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Surveying The Landscape Of AI and ML From An Investor's Perspective

63 min • 15 oktober 2023

Summary
Artificial Intelligence is experiencing a renaissance in the wake of breakthrough natural language models. With new businesses sprouting up to address the various needs of ML and AI teams across the industry, it is a constant challenge to stay informed. Matt Turck has been compiling a report on the state of ML, AI, and Data for his work at FirstMark Capital. In this episode he shares his findings on the ML and AI landscape and the interesting trends that are developing.
Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
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Your host is Tobias Macey and today I'm interviewing Matt Turck about his work on the MAD (ML, AI, and Data) landscape and the insights he has gained on the ML ecosystem

Interview

Introduction
How did you get involved in machine learning?
Can you describe what the MAD landscape project is and the story behind it?
What are the major changes in the ML ecosystem that you have seen since you first started compiling the landscape?
- How have the developments in consumer-grade AI in recent years changed the business opportunities for ML/AI?
What are the coarse divisions that you see as the boundaries that define the different categories for ML/AI in the landscape?
For ML infrastructure products/companies, what are the biggest challenges that they face in engineering and customer acquisition?
What are some of the challenges in building momentum for startups in AI (existing moats around data access, talent acquisition, etc.)?
- For products/companies that have ML/AI as their core offering, what are some strategies that they use to compete with "big tech" companies that already have a large corpus of data?
What do you see as the societal vs. business importance of open source models as AI becomes more integrated into consumer facing products?
What are the most interesting, innovative, or unexpected ways that you have seen ML/AI used in business and social contexts?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on the ML/AI elements of the MAD landscape?
When is ML/AI the wrong choice for businesses?
What are the areas of ML/AI that you are paying closest attention to in your own work?

Contact Info

Website
@mattturck on Twitter

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

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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The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

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