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