https://www.kleinerperkins.com/perspectives/infrastructure-in-23/ Infrastructure in ’23Kleiner PerkinsWednesday, February 22nd 2023 “I set aside some time at the beginning of the year to share what I believe to be the most dynamic and
What I Read: Geometric Deep Learning
https://thegradient.pub/towards-geometric-deep-learning/ Towards Geometric Deep LearningMichael Bronstein18.Feb.2023 “Geometric Deep Learning is an umbrella term for approaches considering a broad class of ML problems from the perspectives of symmetry and invariance.”
What I Read: Teach Computers Math
https://www.quantamagazine.org/to-teach-computers-math-researchers-merge-ai-approaches-20230215/ To Teach Computers Math, Researchers Merge AI ApproachesKevin HartnettFebruary 15, 2023 “Large language models still struggle with basic reasoning tasks. Two new papers that apply machine learning to math
What I Read: More Flexible Machine Learning
https://www.quantamagazine.org/researchers-discover-a-more-flexible-approach-to-machine-learning-20230207/ Researchers Discover a More Flexible Approach to Machine LearningSteve NadisFebruary 7, 2023 ““Liquid” neural nets, based on a worm’s nervous system, can transform their underlying algorithms on the fly,
What I Read: Machines Learn, Teach Basics
https://www.quantamagazine.org/machines-learn-better-if-we-teach-them-the-basics-20230201/ Machines Learn Better if We Teach Them the BasicsMax G. LevyFebruary 1, 2023 “A wave of research improves reinforcement learning algorithms by pre-training them as if they were human.”
What I Read: Optimizing Machine Learning Training Pipelines
https://medium.com/ntropy-network/bag-of-tricks-for-optimizing-machine-learning-training-pipelines-4f8d5cd3d432 Bag of tricks for optimizing machine learning training pipelinesArseny KravchenkoJan 5 “…we are constantly looking for ways to improve the efficiency of our machine learning pipelines, while keeping the