> I wish ML researchers stopped using anthropomorphizing language.

ML researchers don't write articles, journalists do.

Actual language used by the ML researchers: "Intuitive physics learning in a deep-learning model inspired by developmental psychology" [1]

[1]: https://www.nature.com/articles/s41562-022-01394-8

> Actual language used by the ML researchers: "Intuitive physics learning in a deep-learning model inspired by developmental psychology"

In my opinion, this is still anthropomorphizing the algorithms. The term deep-learning is a poor representation of what actually goes on. Someone please correct me if I'm wrong, but all ML does is statistical regressions (in essence). It doesn't "learn" like a person learns. Neural networks are not actually like brains (as far as we understand how the brain works).

I feel like the whole industry is inundated with aphorisms that are kind of true, but not wholly true. Evolutionary algorithms, neural networks, deep learning, deep mind, this stuff all reeks of anthropomorphizing fundamentally mathematical processes. I get it, it's a lot easier to get the gist of "the computer is learning/training" than "the computer is refining the weights and biases to try to optimize the output".

> It doesn't "learn" like a person learns.

Well it's not called person-learning-machine is it? Why would something have to learn "like a person" to be able to use the word "learn", those two concepts are not attached one to the other. If they were, saying "learn like a person" would be a pleonasm, yet it isn't. IMHO "learning" is a fine term, it conveys the idea of what is happening effectively and quickly.

Also, we don't know how a person learns anyway, it might very well be a similar process, just way more efficient and complex.

> Evolutionary algorithms

How would you propose calling them? You have a generation of agents, each with their own specificities, and from the agents most successful to accomplish the task at hand, we derive a new generation, slightly modified from their parent's.

It seems to me "evolution" is again the most suitable and efficient way of describing what is happening.

While I agree that there is definitely too much anthropomorphizing surrounding AI, I feel you are going way too far in the opposite direction. Not every word that can be composed with natural process/humans should be banned from being used anywhere else.

> It doesn't "learn" like a person learns

Who cares? Why should you hold the idea that it would? They are systems moulded after data, 'learn' seemed to be a decent label. If it is not, it is because "learning" is _active_, by philological analysis, and happily consistently with an aim of AGI (intelligent entities learn actively).

A computer does not compute like a human would. Yet, no problem.

For that matter, you are using 'person' in a very individual way - not even "personal" (a "person" learns according to individual nature, while you are using it as a collective term).

As already expressed - nearby I wrote 'biomimicry' -, what you are calling "anthropomorphizing" is a wrong direction: "evolutionary algorithms" were born out of keys after the observation of the natural world, and the terms express that - it is not that you saw the algorithm and went "It looks like my uncle Oscar"¹ (this side is active - it "learns").

(¹Those anthropomorphizing Hollywood cultists and all that sculpture...)

> Who cares?

> a "person" learns according to individual nature, while you are using it as a collective term

There's a very specific definition for learn:

>> to gain knowledge or understanding of or skill in by study, instruction, or experience[0]

There's a few more, but none of the definitions treat learn in a non-collective way. I guess meriam Websters dictionary doesn't like treating people as individuals or something lol.

Additionally, all the definitions there are speaking in human contexts. They talk about learning in the sense of being taught, or gaining experience, or gaining knowledge. Sure a computer kind of does this stuff, but it doesn't really. And that falls into the category of attributing human characteristics to an inanimate object.

I probably shouldn't have said that everything in the short list I wrote reeked of anthropomorphizing processes. But the evolutionary algorithm was more in line with what I mentioned immediately before. My whole comment read:

> I feel like the whole industry is inundated with aphorisms that are kind of true, but not wholly true. Evolutionary algorithms, neural networks, deep learning, deep mind, this stuff all reeks of anthropomorphizing fundamentally mathematical processes.

An evolutionary algorithm definitely falls into the category of kind of true but not wholly true. But it's not anthropomorphic.

> intelligent entities learn actively

Also, this is a very loaded statement. What is an intelligent entity? If you Google "is a computer intelligent" there are various papers, articles, and other pieces of media all claiming that we can't call a computer intelligent, and some claiming that we can consider certain algorithms somewhat intelligent. This is anything but an accepted standard today.

[0]: https://www.merriam-webster.com/dictionary/learn

> kind of true but not wholly true

Give us an example of some relevant label that would be "«wholly true»" instead of "«just kind of true»". Because metaphors, and the whole system of fuzzy pattern relations, are based on fuzzy pattern relations.

> none of the definitions treat learn in a non-collective way

You have misunderstood my post. I would prefer that you read it again.

You are complaining about loose use of the language: I noted that you yourself used the term 'person' more than loosely, with a dubious jump. When one says '«like a person learns»', that is supposed to be "like a specific individual in his own individual characteristics will learn" - instead you used to say "like people in general learn". A "person" is a "definite form", not a general individual representing common features - it is the opposite.

> very loaded statement

Which you are taking out of context. I said that you have to call the moulding of your functions something, and that "learn" seems a very acceptable term, since it is bottom-up instead of top-down, it is automated instead of encoded: it is developed against data, it "learns". And that if the term is disliked, there could be a very good reason, because 'learn' was born as a sort of a hunting term¹ - it really means something like "investigate" -, which is a happy coincidence because what is largely missing in AI is critical thinking, part of the active process of learning ("learning" is active as investigation is). And the day John will have to check «accepted standard[s]» to see how things are, I will be willing to comply to his sad request for mercy.²

¹Irregardless of what the Merriam-Webster will write, because you get a none-the-wiser relative notion but not knowledge from a "dictionary of use", as at the paragraph for 'life' you will not find the meaning of life.

²John must be, tautologically, an "active learner". (He will check personally.)

Seems anthropocentric. Humans don't have a monopoly on learning.

it's metaphors all the way down.

deep learning isn't a bad name imo. it is learning, but nothing in the name suggests it's like a human brain.

You're right that journalists use anthropomorphization much more. But AI researchers also have a long history of choosing terms that are anthropomorphizing or animating. Here the name PLATO -- which evokes an image of an ancient philosopher, a human, who is by cultural tradition considered smart -- is used in the original journal article.

Terms like "neural network" and "artificial intelligence" are frequently used by AI engineers and researchers despite the obvious image they evoke. Sometimes they even call their creations "brains". Also note the name DeepMind.

It's definitely not just the journalists.

To add to that, often EDITORS are the ones who come up with the titles, for reasons beyond being clear, like using words that draw attention and to fit in a specific space.

My pet peeve is when AI researchers coin new terms for objects that can be described by well-established mathematical terms. For example, saying a neural network layer has "256 units" instead of "output dimensionality of 256".

But at some point you need to name things for brevity. I understand why people say "activation function" instead of "elementwise monotonic nonlinear function".

Misuse is also rampant, like using "inference" to describe evaluating a neural network on an input, even when the NN isn't part of a probabilistic model.

To be fair, give high degrees of interdisciplinarity and imperfect acquaintance with all the terminologies (and imperfect memory), and given that we mix natural language and conventional technical language, and with some continuity, and given that natural language itself mixes original core root meanings and posterior conventions, and given that even biologically the best term may be occasionally (polysense) hard to find, the mess is expected.

> artificial neural networks aren't like actual neural networks or brains

Just to zoom right in on neural networks:

People often say this, and I never see a solid argument.

I know very little about biological neural networks.

Clearly they are very different in some respects, for example, meat vs silicon.

But I never see a good argument that there's no perspective from which the computational structure is similar.

Yes, the low level structure, and the optimization is different, but so? You can run quicksort on a computer made of water and wood, or vaccum tubes, or transistors, and it's still quicksort.

Are we sure there aren't similarities in terms of how the various neural networks process information? I would be interested in argument for this claim.

After all, the artificial neural networks are achieving useful high level functionality, like recognizing shapes.

There are many ways one can argue for or against this comparison. This is mostly a matter of terminology. However the problem is that the field of AI has been for many decades consistently shaping its language to evoke human-like connotations in order to boost hype. This article's title is a yet another example of that.

There are a few conceptual differences where artificial neural networks conceptually diverge for computational reasons.

One is the notion of time and connectivity loops - overwhelmingly, ANNs use a feed-forward architecture where the network is a directional graph without loop and some input is transformed to some output in a single pass - and weights can be adjusted in a single reverse pass, which is very practical for training. We do know that biological brains have some behavior that relies on signals "looping through" the neurons, and that is fundamentally different from, for example, running some network iteratively (like generating text word-by-word via GPT-3). We have artificial neural network simulations that do things like this, and also simulations of "spike-train" networks (which can model other time-related aspects which glorified perceptrons can't), but we don't use them in practice since the computational overhead means that for most common ML tasks we can get better performance by using an architecture that's easy to compute and allows to use a few orders of magnitude more parameters, as size matters more.

It is not the case - this is just biomimicry: "let us try imitating feats of a living organism". Perfectly legitimate. Nobody is told to make unduly images out of it.

"DeepMind AI learns simple physics like a baby" clearly makes an unduly image out of it. Calling it PLATO evokes an image of an ancient human philosopher. No other field uses as many bold comparisons to humans as artificial intelligence (its name alone is one).

But you are supposed "not to evoke": that'd be sensational.

"The name of AI": we call "intelligent" in this convention that which finds solutions - normally the natural intelligence of a professional, sometimes the artificial intelligence of a computerized system. As easy as that. It works, no intrinsic issue.

This experiment: children seem to rely on expectations in learning and this ANN based system tries to implement some form of "expectation based learning". No problem.