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 LinkedInSynopsysHow AI HappensSama

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 LinkedInXactlyHow AI HappensSama

 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 KaddevarmuthSrujana Kaddevarmuth on XSrujana Kaddevarmuth on LinkedInUnited Nations Association (UNA) San Francisco The World in 2050American INSIGHTHow AI HappensSama

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 LinkedInUPSHow AI HappensSama

Dr. Martin Riedmiller, a research director at Google DeepMind renowned for his pioneering work in reinforcement learning, dives into the exciting world of AI. He shares insights on how reinforcement learning outperforms traditional methods, illustrated by his experience leading a robotic soccer team. The discussion highlights the challenges of using large language models, their integration with robotics, and the importance of data efficiency. Riedmiller also explores the evolving role of humans in AI development and the potential future disruptions across industries.

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 LinkedInLiveX AIHow AI HappensSama

 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 LinkedInReid Robinson on XZapierCocoNFTHow AI HappensSama

 In this episode of How AI Happens, Justin explains how his project, Wondr Search, injects creativity into AI in a way that doesn’t alienate creators. You’ll learn how this new form of AI uses evolutionary algorithms (EAs) and differential evolution (DE) to generate music without learning from or imitating existing creative work. We also touch on the success of the six songs created by Wondr Search, why AI will never fully replace artists, and so much more. For a fascinating conversation at the intersection of art and AI, be sure to tune in today!Key Points From This Episode:How genetic algorithms can preserve human creativity in the age of AI.Ways that Wondr Search differs from current generative AI models.Why the songs produced by Wondr Search were so well-received by record labels.Justin’s motivations for creating an AI model that doesn’t learn from existing music.Differentiating between AI-generated content and creative work made by humans.Insight into Justin’s PhD topic focused on mathematical optimization.Key differences between operations research and data science.An understanding of the relationship between machine learning and physics.Our guest’s take on “big data” and why more data isn’t always better.Problems Justin focuses on as a technical advisor to Fortune 500 companies.What he is most excited (and most concerned) about for the future of AI.Quotes:“[Wondr Search] is definitely not an effort to stand up against generative AI that uses traditional ML methods. I use those a lot and there’s going to be a lot of good that comes from those – but I also think there’s going to be a market for more human-centric generative methods.” — Justin Kilb [0:06:12]“The definition of intelligence continues to change as [humans and artificial systems] progress.” — Justin Kilb [0:24:29]“As we make progress, people can access [AI] everywhere as long as they have an internet connection. That's exciting because you see a lot of people doing a lot of great things.” — Justin Kilb [0:26:06]Links Mentioned in Today’s Episode:Justin Kilb on LinkedInWondr Search‘Conserving Human Creativity with Evolutionary Generative Algorithms: A Case Study in Music Generation’How AI HappensSama

Jacob shares how Gong uses AI, how it empowers its customers to build their own models, and how this ease of access for users holds the promise of a brighter future. We also learn more about the inner workings of Gong and how it trains its own models, why it’s not too interested in tracking soft skills right now, what we need to be doing more of to build more trust in chatbots, and our guest’s summation of why technology is advancing like a runaway train.Key Points From This Episode:Jacob Eckel walks us through his professional background and how he ended up at Gong.The ins and outs of Gong, and where AI fits in. How Gong empowers its customers to build their own models, and the results thereof. Understanding the data ramifications when customers build their own models on Gong.How Gong trains its own models, and the way the platform assists users in real time. Why its models aren’t tracking softer skills like rapport-building, yet.Everything that needs to be solved before we can fully trust chatbots. Jacob’s summation of why technology is growing at an increasingly rapid rate. Quotes:“We don’t expect our customers to suddenly become data scientists and learn about modeling and everything, so we give them a very intuitive, relatively simple environment in which they can define their own models.” — @eckely [0:07:03]“[Data] is not a huge obstacle to adopting smart trackers.” — @eckely [0:12:13]“Our current vibe is there’s a limit to this technology. We are still unevolved apes.” — @eckely [0:16:27]Links Mentioned in Today’s Episode:Jacob Eckel on LinkedInJacob Eckel on XGongHow AI HappensSama

Bobak further opines on the pros and cons of Perplexity and GPT 4.0, why the technology uses both models, the differences, and the pros and cons. Finally, our guest tells us why Brilliant Labs is open-source and reminds us why public participation is so important. Key Points From This Episode:Introducing Bobak Tavangar to today’s episode of How AI Happens. Bobak tells us about his background and what led him to start his company, Brilliant Labs. Our guest shares his interesting Lord of the Rings analogy and how it relates to his business. How wearable technology is creeping more and more into our lives. The hurdles they face with generative AI glasses and how they’re overcoming them. How Bobak chose the most important factors to incorporate into the glasses. What the glasses can do at this stage of development. Bobak explains how the glasses know whether to query GPT 4.0 or Perplexity AI. GPT 4.0 versus Perplexity and why Bobak prefers to use them both. The importance of gauging public reaction and why Brilliant Labs is open-source. Quotes:“To have a second pair of eyes that can connect everything we see with all the information on the web and everything we’ve seen previously – is an incredible thing.” — @btavangar [0:13:12]“For live web search, Perplexity – is the most precise [and] it gives the most meaningful answers from the live web.” — @btavangar [0:26:40]“The [AI] space is changing so fast. It’s exciting [and] it’s good for all of us but we don’t believe you should ever be locked to one model or another.” — @btavangar [0:28:45]Links Mentioned in Today’s Episode:Bobak Tavangar on LinkedInBobak Tavangar on XBobak Tavangar on InstagramBrilliant LabsPerplexity AIGPT 4.0How AI HappensSama