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The worst part about reading a Wolfram article on HN is having to read for the millionth time all the people complaining about Wolfram's ego, or his rambling. I

The worst part about reading a Wolfram article on HN is having to read for the millionth time all the people complaining about Wolfram's ego, or his rambling. I understand, and agree (to a layerperson's extent) the criticism of his refusal to work within conventions for novel research. Anyway, does anyone have a good rebuttal, elaboration on this idea: > But actually I think there’s no lack of “alien intelligence”; indeed it’s all around us. But the point is that it really is alien. At an abstract computational level it’s like our intelligence. But in its details it’s not aligned with our intelligence. Abstractly it’s intelligence, but it’s not human-like intelligence. It’s alien intelligence. There are a couple of comments making the point that this is reductionism, and is equivalent to saying any computational in theory exists in a dust cloud, but I'm not sure why we can't classify human intelligence/sentience/consciousness as simply a type of emergent computation in our neurons. I've recently been fascinated by Karl Friston's work on the free energy principle, which models intelligent behaviour as a function of us trying to minimize the divergence between the entropy of our perception and a generative, probabilistic model of the world, and the entropy of the likelihood of evidence we collect about the world. From that perspective, at a high-level consiousness just reduces to a really, really, high-dimensional modeling of the world embedded in our neurons that is driven to minimize entropy. Once a species evolves to have its own theory of its mind (which is how I think of sentience/consciousness), the entropy minimization gets really complicated, but ultimately still fundamentally a computational process. I myself don't by this argument, completely, I feel like its missing something, but can't quite figure it out.

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chrispine 4y ago
When we (like, nearly everyone except maybe Wolfram) talk about intelligence, we aren't talking about computational complexity. We're talking about the ability to learn, and to apply that learning to achieve desirable outcomes. Just like swirling storms don't. > At an abstract computational level it’s like our intelligence. True, but abstract computation isn't what matters about intelligence. How about this thing I just made up: By letter count, "computations" is like "intelligence". I mean, it's true, but the number of letters in the word is also not what matters about intelligence. The rebuttal is almost identical to the argument it rebuts: If computational complexity was the same as intelligence, then a swirling dust cloud of sufficient size would be more intelligent than all of humanity. Which is absurd.
- saeranv 4y ago
  Agreed, I think your points get at the right point. I would go further and say - we consider meaningful "intelligence" as having to do with "consciousness", and its absense causes the disconnect between Wolfram's definition and our intuition about human intelligence. Defining consciousness is its own challenge, but one definition that I like (as a layperson) is that it consists of an approximate model of our self, that emerged as the brain recursively modeled itself while attempting to model other's behaviours, a critical trait once group co-operation and adversity became key to our survival. This seems correct to me, a hurricane exhibits computational complexity, but it does not have a sense of self, and does not use that sense of self to make decisions. Of course, that sense of self can also be broken down into manual computation, it's just a very complicated computation. For example, a bayesian interpretation of that "approximate model of our self" might define it as a generative model or joint distribution of actions and states, embedded within a markov decision process (MDP). Modeling, or speculating about your self would therefore consist of sequentially sampling from the joint distribution over several timesteps of the MDP.

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chrispine 4y ago

When we (like, nearly everyone except maybe Wolfram) talk about intelligence, we aren't talking about computational complexity. We're talking about the ability to learn, and to apply that learning to achieve desirable outcomes. Just like swirling storms don't. > At an abstract computational level it’s like our intelligence. True, but abstract computation isn't what matters about intelligence. How about this thing I just made up: By letter count, "computations" is like "intelligence". I mean, it's true, but the number of letters in the word is also not what matters about intelligence. The rebuttal is almost identical to the argument it rebuts: If computational complexity was the same as intelligence, then a swirling dust cloud of sufficient size would be more intelligent than all of humanity. Which is absurd.

saeranv 4y ago
Agreed, I think your points get at the right point. I would go further and say - we consider meaningful "intelligence" as having to do with "consciousness", and its absense causes the disconnect between Wolfram's definition and our intuition about human intelligence. Defining consciousness is its own challenge, but one definition that I like (as a layperson) is that it consists of an approximate model of our self, that emerged as the brain recursively modeled itself while attempting to model other's behaviours, a critical trait once group co-operation and adversity became key to our survival. This seems correct to me, a hurricane exhibits computational complexity, but it does not have a sense of self, and does not use that sense of self to make decisions. Of course, that sense of self can also be broken down into manual computation, it's just a very complicated computation. For example, a bayesian interpretation of that "approximate model of our self" might define it as a generative model or joint distribution of actions and states, embedded within a markov decision process (MDP). Modeling, or speculating about your self would therefore consist of sequentially sampling from the joint distribution over several timesteps of the MDP.