AI Engineering Podcast Barking Up The Wrong GPTree: Building Better AI With A Cognitive Approach
Jul 28, 2024
52:49
Summary
Artificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains his approach to building AI in a more human-like fashion and the emphasis on learning rather than statistical prediction.
Announcements
Parting Question
Artificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains his approach to building AI in a more human-like fashion and the emphasis on learning rather than statistical prediction.
Announcements
- Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
- Your host is Tobias Macey and today I'm interviewing Peter Voss about what is involved in making your AI applications more "human"
- Introduction
- How did you get involved in machine learning?
- Can you start by unpacking the idea of "human-like" AI?
- How does that contrast with the conception of "AGI"?
- The applications and limitations of GPT/LLM models have been dominating the popular conversation around AI. How do you see that impacting the overrall ecosystem of ML/AI applications and investment?
- The fundamental/foundational challenge of every AI use case is sourcing appropriate data. What are the strategies that you have found useful to acquire, evaluate, and prepare data at an appropriate scale to build high quality models?
- What are the opportunities and limitations of causal modeling techniques for generalized AI models?
- As AI systems gain more sophistication there is a challenge with establishing and maintaining trust. What are the risks involved in deploying more human-level AI systems and monitoring their reliability?
- What are the practical/architectural methods necessary to build more cognitive AI systems?
- How would you characterize the ecosystem of tools/frameworks available for creating, evolving, and maintaining these applications?
- What are the most interesting, innovative, or unexpected ways that you have seen cognitive AI applied?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on desiging/developing cognitive AI systems?
- When is cognitive AI the wrong choice?
- What do you have planned for the future of cognitive AI applications at Aigo?
Parting Question
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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- Aigo.ai
- Artificial General Intelligence
- Cognitive AI
- Knowledge Graph
- Causal Modeling
- Bayesian Statistics
- Thinking Fast & Slow by Daniel Kahneman (affiliate link)
- Agent-Based Modeling
- Reinforcement Learning
- DARPA 3 Waves of AI presentation
- Why Don't We Have AGI Yet? whitepaper
- Concepts Is All You Need Whitepaper
- Hellen Keller
- Stephen Hawking