Deep learning (DL) is a critical capability required by Yahoo product teams (ex. Flickr, Image Search) to gain intelligence from massive amounts of online data. Many existing DL frameworks require a separated cluster for deep learning, and multiple programs have to be created for a typical machine learning pipeline (see Figure 1). The separated clusters require large datasets to be transferred among them, and introduce unwanted system complexity and latency for end-to-end learning. As discussed in our earlier Tumblr post, we believe that deep learning should be conducted in the same cluster along with existing data processing pipelines to support feature engineering and traditional (non-deep) machine learning.

I had the opportunity to ponder this while taking cover in a bus shelter during a sudden Austin deluge. While weather forecasts driven by advanced modeling systems are quite useful, a part of me knows to always hedge against their inherent unreliability. In this sense it's not surprising that most of the early success of machine learning in the enterprise has clustered around low-error-cost problems. Models for targeting ads, or recommending products, friends or connections, do not wreak havoc when they misfire. Most end users of the system are not attending closely to the suggestions.

In May 2015, Stanford PhD student Andrej Karpathy wrote a blog post entitled The Unreasonable Effectiveness of Recurrent Neural Networks and released a code repository called Char-RNN. Both received quite a lot of attention from the machine learning community in the months that followed, spurring commentary and a number of response posts from other researchers. I remember reading these posts early last summer. Initially, I was somewhat underwhelmed--as at least one commentator pointed out, much of the generated text that Karpathy chose to highlight did not seem much better than results one might expect from high order character-level Markov chains. Here is a snippet of Karpathy's Char-RNN generated Shakespeare: And without access to affordable GPUs for training recurrent neural networks, I continued to experiment with Markov chains, generative grammars, template systems, and other ML-free solutions for generating text.

Deepdetect is a young open source deep-learning server and API designed to help in bridging the gap toward machine learning as a commodity. It originates from a series of applications built for a handful of large corporations and small startups. It has support for Caffe, one of the most appreciated libraries for deep learning, and it easily connects to a range of sources and sinks. This enables deep learning to fit into existing stacks and applications with reduced effort. Machine learning is the next expected commodity on the developer's stack.

As for who might be buying these computers, NVIDIA is positioning this machine for serious research purposes -- the first machines off of NVIDIA's assembly lines will go to ten universities including MIT, Stanford, NYU and Berkeley. The company is also positioning the DGX-1 as a key component of its new AI Driving machine-learning system called Drive PX, which helps to enable vehicle recognition at 180FPS. The goal of having such a relatively system is to make deploying such massive computing power much easier. "Data scientists and AI researchers today spend far too much time on home-brewed high performance computing solutions," Huang said in a press release. "The DGX-1 is easy to deploy and was created for one purpose: to unlock the powers of superhuman capabilities and apply them to problems that were once unsolvable."

Facebook is using artificial intelligence (AI) tech to describe photos to blind people. In a move to improve accessibility to site, the social network announced Monday that it is using AI to automatically detect what is shown in photos that users upload -- and will then narrate them to people who are visually impaired. In one example Facebook gives, someone uploads a photo of a couple smiling while wearing sunglasses by the coast. "Image may contain: two people, smiling, sunglasses, outdoor, water," the AI says. Previously, blind users would only be told that someone has uploaded a photo -- so if the user didn't manually add a description, they would have no idea what's in it.

Granted AI performs well at identifying, predicting how to respond through analyzing patterns and information, etc. However, AI is not completely hacker proof at this point. AI still requires close monitoring by humans. The bottom line is until the existing net infrastructure and digital platforms are Quantum based; it will be hard to make AI hacker proof and fully autonomous due to the risks with the existing digital technology. In the new battle between man and machine, how does artificial intelligence impact the security professional?

Most of the beginners start by learning regression. It is simple to learn and use, but does that solve our purpose? Because, you can do so much more than just Regression! Think of machine learning algorithms as an armory packed with axes, sword, blades, bow, dagger etc. You have various tools, but you ought to learn to use them at the right time.

Have you ever begun a Google search, only to click on the words the box lays before you? Tagged a friend's face when Facebook prompted it? Have you spoken to your iPhone? The artificial intelligence technology behind these tools is neither self-aware nor homicidal. But they are driven by a computational technique called machine learning, which is, at its simplest, a way to teach machines to teach themselves.

Machine Learning is one of the hottest new technologies impacting everything. Laurence Moroney meets with Joshua Gordon over coffee to talk about Machine Learning and his new show to help developers get started! P.S., in the video we used the word'class' - really, it's a tutorial series, very informal. Check out the first episode here: https://goo.gl/RpvlJl Subscribe to the Google Developers channel at http://goo.gl/mQyv5L