Pascal & Yannick delve into the kind of human involvement SAM-2 needs before discussing the use cases it enables. Hear all about the importance of having realistic expectations of AI, what the cost of SAM-2 looks like, and the the importance of humans in LLMs.

Key Points From This Episode:

• Introducing Pascal Jauffret and Yannick Donnelly to the show.
• Our guests explain what the SAM-2 model is.
• A description of what getting information from video entails.
• What made our guests interested in researching SAM-2.
• A few things that stand out about this tool.
• The level of human involvement that SAM-2 needs.
• Some of the use cases they see SAM-2 enabling.
• Whether manually annotating is easier than simply validating data.
• The importance of setting realistic expectations of what AI can do.
• When LLM models work best, according to our experts.
• A discussion about the cost of the models at the moment.
• Why humans are so important in coaching people to use models.
• What we can expect from Sama in the near future.

Quotes:

“We’re kind of shifting towards more of a validation period than just annotating from scratch.” — Yannick Donnelly [0:22:01]

“Models have their place but they need to be evaluated.” — Yannick Donnelly [0:25:16]

“You’re never just using a model for the sake of using a model. You’re trying to solve something and you’re trying to improve a business metric.” — Pascal Jauffret [0:32:59]

“We really shouldn’t underestimate the human aspect of using models.” — Pascal Jauffret [0:40:08]

Links Mentioned in Today’s Episode:

Pascal Jauffret on LinkedIn

Yannick Donnelly on LinkedIn

How AI Happens

Sama

Today we are joined by Siddhika Nevrekar, an experienced product leader passionate about solving complex problems in ML by bringing people and products together in an environment of trust. We unpack the state of free computing, the challenges of training AI models for edge, what Siddhika hopes to achieve in her role at Qualcomm, and her methods for solving common industry problems that developers face.

Key Points From This Episode:

• Siddhika Nevrekar walks us through her career pivot from cloud to edge computing.
• Why she’s passionate about overcoming her fears and achieving the impossible.
• Increasing compute on edge devices versus developing more efficient AI models.
• Siddhika explains what makes Apple a truly unique company.
• The original inspirations for edge computing and how the conversation has evolved.
• Unpacking the current state of free computing and what may happen in the near future.
• The challenges of training AI models for edge.
• Exploring Siddhika’s role at Qualcomm and what she hopes to achieve.
• Diving deeper into her process for achieving her goals.
• Common industry challenges that developers are facing and her methods for solving them

Quotes:

“Ultimately, we are constrained with the size of the device. It’s all physics. How much can you compress a small little chip to do what hundreds and thousands of chips can do which you can stack up in a cloud? Can you actually replicate that experience on the device?” — @siddhika_

“By the time I left Apple, we had 1000-plus [AI] models running on devices and 10,000 applications that were powered by AI on the device, exclusively on the device. Which means the model is entirely on the device and is not going into the cloud. To me, that was the realization that now the moment has arrived where something magical is going to start happening with AI and ML.” — @siddhika_

Links Mentioned in Today’s Episode:

Siddhika Nevrekar on LinkedIn

Siddhika Nevrekar on X

Qualcomm AI Hub

How AI Happens

Sama

Today we are joined by Developer Advocate at Block, Rizel Scarlett, who is here to explain how to bridge the gap between the technical and non-technical aspects of a business. We also learn about AI hallucinations and how Rizel and Block approach this particular pain point, the burdens of responsibility of AI users, why it’s important to make AI tools accessible to all, and the ins and outs of G{Code} House – a learning community for Indigenous and women of color in tech. To end, Rizel explains what needs to be done to break down barriers to entry for the G{Code} population in tech, and she describes the ideal relationship between a developer advocate and the technical arm of a business.

Key Points From This Episode:

• Rizel Scarlett describes the role and responsibilities of a developer advocate.
• Her role in getting others to understand how GitHub Copilot should be used.
• Exploring her ongoing projects and current duties at Block.
• How the conversation around AI copilot tools has shifted in the last 18 months.
• The importance of objection handling and why companies must pay more attention to it.
• AI hallucinations and Rizel’s advice for approaching this particular pain point.
• Why “I don’t know” should be encouraged as a response from AI companions, not shunned.
• Taking a closer look at how Block addresses AI hallucinations.
• The burdens of responsibility of users of AI, and the need to democratize access to AI tools.
• Unpacking G{Code} House and Rizel’s working relationship with this learning community.
• Understanding what prevents Indigenous and women of color from having careers in tech.
• The ideal relationship between a developer advocate and the technical arm of a business.

Quotes:

“Every company is embedding AI into their product someway somehow, so it’s being more embraced.” — @blackgirlbytes [0:11:37]

“I always respect someone that’s like, ‘I don’t know, but this is the closest I can get to it.’” — @blackgirlbytes [0:15:25]

“With AI tools, when you’re more specific, the results are more refined.” — @blackgirlbytes [0:16:29]

Links Mentioned in Today’s Episode:

Rizel Scarlett

Rizel Scarlett on LinkedIn

Rizel Scarlett on Instagram

Rizel Scarlett on X

Block

Goose

GitHub

GitHub Copilot

G{Code} House

How AI Happens

Sama