Surveying The Landscape Of AI and ML From An Investor's Perspective
AI Engineering Podcast
Shownotes Transcript
SummaryArtificial 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.
- As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES).
- 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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Links
The intro and outro music is from Hitman's Lovesong feat. Paola Graziano) by The Freak Fandango Orchestra)/CC BY-SA 3.0)