ChatGPT and other AI tools based on Large Language Models (LLMs) have grabbed the headlines for their ability to write poems, short stories and other kinds of content – including code. The intuitive interactive interface makes them easy to use and they can be a real time-saver. LLM-based coding tools have begun to proliferate, with multiple options - like Copilot, Ghostwriter, and Codewhisperer - now available. But they all come with the same major drawback: the code they suggest is also often wrong in ways that can be hard to spot – so they require constant human supervision. But LLMs are not the only way to do AI for code: fully autonomous code-writing is possible by using reinforcement learning to write code that is guaranteed to compile, run and be correct. In this session we'll look at how LLMs and reinforcement learning approaches work, their pros and cons, what they are good at doing – and where they struggle, regardless of how much compute power you throw at them.
Mathew is CEO at Diffblue, a pioneer of generative AI for code solutions. He has over twenty-five years of experience in the software industry in developer, product and marketing roles. Before joining Diffblue, Mathew's titles included SVP at Anaconda and Vice President of Cloud Services at VMWare. In each role, his focus has been building and marketing products that customers love.
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