In a sense you can always assume that the answer to these problems is just 'make the neural net bigger' but I find this deeply unhelpful for two reasons:
1. This clearly does not seem to be the way humans learn. Humans can learn from very few examples, in entirely unguided environments, and they don't face the same issues that existing algorithms suffer from. (for example humans have no big problem with rotational invariance, whereas ML vision algorithms do).
2. It's essentially surrendering to the fact that we aren't able to understand how cognition works and build higher-level representations as a result. The goal of AI research can not just be to feed data blindly into enormous primitive structures, it must also be to get a grasp on what sort of complex structures are part of intelligent agents and how they interact.
Humans can learn from very few examples, in entirely unguided environments, and they don't face the same issues that existing algorithms suffer from. (for example humans have no big problem with rotational invariance, whereas ML vision algorithms do).
That's because, contrary to the zeitgeist, humans are not a blank slate. Our brains are the result of billions of years of evolution. They are extremely well adapted to modelling the natural environment and the behaviour of other beings around us. This is in stark contrast to computers which we start from nothing and force feed a huge amount of data without context and then expect results. The fact that this approach works at all for some tasks is staggering.
Not an expert, but as long as there is no way of knowing WHAT it is the algorithm is learning it seems to me that it could never work reliably. It might look perfectly reasonable until you hit one of the triggers the algorithm used to segment the data.
Someone somewhere shared a story about using machine learning to spot the difference between US and Russian tanks ; which apparently worked fine until field testing, where it failed miserably. What the algorithm had learned was the difference between great quality photos of US tanks and poor quality photos of Russian. True or not, this is exactly the kind of issues that will keep popping up.
Plenty of people are spending plenty of time figuring out how to mess with facial recognition as we speak by taking advantage of the same fundamental weakness.
Oh, yep! It's super simple to induce systemic errors like that. Take https://github.com/kevin28520/My-TensorFlow-tutorials/tree/m... . Lighten every dog by 20%. Darken every cat by 20%. Train. Take image of cat, lighten 20%, watch as it's transformed into a dog!
For large corpora, it's impossible to know what features got selected. They probably aren't any feature a human would consider.
1. This clearly does not seem to be the way humans learn. Humans can learn from very few examples, in entirely unguided environments, and they don't face the same issues that existing algorithms suffer from. (for example humans have no big problem with rotational invariance, whereas ML vision algorithms do).
2. It's essentially surrendering to the fact that we aren't able to understand how cognition works and build higher-level representations as a result. The goal of AI research can not just be to feed data blindly into enormous primitive structures, it must also be to get a grasp on what sort of complex structures are part of intelligent agents and how they interact.