How AI Happens

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

Andrew shares how generative AI is used by academic institutions, why employers and educators need to curb their fear of AI, what we need to consider for using AI responsibly, and the ins and outs of Andrew’s podcast, Insight x Design. Key Points From This Episode:Andrew Madson explains what a tech evangelist is and what his role at Dremio entails. The ins and outs of Dremio. Understanding the pain points that Andrew wanted to alleviate by joining Dremio. How Andrew became a tech evangelist, and why he values this role.Why all tech roles now require one to upskill and branch out into other areas of expertise. The problems that Andrew most commonly faces at work, and how he overcomes them. How Dremio uses generative AI, and how the technology is used in academia. Why employers and educators need to do more to encourage the use of AI. The provenance of training data, and other considerations for the responsible use of AI. Learning more about Andrew’s new podcast, Insight x Design. Quotes:“Once I learned about lakehouses and Apache Iceberg and how you can just do all of your work on top of the data lake itself, it really made my life a lot easier with doing real-time analytics.” — @insightsxdesign [0:04:24]“Data analysts have always been expected to be technical, but now, given the rise of the amount of data that we’re dealing with and the limitations of data engineering teams and their capacity, data analysts are expected to do a lot more data engineering.” — @insightsxdesign [0:07:49]“Keeping it simple and short is ideal when dealing with AI.” — @insightsxdesign [0:12:58]“The purpose of higher education isn’t to get a piece of paper, it’s to learn something and to gain new skills.” — @insightsxdesign [0:17:35]Links Mentioned in Today’s Episode:Andrew MadsonAndrew Madson on LinkedInAndrew Madson on XAndrew Madson on InstagramDremio Insights x DesignApache IcebergChatGPTPerplexity AIGeminiAnaconda Peter Wang on LinkedInHow AI HappensSama

Theory Ventures General Partner Tom Tunguz discusses AI financing, the future of data centers vs. the Cloud, predictions for GPUs, and the importance of staying at the forefront of AI developments. He shares insights on investing in AI tech venture capital, the relevance of Generative AI, and the significance of personalized email communication for entrepreneurs in the AI space.

Kordel is the CTO and Founder of Theta Diagnostics, and today he joins us to discuss the work he is doing to develop a sense of smell in AI. We discuss the current and future use cases they’ve been working on, the advancements they’ve made, and how to answer the question “What is smell?” in the context of AI. Kordel also provides a breakdown of their software program Alchemy, their approach to collecting and interpreting data on scents, and how he plans to help machines recognize the context for different smells. To learn all about the fascinating work that Kordel is doing in AI and the science of smell, be sure to tune in!Key Points From This Episode:Introducing today’s guest, Kordel France.How growing up on a farm encouraged his interest in AI.An overview of Kordel’s education and the subjects he focused on.His work today and how he is teaching machines to smell.Existing use cases for smell detection, like the breathalyzer test and smoke detectors.The fascinating ways that the ability to pick up certain smells differs between people.Unpacking the elusive question “What is smell?”How to apply this question to AI development.Conceptualizing smell as a pattern that machines can recognize.Examples of current and future use cases that Kordel is working on.How he trains his devices to recognize smells and compounds.A breakdown of their autonomous gas system (AGS).How their software program, Alchemy, helps them make sense of their data.Kordel’s aspiration to add modalities to his sensors that will create context for smells.Quotes:“I became interested in machine smell because I didn't see a lot of work being done on that.” — @kordelkfrance [0:08:25]“There's a lot of people that argue we can't actually achieve human-level intelligence until we've met we've incorporated all five senses into an artificial being.” — @kordelkfrance [0:08:36]“To me, a smell is a collection of compounds that represent something that we can recognize. A pattern that we can recognize.” — @kordelkfrance [0:17:28]“Right now we have about three dozen to four dozen compounds that we can with confidence detect.” — @kordelkfrance [0:19:04]“[Our autonomous gas system] is really this interesting system that's hooked up to a bunch of machine learning, that helps calibrate and detect and determine what a smell looks like for a specific use case and breaking that down into its constituent compounds.” — @kordelkfrance [0:23:20]“The success of our device is not just the sensing technology, but also the ability of Alchemy [our software program] to go in and make sense of all of these noise patterns and just make sense of the signals themselves.” — @kordelkfrance [0:25:41]Links Mentioned in Today’s Episode:Kordel FranceKordel France on LinkedInKordel France on XTheta DiagnosticsAlchemy by Theta DiagnosticsHow AI HappensSama

After describing the work done at StoneX and her role at the organization, Elettra explains what drew her to neural networks, defines data science and how she overcame the challenges of learning something new on the job, breaks down what a data scientist needs to succeed, and shares her thoughts on why many still don’t fully understand the industry. Our guest also tells us how she identifies an inadequate data set, the recent innovations that are under construction at StoneX, how to ensure that your AI and ML models are compliant, and the importance of understanding AI as a mere tool to help you solve a problem. Key Points From This Episode:Elettra Damaggio explains what StoneX Group does and how she ended up there. Her professional journey and how she acquired her skills. The state of neural networks while she was studying them, why she was drawn to the subject, and how it’s changed. StoneX’s data science and ML capabilities when she arrived, and Elettra’s role in the system. Her first experience of being thrown into the deep end of data science, and how she swam. A data scientist’s tools for success. The multidisciplinary leaders and departments that she sought to learn from when she entered data science.  Defining data science, and why many do not fully understand the industry. How Elettra knows when her data set is inadequate. The recent projects and ML models that she’s been working on. Exploring the types of guardrails that are needed when training chatbots to be compliant.Elettra’s advice to those following a similar career path as hers. Quotes:“The best thing that you can have as a data scientist to be set up for success is to have a decent data warehouse.” — Elettra Damaggio [0:09:17]“I am very much an introverted person. With age, I learned how to talk to people, but that wasn’t [always] the case.” — Elettra Damaggio [0:12:38]“In reality, the hard part is to get to the data set – and the way you get to that data set is by being curious about the business you’re working with.” — Elettra Damaggio [0:13:58]“[First], you need to have an idea of what is doable, what is not doable, [and] more importantly, what might solve the problem that [the client may] have, and then you can have a conversation with them.” — Elettra Damaggio [0:19:58]“AI and ML is not the goal; it’s the tool. The goal is solving the problem.” — Elettra Damaggio [0:28:28]Links Mentioned in Today’s Episode:Elettra Damaggio on LinkedInStoneX GroupHow AI HappensSama

 Mike Miller is the Director of Project Management at AWS, and he joins us today to share about the inspirational AI-powered products and services that are making waves at Amazon, particularly those with generative prompt engineering capabilities. We discuss how Mike and his team choose which products to bring to market, the ins and outs of PartyRock including the challenges of developing it, AWS’s strategy for generative AI, and how the company aims to serve everyone, even those with very little technical knowledge. Mike also explains how customers are using his products and what he’s learned from their behaviors, and we discuss what may lie ahead in the future of generative prompt engineering. Key Points From This Episode:Mike Miller’s professional background, and how he got into AI and AWS. How Mike and his team decide on the products to bring to market for developers. Where PartyRock came from and how it fits into AWS’s strategy. How AWS decided on the timing to make PartyRock accessible to all.   What AWS’s products mean for those with zero coding experience. The level of oversight that is required to service clients who have no technical background. Taking a closer look at AWS’s strategy for generative AI. How customers are using PartyRock, and what Mike has learned from these observations.The challenges that the team faced whilst developing PartyRock, and how they persevered. Trying to understand the future of generative prompt engineering. A reminder that PartyRock is free, so go try it out! Quotes:“We were working on AI and ML [at Amazon] and discovered that developers learned best when they found relevant, interesting, [and] hands-on projects that they could work on. So, we built DeepLens as a way to provide a fun opportunity to get hands-on with some of these new technologies.” — Mike Miller [0:02:20]“When we look at AIML and generative AI, these things are transformative technologies that really require almost a new set of intuition for developers who want to build on these things.” — Mike Miller [0:05:19]“In the long run, innovations are going to come from everywhere; from all walks of life, from all skill levels, [and] from different backgrounds. The more of those people that we can provide the tools and the intuition and the power to create innovations, the better off we all are.” — Mike Miller [0:13:58]“Given a paintbrush and a blank canvas, most people don’t wind up with The Sistine Chapel. [But] I think it’s important to give people an idea of what is possible.” — Mike Miller [0:25:34]Links Mentioned in Today’s Episode:Mike Miller on LinkedInAmazon Web ServicesAWS DeepLensAWS DeepRacerAWS DeepComposerPartyRockAmazon BedrockHow AI HappensSama