GenAI models can be integrated into any process — even those that are not yet automated or even considered
It's cool to develop adaptive, self-learning systems/agents/models, not just static LLM/ML-based pipelines.
YC is hosting its first AI Startup School in San Francisco on June 16th and 17th, featuring top AI experts like Elon Musk, Sam Altman, and Andrew Ng. The free conference is aimed at computer science students and recent graduates in AI, with travel expenses covered. Applications are required due to limited space.
In a related discussion, the Light Cone podcast, hosted by Gary, Jared, Diana, and Harge, explores how to generate startup ideas in the AI space. They emphasize the importance of moving beyond “hackathon ideas” and instead focusing on ambitious, hard-to-build solutions. Key takeaways include:
Look Within or Get Out: Founders should either introspect to find their unique expertise or immerse themselves in industries outside their comfort zone to identify real-world problems.
Examples of Success: Companies like Salient (AI for loan processing) and Diode Computer (AI for circuit board design) were founded by individuals who leveraged their unique industry experiences.
Avoid Shiny Objects: Founders should avoid chasing trendy ideas and instead focus on areas where they have deep expertise or can build new expertise.
Internships and Jobs: Working at cutting-edge companies or even taking on roles in industries you want to disrupt can lead to valuable startup ideas.
Think Big: Founders should aim for ambitious ideas that capture the imagination, like Can of Soup (a new AI-driven social network) or Happenstance (AI-powered intelligent search).
Undercover Research: Founders can gain insights by shadowing workers in industries they want to automate, as seen with ESS Health (AI for dental insurance) and Able Police (AI for police paperwork).
Competition Isn’t Always Bad: Even in crowded markets, technical excellence can set a startup apart, as demonstrated by Gigl in the customer support space.
Persistence Pays Off: Many successful startups take time to find the right idea, especially in the fast-evolving AI landscape.
The overarching message is that founders need to either leverage their existing expertise or deeply immerse themselves in new industries to identify meaningful problems that AI can solve. The key is to avoid staying in a comfort zone and instead push toward ambitious, impactful ideas.
The video discusses the evolution and future of large language models (LLMs) and the concept of scaling in AI. It begins by highlighting the rapid advancements in AI, particularly the exponential growth in model size, data, and compute power, which has led to significant improvements in performance. The “scaling laws” introduced by OpenAI in 2020 demonstrated that increasing model size, data, and compute power results in consistent performance gains, forming the foundation of modern AI development.
However, the video questions whether the era of scaling is coming to an end, as some researchers argue that recent models are hitting diminishing returns despite increased size and cost. Google DeepMind’s research introduced the “Chinchilla scaling laws,” emphasizing the importance of training models with sufficient data rather than just increasing their size. This led to more efficient models like Chinchilla, which outperformed larger models like GPT-3.
The video then explores the potential for a new scaling paradigm, focusing on “test-time compute” or allowing models to think longer during inference, as seen in OpenAI’s 01 and 03 models. These models show significant improvements in complex tasks, suggesting that scaling compute during reasoning could unlock new AI capabilities and potentially lead to artificial general intelligence (AGI).
Finally, the video concludes that while LLMs may be reaching a plateau in traditional scaling, the principles of scaling are still in their early stages for other AI modalities like robotics, image generation, and protein folding. The future of AI scaling remains promising, with new paradigms and innovations on the horizon.
To see the future
