Elementary, my dear Watson: Jeopardy computer offers insight into human cognition

Being the astute Sentient Developments readers that you are, I'm sure you're up to speed on Watson, IBM's Jeopardy playing computer:


The more I think about Watson, the more I'm astounded about what IBM has done here. This isn't just some glorified answer engine. If you think about what this system has to do to get these questions right, you quickly realize that there's a lot more going on behind the scenes.

At its core, Watson is an expert answer engine that utilizes natural language processing technology.

And it's probably doing it in a way that's very, very close to how the human brain does it. I'd be willing to bet that the processes behind Watson's programming is very analogous to how the human mind goes about it. Watson, which has access to a massive repository of information, has to interpret all the nuances of language—synonyms, puns, slang, and all—and quickly come up with an answer. It typically builds a list of around four to five answers, and based on a probability analysis, selects what it thinks is the most likely answer. I'm almost certain that the human mind goes about it in the exact same way. It has been suggested, for example, that the mind applies Bayesian probabilism in its calculations. Wouldn't be amazing if we eventually discover that even the algorithms are the same? If this is the case, then IBM has actually created a stand-alone module of the human brain.

So, in terms of the rule based AI vesus whole brain emulation debate, you can strike this down as a victory for the former.

The big difference, of course, is that Watson is not conscious. But that doesn't make a difference. You are not conscious, either, of how you process natural language, access the memory stores in your brain, and come up with an answer. Your brain does this for you behind the scenes and presents the answer to your consciousness; you're none the wiser. You only think you're clever, and that "you" came up with the answer, but in reality the unconscious mechanistic parts of your brain did all the work.

Some people may complain or freak out about that, but I think it's rather cool. We're biological robots; get over it.

More on Watson:

Making brains: Reverse engineering the human brain to achieve AI

The ongoing debate between PZ Myers and Ray Kurzweil about reverse engineering the human brain is fairly representative of the same debate that's been going in futurist circles for quite some time now. And as the Myers/Kurzweil conversation attests, there is little consensus on the best way for us to achieve human-equivalent AI.

That said, I have noticed an increasing interest in the whole brain emulation (WBE) approach. Kurzweil's upcoming book, How the Mind Works and How to Build One, is a good example of this—but hardly the only one. Futurists with a neuroscientific bent have been advocating this approach for years now, most prominently by the European transhumanist camp headed by Nick Bostrom and Anders Sandberg.

While I believe that reverse engineering the human brain is the right approach, I admit that it's not going to be easy. Nor is it going to be quick. This will be a multi-disciplinary endeavor that will require decades of data collection and the use of technologies that don't exist yet. And importantly, success won't come about all at once. This will be an incremental process in which individual developments will provide the foundation for overcoming the next conceptual hurdle.

But we have to start somewhere, and we have to start with a plan.

Rules-based AI versus whole brain emulation

Now, some computer theorists maintain that the rules-based approach to AI will get us there first. Ben Goertzel is one such theorist. I had a chance to debate this with him at the recent H+ Summit at Harvard. His basic argument is that the WBE approach over-complexifies the issue. "We didn't have to reverse engineer the bird to learn how to fly," he told me. Essentially, Goertzel is confident that the hard-coding of artificial general intelligence (AGI) is a more elegant and direct approach; it'll simply be a matter of identifying and developing the requisite algorithms sufficient for the emergence of the traits we're looking for in an AGI—things like learning and adaptation. As for the WBE approach, Goertzel thinks it's overkill and overly time consuming. But he did concede to me that he thinks the approach is sound in principle.

This approach aside, like Kurzweil, Bostrom, Sandberg and a growing number of other thinkers, I am drawn to the WBE camp. The idea of reverse engineering the human brain makes sense to me. Unlike the rules-based approach, WBE works off a tried-and-true working model; we're not having to re-invent the wheel. Natural selection, through excruciatingly tedious trial-and-error, was able to create the human brain—and all without a preconceived design. There's no reason to believe that we can't figure out how this was done; if the brain could come about through autonomous processes, then it can most certainly come about through the diligent work of intelligent researchers.

Emulation, simulation and cognitive functionalism

Emulation refers to a 1-to-1 model where all relevant properties of a system exist. This doesn't mean recreating the human brain in exactly the same way as it resides inside our skulls. Rather, it implies the recreation of all its properties in an alternative substrate, namely a computer system.

Moreover, emulation is not simulation. We're not looking to give the appearance of human-equivalent cognition. A simulation implies that not all properties of a model are present. Again, it's a complete 1:1 emulation that we're after.

Now, given that we're looking to model the human brain in digital substrate, we have to work according to a rather fundamental assumption: computational functionalism. This goes back to the Church-Turing thesis which states that a Turing machine can emulate any other Turing machine. Essentially, this means that every physically computable function can be computed by a Turing machine. And if brain activity is regarded as a function that is physically computed by brains, then it should be possible to compute it on a Turing machine. Like a computer.

So, if you believe that there's something mystical or vital about human cognition you should probably stop reading now.

Or, if you believe that there's something inherently physical about intelligence that can't be translated into the digital realm, you've got your work cut out for you to explain what that is exactly—keeping in mind that any informational process is computational, including those brought about by chemical reactions. Moreover, intelligence, which is what we're after here, is something that's intrinsically non-physical to begin with.

The roadmap to whole brain emulation

A number of critics point out that we'll never emulate a human brain on account of the chaos and complexity inherent in such a system. On this point I'll disagree. As Bostrom and Sandberg have pointed out, we will not need to understand the whole system in order to emulate it. What's required is a functional understanding of all necessary low-level information about the brain and knowledge of the local update rules that change brain states from moment to moment. What is meant by low-level at this point is an open question, but it likely won't involve a molecule-by-molecule understanding of cognition. And as Ray Kurzweil has revealed, the brain contains masterful arrays of redundancy; it's not as complicated as we currently think.

In order to gain this "low-level functional understanding" of the human brain we will need to employ a series of interdisciplinary approaches (most of which are currently underway). Specifically, we're going to require advances in:

• Computer science: We have to improve the hardware component; we're going to need machines with the processing power required to host a human brain; we're also going to need to improve the software component so that we can create algorithmic correlates to specific brain function.
• Microscopy and scanning technologies: We need to better study and map the brain at the physical level; brain slicing techniques will allow us to visibly study cognitive action down to the molecular scale; specific areas of inquiry will include molecular studies of individual neurons, the scanning of neural connection patterns, determining the function of neural clusters, and so on.
• Neurosciences: We need more impactful advances in the neurosciences so that we may better understand the modular aspects of cognition and start mapping the neural correlates of consciousness (what is currently a very grey area).
• Genetics: We need to get better at reading our DNA for clues about how the brain is constructed. While I agree that our DNA will not tell us how to build a fully functional brain, it will tell us how to start the process of brain-building from scratch.

Essentially, WBE requires three main capabilities: (1) the ability to physically scan brains in order to acquire the necessary information, (2) the ability to interpret the scanned data to build a software model, and (3) the ability to simulate this very large model.

Time-frames

Inevitably the question as to 'when' crops up. Personally, I could care less. I'm more interested in viability than timelines. But, if pressed for an answer, my feeling is that we are still quite a ways off. Kurzweil's prediction of 2030 is uncomfortably short in my opinion; his analogies to the human genome project are unsatisfying. This is a project of much greater magnitude, not to mention that we're still likely heading down some blind alleys.

My own feeling is that we'll likely be able to emulate the human brain in about 50 to 75 years. I will admit that I'm pulling this figure out of my butt as I really have no idea. It's more a feeling than a scientifically-backed estimate.

Lastly, it's worth noting that, given the capacity to recreate a human brain in digital substrate, we won't be too far off from creating considerably greater than human intelligence. Computer theorist Eliezer Yudkowsky has claimed that, because of the brain's particular architecture, we may be able to accelerate its processing speed by a factor of a million relatively easily. Consequently, predictions as to when we may hit the Singularity will likely co-incide with the advent of a fully emulated human brain.

Andy Clark: Supersizing the Mind

There's a new book by Andy Clark worth checking out: Supersizing the Mind: Embodiment, Action and Cognitive Extension. This will be of interest to readers of this blog in that it's a discussion of the so-called "extended mind."

Description:

When historian Charles Weiner found pages of Nobel Prize-winning physicist Richard Feynman's notes, he saw it as a "record" of Feynman's work. Feynman himself, however, insisted that the notes were not a record but the work itself. In Supersizing the Mind , Andy Clark argues that our thinking doesn't happen only in our heads but that "certain forms of human cognizing include inextricable tangles of feedback, feed-forward and feed-around loops: loops that promiscuously criss-cross the boundaries of brain, body and world." The pen and paper of Feynman's thought are just such feedback loops, physical machinery that shape the flow of thought and enlarge the boundaries of mind. Drawing upon recent work in psychology, linguistics, neuroscience, artificial intelligence, robotics, human-computer systems, and beyond, Supersizing the Mind offers both a tour of the emerging cognitive landscape and a sustained argument in favor of a conception of mind that is extended rather than "brain-bound." The importance of this new perspective is profound. If our minds themselves can include aspects of our social and physical environments, then the kinds of social and physical environments we create can reconfigure our minds and our capacity for thought and reason.

Be sure to read Jerry Fodor's review from the London Review of Books and David Chalmer's comments.