This chapter delves into the advancements in large language models (LLMs) and the integration of agentic frameworks that enhance their efficiency in tasks such as coding. It also discusses the implications of reduced training costs, the movement towards no-code solutions, and the evolving relationship between language mastery and AI development.
Are we entering a no-code future? This week, Autonomous Technology and Robotics Director of Research Sam Korus and Associate Portfolio Manager Nick Grous are joined by Next Generation Internet Director of Research Frank Downing to discuss Sakana AI, a project by an AI research lab in Japan that aims to automate the scientific method. The team has created a system that can generate research ideas, write code for experiments, and produce academic papers. While the system shows promise, it has limitations and is better at tasks it was trained on. The conversation also touches on the future of coding, the potential for a no-code future, and the importance of communication skills in working with AI.
If you know ARK, then you probably know about our long-term research projections, like estimating where we will be 5-10 years from now! But just because we are long-term investors, doesn’t mean we don’t have strong views and opinions on breaking news. In fact, we discuss and debate this every day. So now we’re sharing some of these internal discussions with you in our new video series, “The Brainstorm”, a co-production from ARK and Public.com. Tune in every week as we react to the latest in innovation. Here and there we’ll be joined by special guests, but ultimately this is our chance to join the conversation and share ARK’s quick takes on what’s going on in tech today.
Key Points From This Episode:
- Sakana AI is a project that aims to automate the scientific method by generating research ideas, writing code for experiments, and producing academic papers.
- The system shows promise in accelerating the research process and generating new ideas, but it has limitations and is better at tasks it was trained on.
- The future of coding may involve higher levels of abstraction, with AI systems generating code based on human language prompts.
- There is a potential shift towards a no-code future, where people can get what they need from AI systems through simple text prompts.
- Effective communication skills will become increasingly important in working with AI systems, as they rely on clear instructions and prompts.
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