For recognizing relatively simple entities, are there advantages humans still have over neural nets (assuming the same scope of knowledge)?
An idea: We can also run several cat photos through image processing algorithms to filter out details. The output would be outlines similar to the drawings in the Google Quickdraw app. We put those through the app to generalize (perhaps the app needs some training with a few categories of objects, not necessarily animals). Voila! Software can now recognize drawings based on photo examples.
Of course, there's severe bias here, in the sense that what we consider abstraction is by definition "human shaped" abstraction
If multiple humans try to "abstract" a cat, the overlap in underlying processes will be pretty big, making it more likely that we can recognise each other's abstractions.
I can read the words here, but I don't understand the meaning.
We abstract to find a common set of features in things that are supposed to be the same but that are not present in things that are not supposed to be the same. Grouping these features then produces higher level abstractions, and so on.
Where would the bias be?
Even if the features differ, the process is the same.
And even the features are often the same. If you reverse a DCNN to see what it uses to classify things as "cats", expect to see whiskers and fur.
Computers don't look at things from a human perspective; they're still good at abstraction, just different to human abstraction. i.e. there's a human bias in there.
That's OK though; the objective is to make a computer that sees things the way people do; so it's a bias we want.
However the issue isn't that the computer's not a sentient being and therefore can't abstract things it's never seen before; only that the algorithm hasn't been written to sufficiently take account of human bias.
I don't see a fundamental difference between biological and electronic neural nets; so please take the following with a physicalist grain of salt. Imho, precisely because NNs will be fed with nothing else than the reality (physical or virtual) we live in, it should gradually develop the same familiarity as humans have; i.e. nothing more and nothing less than elements of our lives/civs. Visually lots of cats, lots of cars, mountains and coasts; functionally all the tasks we accomplish daily, like driving or cooking or cleaning.
I don't really think you can hard-code "human bias" as it's an emergent property of our biology: too complex (we don't really understand much of it, imho you're bound to miss the mark and induce subjective biases), and somewhat contradictory to how NNs are supposed to evolve (thinking long term here). Basically, I don't think it would be practical nor cost efficient to induce too much perturbations in deep learning, better work on refining the process itself. Think of plants: you can tweak the growing all you want, but the root deciding factors lie in genetics (their potential, and in understanding how to maximize it).
I realize another wording is that we should apply sound evolutionary (Darwin etc.) principles in "growing" AI at large. Because AI and humans share the same environment, we should see converging "intelligence" (skills, familiarity, etc). It's a quite fascinating time from an ontological perspective.
General purpose machine translation is harder, for instance. Brute force algorithms have gotten decent, but aren't in the same ballpark as humans (though professional translation services now often work by correcting a machine translation). However, MT systems trained on a specific domain do much better (medical or legal docs, etc).
What would be the hardest task for machines that's trivial for humans? Maybe deciding if a joke is funny or not?
A literate human scores 100% on this test. No computer system so far scores better than 60%. (And remember that random guessing gets 50%.)
The book, "We are all Completely Beside Ourselves" is fiction, but refers to findings from real studies.
Human perception is heavily biased towards features that had evolutionary advantages, and limited by whatever technical flaws our eyes/brains/etc have. That's a selection bias in our perception of information, in our processing of said information, and therefore in the abstractions that result from it.
I presume it's possible that the limitations of our visual system means we may miss powerful features and hence the ability to build some more powerful abstractions. (I didn't even argue this, just pointed out the process is the same even if features differ)
But I don't see how this supports your original claim of bias, which was: "If multiple humans try to "abstract" a cat, the overlap in underlying processes will be pretty big, making it more likely that we can recognize each other's abstractions."
If humans are good at recognizing each others' abstraction, that's a validation that low-pass (for lack of a better term) filtering the features due to human's physical design still creates very good abstractions and classifiers. That is to say, if anything you're confirming that humans are designed in a way that makes the abstractions they can make maximally useful.
... to other humans.
Are you arguing that the classifications themselves are biased?
Think of the Turing test and its criticisms; it's kind of has the same issues.
PS: I've upvoted every comment of yours; asking questions like this should be encouraged :)
My point is that "good" and "bad" are not objective here, but depend on human use-cases.
Now to be clear: I'm not disagreeing with you! These are good abstractions, for humans. It lets us communicate concepts easily, which is great! But it might not be the best abstraction in every circumstance.
For example, I recall reading an article that said that AI is better at spotting breast cancer from photos (which is essentially interpreting abstract blobs as cancer or not). The main reason seems to be that it is not held back by the human biases in perception.
Second, when we look at a picture of a cat, we're looking at a human's interpretation of what a cat looks like. If we asked a computer to draw a cat, it might look nothing like a cat to us, but another computer could look at it and go "Oh sure, that's a cat." I seem to recall Google did a thing with this a while ago, where they effectively created a feedback loop in a neural net - feeding its own drawing back into itself. As I recall, the result looked like the computer had done way too much LSD.
Google doesn't recognize it as a feline, it recognizes it as Garfield.
https://rocknrollnerd.github.io/assets/article_images/2015-0...
The software does:
https://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.h...
Sure, you can fool a human. But there are things AI is missing that would be embarrassing if a human made the same mistake. It's hard to say, based on anecdotes like this, how big that gap is, but it's there.
I think we do. We see a building we've never seen before and we know it's a building because it has certain features that we use to classify it as a building. The examples aren't scarce.
I also think a good indicator of us doing it is the use of "y" and "ish" and "sort".
As for sthlm's point 2:
>2. The software can't recognize a feather if it's never seen a feather like that. It's not a sentient being.
This is Asimo in 2009:
I feel there is an immense difference between recognizing simple sketches and deriving what an object is based on extended characteristics.
The video you linked furthers that by showing that ASIMO was using three-dimensional observation to calculate certain features and ascertain what that object was.
If you'd give these doodles to people that are not Western males it'll do a lot worse. Someone already pointed out it doesn't recognize woman's shoes.
It is unmistakable how much the difficulty level ramps up when you're paired with those of an unlike-nature to you. Sometimes that level of abstraction is taken way outside of generic context clues.
We use very generic "words" (eg egg, tree, bike, cloud, plate).
When you're using your foot to draw you really have to distill down to the essence of the item. Yes there is a deal of guessing but in some way the image (however unlike the object) has to have some element of the Platonic nature, if you will, of the object being drawn.
Fun!
As for advantages over neural nets, one of the primary ones is that humans can recognise things from unusual angles much more easily. When I tried QuickDraw and doodled things from non-stereotyped angles (like a three-quarter view of a car rather than the usual 2D side view), it had no idea.
The dalmation optical illusion[1] is another example of human ability to pick out patterns and assign them to belong to certain objects. Neural nets have different abilities, and are sometimes better at picking out different sorts of patterns than humans.
[1] http://cdn.theatlantic.com/assets/media/img/posts/2014/05/Pe...