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Interesting that DeepMind is working on a IQ-like test to measure abstract reasoning capabilities. I've been working on a very similar benchmark for the past 6 months (taking a more formal approach). Good to see more action in that space (cc @ChrSzegedy)https://deepmind.com/blog/measuring-abstract-reasoning/ …
The format of the problems looks extremely similar, but the content not so much. The very fact that NNs seem to do well on DeepMind's benchmark seem to indicate that it does not achieve its goal of measuring abstract reasoning, and can be solved through simple pattern recognition
My tests are based on colorful little matrices like these. I expect to release the full dataset and associated paper by the end of 2018. Should have happened much earlier, but Keras development is taking up a lot of my time.pic.twitter.com/mPjxgB1TFW
For more context about why we need such a benchmark, see my talk at RAAIS 2018 (e.g. 13:18):https://www.youtube.com/watch?v=2L2u303FAs8&list=PLht6tyws1YpSOGz2k6bUC1PibVG7ZiRFB&index=6&t=0s …
The NNs do very poorly on many of the regimes! The "strong" performance on the neutral split is arguably the least important/informative result, IMO.
You seem to suggest that a NN by definition can't do (abstract) reasoning. But there's one in your head that seems to do it OK? We are interested in how to replicate that. Note also that NNs do v poorly on more than half of the generalisation splits in our dataset.
The brain is a neural network?
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