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A Small Team of Student AI Coders Beats Google's Machine-Learning Code (technologyreview.com)
Students from Fast.ai, a small organization that runs free machine-learning courses online, just created an AI algorithm that outperforms code from Google's researchers, per an important benchmark. From a report: Fast.ai's success is important because it sometimes seems as if only those with huge resources can do advanced AI research. Fast.ai consists of part-time students keen to try their hand at machine learning -- and perhaps transition into a career in data science. It rents access to computers in Amazon's cloud. But Fast.ai's team built an algorithm that beats Google's code, as measured using a benchmark called DAWNBench, from researchers at Stanford. This benchmark uses a common image classification task to track the speed of a deep-learning algorithm per dollar of compute power. Google's researchers topped the previous rankings, in a category for training on several machines, using a custom-built collection its own chips designed specifically for machine learning. The Fast.ai team was able to produce something even faster, on roughly equivalent hardware.
How does an AI algorithm differ from a plain old algorithm. I am so curious...