I have read it and you’re not missing anything. One of the examples of tacit knowledge they give is walking and that therefore it is impossible to teach a computer to walk. They should watch one of the videos from Boston Dynamics.

That's not teaching a computer to walk, that's building a walking machine. Subtle difference, but it's easy to prove; no matter how well you build a Boston dynamics robot, it will never like walking in the same way your TV will never like entertaining people. There's no "I" there to learn.

Way to change the goal posts. We built a machine than learned how to walk. Mission accomplished. The assertion in the article was either wrong or irrelevant, pick one.

Then a bald assertion, computers will never X. Says you. Just because we’re not there yet is no proof it’s impossible.

My son learned how to stand up at 8 months, pretty soon he was walking and even running. No one had to teach him anything, he did this by observing the world around him and drawing his own conclusions.

This is not about definitions, since we don't even know what exactly we're chasing there are no ways to express the difference unambigously. What we get instead is one side trying to (unsuccessfully) define the difference and the other pretending it doesn't exist.

We're a long, long way from an AI learning how to walk by itself. Neural networks and machine learning is one piece of a puzzle, expert systems are probably in there somewhere as well. Perhaps one day we will identify all the pieces but we're definitely not even close.

Cows can walk minutes after they are born. I guess they're even smarter. You might argue that the walking calf is not intelligent because it came preprogrammed to walk, but human babies will reflexively start making stepping movements when you hold them upright and let their feet touch the ground. I think it's ridiculous to argue that your son somehow learned to walk through observation and reasoning alone.

"Liking things" is just one part of a biological reward system not a metaphysical event, it is possible to create it just not with our current tech (so saying "never" is a stretch imo)

>That's not teaching a computer to walk, that's building a walking machine.

I think that's redefining teaching so that teaching, whatever it is, includes a subjective human 'ghost' inside of it.

But Dreyfus wasn't just saying that machines can do those things, only without a soul. Dreyfus was arguing that things such as walking are clever and subtle in ways that depend on tacit knowledge to execute successfully, and things that depend on it simply aren't even achievable by machines at all, because the nature of those tasks is such that they require a special magical soul. Being able to do the task at all, with or without a special magical soul, stands as a counterpoint to the argument Dreyfus had been making for half of the 20th century.

> it would be practically impossible for people to write programs that articulate and incorporate that knowledge without machine learning

That could be true, but I think that's a different argument. That's more like claiming that it is impossible for a computer to become intelligent unless it can experience its environment and remember those experiences. That seems like a much more plausible and less dualist claim.

> Most "knowledge" deep learning systems have seems tacit to me

it's an interesting question if they're actually tacit in the human sense or not. At the base level even deep learning systems certainly rely on digital manipulation. It's almost certain that the human-brain due to speed constraints of biochemical processes doesn't run on tons of matrix-multiplication or loss functions, so it's an open question I guess if deep learning systems really just resemble the tacit capacity of humans or if there's something fundamentally different in the architecture of organic systems that cannot be replicated at least in today's machines. Which I think is actually fairly likely to be honest and I always wonder why it's disregarded.

It's funny that the top comment of this chain asserts dualism, but I think dualism is overwhelmingly common among CS folks, who almost seem to treat intelligence like some sort of platonic thing, completely ignoring the stuff it's made out of.

I'd argue that deep learning is derived from formal principles of mathematical reasoning in a very concrete sense. Deep learning learns a predictive function of the features that minimizes the loss function (with some caveats). If the loss function and training data are well chosen, that minimizes the probability of being wrong.