[ArtWomb]

Humans observe an object once, such as a cup for drinking water, and we immediately grasp its "cupness". We can identify infinite varieties of cups despite variations in morphology, design, utility and context. Simply based on a single learning instance. This absence of any neural theory of inference is at the crux of the problem ;)

Shortcut Learning in Deep Neural Networks

https://arxiv.org/abs/2004.07780

[hans1729]

> Humans observe an object once, such as a cup for drinking water, and we immediately grasp its "cupness"

Adults do (i.e. the agents pretrained holistic model of its entire observed physical context). By reducing the phenomenon to the single observation, you're conveniently ignoring the early childhood phases spent exploring shapes/3d-geometry that enable this very ability of inference. this isn't fair, because regarding humans, the line between training-phase and trained model is very blurry, whereas a statistical model is trained when the weights are set and done.

Brute forcing through 2d-projections of 3d-objects (further denormalized through camera-artifacts etc.) until something sticks in a convoluted (heh) composition of arbitrarily initialized set of nodes and connections is obviously far different from the physical exploration kids do. Comparing the models resulting from the latter with the former is, in a word, absurd.

Through exploration, humans develop a model of physics itself, from which the nature of cupness can be inferred (which is, in fact, the magic term).

Deep learning alone won't get us there, but it'll probably give us the components that enable us to simulate this intricate process happening in kids brains.

In fact, I'm pretty sure that that's what a lot of the smart people researching general intelligence are working on (because that's what I would do, excuse my hybris).

[ArtWomb]

Good discussion! I'll just respond here, but plenty of though-provoking points all around ;)

I think what I was looking at was the result that has been often observed, that progress in AI research roughly tracks with hardware developments. Looking at AlphaGo to AlphaZero to MuZero. Training time for self-play increases. But parallelism in the tensor units of the hardware is an order of magnitude faster. It's great for problem domains like autonomous vehicles, contactless payments in retail stores and fraud detection in the data center. But what about generalizability? What about the black box communicating how it has learned? Will it be suitable for next-gen applications like robots designed to assist humans in space expansion?

I attended an event in NYC around the creative use of AI by a new breed of emerging artists like Mario Kliegmann from Germany. ArtBreeder can train a GAN on a single input sample and generate paintings in the style of Fragonard or Picasso or Rothko. And someone made a remark along the lines of: "if this had existed in the 1960s, we wouldn't have need Warhol to invent Pop Art!". But in reality, Andy Warhol experimented with a wide variety of media and techniques. From film to "oxidation art". And it struck me that was the truly creative part of the process. One that arises from a place other than rational optimization on a single task or even multiple known tasks.

[kensai]

This is a very insightful comment. I wonder if artificial intelligence can learn anything on how the development of the brain from a child to an adult functions, by actually pruning connections as well as creating/reinforcing new ones.

Well, that's what partly machine learning already does, right? :)

[bulka]

> Simply based on a single learning instance.

Did not get this part. I have limited sample of two kids, but I would say it takes at least a year before humans understand "cupness"

[ghaff]

I assume the parent is referring to kids past some level of neurological development and, of course, it's not necessarily as simple as one and done.

But, in general, deep learning requires far more examples to train image recognition and even then it's relatively fragile. (Not that humans can't be fooled but having models of the world in our brains help a lot. No, that's probably not a flying pig even though it looks like one.)

[zamfi]

> deep learning requires far more examples to train image recognition

Than kids? Who have have video input 10h/day for years (~1B images) and can also choose their examples actively?

There are many ways in which deep approaches differ from kids (understatement of the year?), but to say that kids don’t see a lot of data seems not quite right. They’ve got a huge “world model” to draw on by the time they are good at one- or few-shot learning.

[sp332]

I can go to Flickr and download 3 million photos labelled "cup". How many cups have you seen in your whole life? Probably fewer.

[Frost1x]

I think the point against these "few shot" or "one shot" learning models is that humans typically have a lot more data than are being credited for.

If I pickup a cup and move it around, pour water into it, turn it over so the fluid falls out, do it again, try some other object, compare them... how many data samples did I just receive in my 1 minute endeavor? The answer is: a lot.

Depending on how continuous (or not) space/time is and how fast your brain processes things... you're potentially being exposed to some extremely large discrete sampling of an infinite set.

How many actual discrete samples? Who knows, can probably come up with some bounding estimates with some work but that assumes we understand the brain better than we do. In addition, there's a reasonable amount of information that you likely inherit genetically so think about those massive discrete samplings over the time of your ancestors' lives and summarization and compression you get through evolution, over many thousands of years. The fact I have fingers, thumbs, and walk upright is a solution set found across a massive problem space and I get that out-of-the-womb (tm).

I don't believe modern ANN models are quite right and I suspect there's quite a bit we don't understand about our own intelligence that isn't or may never be captured here (hey, I want to be irreplaceable) but we have to consider the comparison more holistically.

One of the fundamental issues I believe DNN fail at is the fact they train against on high level problems and typically don't couple together for learning processes (it's not quite practical yet). As a human, when I learn how to do something I incorporate and connect it with future learning processes to help me learn faster or gain novel insights. Computationally this would be connecting stupid amounts of DNNs together in all sorts of ways (GANs are one line of thought similar here but I believe this would be a rich field to explore).

Personally, I'm completely against endeavors to search for a real AGI. I find it hard to believe once such a goal was obtained, it could possibly be good for my long term survival but hey, I'm a bit greedy in that respect.

[mLuby]

Exactly this. Show a human a thing for which it doesn't already have 10K hours of "training" and they won't do so well.

Monads might be a good example of a thing for which humans don't have a massive headstart on ML due to "years of experience manipulating monad-like things" (as we do for most physical and social concepts). And I don't think anyone one-shot learns a monad.

[zamfi]

> How many cups have you seen in your whole life? Probably fewer.

How many different cups are there in that dataset?

How many hours have I spent looking at cups? Anecdotally, my 2-year-old spends a huge amount of time playing with cups.

[jklehm]

Thinking about it from the wrong direction. We have lifetimes of images that are "not cups" so when we get some cups flagged to us it's differentiated against a huge non-cup baseline.

[bulka]

A pretty interesting podcast from Quanta Magazine suggests that it might not be a simple matter of training set size: https://www.quantamagazine.org/where-we-see-shapes-ai-sees-t...

[bitL]

One could argue we are either born with some "pre-trained model/architecture" or we spend the first 10 years of our lives "pre-training" (with the first 11 months training the fastest).