AI is mostly about curve fitting (2018) • Hacker News Board

Re: AI is mostly about curve fitting (2018)

#101

Post by account73466 » Sat, Nov 23, 2019, 5:28 PM UTC

EsssM7QVMehFPAs wrote: post #100

nextos wrote: post #71

Earlier quoted context omitted.

I think curve fitting is an important component of future AGI. But it definitely needs causal reasoning baked in, which leads to better models with less data [1,2]. My intuition is that there's a lot of important work to be done using logical representations of models and transforming them back and forth using well understood semantics operators. Deep functions will be part of said models, but the whole model does no…

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 survive.

Re: AI is mostly about curve fitting (2018)

#102

Post by antichronology » Sat, Nov 23, 2019, 5:29 PM UTC

Where is the proof that human "rational" thinking is anything but curve fitting? I'm not convinced

Re: AI is mostly about curve fitting (2018)

#103

Post by cs702 » Sat, Nov 23, 2019, 5:37 PM UTC

This article conflates two separate, very different issues into one:

* Issue #1: There is a tremendous amount of hype, noise, and snake oil surrounding the moniker "AI." Pretty much everyone agrees with this statement. (And anyone who doesn't agree with it... is probably selling snake oil.)

* Issue #2: Is intelligence just a form of "curve fitting," i.e., is it just finding solutions to very complicated, high-dimensional problems with more and more computation via search and self-play? Note that DL via SGD is a form of learning via search, RL via state-action-reward mechanisms is a form of learning via search, and multi-model/multi-agent DL/RL are forms of learning via search with self-play.) There is sharp disagreement on the answer to this question #2. Is that really all there is to intelligence?

The OP believes the answer to #2 is no: intelligence ought to be more than "curve fitting" via search and self-play.

Others believe the answer to #2 is yes. For a typical example of their thinking, here's Rich Sutton, Distinguished Research Scientist at DeepMind: http://www.incompleteideas.net/IncIdeas/BitterLesson.html

Re: AI is mostly about curve fitting (2018)

#104

Post by chongli » Sat, Nov 23, 2019, 5:43 PM UTC

codingslave wrote: post #4

crazygringo wrote: post #3

Earlier quoted context omitted.

I was say fundamentally no -- and Douglas Hofstadter is probably the foremost spokesperson against intelligence being curve fitting. General intelligence is primarily about developing useful conceptual categories (not mapping to existing ones) and drawing cause-and-effect inferences that assist us in achieving goals . Curve fitting is just another name for pattern recognition, mapping to previously defined categories…

We dont know anything about AGI, and we arent sure how it would work. Its impossible to make assertions like this

We know a great deal more about biological general intelligence than we do about AGI. Many animals create their own goals and work to achieve them. We learn what works and what does not, and adapt our strategies to compensate. Humans do this (obviously) but a lot of other intelligent animals can do it as well.

One of my favourite examples is the New Caledonian crows who have learned to use traffic to crack nuts [1]. Here, a crow had no pre-defined objective function apart from "eat food to stay alive" and has accomplished something remarkable. It found a food source that it had never had access to before, it developed a complex model of its urban environment, it combined its knowledge of the problem (the hard nut shell) with its knowledge of its environment (cars crush small objects), and it constructed a sophisticated for strategy for using cars to crack open the nuts and fetching the contents when the traffic lights indicated it was safe to do so.

This is general intelligence!

[1] https://www.youtube.com/watch?v=BGPGknpq3e0

Re: AI is mostly about curve fitting (2018)

#105

Post by sgt101 » Sat, Nov 23, 2019, 5:47 PM UTC

scottlocklin wrote: post #47

tomthe wrote: post #16

Well, most of science is also just curve fitting.

Pretty sure Maxwells Equations and the laws of thermodynamics didn't come from curve fitting! Experimentalists may have verified these ideas using some kind of curve fitting, but thinking in abstractions, aka, "a ball rolled out in the street, maybe a child will follow," is one of the things curve fitting can't do.

As Uber found out.

Re: AI is mostly about curve fitting (2018)

#106

Post by blazespin » Sat, Nov 23, 2019, 5:49 PM UTC

mola wrote: post #99

ipsa wrote: post #33

"Machine learning" used to be a safe haven. You could flee there to escape the Terminators and brain-on-a-chip graphics. Business PR deliberately killed that. They wanted their ML algorithms to be refered to as AI, so they could fully ride the hype train. AI used to be a tight quirky community. Having the brain as inspiration led to all sorts of anthropomorphizing. This was ok. Researchers understood what was meant w…

This is currently true for almost all human endeavors. We're beset with PR people deliberately promoting misconceptions and out right lies. A recent article about "beewashing" is another good example of subverting human attention from real issues by over simplifying for the purpose of corporate PR. We are constantly bombarded by noise and lies so we won't be able to make sound and rational decision about anything. In…

Everything is a lie, until you're tautological. Machine learning itself seems a bit of misnomer. High dimensional curve fitting is a good description, imho.

Re: AI is mostly about curve fitting (2018)

#107

Post by tomerbd » Sat, Nov 23, 2019, 5:49 PM UTC

also humans

Re: AI is mostly about curve fitting (2018)

#108

Post by The_rationalist » Sat, Nov 23, 2019, 5:54 PM UTC

umarniz wrote: post #38

I think intelligence has for some time already been boiled down to curve fitting, even for humans. Our current accepted definition of intelligence in schools is to get a score that is higher than the average to be considered sufficiently intelligent to proceed to the next grade. I feel anything that we develop for AI would fundamentally always be inspired by our own experiences and hence curve fitting is something we…

Curve fitting is only adapted to analysing continuous data and properties. Sparse data such as actual semantics of natural languages or an object oriented database are a mismatch for current machine learning.

Re: AI is mostly about curve fitting (2018)

#109

Post by aplacelikethis » Sat, Nov 23, 2019, 5:57 PM UTC

blazespin wrote: post #106

mola wrote: post #99

Earlier quoted context omitted.

This is currently true for almost all human endeavors. We're beset with PR people deliberately promoting misconceptions and out right lies. A recent article about "beewashing" is another good example of subverting human attention from real issues by over simplifying for the purpose of corporate PR. We are constantly bombarded by noise and lies so we won't be able to make sound and rational decision about anything. In…

Everything is a lie, until you're tautological. Machine learning itself seems a bit of misnomer. High dimensional curve fitting is a good description, imho.

Is nothing a lie, though?

Re: AI is mostly about curve fitting (2018)

#110

Post by mycall » Sat, Nov 23, 2019, 5:59 PM UTC

> Pearl contends that until algorithms and the machines controlled by them can reason about cause and effect, or at least conceptualize the difference, their utility and versatility will never approach that of humans.

Is not cause and effect a temporal-based acyclic directed graph with mutating state (wrt CompSci). Anyways, I think ML needs to look closer at simple cases, like sea squirts which have 8617 neurons. Try to answer how and why do they work better than some of our algorithms.