[sddfd]

I feel uncomfortable at the ubiquitous, silent assumption that what is marketed as AI is a computer implementation of a brain.

I see how the term neuronal network reinforces this believe, but we (especially the researchers among us) should allow for the possibility that we are missing something.

[papito]

Neural networks also have no ability to create new information based on their own mistakes. What is a mistake? When does something look "off" but still very interesting?

For example, you can feed a neural net all the recipes of burgers to create a perfect burger. Great. But how does the same net invent the burger?

The burger, like many foods or accidental art, was invented as a result of scarcity, circumstance, experimentation, or just fortunate error. That sort of imperfection is very hard to achieve with AI, because it is designed to be either perfect or fail.

[GuB-42]

GAN can do that. For example, AlphaZero invented strategies for the game of go from nothing but a random number generator and the rules of the game. As for perfection neither go nor chess AIs play perfectly, and they can still beat the best human players.

Of course, an AI intended to play go isn't going to invent the burger. But I see no reason why, given a list of ingredients, their properties and a model of what human enjoy eating, a neural network couldn't invent the burger.

Creating a new recipe is just an optimization problem at its core.

[gambiting]

>>For example, you can feed a neural net all the recipes of burgers to create a perfect burger. Great. But how does the same net invent the burger?

Wait....but it just....did? It took the information about all possible burger recipes and invented a new one out of these. Like, a human could only invent a new burger if they knew anything about burgers in the first place, at the very least that it's a bun with some filling in between, otherwise you'd have no context to invent anything.

[jayjader]

Not OP, but I think they're not talking about inventing a _new_ burger, but inventing _the_ burger, as in the first one ever.

As in, the neural net in this example is able to improvise a new burger recipe solely because it was given existing recipes to burgers as input; it did not come up with the notion of a burger and then produce a recipe that outputs something fulfilling that notion when followed.

Personally, I would argue that this distinction is not as clear-cut as the tone of the original comment seems to suggest. Humans didn't invent the burger from nothing either. We've been grilling meat and making bread for millennia, and sandwiches have been a thing for over a century.

A 'burger' is just another iteration of our biological neural nets' attempts to make food from ingredients already present in our physical reality. Given that we flow in a single direction through time, any food we make is in turn added to our list of ingredients for making food "the next time". One could argue it is only a matter of time once meat can be ground into patties and grains turned into bread that burgers start being made - given the relative benefits humans gain from consuming both.

This comes back to what others have expressed elsewhere in this thread, that the probable [most] important distinctions aren't between software vs hardware, or organic life vs silicon processors, but the environment & capacity to interact with said environment. Some sense of "innate tendency to experiment" (i.e. curiosity) is probably either equal in importance or a direct runner-up.

[papito]

The burger was invented because a hungry traveler walked into a restaurant in Connecticut that was closing, and the owner had nothing but some beef and bread left. So he improvised - cooked the beef patty and squeezed it between two bread slices.

To this day they serve their burgers between two bread slices - not buns.

If you want to look it up, it's called LOUIS’ LUNCH.

AI my ass :D

[dkersten]

I agree. I think its very widely known that our ANN’s are only very rough approximations of how the brain actually works, I think the people who say its a computer implementation of the brain are either laypeople who don’t know much about machine learning or the brain, are people marketing the hype for personal gain or people without neuroscience knowledge who have bought into the hype.

I also recently heard an argument for why our ANN models won’t spontaneously become sentient: human brains don’t learn from just observation, but also interaction. A young child doesn’t learn abouthow blocks are stacked by looking at images of stacked boxes, they learn through experimentation, by stacking boxes and seeinghow their actions affect the world around them. For an AI, that means we either need to also work on robotics so the AI can interact with its environment, not just sense it, or we need to simulate an interactive virtual environment. Some people are working on this and making great strides, but your average toy ANN won’t exhibit human intelligence in isolation, in my opinion.

Combine those two things and we’re still quite a ways away from human-like intelligence or implementing a human (or animal)-like brain.

[martin-adams]

Interestingly, there are some studies that imply that intense thinking about doing an activity (such as a gym workout[1] or hitting a baseball) can improve your physical skills than if you didn't think about it. So this is supporting the notation that you can rewire your brain by thinking, as well as tactile input.

[1] http://nautil.us/blog/just-imagining-a-workout-can-make-you-...

[dkersten]

That’s not really what I’m referring to (or at least, only a little). Once you have a mental model of something, you can for sure think on it or build on it without interaction, but to initially set up our mental models (as children or whatever), I believe it takes interaction. Once we have a base, we can think abstractly about it and learn, but building that base..

Or, put another way, its my belief that you can “_improve_ your physical skills” by thinking, but to buildthe skill in the first place, interaction is necessary.

But even if its not true and interaction isn’t strictly necessary, I think (wrongly oerhaps) that few people would disagree that usually learning by doing is far superior that only learning by thinking/reading/listening/watching. So even if not neccesary, its at least more efficient (doing both together is probably most efficient).

[martin-adams]

Absolutely. I think what AI has highlighted is that the problem set is now looking more similar to a human experience. For example, how you train based on input and learn from failure and how limited information can confuse even a human brain (think image recognition). That said, because the problem looks the same, doesn't imply the method of processing is the same.

[grenoire]

I am definitely not an expert on this topic but my impression is that the research is not really focusing on structured abstractions of sensory input, or making these abstractions stateful. Shapes, colours, music, and whatnot are clearly stored and retrieved in our brains, which is something NN research is not looking at (enough).