Our guest goes on to share the different kinds of research they use for machine learning development before explaining why he is more conservative when it comes to driving generative AI use cases. He even shares some examples of generative use cases he feels are worthwhile. We hear about how these changes will benefit all UPS customers and how they avoid sharing private and non-compliant information with chatbots. Finally, Sunzay shares some advice for anyone wanting to become a leader in the tech world.

Key Points From This Episode:

• Introducing Sunzay Passari to the show and how he landed his current role at UPS.
• Why Sunzay believes that this huge operation he’s part of will drive transformational change.
• How AI and machine learning have made their way into UPS over the past few years.
• The way Sunzay and his team have decided where AI will be most disruptive within UPS.
• Qualitative and qualitative research and what that looks like for this project.
• Why Sunzay is conservative when it comes to driving generative AI use cases.
• Sunzay shares some of the generative use cases that he thinks are worthwhile.
• The way these new technologies will benefit everyday UPS customers.
• How they are preventing people from accessing non-compliant data through chatbots.
• Sunzay passes on some advice for anyone looking to forge their career as a leader in tech.

Quotes:

“There’s a lot of complexities in the kind of global operations we are running on a day-to-day basis [at UPS].” — Sunzay Passari [0:04:35]

“There is no magic wand – so it becomes very important for us to better our resources at the right time in the right initiative.” — Sunzay Passari [0:09:15]

“Keep learning on a daily basis, keep experimenting and learning, and don’t be afraid of the failures.” — Sunzay Passari [0:22:48]

Links Mentioned in Today’s Episode:

Sunzay Passari on LinkedIn

UPS

How AI Happens

Sama

Martin shares what reinforcement learning does differently in executing complex tasks, overcoming feedback loops in reinforcement learning, the pitfalls of typical agent-based learning methods, and how being a robotic soccer champion exposed the value of deep learning. We unpack the advantages of deep learning over modeling agent approaches, how finding a solution can inspire a solution in an unrelated field, and why he is currently focusing on data efficiency. Gain insights into the trade-offs between exploration and exploitation, how Google DeepMind is leveraging large language models for data efficiency, the potential risk of using large language models, and much more.

Key Points From This Episode:

• What it is like being a five times world robotic soccer champion.
• The process behind training a winning robotic soccer team.
• Why standard machine learning tools could not train his team effectively.
• Discover the challenges AI and machine learning are currently facing.
• Explore the various exciting use cases of reinforcement learning.
• Details about Google DeepMind and the role of him and his team.
• Learn about Google DeepMind’s overall mission and its current focus.
• Hear about the advantages of being a scientist in the AI industry.
• Martin explains the benefits of exploration to reinforcement learning.
• How data mining using large language models for training is implemented.
• Ways reinforcement learning will impact people in the tech industry.
• Unpack how AI will continue to disrupt industries and drive innovation.

Quotes:

“You really want to go all the way down to learn the direct connections to actions only via learning [for training AI].” — Martin Riedmiller [0:07:55]

“I think engineers often work with analogies or things that they have learned from different [projects].” — Martin Riedmiller [0:11:16]

“[With reinforcement learning], you are spending the precious real robots time only on things that you don’t know and not on the things you probably already know.” — Martin Riedmiller [0:17:04]

“We have not achieved AGI (Artificial General Intelligence) until we have removed the human completely out of the loop.” — Martin Riedmiller [0:21:42]

Links Mentioned in Today’s Episode:

Martin Riedmiller

Martin Riedmiller on LinkedIn

Google DeepMind

RoboCup

How AI Happens

Sama

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