Barking Up The Wrong GPTree: Building Better AI With A Cognitive Approach
Data Engineering Podcast
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Advancing Cognitive AI in the Era of Large Language Models
Exploring the disparity in investment between cognitive AI systems and large language models, emphasizing the need for a shift in funding and infrastructure. Discussing the potential of leveraging open-source resources and existing hardware advancements for cognitive AI growth. Highlighting the importance of transparency and explainability in AI models, particularly in sensitive domains like healthcare.
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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 Data Engineering Podcast, the show about modern data management
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- 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. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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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
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