Melissa explains the importance of giving developers the choice of working with open source or proprietary options, experimenting with flexible application models, and choosing the size of your model according to the use case you have in mind. Discussing the democratization of technology, we explore common challenges in the context of AI including the potential of generative AI versus the challenge of its implementation, where true innovation lies, and what Melissa is most excited about seeing in the future.

Key Points From This Episode:

• An introduction to Melissa Evers, Vice President and General Manager of Strategy and Execution at Intel Corporation.
• More on the communities she has played a leadership role in.
• Why open source governance is not an oxymoron and why it is critical.
• The hard work that goes on behind the scenes at open source.
• What to strive for when building a healthy open source community.
• Intel’s perspective on the importance of open source and open AI.
• Enabling developer choices about open source or proprietary options.
• Growing awareness around building architecture around the freedom of choice.
• Identifying that a model is a bad choice or lacking in accuracy.
• Thinking critically about future-proofing yourself with regard to model choice.
• Opportunities for large and smaller models.
• Finding the perfect intersection between value delivery, value creation, and cost.
• Common challenges in the context of AI, including the potential of generative AI and its implementation.
• Why there is such a commonality of use cases in the realm of generative AI.
• Where true innovation and value lies even though there may be commonality in use cases.
• Examples of creative uses of generative AI; retail, compound AI systems, manufacturing, and more.
• Understanding that innovation in this area is still in its early development stages.
• How Wardley Mapping can support an understanding of scale.
• What she is most excited about for the future of AI: Rapid learning in healthcare.

Quotes:

“One of the things that is true about software in general is that the role that open source plays within the ecosystem has dramatically shifted and accelerated technology development at large.” — @melisevers [0:03:02]

“It’s important for all citizens of the open source community, corporate or not, to understand and own their responsibilities with regard to the hard work of driving the technology forward.” — @melisevers [0:05:18]

“We believe that innovation is best served when folks have the tools at their disposal on which to innovate.” — @melisevers [0:09:38]

“I think the focus for open source broadly should be on the elements that are going to be commodified.” — @melisevers [0:25:04]

Links Mentioned in Today’s Episode:

Melissa Evers on LinkedIn

Melissa Evers on X

Intel Corporation

VP of AI and ML at Synopsys, Thomas Andersen joins us to discuss designing AI chips. Tuning in, you’ll hear all about our guest’s illustrious career, how he became interested in technology, tech in East Germany, what it was like growing up there, and so much more! We delve into his company, Synopsys, and the chips they build before discussing his role in building algorithms.

Key Points From This Episode:

• A warm welcome to today’s guest, Thomas Andersen.
• How he got into the tech world and his experience growing up in East Germany.
• The cost of Compute AI coming down at the same time the demand is going up.
• Thomas tells us about Synopsys and what goes into building their chips.
• Other traditional software companies that are now designing their own AI chips.
• What Thomas’ role looks like in machine learning and building AI algorithms.
• How the constantly changing rules of AI chip design continue to create new obstacles.
• Thomas tells us how they use reinforcement learning in their processes.
• The different applications for generative AI and why it needs good input data.
• Thomas’ advice for anyone wanting to get into the world of AI.

Quotes:

“It’s not really the technology that makes life great, it’s how you use it, and what you make of it.” — Thomas Andersen [0:07:31]

“There is, of course, a lot of opportunities to use AI in chip design.” — Thomas Andersen [0:25:39]

“Be bold, try as many new things [as you can, and] make sure you use the right approach for the right tasks.” — Thomas Andersen [0:40:09]

Links Mentioned in Today’s Episode:

Thomas Andersen on LinkedIn

Synopsys

How AI Happens

Sama

Developing AI and generative AI initiatives demands significant investment, and without delivering on customer satisfaction, these costs can be tough to justify. Today, SVP of Engineering and General Manager of Xactly India, Kandarp Desai joins us to discuss Xactly's AI initiatives and why customer satisfaction remains their top priority.
Key Points From This Episode:

• An introduction to Kandarp and his transition from hardware to software.
• How he became SVP of Engineering and General Manager of Xactly India.
• His move to Bangalore and the expansion of Xactly’s presence in India.
• The rapid modernization of India as a key factor in Xactly’s growth strategy.
• An overview of Xactly’s AI and generative AI initiatives.
• Insight into the development of Xactly’s AI Copilot.
• Four key stakeholders served by the Xactly AI Copilot.
• The Xactly Extend, an enterprise platform for building custom apps.
• Challenges in justifying the ROI of AI initiatives.
• Why customer satisfaction and business outcomes are essential.
• How AI is overhyped in the short term and underhyped in the long term.
• The difficulties in quantifying the value of AI.
• Kandarp’s career advice to AI practitioners, from taking risks to networking.

Quotes:

“[Generative AI] is only useful if it drives higher customer satisfaction. Otherwise, it doesn't matter.” — Kandarp Desai [0:11:36]

“Justifying the ROI of anything is hard – If you can tie any new invention back to its ROI in customer satisfaction, that can drive an easy sell across an organization.” — Kandarp Desai [0:15:35]

“The whole AI trend is overhyped in the short term and underhyped long term. [It’s experienced an] oversell recently, and people are still trying to figure it out.” — Kandarp Desai [0:20:48]

Links Mentioned in Today’s Episode:

Kandarp Desai on LinkedIn

Xactly

How AI Happens

Sama

Srujana is Vice President and Group Director at Walmart’s Machine Learning Center of Excellence and is an experienced and respected AI, machine learning, and data science professional. She has a strong background in developing AI and machine learning models, with expertise in natural language processing, deep learning, and data-driven decision-making. Srujana has worked in various capacities in the tech industry, contributing to advancing AI technologies and their applications in solving complex problems. In our conversation, we unpack the trends shaping AI governance, the importance of consumer data protection, and the role of human-centered AI. Explore why upskilling the workforce is vital, the potential impact AI could have on white-collar jobs, and which roles AI cannot replace. We discuss the interplay between bias and transparency, the role of governments in creating AI development guardrails, and how the regulatory framework has evolved. Join us to learn about the essential considerations of deploying algorithms at scale, striking a balance between latency and accuracy, the pros and cons of generative AI, and more.

Key Points From This Episode:

• Srujana breaks down the top concerns surrounding technology and data.
• Learn how AI can be utilized to drive innovation and economic growth.
• Navigating the adoption of AI with upskilling and workforce retention.
• The AI gaps that upskilling should focus on to avoid workforce displacement.
• Common misconceptions about biases in AI and how they can be mitigated.
• Why establishing regulations, laws, and policies is vital for ethical AI development.
• Outline of the nuances of creating an effective worldwide regulatory framework.
• She explains the challenges and opportunities of deploying algorithms at scale.
• Hear about the strategies for building architecture that can adapt to future changes.
• She shares her perspective on generative AI and what its best use cases are.
• Find out what area of AI Srujana is most excited about.

Quotes:

“By deploying [bias] algorithms we may be going ahead and causing some unintended consequences.” — @Srujanadev [0:03:11]

“I think it is extremely important to have the right regulations and guardrails in place.” — @Srujanadev [0:11:32]

“Just using generative AI for the sake of it is not necessarily a great idea.” — @Srujanadev [0:25:27]

“I think there are a lot of applications in terms of how generative AI can be used but not everybody is seeing the return on investment.” — @Srujanadev [0:27:12]

Links Mentioned in Today’s Episode:

Srujana Kaddevarmuth

Srujana Kaddevarmuth on X

Srujana Kaddevarmuth on LinkedIn

United Nations Association (UNA) San Francisco

The World in 2050

American INSIGHT

How AI Happens

Sama

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

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