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

Joelle further discusses the relationship between her work, AI, and the end users of her products as well as her summation of information modalities, world models versus word models, and the role of responsibility in the current high-stakes of technology development.  

Key Points From This Episode:

• Joelle Pineau's professional background and how she ended up at Meta.
• The aspects of AI robotics that fascinate her the most.
• Why elegance is an important element in Joelle's machine learning systems.
• How asking the right question is the most vital part of research and how to get better at it.
• FRESCO: how Joelle chooses which projects to work on.
• The relationship between her work, AI, and the end users of her final products.
• What success looks like for her and her team at Meta.
• World models versus word models and her summation of information modalities.
• What Joelle thinks about responsibility in the current high-stakes of technology development.

Quotes:

“Perhaps, the most important thing in research is asking the right question.” — @jpineau1 [0:05:10]

“My role isn't to set the problems for [the research team], it's to set the conditions for them to be successful.” —  @jpineau1 [0:07:29]

“If we're going to push for state-of-the-art on the scientific and engineering aspects, we must push for state-of-the-art in terms of social responsibility.” —  @jpineau1 [0:20:26]

Links Mentioned in Today’s Episode:

Joelle Pineau on LinkedIn

Joelle Pineau on X

Meta

How AI Happens

Sama

Key Points From This Episode:

• Amii’s machine learning project management tool: MLPL.
• Amii’s ultimate goal of building capacity and how it differs from an agency model. 
• Asking the right questions to ascertain the appropriate use for AI. 
• Instances where AI is not a relevant solution. 
• Common challenges people face when adopting AI strategies. 
• Mara’s perspective on the education necessary to excel in a career in machine learning.

Quotes:

“Amii is all about capacity building, so we’re not a traditional agent in that sense. We are trying to educate and inform industry on how to do this work, with Amii at first, but then without Amii at the end.” — Mara Cairo [0:06:20]

“We need to ask the right questions. That’s one of the first things we need to do, is to explore where the problems are.” — Mara Cairo [0:07:46]

“We certainly are comfortable turning certain business problems away if we don’t feel it’s an ethical match or if we truly feel it isn’t a problem that will benefit much from machine learning.” — Mara Cairo [0:11:52]

Links Mentioned in Today’s Episode:

Maria Cairo

Maria Cairo on LinkedIn

Alberta Machine Intelligence Unit

How AI Happens

Sama

Jerome discusses Meta's Segment Anything Model, Ego Exo 4D, the nature of Self Supervised Learning, and what it would mean to have a non-language based approach to machine teaching. 

For more, including quotes from Meta Researchers, check out the Sama Blog