We hear about Nitzan’s AI expertise, motivation for joining eBay, and approach to implementing AI into eBay's business model. Gain insights into the impacts of centralizing and federating AI, leveraging generative AI to create personalized content, and why patience is essential to AI development. We also unpack eBay's approach to LLM development, tailoring AI tools for eBay sellers, the pitfalls of generic marketing content, and the future of AI in retail. Join us to discover how AI is revolutionizing e-commerce and disrupting the retail sector with Nitzan Mekel-Bobrov!

Key Points From This Episode:

• Nitzan's career experience, his interest in sustainability, and his sneaker collection.
• Why he decided to begin a career at eBay and his role at the company.
• His approach to aligning the implementation of AI with eBay's overall strategy.
• How he identifies the components of eBay's business model that will benefit from AI.
• What makes eBay highly suitable for the implementation of AI tools.
• Challenges of using generative AI models to create personalized content for users.
• Why experimentation is vital to the AI development and implementation process.
• Aspects of the user experience that Nitzan uses to train and develop eBay's LLMs.
• The potential of knowledge graphs to uncover the complexity of user behavior.
• Reasons that the unstructured nature of eBay's data is fundamental to its business model.
• Incorporating a seller's style into AI tools to avoid creating generic marketing material.
• Details about Nitzan’s team and their diverse array of expertise.
• Final takeaways and how companies can ensure they survive the AI transition.

Quotes:

“It’s tricky to balance the short-term wins with the long-term transformation.” — Nitzan Mekel-Bobrov [0:06:50]

“An experiment is only a failure if you haven’t learned anything yourself and – generated institutional knowledge from it.” — Nitzan Mekel-Bobrov [0:09:36]

“What's nice about [eBay's] business model — is that our incentive is to enable each seller to maintain their own uniqueness.” — Nitzan Mekel-Bobrov [0:27:33]

“The companies that will thrive in this AI transformation are the ones that can figure out how to marry parts of their current culture and what all of their talent brings with what the AI delivers.” — Nitzan Mekel-Bobrov [0:33:58]

Links Mentioned in Today’s Episode:

Nitzan Mekel-Bobrov on LinkedIn

eBay

How AI Happens

Sama

Satya unpacks how Unilever utilizes its database to inform its models and how to determine the right amount of data needed to solve complex problems. Dr. Wattamwar explains why contextual problem-solving is vital, the notion of time constraints in data science, the system point of view of modeling, and how Unilever incorporates AI into its models. Gain insights into how AI can increase operational efficiency, exciting trends in the AI space, how AI makes experimentation accessible, and more! Tune in to learn about the power of data science and AI with Dr. Satyajit Wattamwar.

Key Points From This Episode:

• Background on Dr. Wattamwar, his PhD research, and data science expertise.
• Unpacking some of the commonalities between data science and physics.
• Why the outcome of using significantly large data sets depends on the situation.
• The minimum amount of data needed to make meaningful and quality models.
• Examples of the common mistakes and pitfalls that data scientists make.
• How Unilever works with partner organizations to integrate AI into its models.
• Ways that Dr. Wattamwar uses AI-based tools to increase his productivity.
• The difference between using AI for innovation versus operational efficiency.
• Insight into the shifting data science landscape and advice for budding data scientists.

Quotes:

“Around – 30 or 40 years ago, people started realizing the importance of data-driven modeling because you can never capture physics perfectly in an equation.” — Dr. Satyajit Wattamwar [0:03:10]

“Having large volumes of data which are less related with each other is a different thing than a large volume of data for one problem.” — Dr. Satyajit Wattamwar [0:09:12]

“More data [does] not always lead to good quality models. Unless it is for the same use-case.” — Dr. Satyajit Wattamwar [0:11:56]

“If somebody is looking [to] grow in their career ladder, then it's not about one's own interest.” — Dr. Satyajit Wattamwar [0:24:07]

Links Mentioned in Today’s Episode:

Dr. Satyajit Wattamwar on LinkedIn

Unilever

How AI Happens

Sama

Jing explains how Vanguard uses machine learning and reinforcement learning to deliver personalized "nudges," helping investors make smarter financial decisions. Jing dives into the importance of aligning AI efforts with Vanguard’s mission and discusses generative AI’s potential for boosting employee productivity while improving customer experiences. She also reveals how generative AI is poised to play a key role in transforming the company's future, all while maintaining strict data privacy standards.

Key Points From This Episode:

• Jing Wang’s time at Fermilab and the research behind her PhD in high-energy physics.
• What she misses most about academia and what led to her current role at Vanguard.
• How she aligns her team’s AI strategy with Vanguard’s business goals.
• Ways they are utilizing AI for nudging investors to make better decisions.
• Their process for delivering highly personalized recommendations for any given investor.
• Steps that ensure they adhere to finance industry regulations with their AI tools.
• The role of reinforcement learning and their ‘next best action’ models in personalization.
• Their approach to determining the best use of their datasets while protecting privacy.
• Vanguard’s plans for generative AI, from internal productivity to serving clients.
• How Jing stays abreast of all the latest developments in physics.

Quotes:

“We make sure all our AI work is aligned with [Vanguard’s] four pillars to deliver business impact.” — Jing Wang [0:08:56]

“We found those simple nudges have tremendous power in terms of guiding the investors to adopt the right things. And this year, we started to use a machine learning model to actually personalize those nudges.” — Jing Wang [0:19:39]

“Ultimately, we see that generative AI could help us to build more differentiated products. – We want to have AI be able to train language models [to have] much more of a Vanguard mindset.” — Jing Wang [0:29:22]

Links Mentioned in Today’s Episode:

Jing Wang on LinkedIn

Vanguard
Fermilab
How AI Happens

Sama