Horace Wu joins us to discuss his work on Syntheia, a unique product that helps sift through massive amounts of legal data to augment the capacities and function of law firms.

In this episode you will learn:
• Horace’s life and work in New York City [5:00]
• Syntheia and Horace’s role there [6:25]
• Horace’s background [12:07]
• Nearmap [16:35]
• Syntheia NLP use cases [21:46]
• Design, coding, and the team [34:19]
• What skills does one need for this field? [41:41]
• What would Horace do differently and what is he excited for? [46:15]

Additional materials: www.superdatascience.com/455

The SuperDataScience Podcast: Machine Learning | AI | Success

SDS 455: Legal Tech, Powered by Machine Learning

In this episode, I go over what separates a good data scientist from a great one in skills, practices, and approach.

Additional materials: www.superdatascience.com/466

SDS 466: Good vs. Great Data Scientists

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

The Super Data Science Podcast: Machine Learning | AI | Success

SDS 581: Bayesian, Frequentist, and Fiducial Statistics in Data Science

In this episode, Jon kicks off a two-part series that sees him explore the popular topic of artificial general intelligence and why it might–or might not–be only a few years away. Listen in as Jon explains the several reasons why he doesn't believe that AGI is nigh.

Additional materials: www.superdatascience.com/588

SDS 588: Artificial General Intelligence is Not Nigh

Kian Katanforoosh, CEO of Workera and Lecturer at Stanford University, joins Jon Krohn to reveal the tools, frameworks, and machine learning models that power his platform and remote team.

In this episode you will learn:
• What a skills intelligence platform is [3:11]
• How mentorship can be life-changing [7:45]
• Four ways that ML drives Kian’s skills intelligence platform [10:57]
• Kian's day-to-day responsibilities as the CEO of Workera [21:00]
• What frameworks and software languages Kian and his team selected for building their platform and why [24:20]
• What Kian looks for in the data scientists and software engineers he hires [31:48]
• Kian’s Stanford Deep Learning class and mentors [34:58]
• How Kian’s passion for EdTech began [42:47]

Additional materials: www.superdatascience.com/605

SDS 605: Upskilling in Data Science and Machine Learning

Jon Krohn speaks with Shashank Kalanithi, the man who makes a sport out of YouTube and data analytics out of sports. Listen in as he talks about how he got started producing YouTube videos on data science, the essential differences between data science roles, and how data could shape the future of the sports industry.

This episode is brought to you by Datalore (https://datalore.online/SDS), the collaborative data science platform. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• What motivated Shashank to start his YouTube channel [04:31]
• The must-have technical skills for every data scientist [16:59]
• The soft skills needed for data science [20:52]
• The differences between data analyst, data scientist and data engineer [24:26]
• How data are currently being applied in the sports industry [38:38]
• The “needs” divide between digital native and traditional companies [45:34]

Additional materials: www.superdatascience.com/623

623: Data Analyst, Data Scientist, and Data Engineer Career Paths

Interview success, funny memes about data, and stakeholder management: Jon Krohn speaks with Luke Barousse, a full-time YouTuber who produces content to help aspiring data scientists. First, Jon and his guest go underwater to find out how data science can help you while working on a submarine before they emerge onto Luke’s YouTube channel. There, he discloses all the helpful hacks for data science beginners—with a generous helping of humor! As founder of MacroFit, a data-driven company that helps with meal planning, Luke is no stranger to portion sizes…

This episode is brought to you by Iterative (https://iterative.ai), your mission control center for machine learning. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Where Luke gets his inspiration for making YouTube videos [04:46]
• How Luke got into creating comedy skits [08:21]
• Luke’s favorite Python libraries for web scraping [14:41]
• Incorrect assumptions that aspiring data scientists make [15:54]
• The best time to use Power BI [19:15]
• The biggest mistakes Luke made in his data science career [22:17]
• Luke’s experience as a submariner and how it helped him in his data analyst career [38:13]
• The must-have skills for entry-level data analyst roles [43:46]

Additional materials: www.superdatascience.com/631

631: Data Analytics Career Orientation

The top data science trends of 2023 are here. Sadie St. Lawrence joins Jon Krohn to share annual predictions on the future of AI. From the data mesh to multimodal models like ChatGPT, tune in to discover what's next.

This episode is brought to you by Kolena (https://kolena.io), the testing platform for machine learning. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• A recap of 2022 predictions [5:22]
• Our data science trend predictions for 2023: 
  - Data as a product [23:36]
  - Multimodal A.I. models [32:26]
  - The data mesh [42:49]
  - Privacy & AI Trust [50:54]
  - Environmental Sustainability [54:37]
• Sadie's goals for 2023 [1:16:04]

Additional materials: www.superdatascience.com/641

641: Data Science Trends for 2023

SparseGPT is a noteworthy one-shot pruning technique that can halve the size of large language models like GPT-3 without adversely affecting accuracy. In this episode, Jon Krohn provides an overview of this development and explains its commercial and environmental implications.

Additional materials: www.superdatascience.com/650

Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

650: SparseGPT: Remove 100 Billion Parameters but Retain 100% Accuracy

Carlos Aguilar, the founder and CEO of Glean, a data exploration and visualization platform, knows a thing or two about starting and growing a tech startup. After recently raising a $7 million seed round, he sits down with Jon Krohn to dive into the makings of his platform and shares tips for building a great founding team and how to delight early adopters.

In this episode you will learn:
• How Glean extracts actionable insights from their client's data warehouses [06:48]
• What sets Glean apart from other platforms [12:43]
• Glean's software stack [14:43]
• Glean's recent fundraising journey [24:56]
• The essential characteristics of a founding team [30:53]
• How Carlos founded Glean [36:56]
• Carlos's former role at Flatiron Health [40:49]
• How Carlos created a robotic painter [48:57]

Additional materials: www.superdatascience.com/653

Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

653: Efficiently Glean-ing Insights from Vast Data Warehouses

Chip Huyen, co-founder of Claypot AI and author of O'Reilly's best-selling "Designing Machine Learning Systems" is here to share her expertise on designing production-ready machine learning applications, the importance of iteration in real-world deployment, and the critical role of real-time machine learning in various applications. Technical listeners like data scientists and machine learning engineers will definitely enjoy this one!

This episode is brought to you by Pathway, the reactive data processing framework (https://www.pathway.com/?from=superdatascience), and by epic LinkedIn Learning instructor Keith McCormick(linkedin.com/learning/instructors/keith-mccormick). Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Why Chip wrote 'Designing Machine Learning Systems' [08:58]
• How Chip ended up teaching at Stanford [13:18]
• About Chip's book 'Designing Machine Learning Systems' [21:12]
• What makes ML feel like magic [30:53]
• How to align business intent, context, and metrics with ML [37:55]
• The lessons Chip learned about training data [42:03]
• Chip's secrets to engineering good features [53:19]
• How Chip optimizes her productivity [1:07:48]

Additional materials: www.superdatascience.com/661

661: Designing Machine Learning Systems

Angel investor and data science consultant Josh Wills sits down with Jon Krohn to discuss his former roles (Google, Slack, and Cloudera) and the essential skills for engineering scalable machine learning projects.

This episode is brought to you by Pathway, the reactive data processing framework (www.pathway.com/?from=superdatascience), and by epic LinkedIn Learning instructor Keith McCormick(https://linkedin.com/learning/instructors/keith-mccormick). Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Josh's 'Data Engineering for Machine Learning' course [06:50]
• Contextual bandits [10:52]
• Data quality and monitoring [16:45]
• The “infinite loop of sadness” in data product development [25:12]
• Josh’s definition of a data scientist [30:02]
• Josh's role at WeaveGrid [37:36]
• Management-Track vs Independent Contributor [48:47]
• Josh's work on the Covid pandemic [1:06:46] 
• Josh’s favorite tech stack [1:11:13]

Additional materials: www.superdatascience.com/665

665: How to be both socially impactful and financially successful in your data career

Vincent Gosselin, CEO and co-founder of Taipy, an open-source Python library, joins Jon Krohn to discuss how to accelerate productivity in Python and build scalable, reusable, and maintainable data pipelines. Gosselin shares his breadth of wisdom honed over his decades-long AI career.

This episode is brought to you by Pathway, the reactive data processing framework (https://pathway.com/?from=superdatascience), and by Posit, the open-source data science company (https://posit.co/academy). Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• The Taipy library functionality [2:59]
• The future of data pipelines [21:40]
• Common trends of companies that are successful at adopting data pipelines [28:31]
• How no-code and low-code trends impact the data science lifecycle [33:00]
• How Vincent chose the programming languages that underpin Taipy [41:40]
• Common trends on how companies manage their data to learn from it [45:06]
• Vincent's perspective on AI winters [51:03]

Additional materials: www.superdatascience.com/673

673: Taipy, the open-source Python application builder

Unlock the power of XGBoost by learning how to fine-tune its hyperparameters and discover its optimal modeling situations. This and more, when best-selling author and leading Python consultant Matt Harrison teams up with Jon Krohn for yet another jam-packed technical episode! Are you ready to upgrade your data science toolkit in just one hour? Tune-in now!

This episode is brought to you by Pathway, the reactive data processing framework (pathway.com/?from=superdatascience), by Posit, the open-source data science company (posit.co), and by Anaconda, the world's most popular Python distribution (superdatascience.com/anaconda). Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Matt's book ‘Effective XGBoost’ [07:05]
• What is XGBoost [09:09]
• XGBoost's key model hyperparameters [19:01]
• XGBoost's secret sauce [29:57]
• When to use XGBoost [34:45]
• When not to use XGBoost [41:42]
• Matt’s recommended Python libraries [47:36]
• Matt's production tips [57:57]

Additional materials: www.superdatascience.com/681

681: XGBoost: The Ultimate Classifier, with Matt Harrison

AI visionary and CEO of SingularityNET Dr. Ben Goertzel provides a deep dive into the possible realization of Artificial General Intelligence (AGI) within 3-7 years. Explore the intriguing connections between self-awareness, consciousness, and the future of Artificial Super Intelligence (ASI) and discover the transformative societal changes that could arise.

This episode is brought to you by AWS Inferentia (https://go.aws/3zWS0au), the AWS Insiders Podcast (https://pod.link/1608453414), and by Modelbit (https://modelbit.com), for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Decentralized and benevolent AGI [03:13]
• The SingularityNET ecosystem [13:10]
• Dr. Goertzel's vision for realizing AGI - combining DL with neuro-symbolic systems, genetic algorithms and knowledge graphs [25:50]
• How reaching AGI will trigger Artificial Super Intelligence [38:51]
• Dr. Goertzel's approach to AGI using OpenCog Hyperon [42:34]
• Why Dr. Goertzel believes AGI will be positive for humankind [53:07]
• How to ensure the AGI is benevolent [1:06:43]
• How AGI or ASI may act ethically [1:13:50]

Additional materials: www.superdatascience.com/697

697: The (Short) Path to Artificial General Intelligence, with Dr. Ben Goertzel

Take on the world of GPT and learn to develop your own, commercially successful Large Language Models (LLMs) with Jon Krohn’s comprehensive, guided training video for generative AI. Get to grips with the technology, learn which tools to use, and find out how to get an eye for business-viable models with Jon’s (ad-)free educational video.

Additional materials: www.superdatascience.com/704

Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

704: Jon’s “Generative A.I. with LLMs” Hands-on Training

Join Jon Krohn as he chats with Syngenta Group's Feroz Sheikh, Jeremy Groeteke, and Thomas Jung about the digital revolution in agriculture. Learn how data science is evolving farming, from precision techniques to global food solutions. A compelling blend of tech meets nature.

This episode is brought to you by AWS Inferentia (https://go.aws/3zWS0au) and by Modelbit (https://modelbit.com), for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• What is precision agriculture? [09:43]
• What is computational agronomy? [12:30]
• How Syngenta helps growers optimize yields [21:37]
• How to bridge the gap between R&D and out in the real world [33:58]
• What is generative chemistry? [37:52]
• How generative chemistry accelerates the discovery of new compounds [41:55]
• How you could make a big social impact in agriculture with data science [56:22]
• How to go about designing ML models for agriculture [1:00:27]

Additional materials: www.superdatascience.com/705

705: Feeding the World with ML-Powered Precision Agriculture

LLM Vicuña, Chatbot Arena, and the race to increase LLM context windows: This episode’s guest Joey Gonzalez talks to Jon Krohn about developing models and platforms that leverage and improve LLMs, as well as the future of AI development and access.

This episode is brought to you by the AWS Insiders Podcast (https://pod.link/1608453414), by Modelbit (https://modelbit.com), for deploying models in seconds, and by Grafbase (https://grafbase.com), the unified data layer. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Vicuña: How the revolutionary LLM came to be [03:35]
• Chatbot Arena: The leading LLM leaderboard [09:47]
• Trusting LLM results [17:54]
• Gorilla: The open-source ChatGPT plugin alternative [32:13]
• About LMSYS and long context windows [47:48]
• Open- vs closed-source LLMs: Which is better? [1:01:39]
• Aqueduct [1:16:49]
• Founding GraphLab [1:27:02]
• How AI will positively impact society in the coming decades [1:33:23]

Additional materials: www.superdatascience.com/707

707: Vicuña, Gorilla, Chatbot Arena and Socially Beneficial LLMs, with Prof. Joey Gonzalez

On this week’s Five-Minute Friday, host Jon Krohn gives five reasons why he is so excited about ChatGPT’s Code Interpreter and walks listeners through its capabilities with a practical example.

Additional materials: www.superdatascience.com/708

Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

708: ChatGPT Code Interpreter: 5 Hacks for Data Scientists

Meta's Senior Research Director, Dr. Laurens van der Maaten, takes center stage to unravel the captivating realm of AI innovation. Learn about his groundbreaking contributions, including pioneering the t-SNE dimensionality reduction technique and harnessing AI for novel protein synthesis, climate change mitigation, and wearable materials simulation. Join us to explore the transformative power of AI across diverse domains and gain a glimpse into its future societal implications.

This episode is brought to you by AWS Inferentia (https://go.aws/3zWS0au), by Modelbit (https://modelbit.com), for deploying models in seconds, and by Grafbase (https://grafbase.com), the unified data layer. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Large-scale learning of image recognition models on web data [05:05]
• Evolutionary Scale Modeling protein models [16:45]
• Fighting climate change by building an A.I. model [29:49]
• The CrypTen privacy-preserving ML framework [38:36]
• Concerns about adversarial examples [53:25]
• Laurens’ t-SNE algorithm [58:56]
• How to make a big impact [1:07:25]

Additional materials: www.superdatascience.com/709

709: Big A.I. R&D Risks Reap Big Societal Rewards, with Meta's Dr. Laurens van der Maaten

In this episode, host Jon Krohn explores with his guest Ajay Jain, Co-Founder of Genmo.ai, how creative general intelligence could take the video industry by storm. They also discuss the models that got Genmo to this point, the applications of NeRF, and how understanding human psychology is so essential to developing models that output high-fidelity video.

This episode is brought to you by the Zerve data science dev environment (https://zerve.ai), by Grafbase (https://grafbase.com), the unified data layer, and by Modelbit (https://modelbit.com), for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• About Genmo.ai and the term “creative general intelligence” [03:47]
• Why Ajay started Genmo.ai [09:26]
• The increased performance of multimodal models [21:12]
• All about Denoising Diffusion Probabilistic Models (DDPMs) [31:03]
• The application of Neural Radiance Fields (NeRF) [55:26]
• Predicting pedestrian behavior at Uber [1:01:50]
• How to save money in the process of training models [1:12:42]

Additional materials: www.superdatascience.com/711

711: Image, Video and 3D-Model Generation from Natural Language, with Dr. Ajay Jain

Attention and transformers in LLMs, the five stages of data processing, and a brand-new Large Language Models A-Z course: Kirill Eremenko joins host Jon Krohn to explore what goes into well-crafted LLMs, what makes Transformers so powerful, and how to succeed as a data scientist in this new age of generative AI.

This episode is brought to you by Intel and HPE Ezmeral Software Solutions (https://hpe.com/ezmeral/chatwithyourdata), and by Prophets of AI (https://prophetsofai.com), the leading agency for AI experts. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:
• Supply and demand in AI recruitment [08:30]
• Kirill and Hadelin's new course on LLMs, “Large Language Models (LLMs), Transformers & GPT A-Z” [15:37]
• The learning difficulty in understanding LLMs [19:46]
• The basics of LLMs [22:00]
• The five building blocks of transformer architecture [36:29]
- 1: Input embedding [44:10]
- 2: Positional encoding [50:46]
- 3: Attention mechanism [54:04]
- 4: Feedforward neural network [1:16:17]
- 5: Linear transformation and softmax [1:19:16]
• Inference vs training time [1:29:12]
• Why transformers are so powerful [1:49:22]

Additional materials: www.superdatascience.com/747

747: Technical Intro to Transformers and LLMs, with Kirill Eremenko

Google's Gemini Ultra takes the spotlight this week, as host Jon Krohn welcomes Lisa Cohen, Google's Director of Data Science and Engineering, for a conversation about the launch of Gemini Ultra. Discover the capabilities of this cutting-edge large language model and how it stands toe-to-toe with GPT-4. Lisa shares her insights on the development, rollout, and potential of Gemini Ultra in reshaping various sectors. Whether you're a data science professional, tech enthusiast, or curious about the future of AI, this episode offers a deep dive into one of the most significant advancements in artificial intelligence.

This episode is brought to you by Ready Tensor, where innovation meets reproducibility (https://www.readytensor.ai/), and by Intel and HPE Ezmeral Software Solutions (https://hpe.com/ezmeral/chatbots). Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:
• Google’s Gemini model family and Lisa's key responsibilities [04:55]
• How LLMs will transform the practice of Data Science [19:47]
• Lisa on prompt engineering and reinforcement learning from human feedback [24:38]
• How to fine-tune Gemini models with Google's Vertex AI [30:52]
• How AI-assistants will transform life and work for everyone from data scientists to educators to children [47:14]
• The challenges of developing a data-centric culture [57:31]
• Centralized vs decentralized data science teams [1:03:50]

Additional materials: www.superdatascience.com/761

761: Gemini Ultra: How to Release an A.I. Product for Billions of Users, with Google's Lisa Cohen

Data science futurists, bestselling authors, and lively how-to guides from the industry’s top practitioners, which range from applying data science for good to using open-source tools for NLP: This is The Super Data Science Podcast’s top ten most listened-to episodes in 2023, hosted by Jon Krohn. A great snapshot of our great content from 2023.

Additional materials: www.superdatascience.com/764

Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

764: The Top 10 Episodes of 2023

Generative AI is reshaping our world, and Bernard Marr, world-renowned futurist and best-selling author, joins Jon Krohn to guide us through this transformation. In this episode, Bernard shares his insights on how AI is transforming industries, revolutionizing daily life, and addressing global challenges. With his extensive experience advising top organizations worldwide, he also examines the ethical considerations of AI deployment.

This episode is brought to you by Intel and HPE Ezmeral Software (https://bit.ly/hpeintel). Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:
• How Generative AI will transform industries [03:55]
• The evolution of Generative AI [10:19]
• How will Generative AI impact daily life [16:52]
• The ethical challenges of AI [18:55]
• How corporations can harness Generative AI for collaboration [24:36]
• Industries that will be impacted by Generative AI [32:20]
• How Sora-like Generative AI systems will create highly immersive entertainment [42:16]
• How Generative AI could unlock 99% of business data [53:34]

Additional materials: www.superdatascience.com/777

777: Generative AI in Practice, with Bernard Marr

Tidyverse, ggplot2, and the secret to a tech company’s longevity: Hadley Wickham talks to Jon Krohn about Posit’s rebrand, Tidyverse and why it needs to be in every data scientist’s toolkit, and why getting your hands dirty with open-source projects can be so lucrative for your career.

This episode is brought to you by Intel and HPE Ezmeral Software (https://bit.ly/hpeintel). Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:
• All about the Tidyverse [04:46]
• Hadley’s favorite R libraries [17:10]
• The goal of Posit [30:29]
• On bringing multiple programming languages together [36:02]
• The principles for a long-lasting tech company [52:10]
• How Hadley developed ggplot2 [55:24]
• How to contribute to the open-source community [1:05:43]

Additional materials: www.superdatascience.com/779

779: The Tidyverse of Essential R Libraries and their Python Analogues, with Dr. Hadley Wickham

Hear Jon Krohn’s favorite five clips from his April interviews. Chief Scientist at Posit PBC Hadley Wickham on the subtle differences between Python and R. Professor of Business Analytics Barrett Thomas walks through the variables that companies should consider when using drones or any other tech to improve their business operations and bottom line. Aleksa Gordić, Founder of Runa AI believes an overhaul of the current educational system is long overdue. Bernard Marr discusses the future of GenAI and its impact on the world of work. And SuperDataScience founder Kirill Eremenko gives a lively workshop on gradient boosting.


Additional materials: www.superdatascience.com/782

Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

782: In Case You Missed It in April 2024

Jon Krohn takes on a listener's challenge to explain his work in data science to his 94-year-old grandmother, Annie. This heartwarming conversation covers what data is, the role of a data scientist, and breaks down artificial intelligence (AI) and artificial general intelligence (AGI) in simple terms. The episode provides a fresh take on how to communicate complex topics to a lay audience, offering both clarity and insight.

Additional materials: www.superdatascience.com/816

Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

816: Explaining AGI to a 94-Year-Old

Dr. Julia Silge, Engineering Manager at Posit, introduces the brand-new Positron IDE, perfect for exploratory data analysis and visualization. She also lays out her top picks for LLMs that boost coding efficiency and discusses when traditional NLP methods might be the smarter choice over LLMs. Plus, Julia highlights some must-know open-source libraries that make managing MLOps easier than ever. Tune in for insights that every data scientist, ML engineer, and developer will find useful.

This episode is brought to you by Gurobi (https://www.gurobi.com/personas/optimization-for-data-scientists/), the Decision Intelligence Leader, and by ODSC (https://odsc.com/california), the Open Data Science Conference. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:
• Overview of Posit and Positron IDE [05:20]
• How the needs of a data scientist differ from those of a software developer [10:54]
• How to contribute to the open-source Positron [19:50]
• MLOps and Vetiver: Tools for deploying and maintaining ML models [37:01]
• Natural Language Processing (NLP) and the Tidyverse approach [50:34]
• The role of AI and LLMs in data science education [1:24:18]

Additional materials: www.superdatascience.com/817

817: The Positron IDE, Tidy NLP and MLOps with Dr. Julia Silge

Experts from AI and data science discuss the impact and benefits of decentralization, the importance of structuring AI systems in business, and why knowing the basics will always matter for data engineers. Listen to Shingai Manjengwa (episode 809), Daniel Hulme (episode 807), Jerry Yurchisin (episode 813) and Nick Elprin (episode 811) explore a future world of work that rewards continuing learners, sets tasks for the people best suited to complete them rather than those whose job titles reflect the spec, and applies a fleet of ‘AI agents’ to solve complex business tasks.

Additional materials: www.superdatascience.com/818

Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

818: In Case You Missed It in August 2024

SuperDataScience veteran and Udemy teacher Luka Anicin is on the podcast to talk about his brand-new course, “PyTorch: From Zero to Hero”, available exclusively on superdatascience.com. Host Jon Krohn asks Luka why he feels that every data scientist should consider PyTorch as their default Python library, and why “keeping it simple” can secure the success of a machine learning project.

This episode is brought to you by AWS Inferentia (https://go.aws/3zWS0au) and AWS Trainium (https://go.aws/3ycV6K0), and by Gurobi (https://www.gurobi.com/personas/optimization-for-data-scientists), the Decision Intelligence Leader. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:
• About the PyTorch library [03:29]
• Why PyTorch became so popular [25:24]
• How to increase accuracy and efficiency in PyTorch [31:49]
• How to utilize transfer learning [35:44]
• Why real-world projects are essential to data scientists [41:10]
• About Datablooz [46:49]

Additional materials: www.superdatascience.com/819

819: PyTorch: From Zero to Hero, with Luka Anicin

Jon Krohn takes OpenAI’s new models (o1-preview and o1-mini) for a spin in this Five-Minute Friday, learning their key strengths and limitations, and how the o1 series may represent yet another landmark for generative AI.

Additional materials: www.superdatascience.com/820

Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

820: OpenAI's o1 "Strawberry" Models

Marck Vaisman speaks to Jon Krohn about his paradigm for understanding core data practitioner types. Hear Marck detail the four data practitioner personas that he has identified in his research, why he believes the roadmaps that influencers like to promote as surefire ways to a data science career don’t work in practice, and why the term “data scientist” is still so elusive and hard to recruit for.

This episode is brought to you by Gurobi (https://www.gurobi.com/personas/optimization-for-data-scientists), the Decision Intelligence Leader. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:
• How Marck started his work in defining data science roles [08:06]
• The relationship between the four data practitioner personas [15:26]
• About Marck’s “menu” for effective data science [40:43]
• How recruiters can hire the best data scientist for the job [59:31]

Additional materials: www.superdatascience.com/821

821: The Skills You Need to Be an Effective Data Scientist, with Marck Vaisman

NotebookLM, Google’s latest AI tool, takes content creation to a new level. This week, Jon Krohn shares how the platform transformed his 200-page dissertation into a fascinating 11-minute podcast. Discover how AI can turn vast amounts of information into engaging and digestible content, opening up new possibilities for content creation.

Additional materials: www.superdatascience.com/822

Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

822: NotebookLM: Jaw-Dropping Podcast Episodes Generated About Your Documents

Virtual humans are rewriting the rules of digital communication and reshaping entire industries. This week, Jon Krohn welcomes Natalie Monbiot, Head of Strategy at Hour One, to shed light on how AI avatars are revolutionizing L&D and e-commerce by turning traditional training and product listings into captivating, presenter-led content.

This episode is brought to you by epic LinkedIn Learning instructor Keith McCormick (https://www.linkedin.com/learning/instructors/keith-mccormick), by Gurobi (https://www.gurobi.com/personas/optimization-for-data-scientists/), the Decision Intelligence Leader, and by ODSC (https://odsc.com/california), the Open Data Science Conference. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:
• How do you create a virtual being? [10:55]
• Reid Hoffman's avatar [13:40]
• The virtual human economy [31:07]
• Virtual human societies [51:24]
• Virtual humans and creative expression [56:35]
• Challenges in maintaining transparency [01:00:22]

Additional materials: www.superdatascience.com/823

823: Virtual Humans and AI Clones, with Natalie Monbiot

Llama 3.2 brings a new era of AI innovation with lightweight models tailored for on-device applications and powerful vision models for handling complex image inputs. Host Jon Krohn explores how this release pushes the boundaries of open-source AI, making it more accessible and versatile for developers. He also covers the Llama Stack toolkit, designed to streamline deployment, and Llama Guard 3, Meta’s latest content moderation solution. With extensive support from major cloud and hardware partners, Llama 3.2 is set to unlock groundbreaking possibilities for AI across mobile and beyond. Tune in to hear more.

Additional materials: www.superdatascience.com/824

Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

824: Llama 3.2: Open-Source Edge and Multimodal LLMs

The Super Data Science Podcast has moved! But, have no fear: We’re still publishing new episodes every single Tuesday and Friday.

You’ll find “The Super Data Science: ML & AI Podcast with Jon Krohn” via any major podcast-streaming app. Or if you like to watch podcasts, you can subscribe to our YouTube channel, where we have a special video version of the show: https://www.youtube.com/@SuperDataScienceWithJonKrohn

If you’d simply like to listen to the show on the web — while also getting convenient access to the episode transcript, show notes and other useful resources — you can point your browser to https://superdatascience.com/podcast. 

Thanks again for all of your support for the SuperDataScience Podcast over the years! We’re so grateful to have you listening and I can’t wait for you to subscribe and tune in.

Super Data Science Podcast Update

Hadley Wickham, a huge presence in data science, sits down to talk about R, Python, and the future of potential integrations, as well as some Q&A with our listeners through LinkedIn about programming languages and how to make data science accessible for all.

In this episode you will learn:
• Hadley’s R packages [8:26]
• Better integrations between R and Python [20:11]
• LinkedIn Q&A [33:34]
• useR Conference vs. RStudio Conference [50:46]
• LinkedIn Q&A: Career-related questions [1:01:06]
• LinkedIn Q&A: Future-related questions [1:08:01]

Additional materials: www.superdatascience.com/337

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 337: Hadley Wickham Talks Integration and Future of R and Python

Jon Krohn joins me to discuss his work at untapt in designing models for HR purposes. We also discuss the power of data science across fields of medicine and epidemiology, as well as the future of deep learning.

In this episode you will learn:   
• Coronavirus update in New York City [2:36]
• What brought Jon to New York? [5:38]
• Data science and coronavirus [12:50]
• Jon’s work at untapt [18:09]
• Techniques used to design models in untapt [22:02]
• untapt’s approach to explainability and bias [30:19]
• Jon’s other contributions to data science [38:10]
• Jon’s book and visual teaching styles [44:32]
• LinkedIn Q&A [52:05]
• Jon’s recommendation for becoming best at deep learning [1:13:09]

Additional materials: www.superdatascience.com/365

SDS 365: Deep Learning Models For Recruitment

In this episode, I share a series of great tips, plus a bonus tip for getting your application further along in the hiring process and getting the job.

Additional materials: www.superdatascience.com/396

SDS 396: Five Job Hunting Tips

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

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