could do their jobs for less. This is to --- make people feel they are earning a living --- add purpose to their lives --- and keep them and their skills in touch with the real world, other humans, and human needs. For these purposes most people would only need to work 20 to 30 hours a week --- giving them plenty of free time to pursue their personal interests. It is important that individuals and groups be rewarded --- in terms of money and power --- as a function of how much they contribute to others --- and that we develop proper metrics for measuring such contribution. Properly designed feedback loops are essential to continued human well-being.


If we are collectively intelligent there are likely to be more than enough jobs for most people to perform.


One important class of jobs we should keep people in --- even if they could be performed less expensively by machines alone --- are many human service jobs. This includes --- raising and helping educate children --- providing social, medical, and psychological services --- waiting on tables --- being airline stewards and stewardesses --- providing personal grooming --- and many forms of personal counseling, coaching, and religious or spiritual guidance. Keeping people in such roles --- even if their work is amplified by machines --- would make the world seem more "human" and will increase the importance of real world interactions between people. Other types of work we should keep people in include sports, the arts, entertainment, blogging, reporting, and investigating.


Another major occupation for humans should be the monitoring and management of machines, and mechanized manufacturing, farming, transportation, and the provision of other economic goods and services. We should keep private corporations and businesses, so competition can be used to help reward the development of many important human skills, products, and services. Such competition should be monitored and reasonably regulated by a government ultimately controlled by the intelligent public forum.


One type of machine monitoring that is particularly important, is the work of protecting humans and our trusted machines --- including those in manufacturing, infrastructure, transportation, the cloud, and the intelligent public forum, itself --- from malicious people or machines, and their attempts to hack into and take control of our trusted machines.


Yet another major type of work for humans after the Singularity is to provide neighborhood, city, state, national, and world governance --- aided by a superintelligence-enhanced public forum. This would involve having a high percent of people participate in collaborative panels, committees, and governing structures to study and deliberate on issues and problems facing society, and various proposals for dealing with them. It would also involve having more people act as judges and juries, in virtual courtrooms in which judges and jurors have access to superintelligent legal advice --- so as to make the legal process much faster, efficient, fair --- and much less expensive. Presumably much of litigation could be argued by superintelligent virtual litigators --- ones controlled by the client, a trusted friend, or perhaps one or more human lawyers. This way each side would be likely to get a roughly equally talented, attractive, and charming virtual lawyer, all for very little money.


Machines could perform many governing and judging tasks better than people --- but it is important for humanity to keep people in the driver s seat, as much as possible, in such vital feedback loops.






Diversity has value. Computer evolutionary learning systems --- such as genetic algorithms --- benefit from having the transfer of genes come largely from within diverse --- loosely interconnected --- breeding sub-populations. This is because it enables alternative evolutionary approaches to be developed over multiple learning generations.


For similar reasons, it is important to maintain a certain amount of human and cultural plurality, individualism, and competition --- as humans advance deep into the Singularity. In the rapidly changing future --- humanity will need an efficient marketplace of ideas --- one that rewards the creation --- and bringing to the fore --- of multiple good approaches for dealing with important changes as they happen. Similarly we need competition to create feedback loops that reward individuals and organizations for developing productive intelligence, skills, discipline, focuses, ideas, products, and services. This is particularly true because the Singularity will generate many extremely tempting artificial realities, and, thus, it is vital we reward people who stay plugged into the real world.


But too much diversity of thought and values can result in destructive dislike and conflict, and too much selfish competition can hurt society.


Society works best if people are selfish enough to focus on caring sufficiently for themselves and those around them, and if they are motivated to be competitive in productive ways. We want people to have enough ego to seek to distinguish themselves from others in non-harmful ways that make them interesting. But we have all seen the harm that too much selfishness and ego can cause.


Hopefully a superintelligent public forum --- and other means for better communication between human minds --- can help humanity to best decide how to balance the competing demands between diversity and compatibility --- and betwen selfishness and community.






We need to keep humans in the loop --- even if --- as some transhumanist predict --- it is necessary that humanity increasingly become more and more machine. It is important that we not overly rely on machine intelligence. It is important that large numbers of humans have intimate understanding of machine intelligence, machine and network security, and hacking --- and how they all work. It is vital that we know what types of machine intelligences are relatively safe, and those that are relatively dangerous. It is important that many people, in conjunction with trusted machines, monitor all powerful machines for threats.


We need to have many people who know and monitor these things --- because we can be certain malicious people, corporations, nations, or machines will try to hack into our trusted superintelligences and take control of them. It is essential that those of us who abide by the social contract --- when combined with our trusted superintelligences --- can detect and counter such hacking.


If machine intelligences are allowed to evolve independently of supervision by humans and their trusted machines, it is almost certain such intelligences would evolve, over time, to satisfy their own interests --- not ours. If a powerful enough set of superintelligences turned against humans, it is almost certain they could kill us all.


Thetranshumanists may well be right. We humans largely rule the earth because our intelligence and knowledge excels that of all other species. By analogy, it only makes sense that --- starting in several decades when there are likely to be networks of many superintelligences --- each thousands of times smarter than humans --- there will ultimately come a time when machines take domination away from us. That is, perhaps, unless we join them, and make them part us, and us part them. There are already many who look forward to connecting their brains to superintelligences --- and it is almost certain, that once superintelligence arrives, the people who use such implanted, high bandwidth, connections to such machines will be more successful than those who do not. This closeness to machines, and the better use of them it could provide, might help humans to survive.


I am confident extremely powerful and relatively safe superintelligences can be made. What I worry about is: --- will more "dangerous forms of machine intelligence be more useful and more powerful than more "safe" forms --- and if so, by how much? This is a concern, because less safe forms of AI that have more freedom to adapt their goal and control structures and to redesign themselves --- might well --- because of their adaptivity, evolution, and unpredictability --- prove much more powerful and creative than most stable and safe forms of intelligence --- and much more capable of out-foxing them. If this proves true --- it is almost certain unethical people, or machines, would try to use such less-safe forms ofsuperintelligence for competitive advantage --- such as for hacking into other AI s, so as to control them for selfish or dangerous purposes. That may well be why, in the future, we will need something like Goertzel and Bugaj s sousveillance --- with trusted machines watching all other machines.


In the early 1970 s I used to tell the few people that would listen to me --- that the future of mankind might well rest on how well humanity --- using millions of relatively safe, what I called Fido, machines --- could fight off threats created by the more free thinking, and presumably more powerful and hard to control, machines.


I still think this face-off might well determine the fate of humanity.






America s Founding Fathers --- and Sigmund Freud s Society and its Discontents --- both agreed on one thing: --- human nature is deeply flawed.


Human nature did not evolve to be happy or coherent, as much as to breed and have offspring that survive. This has given humans emotions and drives that, at times, are harmful to themselves and others --- such as --- sexual jealousies --- inappropriate selfishness and aggressiveness --- and --- harmful impulsiveness, urges, and addictions. The Singularity will give us enough understanding of our brains that we will be able to dramatically change our minds, drives, emotions, and mental self-control. We need tremendous collective intelligence and wisdom to best decide what, if anything, we should, or must, change in our brains and in our human nature.


Similarly we need to decide how intimate a relationship we want, or need, with machine intelligences and with the consciousnesses of other humans --- and to what degree we want to actually become machine. We will probably start with headsets having --- retnal scanning, to give us see-through HD visual input --- earbuds to give us audio --- eyetracking to give is faster-than-mouse pointing and selection --- microphones and subvocalization detectors for speech input --- cameras to record everything around us --- and a high-bandwidth, wireless connection to the superintelligent cloud. Then we will move on to brain implants that can directly communicate brainstate information from and to the mind. As brain science advances we will be able to use electronics and chemistry to have much greater control over ours, or other people s, brains. Once molecular fabrication becomes commercially feasible, we will be able to make powerful, small superintelligent add-ons that can be implanted into our brains. And, of course, many transhumanist look forward to the day when people can upload their minds to run on artificial brains.


It is not clear how human these progressively altered minds would be.


One of the issues I have with the uploading of human minds is that --- it is not at all clear it would be safe to have minds --- with all the problematic emotions and drives of humans --- running on a superintelligence. It is not clear how long computer hardware running an uploaded mind of someone like Bernie Madoff would be willing to stay within the rules for human friendly AI that people like EliezerYudkowsky, of the Singularity Institute, are trying to write. And if there were millions or billions of human brains uploaded and running on machines --- might not a large schism develop between the flesh-and-blood and the uploaded humans. This schism would tend to grow if it proves easier expand the intelligence of minds running on hardware than those running inside human bodies --- which might well be the case.


Superintelligence will be able to create artificial worlds, friends, and lovers for us that are likely to be much more romantic, emotionally satisfying, and addictive than real ones. The transhumanist suggest this will make it much more pleasant for us to accept the man-machine merger they claim is necessary --- if we want any remnant of what we call human to remain competitive for more than several decades after the advent of machine superintelligence.


But such artificial realities could also be used by machines --- or humans manipulating such machines --- to get emotional control over our minds. That is why I think we need to design a future that keeps humans in important real-world control and feedback loops. Virtual worlds are great for R&R --- and for representing, viewing, and navigating complex scientific, mathematical, economic, and social information --- including information in the intelligent public forum --- but it is important that as many people as possible keep at least one foot firmely grounded in real reality.



In sum, as we transition into the Singularity --- we need to understand: --- what it is we truly value about ourselves and other humans --- what sort of existence we want for our children, and their decendants (even if, as some transhumanist suggest , their descendants might become increasingly more machine) --- and --- how to best maintain those values and goals as far as possible into the coming age of unimaginable change.


Advances in brain science, human psychology, the psychology of machine intelligence --- and the understanding and enginering of consciousness, itself --- in the coming decades --- should help us better understand our own minds and emotions --- and how they can best be served by social institutions and machine intelligence. This understanding--- combined with closer connections between human minds --- and between human minds and collective superintelligence --- might well make --- humanity's transition into the future --- a largely happy and meaningful one.






For a discussion of how to control machines by goal and belief systems derived from the goals and beliefs of humanity see


In the comments that follow Ben s initial post, I argue it will require more collective intelligence to properly derive such Coherent Aggregated Volition.


======Terry (at )

This is a BORG Manifesto Ed. Better Organised Robotic Genius. Bring it on. By the way Ed, I think that the drive of your ideas comes from a enhancement hominist world view, not humanist.



====== Ed Porter

There is a good chance humanity has no choice but to head increasingly toward Borgdom. Humanity's increasing addiction to the Internet is just the start.
In the blog by Goertzel I linked to I am arguing for taking steps to help make the Borgdom that is coming be a more intelligent democratic, reasonably egalitarian, and human-centered.
With regard to "hominist" vs. "humanist," I am not even sure what "hominist" means. One def I found on the web said "One who advocates equal rights for men", which I definately support. Another implied it was a male sexist, which I don't. I had a bright, strong mother, and most of the women I like are bright and strong, and so I am opposed to male sexism --- but I am also opposed to female sexism. Having been in an extremely bitter custody dispute, I can tell you men have no monopoly on selfishness.
When it comes to the man/machine divide, I am probably more of a speciesist than some on this list, even though I think machines are likely to win in the end. But perhaps by then we will have been able to increasingly become more machine ourselves --- and perhaps we will have learned enough about the engineering of conscioiusness to share our consciousnesses with machines, in a way that makes us vastly more consciously aware.




The following is my response to Ben Goertzel s A Cosmist Manifesto as it existed at as of April 12th, 2010, at



The core of Ben s philosophy of Cosmism --- as I understand it --- is to emotionally embrace the radical change the singularity will bring, and to be optimistic that is will be for the good of what we as humans should care about --- for our own future happiness.


Barring some extreme setback to humanity --- the radical change that will be created by the advent of machine superintelligence is unavoidable. Thus, it makes sense for humans to view the inevitable aspects of such change in the most emotionally pleasing possible light --- for our own emotional wellbeing.


So there is very much to say in favor of Ben s joyous embrace of the future.


There is a long tradition of philosophies that emphasize optimism. Studies have shown that a healthy dose of optimism tends to improve the outcomes of individual who indulge in it. And it has long been understood that acceptance of the inevitable is an important part of wisdom.


But like Voltaire s Candide, I believe that --- instead of just being optimistic and believing "all is for the best in the best of all possible worlds" --- we must also cultivate our own garden --- otherwise the weeds and worms of reality will destroy it. Blind optimism alone is not enough. There are many harmful and destructive force within reality, including, those history has proven to be within human nature itself.


It is not clear Ben s Cosmist Manifesto denies the necessity to fight reality s destructive forces --- but it certainly does not emphasize that need.


I think a properly balanced Cosmic Manifesto would match its great hope in the cosmic and spiritual possibilities of the singularity, with a realistic understanding of its great potential dangers --- dangers I have discussed in my first post under [ on this page at ], and in the last section, entitled The Consequences, of my first post under [ on this page at ]


History is full of man s cruelty to man. If summed, all the holocausts of the 20th century killed 150 million to 180 million people. If your spiritualism is anything other than selfish --- preventing killings at such a level should be of concern to you. And if we blow the transition to the future, the level of killing and horror could be much greater. A small group of selfish humans using the power of machines --- or the machines themselves --- could enslave or kill all of humanity. Biotechnology or nanotechnology gone astray could kill all of humanity in something much worse than the Black Death --- that is --- a complete extermination of all mankind, or even of all higher life forms.


Is the possibility of such a disaster something to be blissfully and blindly joyous about?


Yes, one could just say "whatever happens is for the good," so there is no reason to strive to change to the flow of history, even if such a change is necessary to avoid mass exterminations. By this standard, the world *should* have done nothing to avoid Hitler s slaughtering of the Jews, or of the much larger number of non-Jews his forces killed. After all, if Hitler had killed all non-Germans on earth --- man, woman, and child --- and replaced them with an equal number of German Aryans --- there would have been no net loss of intelligence and no net loss of consciousness.


Would the Cosmist Manifesto say that a repeat of such a mass killing would not be something to mightily and fiercely oppose --- alleging that it is only by a narrow, old fashioned, overly humanistic concept of self that the deaths of millions or billions of humans is in anyway a loss --- as long as those lives are replaced by machine consciousnesses of equal number and/or quality.


So --- I would say --- the Cosmist Manifesto SHOULD NOT ONLY ask us to embrace the possibilities of the future, and to embrace to the abilities of machine intelligences to enlarge and expand our notions of what is valuable in a mind ---- BUT SHOULD ALSO ask us to be wide eyed, vigilant, and collectively intelligent about the dangers of the Singularity --- so we can best avoid them --- and have the best chance of reaching the enlightened future the manifesto, itself, envisions.