Some DNNs compute posterior probabilities for classification. You could feed those inputs into a Bayesian network for decision making too but you could also train a NN to make those decisions. Still there may be times when a human derived controller is better with AI as sensory input than an AI controller itself.
AI is mostly about curve fitting (2018)
171–180 of 192 posts
Re: AI is mostly about curve fitting (2018)
#172I tend to think that ML is a kind of automated program generation that maximizes a certain metrics given by human. Some of them are based on real data but some aren't (like RL). So it's certainly more than a curve that needs to be fit. It's more like Turing machine fitting.
Re: AI is mostly about curve fitting (2018)
#173Earlier quoted context omitted.
What our brains do is curve fitting plus experiments . Causality is learned through experiments - and as children we do a lot of experiments (with moving our limbs etc). With just observations you can have only correlations. For example you can correlate smoke with fire, but only through experiment you can learn that it is fire that causes smoke not the other way around.
Just include time in the model and use lagged features. It’s used all the time in automated time series forecasting.
This can tease out transfer entropy but still isn't identifying causal factors.
A common issue you find would be confounding. Then, because you haven't identified the latent connection, you may try to increase level A to have effect on output B, and be disappointed.
This is basically Judea Pearls Book of Why's main hypothesis, that E(Y|X) != E(Y|do(X)), where do(X) is when we modify X somehow.
Re: AI is mostly about curve fitting (2018)
#174Some really insightful discussion of the possibilities and limits of ML can be found on Les Fridman's AI podcast. Especially good were interviews with Yann LeCunn, Jeff Hawkins, Elon Musk and Francois Chollet. One memorable quote from LeCun "There is no intelligence without learning." Here's a link to the LeCun interview, though many aothers are excellent. https://lexfridman.com/yann-lecun/
The interviews with researchers and engineers in the field seem like they'd be interesting, but I don't see it for Musk. What special insights does he offer into AI?
Re: AI is mostly about curve fitting (2018)
#175Earlier quoted context omitted.
Judea Pearl is NOT the author of the piece. The author is someone called Kurt Mako.
True but most of the content of the article is based around Pearl's thoughts on the field.
I would disagree. Most of the article consists of the author's own musings on quotes from Pearl and others.
Re: AI is mostly about curve fitting (2018)
#176What if human intelligence is not computable? Why does no researcher address the fundamental assumption of the field?
It is possible that human intelligence is not computable, but it looks veeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeery difficult to prove it. Nobody is sure. So the options are: 1) Try to prove that human intelligence is not computable. 2) Give up and go to another research field, like particle physics or artificial sweeteners or whatever. 3) Try to see how close to human intelligence can you get with the current…
Why does it seem so difficult?
For example, currently human intelligence accomplishes much more with much less resource usage (many many orders of magnitude) than any AI algorithm we've developed. Someone could look at the trend, and get an idea of the extent of improvement we'd have to reach parity with human intelligence, and see if it seems at all feasible. A simple quantitative analysis of what we know so far.
At any rate, I'm unaware of any sort of significant effort at #1.
Re: AI is mostly about curve fitting (2018)
#177What if human intelligence is not computable? Why does no researcher address the fundamental assumption of the field?
This has been addressed by many authors since modern computation was formulated, for example by Turing himself. We learn about and discuss Newell and Simon's Physical Symbol System hypothesis in undergrad classes, ie. explicitly stating the underlying assumption. Once in a while someone will assert that computation is not sufficient, eg. Penrose, and generate discussion. In practice you can work on AI-the-engineering…
That seems like a pretty uninformed hypothesis. For example, halting oracles can process symbols, but are not computable. So, maybe it is necessary, but easy to show symbol processing may not be sufficient (as I just did).
This has significant implications for the engineering discipline. For example, if the mind is a halting oracle, then you can get much more performant algorithms by incorporating human interaction.
Re: AI is mostly about curve fitting (2018)
#178Earlier quoted context omitted.
Casual reasoning is one thing that is lacking. But what about creativity? What about drive and desire? What about belief and the will to fail on the road to success? What about collective intelligence and the need to peer up in efforts? What about emotional intelligence? I personally do not believe in AGI since I also do not believe in psychology, sociology or neurobiology being anywhere near understanding the holist…
>> But what about creativity? What about drive and desire? What about belief and the will to fail on the road to success? What about collective intelligence and the need to peer up in efforts? What about emotional intelligence? All this resulted from evolutionary processes. Any approximation of AI which will deal with other agents will develop something like that and more in order to be competitive, collaborate and s…
> All this resulted from evolutionary processes. Any approximation of AI which will deal with other agents will develop something like that and more in order to be competitive, collaborate and survive.
How can we assume that a simulated evolutionary process of a simple mathematical model or some arbitrarily sized multi-dimensional matrices yields similar evolutionary results?
Just think of the ongoing debate about quantum entanglement effects inside the neural signaling process. On a rather onthological level, we are still unable to formulate a mere definition of our consciousness or things like creativity that lasts longer than a few academic decades..
Re: AI is mostly about curve fitting (2018)
#179Earlier quoted context omitted.
Casual reasoning is one thing that is lacking. But what about creativity? What about drive and desire? What about belief and the will to fail on the road to success? What about collective intelligence and the need to peer up in efforts? What about emotional intelligence? I personally do not believe in AGI since I also do not believe in psychology, sociology or neurobiology being anywhere near understanding the holist…
> Causal reasoning is one thing that is lacking. But what about creativity? What about drive and desire? What about belief and the will to fail on the road to success? What about collective intelligence and the need to peer up in efforts? What about emotional intelligence? Hi, I work at one of the intersections of machine learning with certain schools of thought in neuroscience. The following is based entirely on my…
Admittedly, that list is an arbitrary poke into areas of debate in your fields of profession.
As a take on your interpretation of creativity: I would argue that the act of forming new and valuable propositions is not related to emotion or aesthetics per se.
Aesthetic theory is observing a very narrow subset of creative processes. And even there, our transition from modernism into the uncertainty of the post-modernist world defies any sound definition of the "aesthetic criteria". Yet we perceive aesthetic human-creativity all the time.
In similar vain is the application of generative machine learning that spurs debate about computational aesthetics today. Nothing proofs better the incapability of modern ML forming real creativity than the imitating nature of adversarial networks spitting out (quite beautiful) permutations of simplified data structures underlying the body of Bach's compositions.
Now we could start on the assumed role of complex neurotransmitters in the creative process of the brain and the trivial way reinforcement learning rewards artificial agents, but that would push the scope of this comment.