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Asimo, the best and last... of the modern day mechanical turks.

To those of you uninitiated to the astonishing revolution of advances that have attended the space of machine learning / AI in the last 15 years who gawk in wonder at the "amazing" tricks that Honda's Asimo robot can achieve be not fooled.

Asimo is the culmination of robotics done precisely the WRONG way.

What do I mean by WRONG? How can I state that given the seemingly impressive things Azimo can do.

Asimo can walk and turn in stride.

Asimo can pick itself up after falling.

Asimo can hold and manipulate objects.

Asimo can identify objects.

Asimo can open bottles.

Asimo can climb up and down stairs.

Asimo can identify obstacles and redirect walking path out of their way.

Asimo can truly run.

So why do I say Asimo is doing things the wrong way??

Because Asimo, unlike an animal capable of similar functionality is using tens of thousands of times the power to get it done.

Why is that important?

Power is how computational ability and requirements are metered out by reality. The more energy it takes you to do something to lazier you are doing it compared to some other agent that requires less power and does it with greater fidelity.

Case in point our biological cousins, a raccoon can do everything I described above. (Yes, including the opening of bottles)

Let me be blunt, Asimo is a parlor trick, a mechanical turk. A simulacrum of intelligence, Asimo doesn't learn at all like you or I or even a roach does, Asimo is a bunch of highly refined programs filled with conditions and sub conditions and should any external stimulus not be handled by those conditions Asimo fails and fails spectacularly.

True AI involves dynamic real time learning of the world environment it is embedded in and constant and continual refinement to that world. This is done not by using a programmed method where you try to identify all the possible cases of success and failure (a futile act just in the short thought of it) but instead in being able to experience *everything* and then determine salience gradually to the needs of the system.

This is what Marc Raibert realized in the 1980's when he was working at MIT's leg lab and it was this realization that led him to radically change how he thought about creating ambulating robots and what a change it has been.

Since that time his method has been adopted by computer scientists to solve all types of problems in machine learning that before were "solved" the same way Asimo does, brute force and programming.

No, no no....the ant has a fraction of a gram of brain and yet is able to forage, climb, descend and ambulate in real time far faster than the best robots we've yet created. How could an ant which requires orders of magnitude less computational ability do MORE than our best robots that require *pentiums* of computing power just to walk and then only if in a prescribed line with no rocks on the floor??

Well that is true for Azimo but not for the results of Raibert's vision. He founded Boston Dynamics in the 90's in order to build robots (with US government and thus citizen money) that could act and react biologically...applying the statistical learning approach he pioneered successfully in his days at the Leg Lab.

The results of that work are Big Dog, Little Dog, Cheetah and PetMan and Atlas.

These robots make ample use of sensors and memory to drastically reduce the computational load (and thus power requirements)  for performing ambulation but more importantly they allow the real time discovery of optimal solutions for performing the same...who did not scream in shock when they saw the robotic Big Dog slip but refuse to fall on ice in the youtube video that went viral?

Who did not gaze in astonishment as Atlas did pushups or balanced one one leg while being pushed by a human being in order to make it fall??

That's what you call robotic success, not a mechanical turk that only has a regime of success but a fully dynamic and agile system that can learn from its mistakes in real time and write its own programs for success into its ever growing memory of the world.

The fire that Raibert has lit has spread through out the field of machine learning and artificial intelligence. The neural network methods that saw traction in the early 90's are now being used in hybrid systems that combine intelligence and memory and sensing along with simple statistical algorithms to enable systems to learn about their environment by being taught (just as animals do). The statistical approach has been applied to solve more efficiently a host of problems that formerly were computationally far more difficult...from language translation to vision and classification to voice recognition. Those of you who have read my posts on my theory of cognition and intelligence understand that this statistical approach in my view is different between animals mostly in scale and some what in connection, how modules in brains are connected. The salience theory of consciousness I proposed last year from the culmination of my thoughts on the matter puts forward my set of hypothesis for how full cognitive function with consciousness can be emerged from the advanced versions of such systems. Raibert's work was always the "right" way to me from as early as 1989 when I read of the successes at the leg lab and of his views regarding biological ambulation. It was obvious to me he was right then...and now over 20 years later that vision is getting closer and closer to emulating biology at least in ambulation.

So next time you see Asimo doing it's parlor tricks at the next Car show...chuckle to yourself as you realize....the best is yet to come!


Marc Raibert:

Big Dog:




Wildcat (Cheetah):


The Turk:


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