How AI Happens

* The rapid evolution of technology excites Anthony, especially in AI. * User preferences are shifting towards more human-like AI interactions. * Empathy in AI is crucial for better customer service experiences. * The partnership between Amdocs and NVIDIA emphasizes the importance of software efficiency * Software and hardware advancements must progress in parallel to maximize productivity. * Physical AI integration will enhance daily life through automation and smart devices. * Emergent behavior in AI represents a new frontier in reasoning and decision-making. * Generative AI can learn and adapt beyond traditional if-then programming. * An audit trail is essential for transparency in AI decision-making processes.

Mica shares the methods behind Augury’s fault testing processes, why they use the highest quality data available, how in-house experts help them filter their data reliably, and their approach to communicating with customers. Our conversation also explores the balance between edge computing and cloud computing, and why both are necessary for optimal performance and monitoring. Key Points From This Episode: * Mica’s journey from studying physics at the Weizmann Institute to her current role at Augury. * How her background in physics and neuroscience inform her work in AI. * Why physicists are drawn to AI and data science; how scientists follow their curiosity. * Mica’s responsibilities in her role as algorithms team lead at Augury. * How they develop algorithms and test for faults; why this requires the highest quality data. * Understanding the role of their in-house expert vibration analysts. * The importance of domain expertise in labeling and annotating data. * Finding the balance between manual and automated processes in data labeling. * How to communicate with customers and present metrics that matter to them. * Augury’s use of edge and cloud computing for optimal performance and monitoring. Quotes: “We look for better ways to adjust our algorithms and also develop new ones for all kinds of faults that could happen in the machines catching events that are trickier to catch, and for that we need highest quality data.” — Mica Rubinson [0:08:20] “At Aubrey, we have internal vibration analysts that are experts in their field. They go through very rigorous training process. There are international standards to how you do vibration analysis, and we have them in-house.” — Mica Rubinson [0:09:07] “[It’s] really helpful for us to have [these] in-house experts. We have massive amounts of records – signal recordings from 10 years of machine monitoring. Thanks to these experts [in] labeling, we can filter out a lot of noisy parts of this data.” — Mica Rubinson [0:10:32] “We quantify [our services] for the customer as their ROI [and] how much they saved by using Augury. You had this [issue, and] we avoided this downtime. [We show] how much does it translates eventually [into] money that you saved.” — Mica Rubinson [0:22:28] Links Mentioned in Today’s Episode: Mica Rubinson on LinkedIn [https://www.linkedin.com/in/mica-rubinson/?originalSubdomain=il] Mica Rubinson on ResearchGate [https://www.researchgate.net/scientific-contributions/Mica-Rubinson-2125815397] Augury [https://www.augury.com/] Weizmann Institute of Science [https://www.weizmann.ac.il/pages/] How AI Happens [https://www.howaihappens.com/] Sama [https://www.sama.com/]

Srini highlights the importance of integrating these agents into real-world applications, enhancing productivity and user experiences across industries. Srini also delves into the challenges of building reliable, ethical, and secure AI systems while fostering developer innovation. His insights offer a roadmap for harnessing advanced agents to drive meaningful technological progress. Don’t miss this informative conversation.  Key Points From This Episode: * Introducing today’s guest, Srini Iragavarapu, a leader at AWS.  * His thoughts on how Agentic and AI are intersecting today.  * The state of the union of agents in the world and at AWS. * How AWS is leveraging agents to build specific tasks for customers. * Two mechanisms that software agents use to operate. * Understanding the reasoning capabilities of large foundational models. * How AWS makes use of a test agent.  * Qdeveloper’s instantaneous conversational capabilities. * Bringing different options to the customers as a long-term strategy. * Three layers at which AWS is innovating today. * Why the end user is ultimately the person who benefits.  Quotes: “Think of it as an iterative way of solving a problem rather than just calling a single API and coming back: that’s in a nutshell how generative AI and the foundation models are working with reasoning capabilities.” — Srini Iragavarapu [0:03:04] “The models are becoming more powerful and more available, faster,  a lot more dependable.” — Srini Iragavarapu [0:29:57] Links Mentioned in Today’s Episode: Srini Iragavarapu on LinkedIn [https://www.linkedin.com/in/isvas/] How AI Happens [https://www.howaihappens.com/] Sama [https://www.sama.com/]

We explore the current trends of AI-based solutions in retail, what has driven its adoption in the industry, and how AI-based customer service technology has improved over time. We also discuss the correct mix of technology and humans, the importance of establishing boundaries for AI, and why it won't replace humans but will augment workflow. Hear examples of AI retail success stories, what companies got AI wrong, and the reasons behind the wins and failures. Gain insights into the value of copilots, business strategies to avoid investing in ineffective AI solutions, and much more. Tune in now! Key Points From This Episode: * Learn about Lisa and Mika's backgrounds in retail technology and AI-based solutions.  * Hear how AI has become more accessible to businesses beyond the typical tech giants. * Explore how AI-powered chatbots and copilots have evolved to improve customer service. * The Coca-Cola AI ad controversy and why oversight on AI-generated content is vital. * Discover the innovative and exciting ways AI can be leveraged in the retail industry. * AI success stories: Target’s AI copilot for employees and Nordstrom’s personalization tool. * How AI is making the return process more efficient and improving inventory management. * Uncover the multimodal connections of AI and how it will enhance customer personalization. * Important considerations for businesses regarding the adoption of AI and the pitfalls to avoid. Quotes: “I think [the evolution] in terms of accessibility to AI-solutions for people who don't have the massive IT departments and massive data analytics departments is really remarkable.” — Mika Yamamoto [0:04:25] “Whether it's generative AI for creative or content or whatever, it's not going to replace humans. It's going to augment our workflows.” — Lisa Avvocato [0:10:46] “Retail is actually one of the fastest adopting industries out there [of] AI.” — Mika Yamamoto [0:14:17] “Having conversations with peers, I think, is absolutely invaluable to figure out what's hype and what's reality [regarding AI].” — Mika Yamamoto [0:30:19] Links Mentioned in Today’s Episode: Lisa Avvocato on LinkedIn [https://www.linkedin.com/in/lisaavvocato/] Mika Yamamoto on LinkedIn [https://www.linkedin.com/in/mikayamamoto/] Freshworks [https://www.freshworks.com] The Coca‑Cola Company [https://www.coca-colacompany.com/] How AI Happens [https://www.howaihappens.com/] Sama [https://www.sama.com/]

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 [https://www.linkedin.com/in/nmekelbobrov/] eBay [https://www.ebayinc.com] How AI Happens [https://www.howaihappens.com/] Sama [https://www.sama.com/]