Pavel holds two gold medals, two silver medals and three bronze on Kaggle. He’s also the author of several well-known libraries, including one that has about 20,000 stars on GitHub. Currently, he’s a member of the open source engineering team at Hugging Face.

This episode covers Pavel’s journey from growing up in a small town in Russia, to becoming a Kaggle competition master and computer vision engineer at Hugging Face. The conversation also covers many insights about Hugging Face itself, including Hugging Face Hub, Enterprise Hub, the Transformers library, the Diffusers library for generating images, pilot projects (about 20-30 every six months), auto-training and showcasing models on Spaces, Remi Cadene’s LeRobot, and Zero GPU. It also covers information about Hugging Face datasets, including a 15-trillion token dataset that’s big enough to train a base NLP model.

This episode features a conversation with Max Mozgovoy, co-founder of VoxDiscover, which plans to use Gen AI to interview customers for user experience (UX) research done by app developers, or for customer lifecycle research, to build customer journey maps or personas, or to identify which features to add or change for the next version of product or service.

The underlying idea of VoxDiscover is to use an AI voice agent to capture customer feedback right in the app or on a website, automating the insight as well, thereby achieving a much lower cost as compared to external service providers that have to pay customers to participate, while also transforming the discovery process into something that happens on an ongoing basis, rather than only during the app or product development phase, or at quarterly or annual or even longer intervals.

With a PhD in machine learning and a specialization in computer vision, Marwa has done deep learning projects across several industries and is currently at Alohi, a Switzerland-based startup with 4 million users of document solutions like Sign.Plus, Fax.Plus and Scan.Plus. In this episode, she describes her journey from formal studies to hands-on work in industry, to her present role as a primary engineer at Alohi. The conversation also touches on what it’s like to maintain a connection to a top university as a Research Fellow, while also working full-time at a startup. Marwa also discusses the challenge of staying current on new developments, because many papers have only marginal value, which makes it hard to quickly identify the important ones. The conversation concludes with a discussion about emerging technologies that Marwa considers to be quite promising in her field, especially diffusion models and LLM foundational vision models.

Iqbal holds a PhD in Computer Science and Engineering, with a specialization in computer algorithms and graph theory. He is currently the Research Data Science Director at the University of Arizona. Aside from his innovative achievement in creating a knowledge map for the university (kmap.arizona.edu), he has one of the most remarkable and inspiring stories you’ll ever hear about overcoming disadvantages. (He grew up in a rural part of Bangladesh and was in 11th grade before he ever saw electricity.)

In this episode, Iqbal describes the brief he was working on when he created the University of Arizona knowledge map. One of the interesting aspects of the project is that it is literally a map, with a visual “geography” of the various departments and disciplines within the university, laid out in a 2D plane, where the size and location of the polygons are based on the relative size and relationships between departments. Everything is fully interactive, including the ability to zoom in or zoom out, like one would do on a physical map, and where every element can be queried to extract more information from more than a dozen data sources, including both structured and unstructured data. The conversation also includes insights into how a large and complex project like this takes shape over time.

Jeffrey is one of those rare people who knew exactly what he wanted to do from an early age: Use mathematics to solve business problems. After earning his Masters Degree in Statistics from the University of Chicago, he went on to do high-impact analytics work in banking, retail and telco, holding senior leadership positions at top companies like Sally Beauty, HSN, AT&T and Bloomin’ Brands. Currently, he leads Digital Analytics at JCPenney.

In this episode, Jeffrey shares a detailed and very insightful analysis of all the factors and trade-offs involved in making a build vs buy decision for analytics or AI solutions in business. He also names what he sees as the best applications for leveraging AI and machine learning in retail – including a few especially promising ones that he has his eye on right now. The show concludes with great career advice for any students who are currently enrolled in college-level data science or AI programs.

Tapan is the Executive Director of Marketing Sciences at OMD. This episode is a very thorough compendium of marketing science principles and practices, as applied to decisions about paid media. Topics include:

• Marketing Mix Modeling (MMM) vs Multi-Touch Attribution (MTA) vs experiments, including how to use each and for what purpose
• KPIs: Incremental sales vs top-of-funnel metrics like awareness and consideration
• How to design incremental changes in the direction that a model is pointing
• Testing a new marketing mix in various geo markets
• Judicious use of model hyperparameters, such as AdStocks
• The best cadence for measurement
• Third-party cookies (Implications of the shift towards user choice)
• Recent tools: Google Meridian (a Bayesian approach) vs Meta Robyn (a frequentist approach)

These ideas are brought to life with an example from a financial services brand, planning a big launch, with a need to answer questions like: How should the paid media budget be allocated? Should the focus be branding first and performance later? How should ad spend be flighted (Always-On vs Burst and Trickle)? In effect, this show is a Master Class regarding marketing science, as described by one of the world’s top authorities on the topic, who’s currently an Executive Director at OMD, which is the world’s largest media agency network.

With a specialization in 3D deep learning, Aida is doing ground-breaking work related to 3D simulations. In this episode, she first describes why meshes or point clouds are computationally-expensive for simulating changes to shapes. She then describes why “implicit fields” are much more efficient for this, as described in the widely-cited “DeepSDF paper.” Next she describes a special-case problem: Predicting the deformation of 2D objects into 3D shapes, such as when metal blanks are stamped into the shape of a car door, for example, which is a case that violates one of the assumptions for DeepSDF. However in this episode, Aida describes a method for using implicit fields to solve this problem too, thereby transforming a simulation process that used to take 20 minutes per trial to just a few seconds per run. In effect, this show is a preview of her soon-to-be-published doctoral dissertation!

Nikhil is the Data Science Director at Sally Beauty Holdings, which is a $3.7 billion specialty retailer, with more than 10,000 products sold through over 4,000 stores, as well as online. Prior to his current role, Nikhil held a senior leadership role at Harman International, working with panel data from top CPG brands like P&G, Unilever and Kraft.

In this episode, Nikhil describes his 19-year journey, starting from a Masters degree in Applied Mathematics, leading up to his current role at Sally Beauty. Within that, he describes several times when he needed to supplement his formal education with ad hoc or certification-led studies, in order to stay current and relevant. He also discusses omni-channel marketing, customer journeys and Segment of One, as well as emotional understanding of the consumer, beyond pure data science. And he describes how one-on-one experiences, like Studio by Sally (in store) or “licensed colorist on demand” (online), can change customer journeys, and can provide valuable insights for new product development.

Victor (Viko) Perrine is the Global Director of New Growth Initiatives at Circle K. Prior to that, he’s held senior leadership roles at Delek US Holdings and at UGP Inc. 

In this episode, Viko describes a major new initiative called Lift that’s unique to Circle K, which just launched in Europe, plus future development plans for that. Building on this, he describes the day-to-day for a global innovation leadership role, and he shares the success factors that help him to identify good target projects for innovation and growth. He also describes a project from earlier in his career that used lidar and computer vision to count banana trees on a 6,000 acre planation in the Philippines, which was the first time that had ever been done in that country. And with all this as context, he concludes with actionable tips for people who’d like to get the nod for leadership roles in innovation.

Ray is the Chief Data and Analytics Officer at Valhalla AI Solutions. Prior to that, he held senior leadership roles at the Advertising Research Foundation, at the Institute for Experiential AI, and at comScore.

In this episode, Ray discusses challenges associates with promoting meaningful AI literacy in business, starting with fundamental questions, like what is “AI literacy” exactly? (Is it merely the ability to use AI tools, or is a deeper understanding of the science behind those tools required?) He also discusses the reverse problem, where recent graduates from AI/ML programs lack sufficient domain knowledge to be full partners with key leaders at the companies that hire them. The end result is that many companies struggle to have meaningful dialog between their domain experts and their experts in AI and ML. On a more optimistic note, Ray describes a variety of initiatives in Canada, Germany and in select states in the US, that show promise for deepening the domain understanding of data scientists, while also empowering domain experts with a better understanding of data science.

Ivan is an Associate VP of Delivery at Robosoft Technologies. His team does application development, engineering and QA for US clients, including web (HTML, CSS), mobile (Android, iOS, Samsung, LG) and streaming, including Roku TV.

In this episode, Ivan shares what he learned in his experiments over the past year using Gen AI to improve the efficiency and quality of code produced by his 300-member team, including results that he describes as “amazing,” with time savings of 40-50% in writing code. The technologies he discusses include GitHub Copilot, Amazon CodeWhisperer, Tabenine, Codeium, TestGrid and LambdaTest. And he describes how he chose between those, and also which ones he has his eye on for the future. Tests were done in various parts of the development lifecycle, including boilerplate code for new projects, bug fixing, unit testing, integration QA, and migrating from one language to another. Since not all of these situations benefited equally from AI-powered tools, Ivan describes what worked well, and where there are gaps that need further development work by vendors.

Sam is the Director of Business Strategy, Solutions & Analytics at the Boston Bruins & TD Garden. Prior to that, he directed strategy and analytics for the Arizona Coyotes, and at VaynerMedia.

In this episode, Sam sheds light on the world of analytics and business strategy for sports teams, including the differences in strategy and focus for teams vs at the league level. He also talks about the size and structure of analytics teams in the sports world, as compared to equivalent teams in consumer packaged goods, for example. And he describes how he uses AI and analytics conferences as part of his overall approach to team development.

Celia is the Chief Innovation Officer at Bits In Glass (BIG), a top Canadian IT consulting firm. Prior to that, she held senior leadership roles at AltaML and at Deloitte Canada. Celia was recognized as the AI Innovator of the Year, by Women in AI.

In this episode, Celia shares insights from trends she’s seen in her recent work involving intelligent automation of business processes at scale. A key topic is the idea of embedding AI agents into multiple steps within business processes, such as contract analysis or banking regulatory change management, in order to achieve business results that would probably not be achievable with a one-shot AI solution. She also describes successful efforts at transforming front-line user experience of field workers, replacing clumsy gadgets with voice-activated workflows. And she discusses critical success factors for taking AI projects into full production, confirming that AI and ML solutions frequently work quite well in a completely different context or industry from the one where they were originally created. She concludes by making a case for a blended approach to resourcing AI projects – one that involves in-house teams, third-party solutions, and external partners.

Andrei holds a PhD in Computer Science, and is the Director of Search and the AI Lab at Neuron7. Prior to that, he was the VP Engineering and AI at Zillow. He’s also held key leadership roles at Google, Apple, Walmart and eBay.

In this episode, Andrei shares insights and advice, based on his experience deploying large-scale, high-load NLP and search applications to billions of customers (5 billion queries per day, 10 billion pages per day). Along the way, he describes how a high-quality engineering culture and a high-quality science culture were nurtured during early his days at Google as one of the first 60 PhDs in 2006, and how he has applied what he learned there later in his career. You’ll also hear a discussion about critical success factors for a transition from POC to production for a large-scale projects, such as 100 million or a billion queries per day – including a discussion about evaluation metrics for LLMs. Andrei also emphasizes the importance of continuous learning for leaders of teams that do AI, and he describes a great approach for staying on top of current research. The episode concludes with valuable career advice for data scientists who are in the early stages of their career.

Dave is a fractional CTO and DevOps engineer with over 25 years of experience in systems and software engineering. He’s the President and Senior Solutions Architect of Stern DevOps Group, which is a consultancy focused on early stage companies. He’s also the author of a new book: Hackproof Your Startup, and that book is a key topic of the show.

In this episode, Dave discusses IT and AI security for early-stage start-ups. The conversation begins with a review of what happened in the famous Codespaces hack. Dave asserts that many companies are still vulnerable to the type of ransomware attack that put Codespaces out of business, and that the risk mitigation solution is fairly straightforward (the elements of which he describes on the show). Other topics include cybersecurity as an asset, infrastructure as code, principle of least privilege, and isolating IT environments. The conversation concludes with a what-if scenario where Dave answers the question: “If someone were to steal my laptop or cell phone. What would I suddenly wish I had done before that happened?”

Shawn is the Global VP of Solutions at FirstHive, which is a customer data platform. Prior to that, he held senior leadership roles at Capgemini, Silicon Valley Bank, JPMorgan Chase, Clorox, Northwestern Mutual and SC Johnson. He is also an advisory board member of the Customer Data Platform (CDP) Institute.

In this conversation, Shawn describes various roadblocks to transformation in large organizations – especially AI-based initiatives in marketing. He then shares an agentic vision for a future-state where a marketing operations user might simply say: “I want to grow my credit card business by 20% in the US. What should I do?” and the platform would develop a plan for that.

Jodi is the VP of Data Science at Cox Automotive, a company that has a diverse portfolio of 17 brands that encompass digital products like Kelley Blue Book and Autotrader, as well as various kinds of physical services – all of which are supported by about 70 in-house data scientists and ML engineers.

In this conversation, Jodi describes her AI initiatives as investments, managed in a way that’s similar to a diversified investment portfolio, where the core projects deliver a baseline of ROI, and are supplemented by strategic bets, plus a very small fraction of high-risk / high-reward projects (“moonshots”) that get a Yes/No decision within 4-6 weeks. The show also includes a discussion about what makes a Gen AI project strategic vs “must have,” as well as insights about the practical and human challenges associated with the kinds of AI-based initiatives that primarily target efficiency gains, rather than top-line growth.

Ramsu holds a PhD in Machine Learning, and is the Head of R&D at solus.ai, which powers Segment of One personalization. Roles prior to that included Senior Scientist at GE Global Research, and Principal at Sabre Airline Solutions, where he developed some of the original algorithms.

Ramsu shares highlights of his journey in AI, with particular focus on personalization in marketing, with insights about how to think about that problem conceptually, including what parts are somewhat easy and which are difficult or tedious. There are also key insights about customer journeys and about the cold start problem. Other topics covered include customer genomes, as well as a discussion about navigating between decisions taken at a zoomed-in perspective at the individual customer record level, while also managing to a zoomed-out perspective that’s driven by KPIs, comps and annual targets. The show concludes with a discussion about the 1-2 year product development roadmap for solus.

This inaugural episode features a very special guest: My father! . . . better known to the rest of the world as G. Edward Griffin, historian, futurist and author of The Creature from Jekyll Island, which sold more than a million copies.

Mr. Griffin would be the first one to point out that he’s not an expert on AI, but this episode nonetheless found an area of intersection between his life work and artificial intelligence.

The show features a thought-provoking discussion about the guardrails that are being erected to ensure that Generative AI does not say “the wrong things.” A conversation ensues about the dangers of creating software that systematically imposes a filtered version of the truth on others, as compared to a world where individuals are able to gain free access to opposing views (including unpopular ones), and to decide for themselves what the truth is.