Data Science Leaders

AI is disrupting marketing, but the biggest threat isn’t AI systems misbehaving, it is the unintended consequences of AI systems performing exactly what they were intended to do.In this interview with Dr. Daniel Hulme, Chief AI Officer at WPP and CEO of Satalia, we discuss the ways that AI is transforming marketing – from accelerating content creation and maximizing activation to exploring the creative landscape and creating “brains” that ensure it is responsible and legal. Also, tune in for fascinating discussions of AI consciousness and what it means to be a Chief AI Officer.    Join us as we discuss:The greatest GenAI opportunities in marketing and beyondHow to maximize AI impact with decision optimizationResponsible AI and the challenges of AI systems going very rightThe emerging field of AI consciousnessThe Chief AI Officer: why you need one and the prerequisites for success  For more information about the new research organization focused on AI consciousness co-founded by Daniel Hulme see conscium.com and his interview on the London Futurists Podcast.

How do you deliver impact with AI and ML and cut development time by weeks and even months? By understanding your customer, building trust, and managing risk. Done well, effective and responsible AI practices can be the secret to faster implementation, adoption, and performance at lower cost and risk.In this episode with Dr. Alex Manasson, Data Science Leader for the Americas at International Flavors and Fragrances (IFF), we uncover their best practices for managing risk and driving rapid AI development and adoption in the safety-focused world of manufacturing.. Dr. Manassof shares insights on balancing statistical process control with predictive modeling, the importance of adapting your data collection processes, and the pros and cons of digital twins. Discover practical tips and strategies for implementing AI and ML tools to boost efficiency and foster trust in high-stakes environments.

How do you deliver value with responsible AI, who is responsible for it, how do you put it into practice, and could we use AI to make our organizations more ethical?  This episode comes to you from the RevX conference in London, where we asked these questions of Chris Wiggins, Chief Data Scientist at the New York Times. He is also Professor of Applied Mathematics at Columbia University and author of the books “How Data Happened: A History from the Age of Reason to the Age of Algorithms” and “Data Science in Context”.Join us as we discuss:What we can learn from the history of research ethics and data legislationThe need for clear principles and defined ownership to ensure ethical AIThe translation of ethical principles into checklists, standards, and product decisionsThe importance of benchmarking AI against human performance and addressing how human biases in data lead to biased AI outcomesTo see all of the sessions at the RevX conferences go to domino.ai/revx.

AI is not all about the data, however, your ability to develop and deploy efficient data pipelines is absolutely critical for unlocking the power of AI at scale. But how do you manage modern data pipelines for AI and how do you deal with fragmented ecosystems and spiraling costs? In this episode, brought to you from the RevX Philadelphia conference,  Richard Swakla, AI/ML Specialist at NetApp, joins us to discuss the current trends and best practices in the life sciences around data and AI. Join us as we discuss:The role of AI in enhancing productivity in healthcare and the life sciences, particularly in drug discovery,  claims processing and fraud detection.The growing importance of hybrid cloud solutions to balance cost, efficiency, and infrastructure access.Challenges in transitioning AI projects from pilots to production due to high costs and rapidly evolving models.To see all of the sessions at the RevX conferences go to domino.ai/revx.

How do you enable AI, data science and analytics on petabyte-scale data, with extremely stringent privacy and security requirements?This episode comes to you from the RevX-New York conference where we had a fireside chat with Ivan Black - Director in charge of ML, AI, and analytics platforms at the US financial services regulator FINRA. Join us as we discuss:The challenges of enabling AI on massive, rapidly growing financial datasetsTalent strategies to support the rapidly changing AI ecosystemThe importance of AI governance and reproducibilityManaging cloud costsTo see all of the sessions at the RevX conferences or to find information about attending upcoming ones go to domino.ai/revx.

How do you craft and implement a strategy to transform an organization with AI? Not just to build a growing portfolio of successful AI projects, but to fundamentally re-engineer the organization’s core processes, to radically increase productivity, to overhaul the company’s tech stack, and to prepare it for a future of AI-driven competition.In this episode, Akshaya Murthy, who leads the AI efforts for Operations at Zendesk joins us to discuss the mandate and toolkit of the AI transformation leader, the importance of strategy for AI impact, the AI transformation efforts at Zendesk, and their successes to date. Join us as we discuss:The AI transformation leader: mandate and skillset Strategy: the misunderstood and frequently forgotten key to AI impactGenAI: the transformation leader’s new best tool for rapid impact Process transformation: the goal of AI transformation

AI leaders. Why do we need them? How do you become one? And above all, how do you keep your job as one?In this episode, we are joined by guest speaker Mike Gualtieri, VP and Principal Analyst at Forrester, and we unpack the opportunities, pitfalls, and best practices of the AI leader role. He shares the pivotal role of AI leaders in catalyzing organizational transformation, their unique skill set that must encompass data science, business acumen, and software engineering, their importance in navigating the evolving regulatory landscape surrounding AI, and the need for platforms to facilitate rigorous auditing and compliance measures to foster trust and transparency.Join us as we discuss:The strategic imperative for AI leaders to curate a diversified portfolio of AI initiatives The multifaceted nature of AI risk management, spanning legal, ethical, and societal dimensionsThe formidable challenges inherent in navigating and enforcing AI-centric regulations amidst rapid technological advancement

GenAI is evolving at a breakneck pace, matched only by the startups that are looking to commercialize it. So what better way to understand the latest GenAI trends than to ask a venture capitalist specializing in AI? In this episode, we speak with James Cham, partner at Bloomberg Beta, about the state of GenAI – where it is delivering value today – and the challenges preventing firms from moving from incremental GenAI-driven productivity gains, to truly disruptive GenAI use cases. Along the way, we cover the problem of treating GenAI like software development, the rapidly changing economics of GenAI, and the key to success with all types of AI which is – as always – understanding people. Join us as we discuss:The cultural challenges preventing companies from unlocking the disruptive potential of GenAIWhy developers don’t get data-powered applications (and why data scientists need product thinking) How GenAI can change our engagement with technology (by killing the GUI)

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