A visualization of a convolutional neural network, which has a color scheme similar to OpenAI's. Since announced in December 2015, Elon Musk and Sam Altman's OpenAI has recruited some of the foremost names in modern artificial intelligence research. Its poached top talent from giants in the field--research director Ilya Sutskever cut his teeth at Google Brain after studying with A.I. veterans Geoff Hinton and Andrew Ng. In their latest round of hires, the company is starting to diversify its staff. OpenAI's newest recruits come from Google Brain (where they have previously tapped), but also from startups and a trading firm.

Silicon Valley is full of chatter about artificial intelligence, deep learning neural networks, and machine learning. And Intel, the world's biggest chip maker, is becoming a lot more conversant in that chatter today. Intel executive Diane Bryant announced today that the company is working on a next-generation version of its high-end server chip, the Xeon Phi, for A.I. applications. Baidu will use the upcoming Xeon Phi chips in the data centers it is building for its Deep Speech platform, where its networks will be able to parse natural language speech as quickly and accurately as possible. By 2020, there will be more servers handling data analytics than any other workload, Bryant said.

If you don't know, Julia is "a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments." Julia is fast, and enjoys support from and integration with the Jupyter notebook environment. Julia can call C directly without a wrapper, integrates top tier open source C and Fortran code into its Base library, and can easily call Python as well. Julia is built for parallel and cloud computing, and has particular interest from the analytics and scientific computing communities. According to KDnuggets' most recent analytics software poll, Julia placed 8th on the list of most used programming languages.

If you passed high school math and can hack around in Python, I want to teach you Deep Learning. Edit: 50% Coupon Code: "mltrask" (expires August 26) I've decided to write a Deep Learning book in the same style as my blog, teaching Deep Learning from an intuitive perspective, all in Python, using only numpy. I wanted to make the lowest possible barrier to entry to learn Deep Learning. The Problem with most entry level Deep Learning resources these days is that they either assume advanced knowledge of Calculus, Linear Algebra, Differential Equations, and perhaps even Convex Optimization, or they just teach a "black box" framework like Torch, Keras, or TensorFlow (where you just hit "train" but you don't actually know what's going on under the hood). Both have their appropriate audience, but I don't believe that either are appropriate for your average python hacker looking for a 101 on the fundamentals.

Following a survey we did back in 2014, I posted on Finextra about how machine learning technologies are progressing from academia, robotics and medical engineering into financial services. At that time, there seemed to be some hesitancy with only 12% of 80 quant-savvy finance professionals saying they used machine learning in their workflows. Has Use of Machine Learning Changed? To provide some answers, we decided to survey attendees at our 2016 finance conference. Our sample was mainly made up of numerically- and model-led quant roles and risk management roles and therefore those most likely to use machine learning.

Here's What We Can Expect from Deep Learning in 2016 and Beyond Is Humanising Chatbots the Next Inevitable Improvement?

Here's What We Can Expect from Deep Learning in 2016 and Beyond Is Humanising Chatbots the Next Inevitable Improvement?

Nano-AI: Nanotech powered compute hardware could surpass the GPU and lead to big breakthroughs in Deep Learning, enabling stochastic processes even beyond bleeding edge work like that in the press today. Imagine nano-enabled hardware with capabilities beyond IBM's creation of the world's first artificial phase-change neurons or Intel's acquistion of Nervana, a hardware-accelerated deep-learning-as-a-service, an automated software-generating startup that Android creator Andy Rubin's Playground incubator said when it was acquired "will replace most of the explicitly designed software we use today."

Microsoft's recent purchase of LinkedIn for a reported 26.2 billion may be the biggest acquisition news so far in 2016. But Twitter is betting that its own recent acquisition of Magic Pony Technology – a neural networks/machine learning company – for a mere 150 million will pay big dividends down the stretch. Commenting on the acquisition in a recent Twitter blog, Twitter CEO and co-founder Jack Dorsey said, "Machine learning is increasingly at the core of everything we build at Twitter." Dorsey went on to say that, "Magic Pony's machine learning technology will help us build strength into our deep learning teams with world-class talent, so Twitter can continue to be the best place to see what's happening and why it matters, first. We value deep learning research to help make our world better, and we will keep doing our part to share our work and learnings with the community." Magic Pony Technology is the third machine-learning startup that Twitter has acquired since Madbits in 2014, which begs the question: Why is Twitter so heavily focused on machine learning?

Deep learning has a wide range of applications, from speech recognition, computer vision, to self-driving cars and mastering the game of Go. While the concept is intuitive, the implementation is often heuristic and tedious. We will take a stab at simplifying the process, and make the technology more accessible. We illustrate our approach with the venerable CIFAR-10 dataset. The following code snippet will download the data from its known location to a folder "data/cifar" inside the current workspace.