Today we say goodbye to @DeepIndaba after six inspiring days in Kigali rich with keynotes, tutorials, workshops, mentorship circles, and insightful posters that kept us learning non-stop. Some of us were only able to make it down to #DLI2025 because of your generous support.

The opportunity gap in AI is more striking than ever. We talk way too much about those receiving $100M or whatever for their jobs, but not enough those asking for <$1k to present their work. For 3rd year in a row, @ml_collective is raising funds to support @DeepIndaba attendees.

The opportunity gap in AI is more striking than ever. We talk way too much about those receiving $100M or whatever for their jobs, but not enough those asking for <$1k to present their work. For 3rd year in a row, @ml_collective is raising funds to support @DeepIndaba attendees.

Next Research Jam is in 14 hours, tomorrow morning at 8am PT. Stop by this virtual lab meeting to hear research ideas and updates on projects in progress! Zoom info at mlcollective.org/events/rese…

Next Research Jam is in 14 hours, tomorrow morning at 8am PT. Stop by this virtual lab meeting to hear research ideas and updates on projects in progress! Zoom info at mlcollective.org/events/rese…

This week at Deep Learning: Classics and Trends we're kicking off a new five part mini-series on LLM Interpretability. Up first: @thesubhashk shows how LLMs represent numbers on a helix and use it to add! Join Friday at 10am PT, zoom here: mlcollective.org/dlct/

Get ready for our first LinkedIn post!

One of my all-time favorite A.I. researchers, Dr. Jason Yosinski (@jasonyo), is my guest today! He details how his startup is using ML to collect wind energy more efficiently and digs into visualizing/understanding deep neural networks. Watch here: superdatascience.com/789 Jason: • Is Co-Founder and CEO of @Windscape_AI, a startup using ML to increase the efficiency of energy generation via wind turbines. • Is Co-Founder and President of the ML Collective, a research group that’s open to ML researchers anywhere. • Was a Co-Founder of the A.I. Lab at the ride-share company Uber. • Holds a PhD in Computer Science from Cornell, during which he worked at the NASA Jet Propulsion Laboratory, Google DeepMind and with the eminent Yoshua Bengio in Montreal. • His work has been featured in The Economist, on the BBC and, coolest of all, in an XKCD comic! Today’s episode gets fairly technical in parts so may be of greatest interest to hands-on practitioners like data scientists and ML engineers, although there are also parts that will appeal to anyone keen to hear how ML is being used to produce more clean energy. In today’s episode, Jason details: • How ML can make wind direction more predictable, thereby making wind turbines and power grids in general more efficient. • How to infer what individual neurons in a deep learning model are doing by using visualizations. • Why freezing a particular layer of a neural net prior to doing any training at all can lead to better results. • How you can get involved in a cutting-edge research community no matter where you are in the world. • What traits make for successful A.I. entrepreneurs. Many thanks to @Crawlbase for supporting this episode of Super Data Science, enabling the show to be freely available on all major podcasting platforms as well as the video version we publish on YouTube. This is Episode #789! #superdatascience #machinelearning #ai #climatechange #windenergy