Key Points From This Episode:

• Ram Venkatesh describes his career journey to founding Sema4.ai.
• The pain points he was trying to ease with Sema4.ai.
• How our general approach to big data is becoming more streamlined, albeit rather slowly.
• The ins and outs of Sema4.ai and how it serves its clients.
• What Ram means by “agent” and “agent agency” when referring to machine learning copilots.
• The difference between writing a program to execute versus an agent reasoning with it.
• Understanding the contextual work training method for agents.
• The relationship between an LLM and an agent and the risks of training LLMs on agent data.
• Exploring the next generation of LLM training protocols in the hopes of improving efficiency.
• The requirements of an LLM if you’re not training it and unpacking modality improvements.
• Why agent input and feedback are major disruptions to SaaS and beyond.
• Our guest shares his hopes for the future of AI.

Quotes:

“I’ve spent the last 30 years in data. So, if there’s a database out there, whether it’s relational or object or XML or JSON, I’ve done something unspeakable to it at some point.” — @ramvzz [0:01:46]

“As people are getting more experienced with how they could apply GenAI to solve their problems, then they’re realizing that they do need to organize their data and that data is really important.” — @ramvzz [0:18:58]

“Following the technology and where it can go, there’s a lot of fun to be had with that.” — @ramvzz [0:23:29]

“Now that we can see how software development itself is evolving, I think that 12-year-old me would’ve built so many more cooler things than I did with all the tech that’s out here now.” — @ramvzz [0:29:14]

Links Mentioned in Today’s Episode:

Ram Venkatesh on LinkedIn

Ram Venkatesh on X

Sema4.ai

Cloudera

How AI Happens

Sama

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