NSF’s Emerging Frontiers in Research and Innovation (EFRI) office funded 4 very futuristic neuroengineering grants.
Disclaimer: I was involved with the second proposal on this page.
An extremely interesting trend in neuroscience has been to use the language of Control Theory to explain brain function. A recent paper by Shadmehr and Krakauer does a very nice job of summarizing this trend and assembling a comprehensive theory of how the brain controls the body. Using control theory, they put forward a mathematically precise description of their theory. Because their theory uses blocks that are direct analogues of specific brain regions like the basal ganglia, motor cortex, and cerebellum, they can use brain lesion studies to undergird their ideas about these components. From the paper:
The theory explains that in order to make a movement, our brain needs to solve three kinds of problems: we need to be able to accurately predict the sensory consequences of our motor commands (this is called system identification), we need to combine these predictions with actual sensory feedback to form a belief about the state of our body and the world (called state estimation), and then given this belief about the state of our body and the world, we have to adjust the gains of the sensorimotor feedback loops so that our movements maximize some measure of performance (called optimal control).
At the heart of the approach is the idea that we make movements to achieve a rewarding state. This crucial description of why we are making a movement, i.e., the rewards we expect to get and the costs we expect to pay, determines how quickly we move, what trajectory we choose to execute, and how we will respond to sensory feedback.
This approach of describing brain lesion studies in the context of a well-thought out theory ought to be further encouraged.
Researchers at the University of Nevada, Reno have an interesting and ambitious set-up for doing research in AI that the describe in a recent paper.
From the paper:
We define virtual neurorobotics as follows: a computer-facilitated behavioral loop wherein a human interacts with a projected robot that meets five criteria: (1) the robot is sufficiently embodied for the human to tentatively accept the robot as a social partner, (2) the loop operates in real time, with no pre-specified parcellation into receptive and responsive time windows, (3) the cognitive control is a neuromorphic brain emulation incorporating realistic neuronal dynamics whose time constants reflect synaptic activation and learning, membrane and circuitry properties, and (4) the neuromorphic architecture is expandable to progressively larger scale and complexity to track brain development, (5) the neuromorphic architecture can potentially provide circuitry underlying intrinsic motivation and intentionality, which physiologically is best described as “emotional” rather than rule-based drive.
What’s interesting to me about this is the combination of a embodied robot in a virtual world with a neurally inspired controller for that robot. While there are pros and cons of embodiment in virtual world (some of which have been touched on here before), I think that if your priority is closing the loop from embodiment to research on neural systems, the importance of this kind of approach cannot be ignored.
From the department of “we are living in a sci-fi movie”, here’s a video of a bionic arm that uses “fluidic muscles”.
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.
But it seems to have been close. The players claim that the program was quite challenging.
The website of the lab that wrote Polaris, with detailed blogs of the games.
Andrew Arensburger presents a neat idea: use the metaphor of the lock-and-key hypothesis for enzymes to route data in genetic programming.
The MIT Media Lab is holding a conference on May 9th, “Human 2.0: New Minds, New Bodies, New Identites” which will launch a number of new initiatives centered around the goal of inventing a better future via direct engineering of the human. Amongst these things will be the initiation of the MIT Center for Human Augmentation, and the launch of a number of novel applied Neurotechnology Projects.
Guest speakers on May 9th will include MIT professors (Roz Picard, Hugh Herr, myself, etc.) and many acclaimed speakers such as Oliver Sacks and John Donoghue. Registration may be close to being full, but it will be webcast.
More information at:
http://h20.media.mit.edu
- Ed