AI is mostly about curve fitting (2018)

Honest question, aren’t the consequences for “real” researchers keeping their heads down quite severe? Won’t we have important policy decisions both public and private and billions in funding misdirected for years when they could best be put elsewhere? Sure the “real” researchers will have easier access to funding, which perhaps is a key motivating factor to not push back on the hype, but isn’t there a large opportunity cost to allowing hype and or bullshit to go unchecked because “they don’t have the time to write a book”?

Thanks for the link to the J.Pearl interview , it 's very interesting. There are many counterpoints that are not examined though:

- There's nothing wrong with curve fitting per se. NNs fit hundreds of curves in parallel and many of them may contain cues about the causal structure of the data.

- Deep learning has become part of reinforcement learning, which is trying to learn a causal structure. The primary determinant of causality is the temporal order of cause and effect. The question is , do humans use other hints apart from time for causal inference?

- There is also not much evidence from neuroscience that wet brains are causality-inferrence machines, most of the evidence is that they are decision-making machines. Humans are also pretty bad at inferring causality when it's not obvious, but we re pretty good at associations/patterns.

- Reasoning (conscious) is often considered to act on a meta-level, which observes the internal action of the human brain itself and vocalizes what it sees. What the brain sees at this level is not the external world, but the representation, and we don't have evidence there is a model of the world in there (except perhaps temporary maps of space that exist in hippocampus). Assuming this is true, it s not impossible that current methods can be extended so that self-explaining an NN ends up being causal reasoning

the more important question is whether any of these methods can lead to the remarkable ability of brains to generate extremely intricate and improbable causal chains. can we get a CNN to start from a photo of maxwell's equations and output the theory of relativity? who knows

It was so refreshing to read the title of this article. This couldn’t be more true and timely. Everyone and their brother is talking about artificial intelligence and it’s frankly annoying at this point. These models are, simply put, just fancy interpolation/ extrapolation approaches.

What’s wrong with anthropomorphizing?

I’ve noticed at least as many people under-anthropomorphize as over. People who seem obsessed with human exceptionalism and are personally offended at the idea that plants and animals (and computers!) might have subjective experiences like our own.

But to me it seems obvious we are far more alike “lower” species than we are unlike them. I would say the cases of human exceptionalism are actually extremely rare. The main source of our uniqueness is that we amalgamate other species, not that we have transcended them.

My theory is that we are terrified that we might be simpler than we think, because socially we behave as if we are so singular. If we are simple, and animals and machines are like us, then maybe we should be treating them with more reverence.

But being afraid of that is OK for a random person. For a machine learning researcher I would hope they are more careful about what we have evidence for (the similarities between us) and what we don’t (that there is some ineffable magic about humans).

> Our current accepted definition of intelligence in schools is to get a score that is higher than the average

I don’t even think educators mistake that for intelligence. It’s, at best, a proxy for intelligence mixed with other factors.