[ipsa]

"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 with "learning", "intelligence", "to perceive" in the context of AI. Nowadays, it is almost irresponsible to do this, not because you'll confuse your co-researchers, but because popular tech articles will write about chatbots inventing their own language and having to be shutdown.

Still, as a business research lab, it is good to get your name out there, so all the wrong incentives are there: Careful researchers avoid anthropomorphizing, and lose their source of inspiration -- you can not be careful with difficult unsolved problems, you need to be a little crazy and "out there". Meanwhile, profit-seeking business engineers and their PR departments, obfuscate their progress and basic techniques, all to get that juicy article with "an AI taught itself to X and you won't believe what happened next".

The researchers actually busy solving the hard problems of vision, natural language understanding, and common sense, do not have time to write books about how AI is not yet general. Nobody from the research community ever claimed that, nobody came forward to claim they've solved these decade-old problems. It is people selling books railing against the popular reporting of AI. Boring, self-serving, and predictive, and you do not need to fit a curve to see that.

All this quarreling about definitions and Venn diagrams and well-known limitations is dust in the wind. Go figure out what to call it on your Powerpoint presentation by yourself, and quit bothering the community.

[erikpukinskis]

What’s wrong with anthropomorphizing?

I’ve noticed at least as many people under-anthropomorphize as over. People who seem obsessed with human exceptionalism and are personally offended at the idea that plants and animals (and computers!) might have subjective experiences like our own.

But to me it seems obvious we are far more alike “lower” species than we are unlike them. I would say the cases of human exceptionalism are actually extremely rare. The main source of our uniqueness is that we amalgamate other species, not that we have transcended them.

My theory is that we are terrified that we might be simpler than we think, because socially we behave as if we are so singular. If we are simple, and animals and machines are like us, then maybe we should be treating them with more reverence.

But being afraid of that is OK for a random person. For a machine learning researcher I would hope they are more careful about what we have evidence for (the similarities between us) and what we don’t (that there is some ineffable magic about humans).

[projektfu]

Anthropomorphizing is dangerous because it leads to metaphor that can both ascribe too much to the subject and create blind spots in the minds of researchers. Saying, for example, "Dogs want love," is fine for the owner but problematic for a researcher because love, as we understand it, is a human state. We'll never really understand what it means for a dog to feel loved. To the ethologist that is not to say that there are not similar emotional processes for dogs, it's to say that they cannot be understood by analogy to the human ones.

It's sort of like the color perception problem [1]. Dogs and machines do see colors, but what do they see?

1. https://newrepublic.com/article/121843/philosophy-color-perc...

[ecorithm]

You should go and read some stuff written by ethologists. Basically everything you said would be vehemently disagreed with by a large group of prominent ethologist. The term anthropodenial has even been coined to criticize your exact thinking and to describe the dangers of not anthropomorphizing enough. Not saying you can't over do it, but the GP's comment is much more in line with thinking by modern ethologist. Frans De Waal is a good place to start.

[projektfu]

Ok things may have changed since I studied ethology

[ecorithm]

Right, to be fair to you this was a hotly debated topic in ethology (and still is to an extent), however I would say most modern ethologist have come out on the side of embracing evolutionary parsimony and viewing our human experience as a valuable asset to understanding animals (especially mammals).

Probably the most cited paper regarding this debate is by Marc Bekoff, "Cognitive Ethology: Slayers, Skeptics, and Proponents" (http://cogprints.org/160/1/199709005.html). Your original comment would be categorized as a "slayer" a position which is widely criticized. In fact Bekoff's focus is on canines and he used your exact example with dogs, but to opposite affect.

[projektfu]

Phew, I'm surprised to see such an emotionally-charged article on the subject. Everyone who is uncomfortable with anthropomorphism is biased and misguided in some way, but extremist proponents are merely overly enthusiastic.

I do wonder about the theoretical bird scientist trying to figure out the "fixed action patterns" of other animals. If anthropomorphism is the way to go, surely it goes in the other direction in some way.

[projektfu]

A review I just read (https://www.frontiersin.org/articles/10.3389/fpsyg.2018.0220...) suggests both of our viewpoints and seems to allow for a continuum of approaches without resorting to name-calling. I think that there's definitely stupidity in the history of "anti-anthropomorphism" if it's really true that people dismissed an article that started by saying bees appear to dance. After all, the fact that they have a behavior like that suggests something interesting is going on. It's also really easy to go overboard in simplifying animal behaviors to our own poorly-understood human behaviors.

[dghughes]

Or people who say "The computer thinks...". No it's a machine that only does what people make it do.

[roenxi]

We've seen that threshold crossed with neural agents like AlphaGo which can be reasonably described as thinking. It decides if moves are good or bad after a little pause for processing, its decisions improve with time, it has an opinion on the state of play, the opinion is formed using basically the same data as a human, different iterations of the neural network can have a different opinion but there is a link between it and the previous one.

I don't see a test that majorly distinguishes it from a human. It appears to be following the same process with a few tweaks around the edges. There are some exceptions in the 2-5 situations in Go where a human can actually use optimised logic to determine what will happen; but they aren't the meat of the game.

[drongoking]

> We've seen that threshold crossed with neural agents like AlphaGo which can be reasonably described as thinking.

I don't recall ever reading in a technical paper, or in an interview, a leader in the field of ANNs claim they were thinking. If you have, I'd like to see a reference. Most are fairly honest about the differences between artificial neurons and real ones, and between human cognition and what ANNs are doing with data.

[erikpukinskis]

Is “thought” even a well defined scientific term? I doubt neuroscientists write about it either.

[projektfu]

Chess is one of those areas where humans have developed computer-like abilities, such as exhaustive search. What's interesting is the appearance of intuition-like movement in modern chess computers, but is it ... intuition?

[Akababa]

I feel that's just a semantics rabbit hole. "Think" is too broad of a term to be picky about.

[shotashotashota]

They are both a problem, people do think human are somehow exceptional. We all agree that we are apes but none of us want to admit when we get horny in public.

But ML, AFAIK, is so simple; its literally a glorified polynomial functions. The only thing it get going for it is the large data set that we can train it on. It cannot "learn" anything from a small data set and extract any information out of it without a human imposing his/her knowledge on it.

For instance, take the concept of an even number. This simple knowledge is so powerful in solving algorithmic problems. But, its very hard to make a machine learn of this concept in general.

I think the problem is really overestimating how "intelligent" human are. We are only as intelligent with respect to our imagination. Its possible that there is an entire class of intelligent outside of our imagination that we cannot fully grasp its intelligent. Similarly, I am only conscious with respect to my own consciousness, but there may be another class of consciousness that is unimaginable to this monkey's brain.

[toolslive]

"Don't antropomorphise computers. They really hate that" (NN)

[ska]

> What’s wrong with anthropomorphizing?

c.f. 'eliza' for some of the issues.

[account73466]

Very well said. Also, curve fitting is not a corner case. Most relevant and intelligent things we care about can be solved with "just" curve fitting + extrapolation.

[nextos]

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 not necessarily need to be deep. We can already see hints of the field going in this direction in deep generative models [3].

[1] http://web.stanford.edu/class/psych209/Readings/LakeEtAlBBS....

[2] https://probmods.org/

[3] http://pyro.ai/examples/

[EsssM7QVMehFPAs]

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 holistic nature of our own intelligence. We are getting better at emulating human traits for specific tasks with ML. We lack the specific knowledge of what the algorithm should mimic to become equal to us in terms of our intellect though.

[account73466]

>> 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.

[EsssM7QVMehFPAs]

> 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..

[eli_gottlieb]

> 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 own understanding, but is at least based on an understanding.

Your list here really only has three problems in it: causal reasoning, theory of mind, and "emotional intelligence". Emotional intelligence works in the service of "drive and desire", considered broadly. Creativity likewise works for the emotions. To be creative, you need aesthetic criteria.

Most of that, we're still really working on putting into mathematical and computational terms.

[EsssM7QVMehFPAs]

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.

[eli_gottlieb]

>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.

You can't really separate emotion and aesthetics from the neurotransmitters helping to implement them! They're considerably more complex than anyone usually gives credit for.

Likewise, to form a valuable proposition, you need a sense of value, which is rooted in the same neurological functionality that creates emotion and aesthetics.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2666711/

[unityByFreedom]

> The researchers actually busy solving the hard problems of vision, natural language understanding, and common sense, do not have time to write books about how AI is not yet general.

I've come to terms with the hype. There are still researchers doing the hard theoretical work, and they will still be toiling away after the next economic downturn. We can all choose every day whether to find fulfillment through seeking attention from other people, money, or satisfying our curiosity to solve problems.

> Nobody from the research community ever claimed that [AGI], nobody came forward to claim they've solved these decade-old problems. It is people selling books railing against the popular reporting of AI. Boring, self-serving, and predictive, and you do not need to fit a curve to see that.

Hear hear! That said, this is a good article by a respected researcher. Here's what LeCun had to say about it,

> ...In general, I think a lot of people who see the field from the outside criticize the current state of affair without knowing that people in the field actively work on fixing the very aspects they criticize.

> That includes causality, learning from unlabeled data, reasoning, memory, etc. [1]

[1] https://www.facebook.com/yann.lecun/posts/10156387222842143

[mola]

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 recent years this transformed from a side effect of bottom line mentality to out right weaponization by powerful entities political and corporate.

[blazespin]

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.

[aplacelikethis]

Is nothing a lie, though?

[nxpnsv]

If everything is a lie, then nothing is a lie, and that’s the truth (or not).

[rzzzt]

If everything is a lie, then "everything is a lie" is a lie.

[aplacelikethis]

Hm. No logic to be found there.

[nxpnsv]

That was the point I tried to make. Arguably not very well...

[notastudent]

"The researchers actually busy solving the hard problems of vision, natural language understanding, and common sense, do not have time to write books about how AI is not yet general."

Stuart Russell recently published a non technical book on AI. I really hope tech journalists take note

[ben_jones]

Honest question, aren’t the consequences for “real” researchers keeping their heads down quite severe? Won’t we have important policy decisions both public and private and billions in funding misdirected for years when they could best be put elsewhere? Sure the “real” researchers will have easier access to funding, which perhaps is a key motivating factor to not push back on the hype, but isn’t there a large opportunity cost to allowing hype and or bullshit to go unchecked because “they don’t have the time to write a book”?

[ska]

The consequences of technical subject matter experts dabbling in policy are often pretty bad.

You can get involved in this, but it takes real work (i.e. time taken away from your research area) and an honest understanding that the policy issues their own deep specialty, and you are likely to be quite naive about it going in.

[ethbro]

Hasn't this almost always been true?

On the plus side, it makes it fairly easy to ask cocktail-party-caliber questions and quickly suss out whether you conversation partner knows what the hell they're talking about.

[hr45eahear5]

> you do not need to fit a curve to see that

You haven't proven this statement. It's possible within your own brain is nothing more than a rudimentary curve fitting algorithm that allowed you to see this pattern.