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In @ScienceMagazine, we present #AlphaCode - the first AI system to write computer programs at a human level in competitions.
It placed in the top 54% of participants in coding contests by solving new and complex problems.
How does it work? 🧵 dpmd.ai/alphacode-scie…
It placed in the top 54% of participants in coding contests by solving new and complex problems.
How does it work? 🧵 dpmd.ai/alphacode-scie…
🔵 Previous AI systems don’t have the problem-solving skills needed to excel at coding competitions.
By combining advances in large-scale transformer models with large-scale sampling and filtering, #AlphaCode made significant progress in this field: dpmd.ai/alphacode-scie…
By combining advances in large-scale transformer models with large-scale sampling and filtering, #AlphaCode made significant progress in this field: dpmd.ai/alphacode-scie…
🔢 AlphaCode is pre-trained on selected public @GitHub code and fine-tuned on our relatively small competitive programming dataset.
At evaluation time, millions of diverse C++ and Python programs are created for each problem, orders of magnitude larger than previous work.
At evaluation time, millions of diverse C++ and Python programs are created for each problem, orders of magnitude larger than previous work.
I spent 1000s of hours on competitive programming (proof-link: codeforces.com/profile/rizar). This makes me qualified to comment on #AlphaCode by @DeepMind
The result is nice, the benchmark will be useful, some ideas are novel. But human level is still light years away.
1/n
The result is nice, the benchmark will be useful, some ideas are novel. But human level is still light years away.
1/n
The system ranks behind 54.3% participants. Note that many participants are high-school or college students who are just honing their problem-solving skills. Most people reading this could easily train to outperform #AlphaCode, especially if time pressure is removed...
Limited time (e.g. 3 hours to solve 6 problems) is a key difficulty in comp. programming. The baseline human is very constrained in this model-vs-human comparison. For #AlphaCode the pretraining data, the fine-tuning data, the model size, the sampling - all was nearly maxed out.
Introducing #AlphaCode: a system that can compete at average human level in competitive coding competitions like @codeforces. An exciting leap in AI problem-solving capabilities, combining many advances in machine learning!
Read more: dpmd.ai/Alpha-Code 1/
Read more: dpmd.ai/Alpha-Code 1/
Below is an example of a problem #AlphaCode can successfully solve, using the exact information seen on @codeforces, & the program that #AlphaCode writes.
By @liyuajia, David Choi, @junyoungch, @NateKushman, @Mononofu, @RemiLeblond, Tom Eccles, James Keeling, @FelixAxelGimeno 2/
By @liyuajia, David Choi, @junyoungch, @NateKushman, @Mononofu, @RemiLeblond, Tom Eccles, James Keeling, @FelixAxelGimeno 2/
Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, @pushmeet, @NandoDF, @koraykv & @OriolVinyalsML. 3/3
