Jia shares the kinds of AI courses she teaches at Stanford, how students are receiving machine learning education, and the impact of AI agents, as well as understanding technical boundaries, being realistic about the limitations of AI agents, and the importance of interdisciplinary collaboration. We also delve into how Jia prioritizes latency at LiveX before finding out how machine learning has changed the way people interact with agents; both human and AI.

Key Points From This Episode:

• The AI courses that Jia teaches at Stanford.
• Jia’s perspective on the future of AI.
• What the potential impact of AI agents is.
• The importance of understanding technical boundaries.
• Why interdisciplinary collaboration is imperative.
• How Jia is empowering other businesses through LiveX AI.
• Why she prioritizes latency and believes that it’s crucial.
• How AI has changed people’s expectations and level of courtesy.
• A glimpse into Jia’s vision for the future of AI agents.
• Why she is not satisfied with the multi-model AI models out there.
• Challenges associated with data in multi-model machine learning.

Quotes:

“[The field of AI] is advancing so fast every day.” — Jia Li [0:03:05]

“It is very important to have more sharing and collaboration within the [AI field].” — Jia Li [0:12:40]

“Having an efficient algorithm [and] having efficient hardware and software optimization is really valuable.” — Jia Li [0:14:42]

Links Mentioned in Today’s Episode:

Jia Li on LinkedIn

LiveX AI

How AI Happens

Sama

Key Points From This Episode:

• Reid Robinson's professional background, and how he ended up at Zapier.
• What he learned during his year as an NFT founder, and how it serves him in his work today.
• How he gained his diverse array of professional skills.
• Whether one can differentiate between AI and mere automation.
• How Reid knew that partnering with OpenAI and ChatGPT would be the perfect fit.
• The way the Zapier team understands and approaches ML accuracy and generative data.
• Why real-world data is better as it stands, and whether generative data will one day catch up.
• How Zapier uses generative data with its clients.
• Why AI is still mostly beneficial for those with a technical background.
• Reid Robinson's next big idea, and his parting words of advice.

Quotes:

“Sometimes, people are very bad at asking for what they want. If you do any stint in, particularly, the more hardcore sales jobs out there, it's one of the things you're going to have to learn how to do to survive. You have to be uncomfortable and learn how to ask for things.” — @Reidoutloud_ [0:05:07]

“In order to really start to drive the accuracy of [our AI models], we needed to understand, what were users trying to do with this?” — @Reidoutloud_ [0:15:34]

“The people who being enabled the most with AI in the current stage are the technical tinkerers. I think a lot of these tools are too technical for average-knowledge workers.” — @Reidoutloud_ [0:28:32]

“Quick advice for anyone listening to this, do not start a company when you have your first kid! Horrible idea.” — @Reidoutloud_ [0:29:28]

Links Mentioned in Today’s Episode:

Reid Robinson on LinkedIn

Reid Robinson on X

Zapier

CocoNFT

How AI Happens

Sama

In this episode of How AI Happens, Justin explains how his project, Wondr Search, injects creativity into AI in a way that doesn’t alienate creators. You’ll learn how this new form of AI uses evolutionary algorithms (EAs) and differential evolution (DE) to generate music without learning from or imitating existing creative work. We also touch on the success of the six songs created by Wondr Search, why AI will never fully replace artists, and so much more. For a fascinating conversation at the intersection of art and AI, be sure to tune in today!

Key Points From This Episode:

• How genetic algorithms can preserve human creativity in the age of AI.
• Ways that Wondr Search differs from current generative AI models.
• Why the songs produced by Wondr Search were so well-received by record labels.
• Justin’s motivations for creating an AI model that doesn’t learn from existing music.
• Differentiating between AI-generated content and creative work made by humans.
• Insight into Justin’s PhD topic focused on mathematical optimization.
• Key differences between operations research and data science.
• An understanding of the relationship between machine learning and physics.
• Our guest’s take on “big data” and why more data isn’t always better.
• Problems Justin focuses on as a technical advisor to Fortune 500 companies.
• What he is most excited (and most concerned) about for the future of AI.

Quotes:

“[Wondr Search] is definitely not an effort to stand up against generative AI that uses traditional ML methods. I use those a lot and there’s going to be a lot of good that comes from those – but I also think there’s going to be a market for more human-centric generative methods.” — Justin Kilb [0:06:12]

“The definition of intelligence continues to change as [humans and artificial systems] progress.” — Justin Kilb [0:24:29]

“As we make progress, people can access [AI] everywhere as long as they have an internet connection. That's exciting because you see a lot of people doing a lot of great things.” — Justin Kilb [0:26:06]

Links Mentioned in Today’s Episode:

Justin Kilb on LinkedIn

Wondr Search

‘Conserving Human Creativity with Evolutionary Generative Algorithms: A Case Study in Music Generation’

How AI Happens

Sama