It's easy for many to think that solving Go and chess means we can also solve household work like cleaning, cooking and washing dishes but it's actually harder.

Next up: Google's Deepmind AI learns to perform arithmetic tabula rasa.

More seriously, it seems Deepmind and the AI community in general is having a Streetlight effect problem, i.e. looking for AI in what works now, rather than coming to terms with the hard challenges. This explains why there are so many papers on GANs. People are just doubling down on what works (where the streetlight is), rather than acknowledging that where we need to look for AI is dark. Since it's become such a cut-throat race to be the next one to say "we made a breakthrough!", it makes much more economic sense to solve simple problems and advertise them as huge challenges.

I wouldn't dismiss GANs so easily. Yann LeCun was singing odes to GANs - as the most interesting idea in the last decade. The interesting thing about GANs is that they don't use a predefined loss, but instead the discriminator acts as the loss function for the generator - thus, it is learning a loss fn instead of using human guesswork to create it. That's quite a powerful new idea. Applications of GANs include making simulated images look more real, which is essential for RL, generating 'artificial' training images for other tasks and using the discriminator as an image embedding generator or classifier.

I agree that the average Joe will misinterpret the significance of AlphaGo, to Google's benefit.

But most people in the research community already know how amazing it would be to make an affordable household robot or a search-and-rescue robot or a self-driving car. Many labs (including mine) are working on it. The streetlight adds a small bias, but the bigger problem is that we have no idea how to build human-level AI.

The biggest problem in robotics is vision. How do you translate pixels to a 3D scene graph with objects attributes and correlate with prior knowledge.

Do that in real time, on device without using a crazy amoubt of power because of batteries.

CNNs and faster GPUs are the biggest breakthrough in that regards but it's still a long way to go before we get to human level visual cortex.

Vision is part of the puzzle--a large part in the case of self-driving cars. But blind people are way better than computers at everyday tasks, so I don't think that it's the Big Problem.

Translating to 3D is low-level and relatively easy. That's not the reason why we don't have household robots/self-driving cars.

Framing vision as "object attributes" and "correlate to prior knowledge" might be a good approach for current research. But humans do more--we understand what we look at. We form concepts and models of the world that allow us to adapt to very novel situations.

The main reason why we haven't solved vision, language, playing chess like a human, etc is that NNs are a poor approximation of human concepts. I agree that we probably need more compute and better compute.

Yes, but it doesn't seem like much of a problem? Exploiting a breakthrough before moving on to harder problems isn't cheating, it's the smart thing to do. It might even turn out to be the fastest way to make progress on the harder problems.