Data Science Leaders

2023 has been an exciting year for AI, but it’s nothing in comparison to what we will see in 2024! Expect to see sensational successes amid the debris of projects that were set up for failure, a flowering of predictive AI, and the emergence of the scariest thing in AI to date (EU regulation). Tune in to this episode where Dr. Kjell Carlsson shares his top predictions for AI in 2024 and get ready for a year of scandal, fraud, plummeting processor prices, and ascendant AI leaders. Also, goodbye quantum computing!

Happy holidays from all of us at Domino Data Lab and the Data Science Leaders podcast.

ChatGPT wasn’t the beginning of generative AI, but it did spark the GenAI revolution. Now, one year since it was launched, how much progress have we made, what impact is GenAI delivering, what are the real risks, and what developments are just around the corner? Join this session with the titan of the data science community, Anaconda CEO Peter Wang, and Dr. Kjell Carlsson, Head of AI Strategy at Domino Data Lab, where we will cover:

• The state of GenAI: where GenAI is delivering and missing expectations
• The challenges: the real risks and remaining barriers to impact
• The future: what advances are underway and what can we expect over the next year

How do you achieve success as a Chief Data Officer? It is a role that is more important, yet more challenging, than it has ever been, with a rapidly expanding set of expectations from stakeholders in every part of the business.

Here to help us understand the CDO role, its evolution, and the keys to success is Gary Barr, Global Chief Data Officer at Legal & General Investment Management (LGIM). Drawing from his wealth of experience, Gary speaks about the three incarnations of the CDO – from data governance champion to air traffic-controller of AI-driven transformation – as well as the dangers of dividing teams into “offense” and "defense”, the goal of the data mesh, and why AI regulation should be welcomed, not feared.

Join us as we discuss:

• The rapid evolution of the CDO mandate and its responsibilities
• Changing the operating model for data and AI adoption
• The importance of qualitative and sentimental measures of ROI

Most experts agree that AI isn’t about replacing human intelligence, but about improving it. When it comes to education, we should take this literally.

In this episode we discuss how to use AI to transform how we learn with Stephen Kosslyn, President of Active Learning Sciences and Founder and Chief Academic Officer of Foundry College. Stephen brings unparalleled expertise when it comes to using AI in education from his remarkable career spanning leadership roles at Harvard, Stanford, and Minerva University, but also thanks to his recent book “Active Learning with AI: A Practical Guide”.

Join us as we discuss:

• How Generative AI can make learning more effective and scalable
• How to design educational programs, create training experiences, and assess student understanding using Generative AI
• Overcoming the challenges of embracing AI in the education sector

For more on the science of active learning and detailed, practical Generative AI examples, please check out Stephen’s new book, available now.

How do you implement an enterprise-grade GenAI application that serves millions of users a day? By focusing your application and building the capabilities for operationalizing it at scale.

Join our upcoming fireside chat with Domino's SVP of Product, Chris Lauren, who will share lessons learned while operationalizing the world’s first enterprise-grade GenAI application to be used on a global scale, Github Copilot.

Join us to learn:

• Success factors for GenAI use cases
• Common challenges and how to avoid them
• Key capabilities for operationalizing GenAI models at scale
• Inferencing GenAI models cost-effectively

Every organization has an abundance of outlier detection use cases, but how do you turn them into repeatable, scalable AI products that drive a virtuous cycle of adoption and impact?

To answer this question, Jan Zirnstein, Senior Data Science Director at Honeywell,. shares their best practices for successfully driving value using anomaly detection, how to build trust with stakeholders, and the importance of both product management and software development resources.

Join us as we discuss:

• How to spark a virtuous cycle with anomaly detection use cases
• Driving continuous improvement by transitioning from unsupervised to supervised machine learning
• Aligning the model development and software development lifecycles

AI has enormous potential for good, not least in helping us make more ethical, sustainable decisions as investors and consumers. In this week’s episode Ron Potok, Head of Data Science at Clarity AI, explains how AI helps us overcome the challenges of collecting, normalizing and assessing Environmental, Social and Governance (ESG) data and making that data useful and convenient to humans when making decisions. Indeed, he reveals how AI can bring transparency to human-only ESG ratings that can be more opaque and prone to bias than an AI model, and the benefits of leveraging humans and AI models in tandem.

Join us as we discuss:

• Overcoming the ESG data quality challenges with AI
• Leveraging AI to contextualize data and drive consistency
• How AI can provide greater transparency than human-only ratings

How do you trust black-box AI models with decisions that will make-or-break your business?

This week we speak with Gregory Zuckerman -- special writer at the Wall Street Journal and author of The New York Times bestseller of The Man Who Solved the Market -- to find out how the pioneers in algorithmic trading learned to stop worrying and trust their AI systems.

Join us as we discuss:

• How trust in AI relies on trust in people and processes
• The limits of explainability and transparency
• The power of systems over stories

Who doesn’t have a data science talent gap? Anyone? Most organizations struggle to realize their AI ambitions because of a lack of data science skills, a disconnect between the technology and the business domain, and a lack of leadership experience with AI.

Halliburton has been solving all three of these challenges with one of the earliest and largest corporate data science programs in the energy sector.

In today’s episode of Data Science Leaders, we are extremely fortunate to be joined by Dr. Satyam Priyadarshy, Managing Director, Technology Fellow and Chief Data Scientist at Halliburton who shares their best practices for upskilling talent, bridging the data science - business divide, and ensuring executive engagement.

Join us as we discuss:

• How to upskill existing domain experts on data science methods
• How to engage and drive alignment with corporate stakeholders through workshops
• The benefits of upskilling domain experts on code-based data science tools
• The importance of involving and upskilling leadership

It’s been said: “When everything is important, nothing is important.” So how do you succeed with AI-driven transformation where everything – across people, process, and technology – is important? It requires leadership, a deliberate strategy, and ongoing organizational change.

Here to share insight on these transformational challenges and best-practices are Jen Stave and Catherine Feldman from the Digital, Data, and Design (D^3) Institute at Harvard. In this wide-ranging conversation, the duo draws upon seminal research from the Harvard Business School – such as professor Clay Christensen’s theory of Disruption – to explain how organizations must adapt their business and operating models, and make experimentation part of their organizational DNA.

Join us as we discuss:

• Disruption and the reasons so many AI projects fail
• The need for a holistic approach and strong leadership for AI success
• Applying a jobs to be done” approach to generative AI

Also don’t miss HBS professor Karim Lakhani’s Rev 4 Keynote, “Competing in the Age of AI”.