There are now many state-of-the-art computer vision algorithms which are a git clone away. We found that existing systems for distributed data analysis were not well suited to dealing with the computational challenges of applying computer vision algorithms to terabyte or petabyte sized video collections, so we designed and built a system called Scanner to make efficient video analysis easier.
Source: cmu.edu
We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.5 month-doubling time (by comparison, Moore’s Law had an 18-month doubling period). Since 2012, this metric has grown by more than 300,000x (an 18-month doubling period would yield only a 12x increase). Improvements in compute have been a key component of AI progress, so as long as this trend continues, it’s worth preparing for the implications of systems far outside today’s capabilities.
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The very first Go AIs used multiple modules to handle each aspect of playing Go – life and death, capturing races, opening theory, endgame theory, and so on. The idea was that by having experts program each module using heuristics, the AI would become an expert in all areas of the game. All that came to a grinding halt with the introduction of Monte Carlo Tree Search (MCTS) around 2008.
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