Key insights

• Company Growth and Cultural Differences

  • 🏢 The co-founder discusses the company's growth and impact over the years, Admitting and fixing mistakes has been crucial for the company's success, Close involvement in the work, effective collaboration, and trust in colleagues are emphasized, Cultural differences between the company's offices are highlighted, each with its unique vibe and work culture, Remote work challenges and the importance of geographic concentration for teams are acknowledged, Skepticism about the homogeneity of San Francisco compared to other cities and the impact on personal life

• Risks, Policy, and Regulation

  • ⚠️ Risks associated with more agency in AI, Potential damaging policy changes affecting AI startups, EU AI legislation and Canadian AI legislation, Challenges in regulating AI due to limited understanding of the technology, Trends in the AI startup space and the emergence of more sophisticated applications, Optimism about AI's increasing role in daily life

• AI in Society

  • 🌐 Ideological divide and cult-like characteristics in EA and EAK movements, AI as a tool for democratization of intelligence, Concerns about misinformation and societal impact of AI, Skepticism towards media and filtering trustworthy sources with the use of AI

• Specialization and Enterprise Use Cases

  • 🎯 Specialization and enhanced capabilities for specific domains are the company's value proposition, Moving towards verticalization and solving enterprise use cases, Concern about the ideological nature of discussions around AI and the impact of academic institutions on policy making

• Future of AI

  • 🚀 Real-world data is preferable over synthetic data for training AI models., Mathematics and code are ideal for synthetic data generation due to their rule-driven nature., A distributed AI system with specialized agents communicating with each other could be the future of AI., AI models are expected to become extremely robust and reliable., Differentiation between AI models will become crucial as the AI landscape evolves, with a focus on tailored capabilities for specific domains.

• Advanced Language Models and Reasoning Capabilities

  • 🔣 Building massive language models with trillions of parameters, Compression of huge models into smaller form factors for practical use, Improvement in reasoning capabilities of models, Teaching models to exhibit intelligent behavior through synthetic data generation, The challenge of scarcity in relevant data for training models, Exploring trade-offs in strategies for training models, such as encoding naive physics into the architecture

• Model Behavior and Improvement

  • 🔄 Models with similar data and training methods lead to similar behavior, making them less interesting and creative., Specialized models are a focus for future improvements as general language models reach high levels of performance., Incremental improvement in models becomes harder to perceive as they reach higher levels of performance.

• AI Technology and Societal Impact

  • 💡 Aiden Gomez discusses AI technology and its societal impact, Cohere's goal is to put AI tech into the hands of enterprises to create value, Barriers to adoption and access include legislative, security, privacy, and familiarity constraints, Efforts to accelerate adoption and ease barriers such as LMU education courses and policy engagement, Need for more robust, intuitive, and less prompt-dependent AI models, Efforts to reduce model brittleness and address tradeoffs between robustness and creativity

Q&A

• What cultural aspects were discussed regarding the company's growth?

  The conversation covers the company's growth journey, embracing mistakes, remote work challenges, and the cultural differences between its various offices.

• What are the concerns and optimism about the future of AI?

  There is caution about potential damaging policy changes, but also optimism about the increasing role of AI in daily life with concerns about policy and regulation.

• What concerns are raised about the impact of AI on society?

  There are concerns about misinformation and societal impact despite seeing AI as a tool for the democratization of intelligence.

• How does the company plan to serve niche markets with AI models?

  The company aims to specialize its models, offer enhanced capabilities for specific domains, and move towards verticalization to solve actual enterprise use cases.

• What is the future outlook for AI models?

  The future of AI will involve using actual real-world data over synthetic data, a distributed AI system with specialized agents, extremely robust and reliable models, and differentiation between models for specific domains.

• What capabilities of large language models are being focused on?

  There is a focus on improving reasoning capabilities, knowledge synthesis, and methods to teach models to exhibit intelligent behavior through techniques like demonstration and synthetic data generation.

• What are the challenges with current AI models?

  Similar data and training methods lead to models with similar behavior, making them less interesting and creative. Efforts to develop specialized models are crucial for future improvements as general language models reach high levels of performance.

• How is the company addressing the need for more robust AI models?

  Efforts are being made to reduce model brittleness, make models less prompt-dependent, and address tradeoffs between robustness and creativity.

• What are the barriers to AI adoption and access discussed?

  Barriers include legislative, security, privacy, and familiarity constraints, and efforts are being made to accelerate adoption and ease these barriers through initiatives like LMU education courses and policy engagement.

• What is Cohere's goal with AI technology?

  Cohere aims to put AI technology into the hands of enterprises to create value for the world.

• 00:00 Aiden Gomez, CEO of Cohere, discusses AI technology, its challenges, and societal impact. Cohere aims to create value for the world by putting AI tech into the hands of enterprises. There are barriers to access and adoption, but the company is working to accelerate adoption and ease these barriers. The models need to be more robust, intuitive, and less prompt-dependent. Efforts are being made to reduce model brittleness and address tradeoffs between robustness and creativity.
• 07:09 The use of similar data and training methods is leading to models with similar behavior, causing them to become less interesting and less creative. Developing specialized models is a key focus for future improvements as general language models reach a high level of performance.
• 14:15 A discussion about the progress in building large language models and their capabilities in reasoning, knowledge, and data synthesis. There's a focus on improving reasoning and teaching models to exhibit intelligent behavior through methods like demonstration and synthetic data generation.
• 22:04 The future of AI will involve using actual real-world data over synthetic data; mathematics and code are examples of domains ideal for synthetic data generation; a distributed AI system with specialized agents might be the way forward; models will become extremely robust and reliable; differentiation between models will become more important as the AI landscape evolves.
• 29:49 The company aims to serve niche markets by specializing its models and offering enhanced capabilities for specific domains. They plan to move towards verticalization and focus on solving actual enterprise use cases. There is concern about the ideological nature of discussions around AI and the impact of certain academic institutions on policy making.
• 37:37 The conversation discusses the ideological divide and cult-like characteristics in EA and EAK movements. The role of AI in society is seen as a tool for democratization of intelligence, but concerns about misinformation and societal impact exist.
• 44:46 The discussion covers the risks of AI, potential damaging policy changes, the state of AI startup scene, and the increasing sophistication of AI tools and applications. Overall, there is cautious optimism about the future of AI with concerns about policy and regulation.
• 52:07 The co-founder discusses the journey of the company's growth, admits to mistakes made, and emphasizes the significance of embracing remote work challenges. They also highlight the cultural differences between the company's various offices.