OpenAssistant: The Open-Source ChatGPT Alternative, with Dr. Yannic Kilcher

Yannic Kilcher — famed Machine Learning YouTuber and creator of OpenAssistant, the best-known open-source conversational A.I. — is today's rockstar guest! Hear from this luminary where the biggest A.I. opportunities are in the coming years 😎

If you’re not already aware of him, Dr. Yannic: 
• Has over 230,000 subscribers on his machine learning YouTube channel.
• Is the CTO of DeepJudge, a Swiss startup that is revolutionizing the legal profession with AI tools.
• Led the development of OpenAssistant, a leading open-source alternative to ChatGPT, that has over 37,000 stars (⭐️⭐️⭐️!!!) on GitHub.
• Holds a PhD in A.I. from the outstanding Swiss technical university, ETH Zürich.

Despite being such a technical expert himself, most of today’s episode should be accessible to anyone who’s interested in A.I., whether you’re a hands-on practitioner or not.

In this episode, Yannic details: 
• The behind-the-scenes stories and lasting impact of his OpenAssistant project.
• The technical and commercial lessons he’s learned while growing his A.I. startup.
• How he stays up to date on ML research.
• The important, broad implications of adversarial examples in ML. 
• Where the biggest opportunities are in A.I. in the coming years.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

How GitHub Operationalizes AI for Teamwide Collaboration and Productivity, with GitHub COO Kyle Daigle

Today's episode features the exceptionally passionate GitHub COO Kyle Daigle detailing how generative A.I. tools improve not only the way individuals work, but also dramatically transform the way people across entire firms collaborate.

Kyle was my on-stage guest for a "fireside chat" live on stage at Insight Partners' ScaleUp:AI conference in New York. It was a terrifically slick conference and a ton of fun to collaborate on stage with Kyle! He's an energizing and inspiring speaker.

Check out the episode for all of our conversation; some of the key takeaways are:
• Generative AI tools like GitHub CoPilot are most useful and efficient when they’re part of your software-development flow.
• These kinds of in-flow generative AI tools can be used for collaboration (such as speeding up code review) not just on an individual basis.
• "Innersourcing" takes open-source principles but applies them within an organization on their proprietary assets.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Seven Factors for Successful Data Leadership

Today's episode is a fun one with the jovial EIGHT-time book author, Ben Jones. In it, Ben covers the seven factors of successful data leadership — factors he's gleaned from administering his data literacy assessment to 1000s of professionals.

Ben:
• Is the CEO of Data Literacy, a firm that specializes in training and coaching professionals on data-related topics like visualization and statistics.
• Has published eight books, including bestsellers "Communicating Data with Tableau" (O'Reilly, 2014) and "Avoiding Data Pitfalls" (Wiley, 2019).
• Has been teaching data visualization at the University of Washington for nine years.
• Previously worked for six years as a director at Tableau.

Today’s episode should be broadly accessible to any interested professional.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

ChatGPT Custom Instructions: A Major, Easy Hack for Data Scientists

Thanks to Shaan Khosla for tipping me off to a crazy easy hack to get markedly better results from GPT-4: providing Custom Instructions that prompt the algorithm to iterate upon its own output while critically evaluating and improving it.

Here's Shaan's full Custom Instructions text, which he himself has been iterating on in recent months:

"I need you to help me with a task. To help me with the task, first come up with a detailed outline of how you think you should respond, then critique the ideas in this outline (mention the advantages, disadvantages, and ways it could be improved), then use the original outline and the critiques you made to come up with your best possible solution.

"Overall, your tone should not be overly dramatic. It should be clear, professional, and direct. Don't sound robotic or like you're trying to sell something. You don't need to remind me you're a large language model, get straight to what you need to say to be as helpful as possible. Again, make sure your tone is clear, professional, and direct - not overly like you're trying to sell something."

Try it out! If you haven't used Custom Instructions before, in today's episode I talk you through how to set it up and explain why this approach is so effective. In the video version, I provide a screenshare that makes getting started foolproof.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Llama 2, Toolformer and BLOOM: Open-Source LLMs with Meta’s Dr. Thomas Scialom

Thomas Scialom, PhD is behind many of the most popular Generative A.I. projects including Llama 2, the world's top open-source LLM. Today, the Meta A.I. researcher reveals the stories behind Llama 2 and what's in the works for Llama 3.

Thomas:
• Is an A.I. Research Scientist at Meta.
• Is behind some of the world’s best-known Generative A.I. projects including Llama 2, BLOOM, Toolformer and Galactica.
• Is contributing to the development of Artificial General Intelligence (AGI).
• Has lectured at many of the top A.I. labs (e.g., Google, Stanford, MILA).
• Holds a PhD from Sorbonne University, where he specialized in Natural-Language Generation with Reinforcement Learning.

Today’s episode should be equally appealing to hands-on machine learning practitioners as well as folks who may not be hands on but are nevertheless keen to understand the state-of-the-art in A.I. from someone who’s right on the cutting edge of it all.

In this episode, Thomas details: 
• Llama 2, today’s top open-source LLM, including what is what like behind the scenes developing it and what we can expect from the eventual Llama 3 and related open-source projects.
• The Toolformer LLM that learns how to use external tools.
• The Galactica science-specific LLM, why it was brought down after a few days, and how it might eventually re-emerge in a new form.
• How RLHF — reinforcement learning from human feedback — shifts the distribution of generative A.I. outputs from approximating the average of human responses to excellent, often superhuman quality.
• How soon he thinks AGI — artificial general intelligence — will be realized and how.
• How to make the most of the Generative A.I. boom as an entrepreneur.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Generative A.I. without the Privacy Risks (with Prof. Raluca Ada Popa)

Consumers and enterprises dread that Generative A.I. tools like ChatGPT breach privacy by using convos as training data, storing PII and potentially surfacing confidential data as responses. Prof. Raluca Ada Popa has all the solutions.

Today's guest, Raluca:
• Is Associate Professor of Computer Science at University of California, Berkeley.
• Specializes in computer security and applied cryptography.
• Her papers have been cited over 10,000 times.
• Is Co-Founder and President of Opaque Systems, a confidential computing platform that has raised over $31m in venture capital to enable collaborative analytics and A.I., including allowing you to securely interact with Generative A.I.
• Previously co-founded PreVeil, a now-well-established company that provides end-to-end document and message encryption to over 500 clients.
• Holds a PhD in Computer Science from MIT.

Despite Raluca being such a deep expert, she does such a stellar job of communicating complex concepts simply that today’s episode should appeal to anyone that wants to dig into the thorny issues around data privacy and security associated with Large Language Models (LLMs) and how to resolve them.

In the episode, Raluca details:
• What confidential computing is and how to do it without sacrificing performance.
• How you can perform inference with an LLM (or even train an LLM!) without anyone — including the LLM developer! — being able to access your data.
• How you can use commercial generative models OpenAI’s GPT-4 without OpenAI being able to see sensitive or personally-identifiable information you include in your API query.
• The pros and cons of open-source versus closed-source A.I. development.
• How and why you might want to seamlessly run your compute pipelines across multiple cloud providers.
• Why you should consider a career that blends academia and entrepreneurship.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Brain-Computer Interfaces and Neural Decoding, with Prof. Bob Knight

In today's extraordinary episode, Prof. Bob Knight details how ML-powered brain computer interfaces (BCIs) could allow real-time thought-to-speech synthesis and the reversal of cognitive decline associated with aging.

This is a rare treat as "Dr. Bob" doesn't use social media and has only made two previous podcast appearances: on Ira Flatow's "Science Friday" and a little-known program called "The Joe Rogan Experience".

Dr. Bob:
• Is Professor of Neuroscience and Psychology at University of California, Berkeley.
• Is Adjunct Professor of Neurology and Neurosurgery at UC San Francisco.
• Over his career, has amassed tens of millions of dollars in research funding, 75 patents, and countless international awards for neuroscience and cognitive computing research.
• His hundreds of papers have together been cited over 70,000 times.

In this episode, Bob details:
• Why the “prefrontal cortex” region of our brains makes us uniquely intelligent relative to all the other species on this planet. 
• The invaluable data that can be gathered by putting recording electrodes through our skulls and directly into our brains.
• How "dynamic time-warping" algorithms allow him to decode imagined sounds, even musical melodies, through recording electrodes implanted into the brain.
• How BCIs are life-changing for a broad range of illnesses today.
• The extraordinary ways that advances in hardware and machine learning could revolutionize medical care with BCIs in the coming years.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Tools for Building Real-Time Machine Learning Applications, with Richmond Alake

Today, the astonishingly industrious ML Architect and entrepreneur Richmond Alake crisply describes how to rapidly develop robust and scalable Real-Time Machine Learning applications.

Richmond:
• Is a Machine Learning Architect at Slalom Build, a huge Seattle-based consultancy that builds products embedded with analytics and ML.
• Is Co-Founder of two startups: one uses computer vision to correct peoples’ form in the gym and the other is a generative A.I. startup that works with human speech.
• Creates/delivers courses for O'Reilly and writes for NVIDIA.
• Previously worked as a Computer Vision Engineer and as a Software Developer.
• Holds a Masters in Computer Vision, ML and Robotics from the University of Surrey.

Today’s episode will appeal most to technical practitioners, particularly those who incorporate ML into real-time applications, but there’s a lot in this episode for anyone who’d like to hear about the latest tools for developing real-time ML applications from a leader in the field.

In this episode, Richmond details:
• The software choices he’s made up and down the application stack — from databases to ML to the front-end — across his startups and the consulting work he does.
• The most valuable real-time ML tools he teaches in his courses.
• Why writing for the public is an invaluable career hack that everyone should be taking advantage of.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Contextual A.I. for Adapting to Adversaries, with Dr. Matar Haller

Today, the wildly intelligent Dr. Matar Haller introduces Contextual A.I. (which considers adjacent, often multimodal information when making inferences) as well as how to use ML to build moat around your company.

Matar:
• Is VP of Data and A.I. at ActiveFence, an Israeli firm that has raised over $100m in venture capital to protect online platforms and their users from malicious behavior and malicious content.
• Is renowned for her top-rated presentations at leading conferences.
• Previously worked as Director of Algorithmic A.I. at SparkBeyond, an analytics platform.
• Holds a PhD in neuroscience from the University of California, Berkeley.
• Prior to data science, taught soldiers how to operate tanks.

Today’s episode has some technical moments that will resonate particularly well with hands-on data science practitioners but for the most part the episode will be interesting to anyone who wants to hear from a brilliant person on cutting-edge A.I. applications.

In this episode, Matar details:
• The “database of evil” that ActiveFence has amassed for identifying malicious content.
• Contextual A.I. that considers adjacent (and potentially multimodal) information when classifying data.
• How to continuously adapt A.I. systems to real-world adversarial actors.
• The machine learning model-deployment stack she uses.
• The data she collected directly from human brains and how this research relates to the brain-computer interfaces of the future.
• Why being a preschool teacher is a more intense job than the military.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Business Intelligence Tools, with Mico Yuk

Today's guest is the straight shooter Mico Yuk, who pulls absolutely no punches in her assessment of, well, anything! ...but particularly about vendors in the business intelligence and data analytics space. Enjoy!

Mico:
• Is host of the popular Analytics on Fire Podcast (top 2% worldwide).
• Co-founded the BI Brainz Group, an analytics consulting and solutions company that has taught over 15,000 students analytics, visualization and data storytelling courses — included at major multinationals like Nestlé, FedEx and Procter & Gamble.
• Authored the "Data Visualization for Dummies" book.
• Is a sought-after keynote speaker and TV-news commentator.

In this episode, Mico details:
• Her BI (business intelligence) and analytics framework that persuades executives with data storytelling.
• What the top BI tools are on the market today.
• The BI trends she’s observed that could predict the most popular BI tools of the coming years.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Automating Industrial Machines with Data Science and the Internet of Things (IoT)

Despite poor lighting on my face in today's video version (my bad!), we've got a fascinating episode with the brilliant (and well-lit!) Allegra Alessi, who details how data science is automating industrial machines.

Allegra:
• Is Product Owner for IoT (Internet of Things) devices at BOBST, a Swiss industrial manufacturing giant.
• Previously, she worked as a Product Owner and Data Scientist for Rolls-Royce in the UK and as a Data Scientist for Alstom, the enormous train manufacturing company, in Paris.
• She holds a Master’s in Engineering from Politecnico di Milano in Italy.

In this episode, Allegra details:
• How modern industrial machinery depends on data science for real-time performance analytics, predicting issues before they happen, and fully automating their operations.
• The tech stack her team uses to build data-driven IoT platforms.
• The key methodologies she uses to be effective at product management.
• The kinds of data scientists that might be ideally suited to moving into a product role.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

GPT-4 Has Arrived

SuperDataScience episode #666 — appropriate for an algorithm that has folks (quixotically) signing a letter to pause all A.I. development. In this first episode of the GPT-4 trilogy; in ten minutes, I introduces GPT-4's staggering capabilities.

A Leap in AI Safety and Accuracy

GPT-4 marks a significant advance over its predecessor, GPT-3.5, in terms of both safety and factual accuracy. It is reportedly 82% less likely to respond with disallowed content and 40% more likely to produce factually correct responses. Despite improvements, challenges like sociodemographic biases and hallucinations persist, although they are considerably reduced.

Academic and Professional Exam Performance

The prowess of GPT-4 becomes evident when revisiting queries initially tested on GPT-3.5. Its ability to summarize complex academic content accurately and its human-like response quality are striking. In one test, GPT-4’s output was mistaken for human writing by GPTZero, an AI detection tool, underscoring its sophistication. In another test, the uniform bar exam, GPT-4 scored in the 90th percentile, a massive leap from GPT-3.5's 10th percentile.

Multimodality

GPT-4 introduces multimodality, handling both language and visual inputs. This capability allows for innovative interactions, like recipe suggestions based on fridge contents or transforming drawings into functional websites. This visual aptitude notably boosted its performance in exams like the Biology Olympiad, where GPT-4 scored in the 99th percentile.

The model also demonstrates proficiency in numerous languages, including low-resource ones, outperforming other major models in most languages tested. This linguistic versatility extends to its translation capabilities between these languages.

The Secret Behind GPT-4’s Success

While OpenAI has not disclosed the exact number of model parameters in GPT-4, it's speculated that they significantly exceed GPT-3's 175 billion. This increase, coupled with more and better-curated training data, and the ability to handle vastly more context (up to 32,000 tokens), are likely contributors to GPT-4's enhanced performance.

Reinforcement Learning from Human Feedback (RLHF)

GPT-4 incorporates RLHF, a method that refines its output based on user feedback, allowing it to align more closely with desired responses. This approach has already proven effective in previous models like InstructGPT.

GPT-4 represents a monumental step in AI development, balancing unprecedented capabilities with improved safety measures. Its impact is far-reaching, offering new possibilities in various fields and highlighting the importance of responsible AI development and use. As we continue to explore its potential, the conversation around AI safety and ethics becomes increasingly vital.
The SuperDataScience GPT-4 trilogy is comprised of:
• #666 (today): an introductory overview by yours truly
• #667 (Tuesday): world-leading A.I.-monetization expert Vin Vashishta joins me to detail how you can leverage GPT-4 to your commercial advantage
• #668 (next Friday): world-leading A.I.-safety expert Jeremie Harris joins me to detail the (existential!) risks of GPT-4 and the models it paves the way for

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Open-Source Tools for Natural Language Processing

In today's episode, the brilliant Vincent Warmerdam regales us with invaluable ideas and open-source software libraries for developing A.I. (particularly Natural Language Processing) applications. Enjoy!

Vincent:
• Is an ML Engineer at Explosion, the German software company that specializes in developer tools for A.I. and NLP such as spaCy and Prodigy.
• Is renowned for several open-source tools of his own, including Doubtlab.
• Is behind an educational platform called Calmcode that has over 600 short and conspicuously enjoyable video tutorials about software engineering concepts.
• Was Co-Founder and Chair of PyData Amsterdam.
• Has delivered countless amusing and insightful PyData talks.
• Holds a Masters in Econometrics and Operations Research from Vrije Universiteit Amsterdam (VU Amsterdam)).

Today’s episode will appeal primarily to technical listeners as it focuses primarily on ideas and open-source software libraries that are indispensible for data scientists, particularly those developing A.I. or NLP applications.

In this episode, Vincent details:
• The prompt recipes he developed to enable OpenAI GPT architectures to perform tremendously helpful NLP tasks such as data labeling.
• The super-popular open-source libraries he’s developed on his own as well as with Explosion.
• The software tools he uses daily including several invaluable open-source packages made by other folks.
• How both linguistics and operations research are extremely useful fields to be a better NLP practitioner and ML practitioner, respectively.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Hg Capital's "Digital Forum"

At Hg Capital's "Digital Forum" in London, I delivered a keynote on "Getting Value from A.I." — my slides and the slickly-edited video production on YouTube are available now.

With a focus on B2B SaaS applications, over 45 minutes I covered:
1. What Deep Learning A.I. is and How it Works
2. Tasks that are Replaceable with A.I. vs Tasks that can be Augmented
3. How to Effectively Implement A.I. Research into Production

The audience engagement was terrific and the on-stage Q&A carried on afterward for an energizing 30 additional minutes. It felt like we could have kept on going much longer!

NLP with ChatGPT (and other LLMs)

Over 1400 people registered for yesterday's "NLP with ChatGPT (and other LLMs)" conference that I hosted in the O'Reilly Media platform. Kudos to speakers Sinan, Melanie and Shaan for making it a smashing success 🎉

This screenshot is a taste of what it looked like from inside the broadcasting platform, captained flawlessly by producers Joan Baker and Nurul Ishak, PMP.

The presenters each spent 30 minutes presenting on their topics and then engaged in riveting Q&A with the highly engaged attendees:
• Sinan Ozdemir: The A.I. entrepreneur and author introduced the theory behind Transformer Architectures and LLMs like BERT, T5, and GPT.
• Melanie Subbiah: A first author on the original GPT-3 paper, she led interactive demos of the broad range of capabilities of LLMs like ChatGPT.
• Shaan Khosla: A data scientist on my team at Nebula.io, he detailed practical tips on training, validating, and productionizing LLMs hands-on in Python.

I've heard word that, unusually for a live event in O'Reilly, the footage of this conference will be made available as a video within the platform. Stay tuned for details!

Getting Value From A.I.

My keynote on "Getting Value from A.I." — which I delivered at Hg Capital's "Digital Forum" in London — is now live on YouTube!

With a focus on B2B SaaS applications, over 45 minutes I covered:
1. What Deep Learning A.I. is and How it Works
2. Tasks that are Replaceable with A.I. vs Tasks that can be Augmented
3. How to Effectively Implement A.I. Research into Production

The audience engagement was terrific and the on-stage Q&A carried on afterward for an energizing 20 additional minutes. All of this is captured in the slickly-edited video production.

A.I. Talent and the Red-Hot A.I. Skills

What skills and traits do the best A.I. talent have? And how do you attract the best A.I. talent to your firm? Jaclyn Rice Nelson of Tribe AI, the world's most prestigious ML collective, fills us in in today's episode.

Jaclyn:
• Is Co-Founder/CEO of Tribe A.I., a "collective" of ML engineers and data scientists that drop into companies to accelerate their A.I. capabilities.
• Previously worked in senior roles at Google and CapitalG, Alphabet's growth equity fund.

In today's episode, she details:
• What characterizes the very best A.I. talent.
• What skills you should learn today to be tomorrow’s top A.I. talent.
• How to attract the top engineers and data scientists to your firm.
• The specific category of A.I. project that her clients are suddenly demanding tons of help with.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Simplifying Machine Learning

Today, Mariya Sha — host of the wildly popular "Python Simplified" YouTube channel (140k subscribers!) — taps her breadth of A.I. expertise to provide a fun and fascinating finale to SuperDataScience guest episodes for 2022.

Mariya:
• Is the mind behind the "Python Simplified" YouTube channel that makes advanced concepts (e.g., ML, neural nets) simple to understand.
• Her videos cover Python-related topics as diverse as data science, web scraping, automation, deep learning, GUI development, and OOP.
• Is renowned for taking complex concepts such as gradient descent or unsupervised learning and explaining them in a straightforward manner that leverages hands-on, real-life examples.
• Is pursuing a bachelor's in Computer Science (with a specialization in A.I. and Machine Learning) from the University of London.

Today’s episode should appeal to anyone who’s interested in or involved with data science, machine learning, or A.I.

In this episode, Mariya details:
• How the incredible potential of ML in our lifetimes inspired her to shift her focus from web-development languages like JavaScript to Python.
• Why automation and web scraping are critical skills for data scientists.
• How to make learning any apparently complex data science concept straightforward to comprehend.
• Her favorite Python libraries and software tools.
• One rarely-mentioned topic that every data scientist would benefit from.
• The pros and cons of pursuing a 100% remote degree in computer science.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.