And, of course, if we had some really good mathematical framework for describing and reasoning about object recognition, we probably wouldn't need to turn to ML to solve it ;)

Feature extraction also works much better when you toss a lot of data and processing power behind it. So, a lot of progress is simply more data and computing power vs better approaches. Consider how poorly deep leaning works when using a single 286.

But that's true of people too. How quickly can you read upside-down?

If you trained on a mixture of upside-down and right way up images, and tested on upside-down images, performance wouldn't take that much of a hit.

Sure, the problem is we are more willing to ignore failures that are similar to how we fail. IMO, when we compare AI approach X vs. Y we need to consider absolute performance not just performance similar to human performance.

Deep learning for example gains a lot from texture detection in images. But, that also makes it really easy to fool.

If I had to describe the root cause of this problem it would be that humans process "problems" rather than "things" and we "learn" by building an ever growing mental library of problem solving algorithms. As we continue to "learn", we refine our problem solving algorithms to be more general than specific. Compare that to a deep learning AI that learns by building an ever greater data library of things while refining algorithms to suit ever more specific use cases.

I tend to imagine our brains works similarly. It's not that you have a single "network" in your brain that recognizes test from all angle, but your brain is a "general purpose" machine with many networks that work together. I think current deep learning techniques are great for discrete tasks, and the improvement needed is to have many networks that work together properly with some form of intuition as to what should be done with the information at hand.

As much as architecture research gets denigrated these days, MLPs aren't what set off the revolution.

How 'bout "creating models that can work with more dimensions of the problem domain than are conveyed by standard data labeling"?

I mean, we don't simply want AI but actually "need" it in the sense that problems like biological system are too complex to understand without artificial enhancements to our comprehension processes - thus to "understand the problem domain better" we need AI. If it's true that "to build AI, we need to understand the problem domain better", it leaves us stuck in a chicken-and-problem. That might be the case but if we're going find a way out, we are going to need to build tools in the fashion humans used to solve problems many times before.

As an aside, I think it's important that we find a way to examine and inspect how an ML model "works". If you have some neural network that does really well at the problem, it would be nice if you could somehow peer into it and explain, in human terms, what insight the model has made into the problem. That might not be feasible with neural networks, as they're really just a bunch of weights in a matrix, but this is practical for something like decision trees. Just food for thought.

An explanation/tutorial, with clean images of the process: https://github.com/tensorflow/tensorflow/blob/r0.10/tensorfl...

Google’s investigation of it’s GoogLeNet architecture: http://storage.googleapis.com/deepdream/visualz/tensorflow_i...

Now, I say somewhat because results can be visually confusing, ex Google’s analysis. Even then, we can see the progression of layer complexity as we go deeper into ImageNet. Plus, we can see mixed4b_5x5_bottleneck_pre_relu has kernels that seem to correspond with noses and eyes. mixed_4d_5x5_pre_relu has a kernel that seems to correspond with cat faces.

Mmmaybe,

It's tricky to articulate what pattern the data-scientist could see ... that an automated system couldn't see. Or otherwise, perhaps the whole "loop" could be automated. Or possibly the original neural already finds all the patterns available and what's left can't be interpreted.

What is interesting to note, now that above idea is considered, is that this process model itself belongs to the set of human-machine coordinations. Another process model is where low level human mind is used to perform recognition tasks too hard (or too slow) for machine to perform, for example using porn surfers to perform computation tasks via e.g. captcha like puzzles.

Long term social ramifications of all this is also interesting to consider as it motivates machines to breed distinct types of humans ;)

However, I would mention that there's a larger "overhead" than many realize to methods which work without the creator or the user understanding why. You have "racist" AI which don't undertstand that correlation may not be causation in questions like whether someone should be paroled or get a loan, you have the AIs subject to adversial attacks of various sorts, where not knowing why the AI works is also problematic, you have a situation where the target to match varies over time and so-forth.

Which adds up to AI having more dimensions to it than simply "working well" and "working less well". Indeed, AI is effectively ad-hoc statistics with result derived heuristically.

So in the process of "getting things right" exploring all sorts of things certainly sounds good, it seems like there's an "understanding gap" that needs to be closed and some broader model of what's happening would be useful but naturally there's no guarantee we can find one.

You just described all of software engineering.

bridges aren't catenaries and Euler and Lagrange gave us the Euler-Lagrange equations, not Newton.