Elon Musk has not been shy about his concerns over artificial intelligence turning evil. So it wasn't a surprise in December when Musk announced the formation of OpenAI, an open-source, non-profit focused on advancing "digital intelligence in the way that is most likely to benefit humanity as a whole." That's all well and good, but not much has been revealed about what exactly OpenAI is working on. OpenAI's co-founder and CTO Greg Brockman told Tech Insider that OpenAI is primarily focusing on advancing machine learning, which is the technology that enables computers to learn how to complete tasks through experience. Specifically, the company is focusing on two key types of machine learning that every major tech company is investing in right now.

Lessons from Kaggle competitions, including why XG Boosting is the top method for structured problems, Neural Networks and deep learning dominate unstructured problems (visuals, text, sound), and 2 types of problems for which Kaggle is suitable. Here is a summary of Anthony Goldbloom presentation at the Data Science Chicago Meetup, Nov 2 2015. Nice to see Anthony coming from financial statistics/econometrics (he mentioned his first job was with the Reserve Bank of Australia).

Deep learning with Java script: The library allows you to formulate and solve Neural Networks in Javascript, and was originally written by Andrej Karpathy (a PhD student at Stanford). However, the library has since been extended by contributions from the community.

Anyone have an issue with keras/theano on ubuntu 16.04? Has anyone seen this issue where keras seems to want to run an epoch and just sits there? I pulled theano and keras from git. Have you tried the built-in Keras example scripts (link here)?

Recurrent Neural Networks (RNN) make use of sequential information. Unlike traditional neural networks, where it is assumed that all inputs and outputs are independent of one another, RNNs are reliant on preceding computations and what has previously been calculated. RNNs can be conceptualized as a neural network unrolled over time. Where you would have different layers in a regular neural network, you apply the same layer to the input at each timestep in an RNN, using the output, i.e. the state of the previous timestep as input. Connections between entities in a RNN form a directed cycle, creating a sort of internal memory, that helps the model leverage long chains of dependencies.

Over the past few years the field of Machine Learning has entered the general parlance. From free massive open online courses to image recognition benchmarks being broken and decades of Atari games being mastered. During the same period developers have witnessed the release of several popular open source frameworks and libraries. The chart below shows different open source machine learning projects by initial commit date and programming language. The size represents the popularity of a project based on number of Github stargazers.

RE•WORK will host its annual East Coast events on Deep Learning and the Internet of Things in Boston on 12 & 13 May. Over 300 machine learning and IoT enthusiasts and experts will come together to hear keynote presentations, panel discussions, fireside chats and to explore the startup showcase area. The Deep Learning Summit brings together leaders from industry, academia and startups to explore advances in deep learning methods and techniques, as well as their business applications in areas including finance, manufacturing, healthcare & transportation. Confirmed speakers and presentations include: -Keynote presentation from deep learning expert Yoshua Bengio, Full Professor at Université de Montréal Professor Bengio is also Head of the Machine Learning Laboratory, Co-director of the CIFAR Neural Computation & Adaptive Perception program, and editor of many prestigious machine learning publications. Daniel is also a Research Affiliate at the MIT Media Lab.

The match sent a shock wave around the world. AlphaGo, an artificial intelligence program developed by Britain-based Google DeepMind, defeated a top Go player. The Go board is larger than those used for chess and shogi, and many believed AI programs would defeat humans 10 years from now at the earliest. Remarkable technological development demonstrated ability in a field requiring a "wide perspective" to read situations every time a move was made. This is the third AI boom since the end of World War II. In the last two, AI programs could not do anything unless they were taught "knowledge" and "ways of thinking" by humans.

Meet other students from Berlin and Brandenburg universities interested in Artificial Intelligence and related fields like Natural Language Processing, Deep Learning, Computational Linguistics, etc. We will have a short impulse talk about a current AI-related topic (15 minutes) and discussion (15-20 minutes) by somebody from either industry, science, or also from the student community. During the open-ended get-together we mingle and students will have the opportunity to meet potential employers, co-founders, project partners, or find inspiration for their own project ideas. Our meetup takes place at gtec.berlin (German Tech Entrepreneurship Center), 4th floor of the Admin Building of the European School of Management and Technology (ESMT).

If you don't know, docker has been around for a few years now to help with the deployment of applications using operating system level virtualization on Linux. It has a bunch of great features to help with this, but I would say the main use case for docker is that you can run any docker container on any docker host and it will run. "Docker containers run on any computer, on any infrastructure and in any cloud." Unfortunately that is wrong for deep learning applications. For any serious deep learning application, you need NVIDIA graphics cards, otherwise it could take months to train your models.