Superdatascience

In this episode founding Editor-in-Chief of the Harvard Data Science Review and Professor of Statistics at Harvard University, Prof. Xiao-Li Meng, joins Jon Krohn to dive into data trade-offs that abound, and shares his view on the paradoxical downside of having lots of data. In this episode you will learn: What the Harvard Data Science Review is and why Xiao-Li founded it [5:31] The difference between data science and statistics [17:56] The concept of 'data minding' [22:27] The concept of 'data confession' [30:31] Why there’s no “free lunch” with data, and the tricky trade-offs that abound [35:20] The surprising paradoxical downside of having lots of data [43:23] What the Bayesian, Frequentist, and Fiducial schools of statistics are, and when each of them is most useful in data science [55:47] Additional materials: www.superdatascience.com/581

In this episode, Jon resumes his series on strategies for getting business value from machine learning. Part one saw him review several ways to identify a commercial problem before starting data collection or ML model development. And now, in part two, Jon digs into the data collection process. Additional materials: www.superdatascience.com/580

In this episode, the CEO of Overjet, Dr. Wardah Inam, joins Jon Krohn to discuss the classification and quantification of dental diagnoses with computer vision, her data labeling challenges, and tips for building a successful A.I. business. In this episode you will learn: How Overjet leverages computer vision to qualify and quantify dental diagnoses [5:11] How A.I. solutions reduce the under-diagnosis of common diseases like periodontal disease [8:15] Overjet's particular ML challenges within the dental industry [15:45] Wardah's experience in introducing A.I. to the dental industry [20:12] Wardah's tips for building a successful A.I. business [23:34] What she looks for in the data scientists and software engineers she hires [39:36] Additional materials: www.superdatascience.com/579

In this episode, Jon kicks off a new Five-Minute Friday series that explores the strategies for getting business value from machine learning. Part one sees him review several ways to identify a commercial problem before starting data collection or ML model development. Additional materials: www.superdatascience.com/578

In this episode, the former CEO and co-founder behind Onfido, an AI-based ID verification, joins Jon Krohn to discuss his path to start-up success. Tune in to hear valuable information from Husayn Kassai. In this episode you will learn: How Husayn's start-up journey began [5:55] How Husayn determined that his challenge could be solved by machine vision [11:18] Onfido's initial seed stages [18:23] Launching and scaling your start-up in the U.S. market [22:00] The most important component in building the best product [26:30] Husayn's latest start-up [28:52] Husayn’s startup project decision-making process [37:49] Choosing your co-founding team [44:04] Additional materials: www.superdatascience.com/577

Hollywood has officially fallen for the drama of tech startups! Tune in to hear Jon Krohn review the small-screen adaptations of WeWork (WeCrashed), Uber (Super Pumped), and Theranos (The Dropout). Additional materials: www.superdatascience.com/576

In this episode, the Director of Architecture at NVIDIA, Dr. Magnus Ekman, joins Jon Krohn to discuss how machine learning, including deep learning, can optimize computer hardware design. The pair also review his exceptional book 'Learning Deep Learning.' In this episode you will learn: What hardware architects do [10:15] How ML can optimize hardware speed [ 13:19] Magnus’s Deep Learning Book [21:14] Is understanding how ML models work important? [36:16] Algorithms inspired by biological evolution [41:25] How artificial general intelligence won’t be obtained by increasing model parameters alone [51:24] Why there will always be a place for CNNs and RNNs [54:51] How people can "transition" realistically into ML [1:09:15] Additional materials: www.superdatascience.com/575

In this episode, Jon shares how the right music can power your productivity. It's no secret that he's a big fan of 'deep work,' but this week, he opens up about the artists, sites, and playlists that propel his productivity to new levels. Additional materials: www.superdatascience.com/574

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

In this episode, superstar data science YouTuber Tina Huang joins us to discuss what it's like to work at one of the world's largest tech companies, her strategies for efficient learning, and how best to prepare for a career in data science from scratch. In this episode you will learn: The key areas to focus on when getting started in data science [6:01] Tina’s five steps to consistently doing anything [11:55] Tina's day-to-day life as a data scientist at one of the world’s largest tech companies [20:02] How Tina's computer science background helps her work [26:20] Traditional banking culture vs big tech [32:12] How Tina's background in pharmacology impacts her work in data science [36:15] The software languages that Tina uses daily in her work [45:30] How Tina’s SQL course practically prepares you for data science interviews [47:24] Additional materials: www.superdatascience.com/563

In this episode, Jon shares his daily technical exercise, which is part of an extensive habit tracking system that allows him to achieve more, create more structure within his day, and cut out bad habits. By completing mathematics, computer science, or programming exercise daily, Jon is able to hone his technical skills in a limitlessly broad field and open new professional opportunities in the long run. Additional materials: www.superdatascience.com/562