Humans are more than pattern matchers because we do not passively receive and imitate information. We learn cause and effect by perturbing our environment, which is not possible by passively examining data.

An AI agent can interact with an environment and learn from its environment by reinforcement learning. It is important to remember that pattern matching is different from higher forms of learning, like reinforcement learning.

To summarize, I think there are real limitations with this AI, but these limitations are solvable problems, and I anticipate significant future progress

Fortunately the environment for coding AI is a compiler and a CPU which is much faster and cheaper than physical robots, and doesn't require humans for evaluation like dialogue agents and GANs.

Well you still have to assess validity and code quality which is a difficult task , but not unsolvable.

Also Generative Adversarial Networks original implementation was to pit neural networks against each other to train them , they don't need human intervention.

> They feed you all these algorithms in college and your brain suggests new algorithms based on those patterns.

Some come from the other end of the process.

I want to solve that problem -> Functionally, it’d mean this and that -> How would it work? -> What algorithms / patterns are there out there that could help.

Usually people with less formal education and more hands on experience, I’d wager.

More prone to end up reinventing the wheel and spend more time searching for solutions too.

What?

Most people I know who’ve been to college, or otherwise educated in the area they work in, tend to solve problems using what they know (not implying it’s a hard limit. Just an apparently well spread first instinct).

Which fits the pattern matching described by the grandparent.

A few people I know, most of which haven’t been to college, or done much learning at all, but are used to work outside of what they know (that’s an important part), tend to solve problems with things they didn’t know at the time they set out to solve said problems.

Which doesn’t really fit the pattern matching mentioned by the grandparent. At least not in the way it was meant.

To reduce intelligence to pattern matching begs the question: How do you know which patterns to match against which? By some magic we can answer questions of why, of what something means. Purpose and meaning might be slippery things to pin down, but they are real, we navigate them (usually) effortlessly, and we still have no idea how to even begin to get AI to do those things.

I think those are the distance metrics, which is what produces inductive bias, which is the core essence of what we consider 'intelligence'. - Consider a more complicated metric like a graph distance with a bit of interesting topology. That metric is the unit by which the feature space is uniformly reduced. Things which are not linearized by the metric are considered noise, so this forms a heuristic which overlooks features which may have been in reality salient. - This makes it an inductive bias.

(Some call me heterodox, I prefer 'original thinker'.)