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Published Dec 28, 2023

2023 in AI, with Nathan Benaich

Daniel Bashir and Nathan Benaich delve into the transformative power of generative AI across various sectors, examining its impact on productivity, protein engineering, and creative fields. They also discuss the challenges in AI benchmarking, the open vs. closed-source debate, and the complexities of global AI regulation, emphasizing the UK's balanced approach.
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  • AI Benchmarks

    The evolving landscape of AI benchmarks presents significant challenges as models become more general-purpose. highlights the difficulty for developers to anticipate all potential uses of their systems, making it unrealistic to create benchmarks that cover every edge case 1. He argues that the most relevant benchmarks are those that reflect the product experience and align with the intended outcomes.

    Ultimately, I think the benchmarks that matter the most are the ones that are reflective of your product experience and what you're trying to achieve.

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    agrees, noting that few companies aim to build truly general AI systems, which further complicates the benchmarking process 1.

       

    Open vs Closed Source

    The debate over state-of-the-art AI models often centers on the choice between open and closed-source development. observes that companies initially opt for third-party APIs to quickly test capabilities, but must later evaluate the cost-effectiveness and control over data privacy and deployment nuances 2. This decision-making process is influenced by recent developments in the tech world, such as the shake-up at OpenAI.

    Builders and companies will often choose the path of least resistance that gives them the feeling for what a capability can potentially provide their customers.

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    The choice between open and closed-source models remains a pivotal decision for companies seeking to scale their AI capabilities.

       

    Open Source Impact

    Open-source AI models are gaining traction, particularly in vertical-specific applications. notes that open-source models can outperform larger models like GPT-3.5 turbo in specific contexts, such as search engines 3. He emphasizes the importance of redundancy and security when relying on third-party solutions, suggesting that open-source offers a viable alternative.

    Open source does have a genuine shot, and particularly within sort of vertical specific products where you might not need all of the bells and whistles of a general purpose model.

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    adds that while small language models may not match the capabilities of larger ones, they hold promise in niche areas 3.

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