AI And Deep Learning. From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.
a16z Podcast: The Product Edge in Machine Learning Startups. A lot of machine learning startups initially feel a bit of “impostor syndrome” around competing with big companies, because (the argument goes), those companies have all the data; surely we can’t beat that! Yet there are many ways startups can, and do, successfully compete with big companies. You can actually achieve great results in a lot of areas even with a relatively small data set, argue the guests on this podcast, if you build the right product on top of it.
When Humanity Meets AI. Andreessen Horowitz Distinguished Visiting Professor of Computer Science is Fei-Fei Li [who publishes under Li Fei-Fei], associate professor at Stanford University, argues we need to inject a stronger humanistic thinking element to design and develop algorithms and A.I. that can co-habitate with people and in social (including crowded) spaces.
Accelerating Understanding: Deep Learning, Intelligent Applications, and GPUs. The Institute for Scientific Computing Research (ISCR) sponsored this talk entitled "Deep Learning" on April 16, 2015, at the Lawrence Livermore National Laboratory. The talk was presented by Yann LeCun, director of AI research at Facebook and professor of data science, computer science, neural science and electrical engineering at NYU.
A DARPA Perspective on Artificial Intelligence. What's the ground truth on artificial intelligence (AI)? In this video, John Launchbury, the Director of DARPA's Information Innovation Office (I2O), attempts to demystify AI: what it can do, what it can't do, and where it is headed.
Andrej Karpathy's http://cs231n.github.io/convolutional-networks/. Make sure to scroll down to see the cool animation that shows you what a convolution is.">class notes from Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition
Play with Andrej Karpathy's ConvNetJS demo which trains a Convolutional Neural Network on the MNIST digits dataset (consisting of handwritten numerical digits) in the comfort of your own browser.
DeepMind published a paper in Nature describing a system that combines reinforcement learning with deep learning to learned to play a set of Atari video games, some with great success (like Breakout) and others terribly (like Montezuma's Revenge).