How AI Happens

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

 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 LinkedIn

Wondr Search

‘Conserving Human Creativity with Evolutionary Generative Algorithms: A Case Study in Music Generation’

How AI Happens

Sama

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 LinkedIn

Jacob Eckel on X

Gong

How AI Happens

Sama

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 LinkedIn

Bobak Tavangar on X

Bobak Tavangar on Instagram

Brilliant Labs

Perplexity AI

GPT 4.0

How AI Happens

Sama

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 Madson

Andrew Madson on LinkedIn

Andrew Madson on X

Andrew Madson on Instagram

Dremio 

Insights x Design

Apache Iceberg

ChatGPT

Perplexity AI

Gemini

Anaconda 

Peter Wang on LinkedIn

How AI Happens

Sama

Tom shares further thoughts on financing AI tech venture capital and whether or not data centers pose a threat to the relevance of the Cloud, as well as his predictions for the future of GPUs and much more. 

Key Points From This Episode:

• Introducing Tomasz Tunguz, General Partner at Theory Ventures.
• What he is currently working on including AI research and growing the team at Theory.
• How he goes about researching the present to predict the future.
• Why professionals often work in both academia and the field of AI.
• What stands out to Tom when he is looking for companies to invest in.
• Varying applications where an 80% answer has differing relevance.
• The importance of being at the forefront of AI developments as a leader. 
• Why the metrics of risk and success used in the past are no longer relevant.
• Tom’s thoughts on whether or not Generative AI will replace search.
• Financing in the AI tech venture capital space.
• Differentiating between the Cloud and data centers.
• Predictions for the future of GPUs.
• Why ‘hello’ is the best opener for a cold email.

Quotes:

“Innovation is happening at such a deep technological level and that is at the core of machine learning models.” — @tomastungusz [0:03:37]

“Right now, we’re looking at where [is] there rote work or human toil that can be repeated with AI? That’s one big question where there’s not a really big incumbent.” — @tomastungusz [0:05:51]

“If you are the leader of a team or a department or a business unit or a company, you can not be in a position where you are caught off guard by AI. You need to be on the forefront.” — @tomastungusz [0:08:30]

“The dominant dynamic within consumer products is the least friction in a user experience always wins.” — @tomastungusz [0:14:05]

Links Mentioned in Today’s Episode:

Tomasz Tunguz

Tomasz Tunguz on LinkedIn

Tomasz Tunguz on X

Theory Ventures

How AI Happens

Sama

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 France

Kordel France on LinkedIn

Kordel France on X

Theta Diagnostics

Alchemy by Theta Diagnostics

How AI Happens

Sama

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 LinkedIn

StoneX Group

How AI Happens

Sama