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

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 LinkedIn

Amazon Web Services

AWS DeepLens

AWS DeepRacer

AWS DeepComposer

PartyRock

Amazon Bedrock

How AI Happens

Sama

Key Points From This Episode:

• Welcoming Seth Walker to the podcast.
• The importance of being agile in AI.
• All about Seth’s company, Carrier, and what they do.
• Seth tells us about his background and how he ended up at Carrier.
• How Seth goes about unlocking the power of AI.
• The different levels of success when it comes to AI creation and how to measure them.
• Seth breaks down the different things Carrier focuses on.
• The importance of prompt engineering.
• What makes him excited about the new iterations of machine learning.

Quotes:

“In many ways, Carrier is going to be a necessary condition in order for AI to exist.” — Seth Walker [0:04:08]

“What’s hard about generating value with AI is doing it in a way that is actually actionable toward a specific business problem.” — Seth Walker [0:09:49]

“One of the things that we’ve found through experimentation with generative AI models is that they’re very sensitive to your content. I mean, there’s a reason that prompt engineering has become such an important skill to have.” — Seth Walker [0:25:56]

Links Mentioned in Today’s Episode:

Seth Walker on LinkedIn

Carrier

How AI Happens

Sama

Philip recently had the opportunity to speak with 371 customers from 15 different countries to hear their thoughts, fears, and hopes for AI. Tuning in you’ll hear Philip share his biggest takeaways from these conversations, his opinion on the current state of AI, and his hopes and predictions for the future. Our conversation explores key topics, like government and company attitudes toward AI, why adversarial datasets will need to be audited, and much more. To hear the full scope of our conversation with Philip – and to find out how 2024 resembles 1997 – be sure to tune in today!

Key Points From This Episode:

• Some background on Philip Moyer and his role as part of Google’s AI engineering team.
• What he learned from speaking with 371 customers from 15 different countries about AI.
• Philip shares his insights on how governments and companies are approaching AI.
• Recognizing the risks and requirements of models and how to manage them.
• Adversarial datasets; what they are and why they need to be audited.
• Understanding how adversarial datasets can vary between industries.
• A breakdown of Google’s approach to adversarial datasets in different languages.
• The most relevant takeaways from Philip’s cross-continental survey.
• How 2024 resembles the technological and competitive business landscape of 1997.
• Google’s partnership with Nvidia and how they are providing technologies at every layer.
• The new class of applications that come with generative AI.
• Using a company’s proprietary data to train generative AI models.
• The collective challenges we are all facing when it comes to creating generative AI at scale.
• Understanding the vectorization of knowledge and why it will need to be auditable.
• Philip shares what he is most excited about when it comes to AI.

Quotes:

“What's been so incredible to me is how forward-thinking – a lot of governments are on this topic [of AI] and their understanding of – the need to be able to make sure that both their citizens as well as their businesses make the best use of artificial intelligence.” — Philip Moyer [0:02:52]

“Nobody's ahead and nobody's behind. Every single company that I'm speaking to, has about one to five use cases live. And they have hundreds that are on the docket.” — Philip Moyer [0:15:36]

“All of us are facing the exact same challenges right now of doing [generative AI] at scale.” — Philip Moyer [0:17:03]

“You should just make an assumption that you're going to be somewhere on the order of about 10 to 15% more productive with AI.” — Philip Moyer [0:25:22]

“[With AI] I get excited around proficiency and job satisfaction because I really do think – we have an opportunity to make work fun again.” — Philip Moyer [0:27:10]

Links Mentioned in Today’s Episode:

Philip Moyer on LinkedIn

How AI Happens

Sama