Probabilistic AI can't be AI

Where do you get that conclusion? Think of a baseball player with exceptional hand-eye coordination, who also knows absolutely nothing about any of the math or calculus behind it. There is a vast chasm between conscious and subconscious thought (where the latter might be viewed as the underlying "intelligence model"). I don't agree that a (partially) probabilistic model would necessarily imply a proficiency at the conscious, communicable level.

we wouldn't benefit from learning about and consciously using probability to solve problems, as our brain would already do it on a lower level.

Don't understand this one either. Back to the baseball player - there is a lot of value we gain from our brains "naturally" solving e.g. inverse kinematics problems on the fly. But, there is also a lot we gain from understanding these mathematical concepts at the conscious level. Totally different realms, both useful.

Statistical methods generally need a big learning set to learn anything.

Ok, that's a limitation of the current AI models. They're not smart enough to infer the essential characteristics of an elephant from a small set (it's also a very narrow approach...you don't start a child off by showing them pictures of elephants, first they have to spend a long time acquiring basic fundamentals of knowledge and perception). I don't see how that implies a more deterministic nature of organic intelligence.

Even the child might think something is an elephant and be wrong. But (IMO) it's the same process of using the bits of knowledge you have to make a guess (and an estimated degree of certainty). "It could be this thing or it could be that thing, and my model says it has the greatest probability of being this one, so that's what I'm going with."

Human bodies are based on molecules, it doesn't imply that people understand chemistry.

Computer chips are based on transistors, it doesn't imply that computers understand electronics.

Sorry, but your assumption that knowledge is inherited by foundations would imply that everything understands the Universe.

Second off, I personally find your theses unconvincing because the simple statements are easily refuted, and there's not enough supporting information to make them into tighter arguments.

For example, for your first bullet point, the underlying mechanism could be highly probabilistic, without our consciousness having access to the probabilities as they are executed. We do not have conscious knowledge of neuron potentials, or of any other internal state of the physical mechanisms of our consciousness, but that does not mean that they don't exist.

Further, people are bad at reasoning period, and have to be taught how to do it in school. It's only through much training that we learn reasoning from certainties, and reasoning with uncertainty will similarly require training.

Finally, I don't think that anybody is saying that our brains operate SVMs or RBMs or anything like that, they are merely computational mechanisms to replicate the behavior of complex networks of neurons; there may be direct analogs to computation in how neurons integrate chemical, electrical, and epigenetic signals, but it's not clear that they're exactly the same thing; and that doesn't mean that even if it's completely different, that a probabilistic AI can't be an Intelligence.

These are heady matters that people put a lot of careful thought into, and while there's definitely room for light discussion among friends, I'm not sure that this is the right material for HN.

As for the subject of probabilistic AI there's quite a lot of evidence that natural language grammar to a large extent is probabilistic. In fact I'd say that most natural language grammar rules are just surface representations of probabilistic models such as Hidden Markov models.

Example from supporting evidence:

"then people would be naturally great at probability, and we know we suck at probability"

No, this does not follow at all. There is absolutely no reason to assume that a probabilistic algorithm would somehow lead to better results when dealing with probabilities at a much higher level.

Example from premise:

- No argument for why "if real intelligences aren't probabilistic, therefore probabilistic intelligences can't work" is given.

- Just because your models come from statistical learning (such as SVMs or Bayesian methods, as the author names) does not mean your resulting algorithm is probabilistic.

Flagging.

- Reality is a probabilistic process. Brains work probabilistically at least in the sense that the underlying biomechanics have some statistical distribution.

I have the feeling the author doesn't really understand what "probability" means, as he more or less admits.

The basic flaw in the argument is that there is only one "mind" and that all its workings are available to us.

But as much recent research shows, especially that of Tversky and Kahneman, we have at least two "minds", the so-called "fast" and "slow" systems, and the "fast" system is not directly accessible to "us" - and "us", what we recognize as "us", is the working of "slow", the deliberate, conscious, effortful problem solver, often called upon to cook up a "good enough story" to explain an exception "fast" couldn't make sense of.

"Fast" might be terrifically good at probability, with some survival heuristics favouring particular conclusions - better to always conclude that those weird pattens in the bush are a tiger than to be wrong once.

Since "fast" is not really accessible to "slow", it matters not how good "fast" is at probability - and "fast" could be 100% probabilistic AI and we'd never know it...

...not without detailed neurophychological study, at least. It certainly wouldn't "just be evident".

I still disagree that the brain doesn't use probability at all or that we shouldn't focus on it in AI research.

>There are more external signs that we don't do probability. Statistical methods generally need a big learning set to learn anything. A small child doesn't have to see a set of 500 cartoon elephants in different poses to recognize elephants from then on in various different cartoons and in real life *)

No but they do see hundreds of hours of visual feed from their eyes from which they learn high level features. Learning an elephant from one example wouldn't be possible without first learning thousands of other concepts such as how to detect edges, shapes, 3d objects, the properties of animals, etc.

The fact that people seem to be universally bad at assessing probabilities means only that however their brains work, that mechanism doesn't produce intelligences that are finely adapted at assessing probabilities. The machinery that is producing that intelligence could still be completely probabilistic itself.

Humans are fantastic at pattern recognition, but that doesn't mean that our intelligence must have been created by a pattern recognition algorithm.

We are great at probability, we compute it unconsciously. Every person knows the approximate probability of flipping a coin to show heads, pulling a card from a stack, or being rejected/successful when asking a date out (we always say our 'chances' are good/bad, depending on factors we think are important).

In our day to day lives, many decisions are driven by what we think the 'odds' are of our endeavor being successful.