If there’s one lesson to be learned from almost 60 years of AI research it’s almost certainly to be skeptical of anyone who says they have found THE ANSWER to producing human-level intelligence from computers. Even in the face of this, however, I am intrigued by a new company’s approach to developing Artifical General Intelligence (AGI), a term which is meant to indicate Strong AI rather than Weak AI. That’s probably because its founder, Ben Goertzel, manages to skillfully walk the tightrope between staying conservative about how much they can realistically accomplish and still managing to inspire hope that their methodology has the potential to get close to AGI.
Interview with Steve Grand on building human level artificial intelligence at Machines Like Us. Really interesting. Via Chris Chatham at (the excellent) Developing Intelligence.
In particular, MLU asks why his current project to create an android was done as a physical robot rather than as a simulation. The answer, that you can cheat too much in a simulation, is familiar to those from the Brooksian school of embodied intelligence. He says that simulations still aren’t good enough to provide the kinds of physical constraints, like gravity and friction, etc, that you get when building real robots .
However, with the availability of free 3D simulation environments that handle physics, like Breve, we are getting a lot closer. Building a robot within a simulation like this, particularly where you don’t modify the code of the the simulation environment itself, is a terrific way to balance the competing interests of keeping yourself honest and avoiding the painstaking mechanical engineering required to construct complicated robots. This kind of environment allows you to build a body with primary sensory systems and primary motor outputs in a similar fashion as one would with real robots.
Why there aren’t more who have adopted this kind of “in silico embodiment” philosophy I think is the result of taking Brooks’ a bit too seriously. Brooks idea of embodiment is very well founded, but back in the day when he first made those statements, there really were no good ways to simulate the physics of an embodied creature very faithfully. Today that is not the case. Moreover, building real physical robots is great if you have a lot of time, or an engineering team, but it’s a huge investment that distracts from the real problem of understanding the nature of intelligence. The fact that the world has extremely few labs that can make that investment is one of the many reasons there aren’t more serious strong AI researchers any more.
Update: Steve apparently received a few comments along these lines and replies.
The man had a normal job and is a married father of two children.
Lionel Feuillet, Henry Dufour and Jean Pelletier. Brain of a white-collar worker. The Lancet, Volume 370, Issue 9583, 21 July 2007-27 July 2007, Page 262.
Science has a special online feature this week on behavioral science. One of the articles is a review by Paul Bloom and Deena Skolnick Weisberg (a fellow SymSys alum!) presents some interesting evidence about how dualistic ideas about mind/brain are present from an early age. They state:
Another consequence of people’s common-sense psychology is dualism, the belief that the mind is fundamentally different from the brain (5). This belief comes naturally to children. Preschool children will claim that the brain is responsible for some aspects of mental life, typically those involving deliberative mental work, such as solving math problems. But preschoolers will also claim that the brain is not involved in a host of other activities, such as pretending to be a kangaroo, loving one’s brother, or brushing one’s teeth (5, 17). Similarly, when told about a brain transplant from a boy to a pig, they believed that you would get a very smart pig, but one with pig beliefs and pig desires (18). For young children, then, much of mental life is not linked to the brain.
For one thing, debates about the moral status of embryos, fetuses, stem cells, and nonhuman animals are sometimes framed in terms of whether or not these entities possess immaterial souls (20, 21). What’s more, certain proposals about the role of evidence from functional magnetic resonance imaging in criminal trials assume a strong form of dualism (22). It has been argued, for instance, that if one could show that a person’s brain is involved in an act, then the person himself or herself is not responsible, an excuse dubbed “my brain made me do it” (23).
The authors conclude that adult resistance to science is strongest in fields where scientific claims are contested by the society (that is, contested by non-science alternatives rather than by scientific uncertainty). They claim that this accounts for the difference in the United States (versus other countries with less vociferous advocacy of non-science) in the resistance to the central tenets of evolutionary biology and neuroscience.
I think this says something important about science education, namely that it should start earlier in life. And there’s no reason that neuroscience should be left as a “college-level” subject. I think modern neuroscience has progressed to the point where we can confidently teach some basics at a high-school or earlier stage. Judging from my own experiences, I think the desire to learn about neuroscience is certainly there in younger children.
The field of neuroscience naturally focuses its inquiry into neurons. This approach to understanding the brain by studying its parts has been thought to have a greater potential than that of psychology to understand how the brain works, a comment made by no less than Daniel L. Schacter, chair of Harvard’s Department of Psychology, in his book, The Seven Sins of Memory.
However promising the field has been thus far, even the most accomplished neuroscientists will admit that we still do not understand how the brain really works. I would submit that the current reductionist nature of neuroscience has shed much light on the dynamics of how neurons work, but has to a far lesser degree shed light on how neurons process information. The difference between these two lines of inquiry is important for making progress in understanding how the brain works.
Entrepreneur-turned-cognitive neuroscientist Jeff Hawkins is distributing a “research release” of their experimental code base implementing his idea of hierarchical temporal memory described in his book, “On Intelligence”. Hawkins drew inspiration for the model from his own reading about the structure and function of the human neocortex and believes that it represents the foundation for developing intelligent machines.
Jeff explains this surprising move to open source the code for the Numenta Platform for Intelligent Computing (NuPIC) on the Numenta web site:
Why are we making NuPIC available now?
We have been contacted by dozens of researchers and scientists who are excited about HTM and by our work at Numenta. These people are anxious to work on HTM, are willing to be pioneers, and are willing to accept the uncertainty associated with a new technology. We are making our tools available so that these sophisticated developers can start building a community around HTM technology. NuPIC has been under development for 18 months, is pretty solid, and is well documented – including several examples to make it easy to get started – so we’re ready to open up to more developers, even while knowing that we do not yet have benchmarking data, and we cannot make guarantees about applicability to specific problems.
Here’s why Hawkins thinks that HTMs are new.
We have been covering Hawkins’ work for a while now. See these previous posts for more background info.
Neurodudes is actively soliciting code reviews of the newly released software. Is NuPIC the next big thing, or are you left feeling cold? Post your thoughts yourself using the instructions on the right-hand column, or let us know at contactus -AT- neurodudes.com!
In September, 2006, I described my “new brain/mind theory” here and received some challenging criticism from Eric Thomson and Mike S. (see below). To meet these challenges, I prepared a reduced model discussed in a web page linked to a paper in .pdf form. Since my approach is based on little-known thermodynamics, I have also written about mechanical metaphors that may be helpful in explaining my ideas.