Super Data Science: ML & AI Podcast with Jon Krohn

In this episode, co-founder and CEO of Linea, Dr. Doris Xin, joins Jon Krohn to discuss how automating ML model deployment delivers groundbreaking change to data science productivity, and shares what it's like being the CEO of an exciting, early-stage tech start-up.In this episode you will learn: How Linea reduces ML model deployment down to a couple of lines of Python code [5:14] Linea use cases [11:30] How DAGs can 10x production workflow efficiency [22:12] ML model graphlets and reducing wasted computation [24:14] What future Doris envisions for autoML [35:23] Doris’s day-to-day life as a CEO of an early-stage start-up [42:43] What Doris looks for in the engineers and data scientists that she hires [52:21] The future of Data Science and how to prepare best for it [53:58] Additional materials: www.superdatascience.com/573

In this episode, Jon shares his habit of blocking out two hours in his mornings that are free from email and social media distractions. Tune in to learn how this habit helps him deeply focus on his most delightful tasks of the day.Additional materials: www.superdatascience.com/572

Einblick co-founder and associate professor at MIT, Tim Kraska, joins Jon Krohn to discuss no-code collaboration tools for data science and uncovers the clever database and machine learning tricks under the hood of the visual data computing platform.In this episode you will learn: The inspiration behind Einblick [2:45] Einblick's progressive approximation engine [6:43] How no-code tools impact productivity [17:18] The critical steps to become more data-driven as an organization [24:30] How research universities like MIT support high-risk, long-term research [38:37] How ML applied to databases enables them to be faster and more efficient [42:03] How real-time collaboration environments like Google Docs are likely to become more widespread for data science tasks [ 49:24] Additional materials: www.superdatascience.com/571

In this episode, Jon is back with another A.I. model breakthrough! He updates listeners on OpenAI's outstanding DALL-E 2 model. The new natural language processing model churns out staggering visual examples of whatever text your mind can dream up.Additional materials: www.superdatascience.com/570

Research Scientist at Meta AI, Dr. Noam Brown, joins Jon Krohn to discuss his award-winning no-limit poker-playing algorithms and the real-world implications of his game-playing A.I. breakthroughs.In this episode you will learn: What Meta A.I. is and how it fits into Meta, the company [3:01] Noam's award-winning no-limit poker-playing algorithms, Libratus and Pluribus algorithms. [4:33] What game theory is and how does Noam integrate it into his models? [8:45] The real-world implications of Noam’s game-playing A.I. breakthroughs [25:24] Why Noam elected to become a researcher at a big tech firm instead of in academia [27:06] The main barriers to getting AI game theory techniques beyond games to self-driving cars [30:16] Recommendations for people who want to break into poker AI [37:45] Additional materials: www.superdatascience.com/569

In this episode, Jon updates listeners on one of the industry's biggest breakthroughs to date –Google's new natural language processing model, PaLM. The key innovation with PaLM is scaling up Google's Pathways modeling approach to half a trillion parameters — many-fold more parameters than had previously been trained using this approach.Additional materials: www.superdatascience.com/568

In this episode, the MIT Press Director and Publisher, Dr. Amy Brand, joins Jon Krohn to discuss open-access publishing in data science and how to address the inequalities that exist for women and minorities in STEM.In this episode you will learn: What it’s like to run the prestigious MIT Press [4:34] How open access makes scholarly work more impactful [6:34] How publishing outstanding STEM books for broader audiences, including for children, can help address STEM biases [19:28] Amy's award-winning documentary Picture A Scientist [25:28] What it's like to executive produce a documentary [37:24] What can be done to change STEM to make it more welcoming to minorities [48:44] The best open-source model going forward [58:26] What fascinates Amy about natural language processing [1:01:30] How author metadata in standardized taxonomies can help authors receive the credit they deserve [1:04:50] Additional materials: www.superdatascience.com/567

In this episode, Jon reflects on the Chinese proverb: "The best time to plant a tree was 20 years ago. The second best time is now." He also challenges listeners to reflect on their long-term goals that have gone unfulfilled.Additional materials: www.superdatascience.com/566

In this episode, Jeremie Harris dives into the stirring topic of AI Safety and the existential risks that Artificial General Intelligence poses to humankind.In this episode you will learn: Why mentorship is crucial in a data science career development [15:45] Canadian vs American start-up ecosystems [24:18] What is Artificial General Intelligence (AGI)? [38:50] How Artificial Superintelligence could destroy the world [1:04:00] How AGI could prove to be a panacea for humankind and life on the planet. [1:27:31] How to become an AI safety expert [1:30:07] Jeremie's day-to-day work life at Mercurius [1:35:39] Additional materials: www.superdatascience.com/565

In this episode, Jon speaks with the CEO of Hugging Face, Clem Delangue, about open-source machine learning and transformer architectures, while attending the ScaleUp:AI Conference in New York.Additional materials: www.superdatascience.com/564