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Data Science Leaders

Author: Domino Data Labs

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Data Science Leaders: The premiere podcast for executives tackling the world’s most important challenges with the power of machine learning and artificial intelligence. Join host, Dr. Kjell Carlsson, for Season 2 as we interview pioneering data science leaders and industry watchers to unearth the secrets to driving transformative business outcomes—and avoiding a myriad of pitfalls—with the latest ML & AI technologies. Our conversations are full of real stories, breakthrough strategies, and unique insights—to help you build your own model for enterprise data science success.
71 Episodes
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A human being consists of billions of cells, each with the same genetic code but interacting in a myriad ways that can eventually translate into disease. Understanding and treating that disease is, in essence, a data problem. But how do you unlock that data and how do you change an organization to systematically use that data to improve decision-making and accelerate drug discovery? In this episode, we speak with Volodimir Olexiouk, Director of Scientific Engagement and Data Science Team Lead at BioLizard, about best practices for overcoming the data challenges for AI-driven drug discovery and combining scientific expertise with data science for augmented intelligence in the life sciences. Join us as we discuss:The challenges in discerning correlation from causation and integrating domain expertiseHow bridging expertise gaps and merging data silos in pharmaceutical companies radically improves drug-discovery processes The promise AI holds for swifter and more effective responses to future pandemics
Trip planning may well be the perfect AI use case. Too much information, too many combinations, and too little time —for humans, but not for Tripadvisor’s AI Trips. In this episode Rahul Todkar, VP Head of Data and AI, shares the secrets to building a trusted GenAI solution at internet scale and discusses the similarities and differences between data leadership roles at digitally native companies and more traditional enterprises. Join us as we discuss:How to use GenAI to unlock first party dataThe ideal GenAI development teamThe evolving role of data and AI leaders
Wouldn’t it be great if there was a commonly agreed-upon framework for executing all AI projects successfully? Well, there isn’t one. However, there is CRISP-DM, the antediluvian “Cross-Industry Standard Process for Data Mining”, but you need to expand, modernize and adapt this framework for success at your organization.In this episode from the archive, Dave Cole interviews David Von Dollen, former Head of AI at Volkswagen of America, about how they integrated CRISP-DM into an Agile process to drive more rapid iteration and, ultimately, more successful AI projects.
What’s just as important as the government keeping us safe from AI? Government leveraging AI to keep us safe!In this episode, we interview Joel Meyer – former head of strategy at the Department of Homeland Security (DHS) and the person who drove the creation of the DHS AI Task Force. Joel shares how they identified key areas where they could apply AI to improve national safety and security, such as combating fentanyl and child sexual exploitation and abuse, and the steps that the federal government is taking to build AI capabilities across the public sector.Join us as we discuss:Key areas where US government agencies are looking to leverage AI to improve mission effectivenessThe people, process, and technology steps that government agencies are implementing to scale AI and how they apply to the private sectorThe importance and value of Responsible AI in public sector use cases and beyond
There is no such thing as an AI drug, but AI and ML-models are driving the next wave of new treatments. In this episode, Brandon Allgood, Chief Data Officer at FogPharma and serial entrepreneur at the intersection of ML and Biopharma, shares his insights on how AI is disrupting the traditional process of drug discovery and development.Join us as we discuss: Why AI is so powerful for drug discoveryWhat data science needs to learn from engineeringHow drug discovery processes need to be rebuilt with AI models at their core
What’s the biggest problem in AI today? It’s that far too few projects make it to deployment. In this episode, Eric Siegel, founder of the long-running Machine Learning Week conference and creator of the first (and perhaps only) ML music video, tells us about his new book, The AI Playbook and the bizML framework for aligning stakeholders and maximizing the chance for deployment and impact.Join us as we discuss:Causes behind the high rates of AI project failureCritical project steps for ensuring deploymentHumor as a means to bridge the gap in AI understandingAnd check out:The AI Playbook: Mastering the Rare Art of Machine Learning DeploymentThe entire Predict This music video. You won’t regret it.Machine Learning Week June 4-7, 2024
The biggest challenges to driving impact with AI have little to do with AI and everything to do with humans. Nowhere is this greater than with GenAI where myths and misconceptions abound as to how organizations should be designing, developing and operationalizing GenAI-based applications. In this episode with Rowan Curran, industry analyst at Forrester Research, we debunk the most harmful myths and discuss how AI teams are shattering these myths and delivering transformative outcomes.Join us as we discuss:The role of data scientists and ML engineers in GenAI projectsSuccessful approaches to prompt engineeringThe linkages between MLOps and LLMOps
Imagine Generative AI handling tens of thousands of conversations with your customers daily. Science fiction? Not at Bolt where this has been in production since the summer of 2023. In this episode, Mikhail Korolev – head of the data science team at Bolt’s food delivery service – shares the challenges and hard earned best practices for operationalizing a GenAI application that dramatically lowers cost while also increasing customer satisfaction. Join us as we discuss:How to leverage GenAI to automate customer service conversationsHow to manage inconsistency and mitigate risk in GenAI appsHow to protect sensitive data and comply with regulatory requirements with GenAI
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 expectationsThe challenges: the real risks and remaining barriers to impactThe 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 responsibilitiesChanging the operating model for data and AI adoptionThe 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 scalableHow to design educational programs, create training experiences, and assess student understanding using Generative AIOvercoming the challenges of embracing AI in the education sectorFor 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 casesCommon challenges and how to avoid themKey capabilities for operationalizing GenAI models at scaleInferencing 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 casesDriving continuous improvement by transitioning from unsupervised to supervised machine learningAligning 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 AILeveraging 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 processesThe limits of explainability and transparencyThe 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 methodsHow to engage and drive alignment with corporate stakeholders through workshopsThe benefits of upskilling domain experts on code-based data science toolsThe 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 failThe need for a holistic approach and strong leadership for AI successApplying a jobs to be done” approach to generative AIAlso don’t miss HBS professor Karim Lakhani’s Rev 4 Keynote, “Competing in the Age of AI”.
Generative AI is here and, unless you’ve been cloistered in a cave, you already know it’s making waves in nearly every industry. But when it comes to this shiny new technology, separating fact from fiction can become quite a challenge.Luckily, in this episode, Rowan Curran, Analyst at Forrester, joins the show to demystify the latest leaps in AI tech, help you apply it to your business today, and give a glimpse of how it will affect the business landscape of tomorrow.Join us as we discuss:Separate generative AI facts and fictionTake a closer look at AI applications you can start using todayExamine the future of AI and its impacts on the workforce and the workplace
“It is on the shoulders of leaders that they build and maintain an ethical AI risk program.” That’s the message Reid Blackman – author of “Ethical Machines” and founder CEO at Virtue Consultants – shares in this episode. He discusses the real ethical AI concerns — blackbox models, bias, hallucinations, privacy violations and more — and explains the crucial need for leadership accountability, buy-in from the very top of the organization, and a multi-party effort in building and maintaining AI ethical risk programs.Join us as we discuss:Why AI poses greater ethical risks than other technologies (and humans)Leadership and the other key elements of a successful AI / digital ethics programThe importance of explainability
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