Category: AI

On the Non-Simulation of Human Intelligence

Mark DavisLeave a comment

There is a curious dilemma that pervades much machine learning research. The solutions that we are trying to devise are supposed to minimize behavioral error by formulating the best possible model (or collection of competing models). This is also the assumption of evolutionary optimization, whether natural or artificial: optimality is the key to efficiently outcompeting alternative structures, alternative alleles, and alternative conceptual models. The dilemma is whether such optimality is applicable to the notoriously error prone, conceptual flexible, and inefficient reasoning of people. In other words, is machine learning at all like human learning?

I came across a paper called “Multi-Armed Bandit Bayesian Decision Making” while trying to understand what Ted Dunning is planning to talk about at the Big Data Science Meetup at SGI in Fremont, CA a week from Saturday (I’ll be talking, as well) that has a remarkable admission concerning this point:

Human behaviour is after all heavily influenced by emotions, values, culture and genetics; as agents operating in a decentralised system humans are notoriously bad at coordination. It is this fact that motivates us to develop systems that do coordinate well and that operate outside the realms of emotional biasing. We use Bayesian Probability Theory to build these systems specifically because we regard it as common sense expressed mathematically, or rather `the right thing to do’.

The authors continue on to suggest that therefore such systems should instead be seen as corrective assistants for the limitations of human cognitive processes! Machines can put the rational back into reasoned decision-making. But that is really not what machine learning is used for today. Instead, machine learning is used where human decision-making processes are unavailable due to the physical limitations of including humans “in the loop,” or the scale of the data involved, or the tediousness of the tasks at hand.… Read the rest

Eusociality, Errors, and Behavioral Plasticity

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I encountered an error in E.O. Wilson’s The Social Conquest of Earth where the authors intended to assert an alternative to “kin selection” but instead repeated “multilevel selection,” which is precisely what the authors wanted to draw a distinction with. I am sympathetic, however, if for no other reason than I keep finding errors and issues with my own books and papers.

The critical technical discussion from Nature concerning the topic is available here. As technical discussion, the issues debated are fraught with details like how halictid bees appear to live socially, but are in fact solitary animals that co-exist in tunnel arrangements.

Despite the focus on “spring-loaded traits” as determiners for haplodiploid animals like bees and wasps, the problem of big-brained behavioral plasticity keeps coming up in Wilson’s book. Humanity is a pinnacle because of taming fire, because of the relative levels of energy available in animal flesh versus plant matter, and because of our ability to outrun prey over long distances (yes, our identity emerges from marathon running). But these are solutions that correlate with the rapid growth of our craniums.

So if behavioral plasticity is so very central to who we are, we are faced with an awfully complex problem in trying to simulate that behavior. We can expect that there must be phalanxes of genes involved in setting our developmental path (our nature and the substrate for our nurture). We should, indeed, expect that almost no cognitive capacity is governed by a small set of genes, and that all the relevant genes work in networks through polygeny, epistasis, and related effects (pleiotropy). And we can expect no easy answers as a result, except to assert that AI is exactly as hard as we should have expected, and progress will be inevitably slow in understanding the mind, the brain, and the way we interact.… Read the rest

Experimental Psychohistory

Mark DavisLeave a comment

Kalev Leetaru at UIUC highlights the use of sentiment analysis to retrospectively predict the Arab Spring using Big Data in this paper. Dr. Leetaru took English transcriptions of Egyptian press sources and looked at aggregate measures of positive and negative sentiment terminology. Sentiment terminology is fairly simple in this case, consisting of positive and negative adjectives primarily, but could be more discriminating by checking for negative modifiers (“not happy,” “less than happy,” etc.). Leetaru points out some of the other follies that can arise from semi-intelligent broad measures like this one applied too liberally:

It is important to note that computer–based tone scores capture only the overall language used in a news article, which is a combination of both factual events and their framing by the reporter. A classic example of this is a college football game: the hometown papers of both teams will report the same facts about the game, but the winning team’s paper will likely cast the game as a positive outcome, while the losing team’s paper will have a more negative take on the game, yielding insight into their respective views towards it.

This is an old issue in computational linguistics. In the “pragmatics” of automatic machine translation, for example, the classic example is how do you translate fighters in a rebellion. They could be anything from “terrorists” to “freedom fighters,” depending on the perspective of the translator and the original writer.

In Leetaru’s work, the end result was an unusually high churn of negative-going sentiment as the events of the Egyptian revolution unfolded.

But is it repeatable or generalizable? I’m skeptical. The rise of social media, enhanced government suppression of the media, spamming, disinformation, rapid technological change, distributed availability of technology, and the evolving government understanding of social dynamics can all significantly smear-out the priors associated with the positive signal relative to the indeterminacy of the messaging.… Read the rest