goodlisten

Clip

The Challenges of Processing Large Amounts of Data Generated by the Large Hadron Collider
listen on Spotify
1:24:12 - 1:26:45 (02:33)

The Large Hadron Collider generates large amounts of data, which can pose a challenge due to its volume. Off-the-shelf machine learning packages can be used to help clean up the data by filtering signal noise.

Similar Clips
In this episode, the host and the guest discuss the limitations of science, such as the mystery of consciousness, and whether artificial intelligence may help answer these questions.
54:01 - 55:24 (01:23)
listen on Spotify
Science
Summary

In this episode, the host and the guest discuss the limitations of science, such as the mystery of consciousness, and whether artificial intelligence may help answer these questions.

Chapter
The Future of Understanding Black Holes
Episode
Leonard Susskind: Quantum Mechanics, String Theory, and Black Holes
Podcast
Lex Fridman Podcast
Paul Dubois recognized in the mid-90s that scientists needed a higher level language to tie together fundamental mathematical algorithms for scientific computing, which led to the development of packages like PyTorch, TensorFlow, and NumPy.
2:42:19 - 2:48:23 (06:03)
listen on SpotifyListen on Youtube
Scientific Computing
Summary

Paul Dubois recognized in the mid-90s that scientists needed a higher level language to tie together fundamental mathematical algorithms for scientific computing, which led to the development of packages like PyTorch, TensorFlow, and NumPy.

Chapter
The History and Importance of TensorFlow in the Data Science Community
Episode
#341 – Guido van Rossum: Python and the Future of Programming
Podcast
Lex Fridman Podcast
The basics of deep learning, such as convolutional models, will most likely still exist in some form in five years.
48:07 - 49:52 (01:45)
listen on Spotify
Deep Learning
Summary

The basics of deep learning, such as convolutional models, will most likely still exist in some form in five years. Directionally, eager execution and graphs are being combined to make programming feel more natural.

Chapter
Cohesion and Change in Programming Teams
Episode
Rajat Monga: TensorFlow
Podcast
Lex Fridman Podcast
Andrew Ng discusses the future of AI in regards to the Turing test, natural language processing and sequence modeling, and the development of new research for other applications.
1:05:36 - 1:07:32 (01:55)
listen on Spotify
AI
Summary

Andrew Ng discusses the future of AI in regards to the Turing test, natural language processing and sequence modeling, and the development of new research for other applications.

Chapter
Using Games to Advance Technology
Episode
Oriol Vinyals: DeepMind AlphaStar, StarCraft, Language, and Sequences
Podcast
Lex Fridman Podcast
While simulations can be a useful tool for machine learning, relying solely on simulated data can hinder perpetual improvement, making it necessary to eventually incorporate real data to account for real-world limitations and challenges.
1:05:25 - 1:13:04 (07:39)
listen on Spotify
Machine Learning
Summary

While simulations can be a useful tool for machine learning, relying solely on simulated data can hinder perpetual improvement, making it necessary to eventually incorporate real data to account for real-world limitations and challenges. This is particularly important for developing reliable systems and addressing issues that may not arise in purely simulated environments.

Chapter
The Role of Simulation in Machine Learning
Episode
#108 – Sergey Levine: Robotics and Machine Learning
Podcast
Lex Fridman Podcast