Understand what AI sees

https://hackernoon.com/understanding-what-artificial-intelligence-actually-sees-7d4e5b9e648e

by dirtPUNK • 8 years ago

100 42 8 years ago

grenoire• 8 years ago

I was actually giving an example to my friend on ML vision applications over the hotdog app. The underlying network probably only recognises a red shape surrounded by lighter parts, as opposed to understanding conceptually what a hotdog is. The USB example of course is an extreme case of that.

memebox3v• 8 years ago

Eh? What does conceptually mean?

red75prime2• 8 years ago

I don't know what "conceptually" means on such a level that I can program it. But it probably has something to do with a network of differing representations we have for a hotdog. A low-level visual cortex representation (probably not too much different from artificial NNs). Representation as parts arranged in particular spacial order. A word. Related representations, like a process of making a hotdog. And so on.

k_sh• 8 years ago

The AI sees data/markers/patterns that look like something it's seen before, as opposed to actually comprehending that it sees a tube of meat that people call a hot dog.

The best metaphor I can think of is the cognitive difference between navigating a transit station that has signs in your native language, and one that you spent a couple of hours learning on Duolingo - with the latter, you aren't really understanding anything, just associating a:b::x:y.

notahacker2• 8 years ago

A human sees a 3D scene with objects, creatures, texture and lighting (and it evaluates the scene based on these concepts and how they related to each other even if it's never seen green fields, sheep, dry stone walls or fog before).

The computer generally sees a set of pixel values, and takes a plenty of training to distinguish between "sheep" and "field the same shade of green as usually found in images containing sheep" because it doesn't have an innate concept of animate objects and habitats, how they relate to each other and which is more important. Whilst the computer's busy seeing a white pile of stones as a false positive for the presence of sheep, the human's looking at the way the stones are piled as possible evidence of human activity and noting the presence of droppings in the foreground might mean sheep were here recently.

(of course, it's not entirely impossible for computer vision systems to deal with higher levels of abstraction: autonomous vehicles model the world in 3D and classify objects as vehicles in order to predict their near future behaviour and signals in order to regulate their own behaviour, but that goes well beyond mere learning processes. And of course a pixel-by-pixel understanding of the world has its use cases in spotting changes in colour and texture which are so subtle humans abstract away from, like crop discolouration on satellite images or cracks in rough surfaces)

We're much better at abstraction than other animals too: show us a 20,000 year old cave painting and we'll easily grasp that it was produced by humans and the lines represent shapes of animals broadly similar to today's livestock. Same goes for 2000 year old marble bas reliefs. You might well be able to train an algorithm to recognise "paintings of animals" and "carvings of animals", but you'll struggle with a training set consisting purely of photographs of real world livestock

foota2• 8 years ago

Isn't this pretty far behind the state of the art for white box neural nets? Which would use the first layer's activation to do something similar?

shawn2• 8 years ago

Could you give some references for an ML novice to learn these techniques? (Papers, books, anything.) I've been studying ML, but I'm not advanced enough yet to know effective ways to learn state of the art methods.

foota2• 8 years ago

I just try to follow along, I don't really get into the details. The bit I was referencing was from a blog post that was on here a while ago: https://ai.googleblog.com/2018/03/the-building-blocks-of-int.... That said, I've seen the following listed as good resources:

fast.ai

The Google ML crash course

Andrew Ng's Coursera course

If reddit's your thing, it also looks like there's a sub, https://www.reddit.com/r/learnmachinelearning/, that might be helpful.

buboard• 8 years ago

I believe models with attention are more appropriate for ‘telling’ what they re focusing on , at least if the question is which part of the image they re focusing on

minimaxir• 8 years ago

Attention is only (edit: typically) for sequential models (e.g. time series), not image-based convolution models.

sdenton4• 8 years ago

Untrue! Three are a number of valid strategies for using attention in non sequential models. Attention is really just generating a mask to apply on a feature representation; it generalized perfectly well to classical models.

Viker• 8 years ago

We need to invent a quad type machine ( opposed to the current binary model) where we have a "maybe" type of data structure.

Yes and a No and a MaybeYes and MaybeNo.

shakna• 8 years ago

Other kinds have existed.

Ternary computers [0], where the most famous examples come from the Soviet Union.

And whilst I'm not aware of any computer that used the quaternary numerical system, it should be possible.

However, none of that necessarily means it's a good idea or necessary to perform quaternary logic.

[0] https://en.m.wikipedia.org/wiki/Ternary_computer

groestl• 8 years ago

Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth.

lottin3• 8 years ago

To state the obvious, AI doesn't see anything. What people call AI is simply a statistical model, a system of equations. By solving it, we find a solution that solves the equations simultaneously. The model doesn't see anything, because it doesn't exist as such. It simply is a series of calculations done on a computer or on a piece of paper.

gok4• 8 years ago

Then biological brains don’t see anything either.

oliveshell• 8 years ago

I’m not sure your conclusion follows. We agree that we “see” things because we have the conscious experience of doing so and we talk about it.

Nothing remotely analogous happens with current AI algorithms.

lottin3• 8 years ago

Not sure what your point is. Humans can see. So can most animals. We have plenty of evidence of that. We have absolutely no evidence that inanimate objects can see. If they could, it is reasonable to expect that we would have some evidence of it by now. The fact that we haven't got any, leads us to conclude pretty solidly that they can't.

wetpaws• 8 years ago

This is absolutely true.

hal9000xp• 8 years ago

I was always puzzled by human's experience of "seeing". When you "see redness" for example. It was very hard for me to explain it to other people before I found that there is special term called "qualia":

https://en.wikipedia.org/wiki/Qualia

And I have absolutely no idea how to make machines experience "qualia". Any sophisticated image/motion recognition is rather trivial stuff compared to achieving mysterious "qualia".

Understand what AI sees

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