DeepMind’s AlphaCode can outperform human programmers

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When it comes to tracking the incremental progression of You have potentialAnd the Humans are oddly inclined to think in terms of our board games Maybe he hasn’t played since childhood. Although there is no shortage of examples, even Recently Of whichThese tests go only so far in demonstrating the technology’s effectiveness in solving real-world problems.

Possibly a much better “challenge”, pitting AI alongside humans in a programming competition. the alphabet-DeepMind owned has done exactly that with alpha code Model. Results? Well, AlphaCode did well but not exceptional. The overall performance of the model, according to a paper published in Sciences Shared with Gizmodo, it corresponds to a “junior programmer” with training from a few months to a year. Part of those results were made general by DeepMind earlier this year.

In testing, AlphaCode was able to achieve “almost human-level performance” and solve natural language problems previously unseen in a competition by anticipating pieces of code and generating millions of potential solutions. After generating a large number of solutions, AlphaCode then filters them down to a maximum of 10 solutions, all of which the researchers say were generated, “without any built-in knowledge about the structure of computer code.”

AlphaCode has an average rating in the top 54.3% in simulated ratings in recent coding competitions on competitive coding platform Codeforces when limited to generating 10 solutions per problem. 66% of these problems, however, were solved using its first submission.

This may not sound very impressive, especially when compared to the models’ seemingly stronger performance against humans in complex board games, although the researchers note that success in programming competitions is very difficult. To be successful, AlphaCode first had to understand complex coding problems in natural languages ​​and then “reason” about unexpected problems rather than just saving code snippets. AlphaCode was able to solve problems it hadn’t seen before, and the researchers claimed they found no evidence that their model simply copied the underlying logics from the training data. Taken together, the researchers say that these factors make AlphaCode’s performance “a huge step forward.”

J. Zico Coulter, Bush Center for Artificial Intelligence, Carnegie Mellon University, wrote in a recent interview: impression Article attached to the study.

AlphaCode isn’t the only AI model being developed with coding in mind. Most notably, OpenAI has it adaptation Its own GPT-3 natural language model to create autocomplete functionality that can harm lines of code. GitHub also has its own AI programming tool called copilot. However, neither of these two programs has shown equal adeptness in competing with humans in solving complex competitive problems.

Although we’re still in the relatively early days of AI-assisted code generation, DeepMind researchers are confident that AlphaCode’s recent successes will lead to useful applications for human programmers in the future. In addition to increasing overall productivity, the researchers say AlphaCode can also “make programming more accessible for a new generation of developers.” At the highest level, the researchers say AlphaCode could one day lead to a cultural shift in programming where humans exist primarily to formulate the problems used by AI.Then s is assigned to solve it.

At the same time, some AI critics have questioned the effectiveness of basic training models that support many advanced AI models. Just last month, a programmer named Matthew Patrick submitted the first work of its kind lawsuit against Microsoft-owned by GitHub, arguing that its Copilot AI assistant tool blatantly ignores or removes licenses given by software engineers during the learning and testing phase. Patrick argues that liberal use of other programmers’ code amounts to “software piracy on an unprecedented scale”. The outcome of that lawsuit can play an important role in Theermget downThe ease with which AI developers, especially those who train their models on previous human code, can improve and evolve their models.

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