Inc. (CRM - Analyst Report) acquired MetaMind, a start-up specialized in offering "deep-learning" services. However, the financial terms of the deal remain under wraps. Founded in 2014, MetaMind received a combined investment of 8 million from Salesforce CEO Marc Benioff and Khosla Ventures in December the same year. MetaMind's "deep-learning" AI technique, according to Bloomberg "helps business users employ deep learning technology, which trains networks of computers to mimic the human brain's ability to recognize and analyze pictures or text without explicit instructions". MetaMind's technology and tools span natural language and image recognitions which help users to analyze data and make better decisions.

As a way to meet the demands of artificial intelligence, NVIDIA developed the first deep learning supercomputer called NVIDIA DGX-1, giving researchers and data scientists a new class of intelligent machines that could learn and perceive the world as humans do. The DGX-1, which was launched today at NVIDIA's GPU Technology Conference, is the first system designed specifically for deep learning. It comes fully integrated with hardware, deep learning software, and development tools for easy deployment. It also has a turnkey system that contains a new generation of GPU accelerators, which delivers the equivalent throughput of 250 x86 servers, the company said. The deep learning system allows researchers to reduce the time for training larger, more sophisticated deep neural networks.

Here's What We Can Expect from Deep Learning in 2016 and Beyond

Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you'll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. We'll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

Salesforce is set to acquire deep learning start up MetaMind in an effort to bolster it artificial intelligence capabilities. While terms of the deal have not been released, it would appear to be an "acqhire" based agreement, as Salesforce will integrate MetaMind's technology into its current services. Long-term intentions have not been announced, though MetaMind's capabilities will be used to automate and personalize customer support in the first instance. "With MetaMind and Salesforce coming together, we'll be able to offer customers real AI solutions with breakthrough capabilities that further automate and personalize customer support, marketing automation, and many other business processes," said MetaMind Founder Richard Socher. "We'll extend Salesforce's data science capabilities by embedding deep learning within the Salesforce platform."

The folks over at x.ai – creators of Amy, the artificial intelligence answer to scheduling meetings – have had a shot at showing exactly what it looks like inside their bot's brain, using AI, of course. The team used a powerful deep-learning model, a Recurrent Neural Network (RNN), to trawl 500,000 words in its database, looking at their sequence in a sentence to understand what they mean, then predicting how to categorize them. Don't miss our biggest TNW Conference yet! Without a human ever telling the RNN the definitions of different word groups, it has managed to understand that Stanford is different from Instagram, and that Jesse, Luke and Jason are names. This data was cut to down to the 3,500 most frequently used words and has then been projected into a 2D shape in order to show the relationships the AI has made between different words.

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.

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."

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.