![]() The latest version of Bard identifies prompts that might benefit from logical code, writes the code “under the hood,” tests it and uses the result to generate an ostensibly more accurate response. Motivated to address the coding and mathematics shortcomings in general LLMs, Google developed implicit code execution, which allows Bard to write and execute its own code. ![]() ![]() Unlike Bard and rivals along the lines of ChatGPT, which were trained using a vast range of text samples from the web, e-books and other resources, Copilot, CodeWhisperer and comparable code-generating models were trained and fine-tuned almost exclusively on code samples. That makes them exceptionally good email and essay writers, but somewhat error-prone software developers.īut wait, you might say - what about code-generating models like GitHub’s Copilot and Amazon’s CodeWhisperer? Well, those aren’t general-purpose. When given a prompt, they generate a response by anticipating what words are likely to come next in a sentence. That’s according to a blog post published today by the tech giant, which suggests that - thanks to a technique called “implicit code execution” - Bard is now improved specifically in the areas of math and coding.Īs the blog post explains, large language models (LLMs) such as Bard are essentially prediction engines. Bard, Google’s beleaguered AI-powered chatbot, is slowly improving at tasks involving logic and reasoning.
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