There is no doubt that today’s programming paradigms lack plasticity. If one part of a system fails, it generally brings the entire system down. While there has been work to develop fault tolerance, and “self-healing” there have not been any noticeable large scale successes.

Developing millions or billions of capabilities will be a huge undertaking. Not all of the capabilities will be fully fleshed out by programmers, since some are likely going to involve neural networks such as those proposed by Jeff Hawkins in On Intelligence, or genetic algorithms, or some other learning model which may or may not be based on biology.

Unfortunately, at this point it is unclear how many discrete capabilities are required to handle balancing, walking, catching a ball, crossing the street, and so on. My current guesstimate would be that this would be in the billions, and therefore the equivalent of trillions of lions of code would be required. This is definitely a huge undertaking. However, how much of this code is hand written, how much is generated automatically, and how much is data or biological driven remains to be unseen. As engineers and researchers try to figure out how to develop robots which can act (and maybe even be) intelligent, there is going to be a lot of design space investigated.

Even if robots are developed via models of the brain, the set of capabilities that robot can perform should be a good measure. Of course, if the capabilities develop via learning, as opposed to being added incrementally by engineers, counting them will be a bit more challenging. So a good question based on this metric would be how many capabilities does a human have today? What are the capabilities of an ant, spider, etc. Knowing capabilities, would also help to emulate.

In an upcoming article, I do plan to take a stab at examining the challenges that would be involved in trying to develop a robot in a community driven manner. As you point out, the sheer manpower required means that no particular group would be able to develop everything. Development will occur over large periods of time, with new additions building up, until the underlying architecture gets overwhelmed, and then the architecture gets revamped, and progress continues, and so on. Development will require substantial automation.

1) The crucial aspect of intelligence is plasticity. The human brain is capable of using many different parts for the same task. Visual data can be given to a person through their back (on pressure pads) or even their tongue and their brain can learn to treat it just like data sent in through the optic nerve. In some animals (and possibly humans too) the optic nerve can be rerouted to an entirely different area of the brain (during embryology) and the animals brain grows with a completely different structure but still able to function normally. In addition people routinely use mental modules they developed for one task to perform an entirely different one (e.g. counting in ones head to do simple sums).

In my opinion with AI we should create an AI brain with the same or similar degree of plasticity and then teach it these tasks. Any other route will take much longer because their are only so many available programmers.

2) The algorithm behind the brain’s plasticity and its learning capacity must be pretty simple for several reasons. Firstly there are not many genes responsible for the nervous system in people and most of these probably code for essentially irrelevant internal aspects of neurons. This leaves very little space for a complex algorithm (the emergent behaviour is certainly complex, however). This means that it should not be beyond the 21st century wit of man to reverse engineer this basic algorithm or to create an equivalent algorithm.

If we can create a suitably flexible AI brain then I agree that we should expect exponential growth in its capacities (whilst Moore’s law itself holds). However, without that I am much less confident in this as it seems to me that the speed with which capacities could be added is limited by programmer time.

While the original work was aimed at audio compression, the instrument model seems to be one step closer to allowing instrument based computer assistance.

Reports of this research can be found in the University of Rochester press release, or a report from znet.