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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 …
Love this idea. Perphaps a small report will be nice to update us on your ideas, even if the dataset is not ready. I sense there is something important to discuss here and we may different perspectives.
Excited to see this! Will this be able to measure human performance as well, or is it meant for NNs only?
Both, all problems are meant to be solvable by humans (that's how we know they're relevant problems!) and the percentage of problems a human can solve should correlate with other measures of intelligence (such as IQ). Human-machine generalization quotient.
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