Learning from Machine Learning Seth Levine

In this episode, we are joined by Chris Van Pelt, co-founder of Weights & Biases and Figure Eight/CrowdFlower. Chris has played a pivotal role in the development of MLOps platforms and has dedicated the last two decades to refining ML workflows and making machine learning more accessible.
Throughout the conversation, Chris provides valuable insights into the current state of the industry. He emphasizes the significance of Weights & Biases as a powerful developer tool, empowering ML engineers to navigate through the complexities of experimentation, data visualization, and model improvement. His candid reflections on the challenges in evaluating ML models and addressing the gap between AI hype and reality offer a profound understanding of the field's intricacies.
Drawing from his entrepreneurial experience co-founding two machine learning companies, Chris leaves us with lessons in resilience, innovation, and a deep appreciation for the human dimension within the tech landscape. As a Weights & Biases user for five years, witnessing both the tool and the company's growth, it was a genuine honor to host Chris on the show.

References and Resources
https://wandb.ai/
https://www.youtube.com/c/WeightsBiases
https://x.com/weights_biases
https://www.linkedin.com/company/wandb/
https://twitter.com/vanpelt

Resources to learn more about Learning from Machine Learning
https://www.youtube.com/@learningfrommachinelearninghttps://www.linkedin.com/company/learning-from-machine-learninghttps://mindfulmachines.substack.com/https://www.linkedin.com/in/sethplevine/https://medium.com/@levine.seth.p

This episode features Dr. Michelle Gill, Tech Lead and Applied Research Manager at NVIDIA, working on transformative projects like BioNemo to accelerate drug discovery through AI. Her team explores Biofoundation models to enable researchers to better perform tasks like protein folding and small molecule binding.
Michelle shares her incredible journey from wet lab biochemist to driving cutting edge AI at NVIDIA. Michelle discusses the overlap and differences between NLP and AI in biology. She outlines the critical need for better machine learning representations that capture the intricate dynamics of biology.
Michelle provides advice for beginners and early career professionals in the field of machine learning, emphasizing the importance of continuous learning and staying up to date with the latest tools and techniques. She also shares insights on building successful multidisciplinary teams
After hearing her fascinating PyData NYC keynote, it was such an honor to have her on the show to discuss innovations at the intersection of biochemistry and AI.
References and Resources
https://michellelynngill.com/
Michelle Gill - Keynote - PyData NYC https://www.youtube.com/watch?v=ATo2SzA1Pp4
AlexNet
AlphaFold - https://www.nature.com/articles/s41586-021-03819-2
OpenFold - https://www.biorxiv.org/content/10.1101/2022.11.20.517210v1
BioNemo - https://www.nvidia.com/en-us/clara/bionemo/
NeurIPS - https://nips.cc/
Art Palmer - https://www.biochem.cuimc.columbia.edu/profile/arthur-g-palmer-iii-phd
Patrick Loria - https://chem.yale.edu/faculty/j-patrick-loria
Scott Strobel - https://chem.yale.edu/faculty/scott-strobel
Alexander Rives - https://www.forbes.com/sites/kenrickcai/2023/08/25/evolutionaryscale-ai-biotech-startup-meta-researchers-funding/?sh=648f1a1140cf
Deborah Marks - https://sysbio.med.harvard.edu/debora-marks
Resources to learn more about Learning from Machine Learning
https://www.linkedin.com/company/learning-from-machine-learninghttps://mindfulmachines.substack.com/https://www.linkedin.com/in/sethplevine/https://medium.com/@levine.seth.p

This episode features co-founder and CEO of Explosion, Ines Montani. Listen in as we discuss the evolution of the web and machine learning, the development of SpaCy, Natural Language Processing vs. Natural Language Understanding, the misconceptions of starting a software company, and so much more! Ines is a software developer working on Artificial Intelligence and Natural Language Processing technologies.
She's the co-founder and CEO of Explosion, the company behind SpaCy, one of the leading open-source libraries for NLP in Python and Prodigy, an annotation tool to help create training data for Machine Learning Models. Ines has an academic background in Communication Science, Media Studies and Linguistics and has been coding and designing websites since she was 11. She's been the keynote speaker at Python and Data Science conferences around the world.
Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts.
Listen on YouTube: https://youtu.be/XNFqFT-DZwo?si=Aj75TmsCyBQTyWqq
Listen on your favorite podcast platform:
https://rss.com/podcasts/learning-from-machine-learning/1190862/

References in the Episode
https://explosion.ai/https://spacy.io/https://ines.io/Applied NLP ThinkingInes Montani - How to Ignore Most Startup Advice and Build a Decent Software Business Ines Montani: Incorporating LLMs into practical NLP workflowsInes Montani (spaCy) - Large Language Models from Prototype to Production [PyData Südwest] Confectionhttps://github.com/explosion/confection
Resources to learn more about Learning from Machine Learning
https://www.linkedin.com/company/learning-from-machine-learninghttps://mindfulmachines.substack.com/https://www.linkedin.com/in/sethplevine/https://medium.com/@levine.seth.p

This episode features Lewis Tunstall, machine learning engineer at Hugging Face and author of the best selling book Natural Language Processing with Transformers. He currently focuses on one of the hottest topic in NLP right now reinforcement learning from human feedback (RLHF). Lewis holds a PhD in quantum physics and his research has taken him around the world and into some of the most impactful projects including the Large Hadron Collider, the world's largest and most powerful particle accelerator. Lewis shares his unique story from Quantum Physicist to Data Scientist to Machine Learning Engineer.
Resources to learn more about Lewis Tunstall
https://www.linkedin.com/in/lewis-tunstall/https://github.com/lewtunReferences from the Episode
https://www.fast.ai/https://jeremy.fast.ai/SetFit - https://arxiv.org/abs/2209.11055Proximal Policy OptimizationInstructGPTRAFT BenchmarkBidirectional Language Models are Also Few-Shot LearnersNils Reimers - Sentence TransformersJay Alammar - Illustrated TransformerAnnotated TransformerMoshe Wasserblat, Intel, NLP, Research ManagerLeandro von Werra, Co-Author of NLP with Transformers, Hugging Face ResearcherLLMSys - https://lmsys.org/LoRA - Low-Rank Adaptation of Large Language Models
Resources to learn more about Learning from Machine Learning
https://www.linkedin.com/company/learning-from-machine-learninghttps://mindfulmachines.substack.com/https://www.linkedin.com/in/sethplevine/https://medium.com/@levine.seth.p

The episode features Paige Bailey, the lead product manager for generative models at Google DeepMind. Paige's work has helped transform the way that people work and design software using the power of machine learning. Her current work is pushing the boundaries of innovation with Bard and the soon to be released Gemini.
Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts.
Resources to learn more about Paige Bailey
https://twitter.com/DynamicWebPaige
https://github.com/dynamicwebpaige
References from the Episode
Diamond Age - Neal Stephenson - https://amzn.to/3BCwk4n
Google Deepmind - https://www.deepmind.com/
Google Research - https://research.google/
Jax - https://jax.readthedocs.io/en/latest/
Jeff Dean - https://research.google/people/jeff/
Oriol Vinyals - https://research.google/people/OriolVinyals/
Roy Frostig - https://cs.stanford.edu/~rfrostig/
Matt Johnson - https://www.linkedin.com/in/matthewjamesjohnson/
Peter Hawkins - https://github.com/hawkinsp
Skye Wanderman-Milne - https://www.linkedin.com/in/skye-wanderman-milne-73887b29/
Yash Katariya - https://www.linkedin.com/in/yashkatariya/
Andrej Karpathy - https://karpathy.ai/
Resources to learn more about Learning from Machine Learning
https://www.linkedin.com/company/learning-from-machine-learning
https://www.linkedin.com/in/sethplevine/
https://medium.com/@levine.seth.p

This episode we welcome Sebastian Raschka, Lead AI Educator at Lightning and author of Machine Learning with Pytorch and Scikit-Learn to discuss the best ways to learn machine learning, his open source work, how to use chatGPT, AGI, responsible AI and so much more. Sebastian is a fountain of knowledge and it was a pleasure to get his insights on this fast moving industry. Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts. Resources to learn more about Sebastian Raschka and his work:
https://sebastianraschka.com/
https://lightning.ai/
Machine Learning with Pytorch and Scikit-Learn
Machine Learning Q and AI
Resources to learn more about Learning from Machine Learning and the host: https://www.linkedin.com/company/learning-from-machine-learning
https://www.linkedin.com/in/sethplevine/
https://medium.com/@levine.seth.p
twitter
References from Episode
https://scikit-learn.org/stable/
http://rasbt.github.io/mlxtend/
https://github.com/BioPandas/biopandas
Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch
Andrew Ng - https://www.andrewng.org/
Andrej Karpathy - https://karpathy.ai/
Paige Bailey - https://github.com/dynamicwebpaige

Contents
01:15 - Career Background
05:18 - Industry vs. Academia
08:18 - First Project in ML
15:04 - Open Source Projects Involvement
20:00 - Machine Learning: Q&AI
24:18 - ChatGPT as Brainstorm Assistant
25:38 - Hype vs. Reality
27:55 - AGI
31:00 - Use Cases for Generative Models
34:01 - Should the goal to be to replicate human intelligence?
39:18 - Delegating Tasks using LLM
42:26 - ML Models are overconfident on Out of Distribution
44:54 - Responsible AI and ML
45:59 - Complexity of ML Systems
47:26 - Trend for ML Practitioners to move to AI Ethics
49:27 - What advice would you give to someone just starting out?
52:20 - Advice that you’ve received that has helped you
54:08 - Andrew Ng Advice
55:20 - Exercise of Implementing Algorithms from Scratch
59:00 - Who else has influenced you?
01:01:18 - Production and Real-World Applications - Don’t reinvent the wheel
01:03:00 - What has a career in ML taught you about life?