Arguably one of the verticals that is both at the same time most ripe for disruption by AI and the hardest to disrupt is search. We've seen many attempts at reimagining search using AI, and many are trying to usurp Google from its throne as the top search engine on the planet, but I think no one is laying the case better for AI assisted search than perplexity. AI. Perplexity doesn't need an introduction. It is an AI powered search engine that lets you get the information you need as fast as possible.

Denis Yarats is the Co-Founder and Chief Technology Officer of Perplexity AI. He previously worked at Facebook as an AI Research Scientist. Denis Yarats attended New York University. His previous research interests broadly involved Reinforcement Learning, Deep Learning, NLP, robotics and investigating ways of semi-supervising Hierarchical Reinforcement Learning using natural language.

In the episode, Adel and Denis explore Denis’ role at Perplexity.ai, key differentiators of Perplexity.ai when compared to other chatbot-powered tools, culture at perplexity, competition in the AI space, building genAI products, the future of AI and search, open-source vs closed-source AI and much more. 

Links Mentioned in the Show:

• Perplexity.ai
• NeurIPS Conference
• [Course] Artificial Intelligence (AI) Strategy
• Related Episode: The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at Pinecone
• Sign up to RADAR: AI Edition

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Spatial computing is revolutionizing the way we interact with digital and physical worlds, but its adoption comes with questions about practicality and return on investment. As businesses explore this cutting-edge technology, they must consider how it can enhance productivity and streamline operations. What are the best strategies to integrate spatial computing into your current systems? How can you ensure that it not only boosts efficiency but also delivers measurable benefits to your bottom line? 

Cathy Hackl is a web3 and metaverse strategist, tech futurist, speaker and author. She's worked with metaverse-related companies such as HTC VIVE, Magic Leap, and AWS, and currently consults with some of the world's leading brands, including P&G, Clinique, Ralph Lauren, Orlando Economic Partnership and more. Hackl is one of the world's first Chief Metaverse Officers and the co-founder of Journey, where she works with luxury, fashion, and beauty brands to create successful metaverse and web3 strategies and helps them build worlds in platforms like Roblox, Fortnite, Decentraland, The Sandbox, and beyond. She is widely regarded as one of the leading thinkers on the Metaverse.

Irena Cronin is SVP of Product for DADOS Technology, which is making an Apple Vision Pro data analytics and visualization app. She is also the CEO of Infinite Retina, which helps companies develop and implement AI, AR, and other new technologies for their businesses. Before this, she worked as an equity research analyst and gained extensive experience in evaluating both public and private companies.

In the episode, Richie, Cathy and Irina explore spatial computing, the current viability of spacial computing and it's prominence alongside the release of Apple's Vision Pro, expected effects of spatial computing on gaming and entertainment, industrial applications as well as data visualization and AI integration opportunities of spatial computing, how businesses can leverage spatial computing, future developments in the space and much more. 

Links Mentioned in the Show:

• Cathy’s Book
• Irena’s Books
• Apple Vision Pro
• Marvel Studios and ILM Immersive Announce 'What If...? - An Immersive Story'
• [Course] Artificial Intelligence (AI) Strategy
• Related Episode: Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot
• Sign up to RADAR: AI Edition

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Memory, the foundation of human intelligence, is still one of the most complex and mysterious aspects of the brain. Despite decades of research, we've only scratched the surface of understanding how our memories are formed, stored, and retrieved. But what if AI could help us crack the code on memory? How might AI be the key to unlocking problems that have evaded human cognition for so long?

Kim Stachenfeld is a Senior Research Scientist at Google DeepMind in NYC and Affiliate Faculty at the Center for Theoretical Neuroscience at Columbia University.  Her research covers topics in Neuroscience and AI. On the Neuroscience side, she study how animals build and use models of their world that support memory and prediction. On the Machine Learning side, she works on implementing these cognitive functions in deep learning models. Kim’s work has been featured in The Atlantic, Quanta Magazine, Nautilus, and MIT Technology Review. In 2019, she was named one of MIT Tech Review’s Innovators under 35 for her work on predictive representations in hippocampus. 

In the episode, Richie and Kim explore her work on Google Gemini, the importance of customizability in AI models, the need for flexibility and adaptability in AI models, retrieval databases and how they improve AI response accuracy, AI-driven science, the importance of augmenting human capabilities with AI and the challenges associated with this goal, the intersection of AI, neuroscience and memory and much more. 

Links Mentioned in the Show:

• DeepMind
• AlphaFold
• Dr James Whittington - A unifying framework for frontal and temporal representation of memory
• Paper - Language models show human-like content effects on
• reasoning tasks
• Kim’s Website
• [Course] Artificial Intelligence (AI) Strategy
• Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
• Sign up to RADAR: AI Edition

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In the fast-paced work environments we are used to, the ability to quickly find and understand data is essential. Data professionals can often spend more time searching for data than analyzing it, which can hinder business progress. Innovations like data catalogs and automated lineage systems are transforming data management, making it easier to ensure data quality, trust, and compliance. By creating a strong metadata foundation and integrating these tools into existing workflows, organizations can enhance decision-making and operational efficiency. But how did this all come to be, who is driving better access and collaboration through data?

Prukalpa Sankar is the Co-founder of Atlan. Atlan is a modern data collaboration workspace (like GitHub for engineering or Figma for design). By acting as a virtual hub for data assets ranging from tables and dashboards to models & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Slack, BI tools, data science tools and more. A pioneer in the space, Atlan was recognized by Gartner as a Cool Vendor in DataOps, as one of the top 3 companies globally. Prukalpa previously co-founded SocialCops, world leading data for good company (New York Times Global Visionary, World Economic Forum Tech Pioneer). SocialCops is behind landmark data projects including India’s National Data Platform and SDGs global monitoring in collaboration with the United Nations. She was awarded Economic Times Emerging Entrepreneur for the Year, Forbes 30u30, Fortune 40u40, Top 10 CNBC Young Business Women 2016, and a TED Speaker.

In the episode, Richie and Prukalpa explore challenges within data discoverability, the inception of Atlan, the importance of a data catalog, personalization in data catalogs, data lineage, building data lineage, implementing data governance, human collaboration in data governance, skills for effective data governance, product design for diverse audiences, regulatory compliance, the future of data management and much more. 

Links Mentioned in the Show:

• Atlan
• Connect with Prukalpa
• [Course] Artificial Intelligence (AI) Strategy
• Related Episode: Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake
• Sign up to RADAR: AI Edition

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One thing we like to do on DataFramed is cover the current state of data & AI, and how it will change in the future. But sometimes to really understand the present and the future, we need to look into the past. We need to understand just exactly how data became so foundational to modern society and organizations, how previous paradigm shifts can help inform us about future ones, and how data & AI became powerful social forces within our lives.

Cristina Alaimo is Assistant Professor (Research) of Digital Economy and Society at LUISS University, Rome. She co-wrote the book Data Rules, Reinventing the Market Economy with Jannis Kallinikos, Professor of Organization Studies and the CISCO Chair in Digital Transformation and Data Driven Innovation at LUISS University. The book offers a fascinating examination of the history and sociology of data. 

In the episode, Adel and Cristina explore the many of the themes covered in the book, from the first instance of where data was used, to how it became central for how organizations operate, to how usage of data introduced paradigm shifts in organizational structure, and much more.

Links Mentioned in the Show:

• Data Rules, Reinventing the Market Economy
• The Age of Surveillance Capitalism by Shoshana Zuboff
• Connect with Cristina
• [Course] Artificial Intelligence (AI) Strategy
• Related Episode: What to Expect from AI in 2024 with Craig S. Smith, Host of the Eye on A.I Podcast
• Sign up to RADAR: AI Edition

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In today's fast-paced digital world, managing IT operations is more complex than ever. With the rise of cloud services, microservices, and constant software deployments, the pressure on IT teams to keep everything running smoothly is immense. But how do you keep up with the ever-growing flood of data and ensure your systems are always available? AIOps is the use of artificial intelligence to automate and scale IT operations. But what exactly is AIOps, and how can it transform your IT operations?

Assaf Resnick is the CEO and Co-Founder of BigPanda. Before founding BigPanda, Assaf was an investor at Sequoia Capital, where he focused on early and growth-stage investing in software, internet, and mobile sectors. Assaf’s time at Sequoia gave him a front-row seat to the challenges of IT scale, complexity, and velocity faced by Operations teams in rapidly scaling and accelerating organizations. This is the problem that Assaf founded BigPanda to solve.

In the episode, Richie and Assaf explore AIOps, how AIOps helps manage increasingly complex IT operations, how AIOps differs from DevOps and MLOps, examples of AIOps projects, a real world application of AIOps, the key benefits of AIOps, how to implement AIOps, excitement in the space, how GenAI is improving AIOps and much more. 

Links Mentioned in the Show:

• BigPanda
• Gartner: Market Guide for AIOps Platforms
• [Course] Implementing AI Solutions in Business
• Related Episode: Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake
• Sign up to RADAR: AI Edition

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Trust is the foundation of any relationship, whether it's between friends or in business. But what happens when the entity you're asked to trust isn't human, but AI? How do you ensure that the AI systems you're developing are not only effective but also trustworthy? In a world where AI is increasingly making decisions that impact our lives, how can we distinguish between systems that genuinely serve our interests and those that might exploit our data? 

Bruce Schneier is an internationally renowned security technologist, called a “security guru” by The Economist. He is the author of over one dozen books—including his latest, A Hacker’s Mind—as well as hundreds of articles, essays, and academic papers. His influential newsletter “Crypto-Gram” and his blog “Schneier on Security” are read by over 250,000 people. He has testified before Congress, is a frequent guest on television and radio, has served on several government committees, and is regularly quoted in the press. Schneier is a fellow at the Berkman Klein Center for Internet & Society at Harvard University; a Lecturer in Public Policy at the Harvard Kennedy School; a board member of the Electronic Frontier Foundation and AccessNow; and an Advisory Board Member of the Electronic Privacy Information Center and VerifiedVoting.org. He is the Chief of Security Architecture at Inrupt, Inc.

In the episode, Richie and Bruce explore the definition of trust, the difference between trust and trustworthiness, how AI mimics social trust, AI and deception, the need for public non-profit AI to counterbalance corporate AI, monopolies in tech, understanding the application and potential consequences of AI misuse, AI regulation, the positive potential of AI, why AI is a political issue and much more.

Links Mentioned in the Show:

• Schneier on Security
• Books by Bruce
• [Course] AI Ethics
• Related Episode: Building Trustworthy AI with Alexandra Ebert, Chief Trust Officer at MOSTLY AI
• Sign up to RADAR: AI Edition

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Building a successful data engineering team involves more than just hiring skilled individuals—it requires fostering a culture of trust, collaboration, and continuous learning. But how do you start from scratch and create a team that not only meets technical demands but also drives business value? What key traits should you look for in your early hires, and how do you ensure your team’s projects align with the company’s goals?

Liya Aizenberg is Director of Data Engineering at Away and a seasoned data leader with over 22 years of experience spearheading innovation in scalable data engineering pipelines and distribution solutions. She has built successful data teams that integrate seamlessly with various business functions, serving as invaluable organizational partners. She focuses on promoting data-driven approaches to empower organizations to make proactive decisions based on timely and organized data, shifting from reactive to proactive business strategies. Additionally, as a passionate advocate for Women in Tech, she actively contributes to fostering diversity and inclusion in the technology industry.

In the episode, Adel and Liya explore the key attributes that forge an effective data engineering team, traits to look for in new hires, what technical skill sets set people up for success in a data engineering team, leveraging knowledge transfer between external experts and internal stakeholders, upskilling and career growth, aligning data engineering initiatives with business goals, measuring the ROI of data projects, working agile in data engineering, balancing innovation and practicality, future trends and much more. 

Links Mentioned in the Show:

• Away Travel
• Connect with Liya on Linkedin
• [Career Track] Data Engineer with Python
• Related Episode: Scaling Data Engineering in Retail with Mo Sabah, SVP of Engineering & Data at Thrive Market
• Sign up to RADAR: AI Edition

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Everything in the world has a price, including improving and scaling your data and AI functions. That means that at some point someone will question the ROI of your projects, and often, these projects will be looked at under the lens of monetization. But how do you ensure that what you’re working on is not only providing value to the business but also creating financial gain? What conditions need to be met to prove your project's success and turn value into cash?

Vin Vashishta is the author of ‘From Data to Profit’ (Wiley), the playbook for monetizing data and AI. He built V-Squared from client 1 to one of the oldest data and AI consulting firms. For the last eight years, he has been recognized as a data and AI thought leader. Vin is a LinkedIn Top Voice and Gartner Ambassador. His background spans over 25 years in strategy, leadership, software engineering, and applied machine learning.

Dr. Tiffany Perkins-Munn is on a mission to bring research, analytics, and data science to life. She earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and their real-world applications.

As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.

In the episode, Richie, Vin, and Tiffany explore the challenges of monetizing data and AI projects, including how technical, organizational, and strategic factors affect your input, the importance of aligning technical and business objectives to keep outputs focused on core business goals, how to assess your organization's data and AI maturity, examples of high data maturity businesses, data security and compliance, quick wins in data transformation and infrastructure, why long-term vision and strategy matter, and much more.

Links Mentioned in the Show:

• Connect with Tiffany on Linkedin
• Connect with Vin on Linkedin
• Vin’s Website
• [Course] Data Governance Concepts 
• Related Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx

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As we close out our focus on how the venture capital industry identifies and decides which future companies to fund, it might be easy to fall into the trap of thinking that the latest methods for discovering future unicorns are ubiquitous among all VCs. However, many VCs still work ‘the old way,’ using data to back up human assumptions. But what happens when a data engineer pivots to VC? What does a data-driven, data-first approach look like, and how does it compare to the incumbent processes?

Dr. Andre Retterath is a Partner in Earlybird’s Munich Office, focussing on enterprise software with a particular interest in developer, data and productivity tools, alongside AI-centric products and robotics. Before transitioning into VC in 2017, he gained more than 5 years of experience as a process automation and predictive maintenance engineer at ThyssenKrupp and further insights as a management consultant at GE North America. Andre also has his own VC, AI & data newsletter, Data-Driven VC.

In the episode, Richie and Andre explore the concept of data-driven venture capital, the challenges of traditional VC and why digitization has had a huge impact on the industry, the data-driven VC process, the use of modern data and AI technologies in identifying potentially successful projects, the human element in VC, the challenges and opportunities of early-stage investments, the importance of early identification of these ventures, cultural and organizational indicators and much more. 

Links Mentioned in the Show:

• Data-Driven VC
• Earlybird VC
• Aleph Alpha
• Pareto Principle
• Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
• Sign up to RADAR: AI Edition

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