Human learning, itself, is based on millions of years of evolution: innate biology mixed with instinctive behavior that led us to the point where we were eventually able to see into the abstract and learn beyond our personal experiences. But our ability to learn is still based on all that evolved biology and instinct. Are we also clever machines that rely on our evolved baseline routines in ways that only appear to be intelligence and learning?

More important, how do we create machines to do even this?

I think it’s not enough to create a “brain”. We have to be able to figure out how to make machines that can use their base programming explore and learn about their enviornment around them. Maybe even more than that, I think that the machines will have to have a “drive” to learn; an inherent need to find out more about its environment, much the way that an infant takes its base genetic programming and learns/explores its own environment.

Right now, we’re good at creating machines that operate quite nicely on “instinct” alone. But even an infant has the innate ability to learn what it likes or dislikes, needs or doesn’t need. We might be able to eventually create machines that can learn about their environment, but unless we can find a way of making machines that can respond to stimulus out of its own needs and values, we’ll have a hard time creating the separation between clever program and intelligent machines.

Emergent Order is THE central question in AI (as the cliche has it, intelligence as such is not artificial; by definition, it transcends programmed design).

AI researchers can only start things off. Like “genetic algorithms”, no-one knows or can know where Emergent Order leads. When different central foci exist in competition, the result will be a second-order Emergent Organism, and so on down the line.

Once that line is breached, true intelligence can emerge. Until then, self-awareness means nothing and AI will remain a propeller-head dominated circle jerk.

On the other hand, the brain’s developmental wiring–the genetics that governs how white matter connects one cortical region to brainstem structures, limbic system structures, and other cortical regions–is fearsomely complex and the product of tens of millions of years of evolution. Whether the engineering required to mimic that evolution is more or less complex than the engineering required to produce a completely synthetic method for self-constructing ontologies will govern what happens.

In the example of teaching a machine how to sweep, the machine would already be at a disadvantage if it did not first have built into it the ingrained capability of having hands, arms, musculature, etc. like ours…along with the years of coordinated developmental practice of using them all.

So, do we judge the machine’s inability to intuit how to sweep by observation entirely on its lack of intelligence or do we factor in the innate advantage we have by having designed brooms that work with the way we humans are built?

Put another way, it took us a long time to figure out the basic intelligence we now perceive in, say, dolphins or ravens. Dolphins have been shown to have self-awareness, to pass on skills to their young, etc. Ravens show remarkable problem-solving skills, including the adapted use of tools (like, for instance, car tires to crack open nuts). Would we doubt the overall intelligence of a dolphin or a raven for not being able to “get” how to operate a broom?

I agree that we may not have reached a point where our computers are capable of transcending their baseline programming to intuit their own conclusions. What I’m not quite certain is how much of our own intelligence is transcendent of our own baseline programming.

Maybe to get at true AI, we have to focus on simple learning machines first. The rest should come on its own.

I suspect it’s the same thing that used to attract young males to philosophy.

Males abstract and simplify. Wittgenstein showed how that eliminated the solution to the problem.

Lots of things are like that. AI is probably one.

There comes a time in each of our lives where we grab a thick section of the morning paper, roll it up and set off to do battle with one of nature’s most accomplished aviators – the fly. If however, instead of swatting we could magnify our view and experience the world in slow motion we would be privy to a world-class ballet full of graceful figure-eight wing strokes, effortless pirouettes, and astonishing acrobatics. After watching such a magnificent display, who among us could destroy this virtuoso? How do flies produce acrobatic maneuvers with such precision? What control mechanisms do they need to maneuver? More abstractly, what problem are they solving as they fly? Despite pioneering studies of flight control in tethered insects, robotic wing experiments, and fluid dynamics simulations that have revealed basic mechanisms for unsteady force generation during steady flight, the answers to these questions remain elusive.

Even abstraction or novel thought might be nothing more then the averaging out of thousands of random associations. To but integrity in a machine like is pretty risky but something we should definitely do.