Barking Up The Wrong GPTree: Building Better AI With A Cognitive Approach
Data Engineering Podcast
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Exploring the features of DAGS-TUR cloud native orchestrator for data platforms and pipelines, alongside the benefits of Starburst Data Lake analytics platform for petabyte-scale SQL analytics with support for various data formats.
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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
- Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free!
- Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
- 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.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
- 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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