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AI governance is no longer a hypothetical consideration—it's critical not only to meet regulatory requirements but also to build trust, drive adoption, and deliver tangible impact with AI. In this episode, we bring together experts Jared Vaudrey and Dr. Dylan Bobby Storey, who have spent years developing AI solutions in heavily regulated industries. They share their hard-won insights on the realities of implementing AI governance across a wide range of organizations and use cases.
Join us as we discuss:
The value of AI governance for driving adoption and innovationHow governance principles apply to traditional ML and genAIThe common challenges of governance Real-world examples and best practices from advanced teams integrating AI governanceHow automation helps scale governance and provide the flexibility to support future regulation -
AI governance is more important than ever, but confusion reigns about basic questions such as: what it is, why it is important and what we should do about it. As an AI, data science, or business leader, you need to dispel these governance misconceptions in order to manage AI risk and ensure the safety and reliability necessary to drive adoption and impact. The ability of your organization to drive impact with AI, and potentially your career, depends on it.
Join us as host Kjell Carlsson, gives an opinionated take on the basic questions of AI governance:
Why you should care about AI governance (and why it goes beyond ethics and regulation)What AI governance is (and how it differs from ethical AI, responsible AI, and trustworthy AI)Why AI governance is difficult (and the gap between frameworks and practices)What AI governance looks like in practice (across the AI/ML lifecycle)What we need for AI governance (and the capabilities to do it at scale) -
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Data privacy - why is it so important in the age of AI, why is it so difficult, and what should we be doing to improve it in our organizations?
In this episode we speak with Dr. Rebecca Balebako, privacy engineer and Chief Privacy Officer at Lotic.ai, about why AI makes privacy more important than ever, the common misconceptions around data protection, who should own privacy, and the benefits and limitations of privacy enhancing technologies (PETs).
Join us as we discuss:The value of privacy (~5% of annual profit)The many definitions of privacy.The top 3 misconceptions: no one cares, it can be fixed with a privacy policy, and privacy is a blocker.Who should own privacy in your organization.PETs vs. data hygiene
For more on Rebecca’s consulting on privacy strategy, coaching and how to build a privacy advocate team see https://www.privacyengineer.ch/ and for more on privacy methods check out the book “Practical Data Privacy” by Katharine Jarmul. -
What capabilities do you need to take advantage of AI and what changes will you need to make to your IT architecture? Well, let’s look at the data.
In this episode, Shawn Rogers, CEO and Fellow at BARC US, shares the results of their survey on how enterprises are optimizing their architecture for AI innovation. Shawn unpacks data on everything from the biggest obstacles to delivering AI impact to how companies are sourcing their AI capabilities.
Join us as we discuss:
The AI talent gap and the strategies that firms are using to close itThe myriad AI capabilities that high-readiness firms are implementingThe need to manage AI costs upfront to ensure deploymentThe perpetual question of build vs. buy (or both!)The crucial need for AI governance to ensure approval and adoption
Download the full report here. -
How do you drive digital strategy and transformation with AI? Do you need an AI strategy or a business strategy that intelligently leverages AI?
In this episode, we delve into the challenge of driving transformation with AI in insurance with Fu'ad Butt, VP Head of Digital Strategy and Automation at OneAmerica. Fu’ad shares his best practices for identifying and executing AI projects. These range from how to identify the most promising use cases (hint: focus on augmented intelligence, but tie it to business value) to executing them successfully (build a test and learn process, and use multifunctional pods).
Join us as we discuss:The role of AI in digital strategy today.Overcoming the challenge of aligning AI to business value.Experimenting efficiently with digital twins and a contrarian in the loop.Breaking hierarchies with multifunctional pods for faster impact. -
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 costs
To 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 -
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 understanding
And 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 - もっと表示する
