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action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home4/scienrds/scienceandnerds/wp-includes/functions.php on line 6114Source:https:\/\/techcrunch.com\/2023\/06\/07\/google-claims-that-bard-is-improving-at-math-and-programming\/<\/a><\/br> Bard, Google\u2019s beleaguered<\/a> AI-powered chatbot, is slowly improving at tasks involving logic and reasoning. That\u2019s according to a blog post published<\/a> today by the tech giant, which suggests that \u2014 thanks to a technique called \u201cimplicit code execution\u201d \u2014 Bard is now improved specifically in the areas of math and coding.<\/p>\n As the blog post explains, large language models (LLMs) such as Bard are essentially prediction engines. When given a prompt, they generate a response by anticipating what words are likely to come next in a sentence. That makes them exceptionally good email and essay writers, but somewhat error-prone software developers.<\/p>\n But wait, you might say \u2014 what about code-generating models like GitHub\u2019s Copilot<\/a> and Amazon\u2019s CodeWhisperer<\/a>? Well, those aren\u2019t general-purpose. Unlike Bard and rivals along the lines of ChatGPT, which were trained using a vast range of text samples from the web, ebooks and other resources, Copilot, CodeWhisperer and comparable code-generating models were trained and fine-tuned almost exclusively on code samples.<\/p>\n Motivated to address the coding and mathematics shortcomings in general LLMs, Google developed implicit code execution, which allows Bard to write and execute<\/em> its own code. The latest version of Bard identifies prompts that might benefit from logical code, writes the code \u201cunder the hood,\u201d tests it and uses the result to generate an ostensibly more accurate response.<\/p>\n
\nGoogle claims that Bard is improving at math and programming<\/br>
\n2023-06-07 21:46:18<\/br><\/p>\n