Today we're excited to announce the availability of NVIDIA DIGITS 4. DIGITS 4 introduces a new object detection workflow and DetectNet, a new deep neural network for object detection that enables data scientists and researchers to train models that can detect instances of faces, pedestrians, traffic signs, vehicles and other objects in images. Object detection is one of the most challenging problems in computer vision and is the first step in several computer vision applications. The goal of an object detection system is to detect all instances of objects of a known category in an image. Figure 1 shows the final results of an object detection system trained with DIGITS which can detect vehicles on a construction site. Starting with a successful vehicle detection system like this, you can solve a number of other problems such as recognizing the makes and models of the vehicles, counting and tracking vehicle locations over time, generating natural language descriptions of the images and so on.

Kyulux Inc announced that it has a license with Harvard University's Molecular Space Shuttle deep learning system to develop new display and lighting application materials, according to a news release. Kyulux is an advanced materials start-up company that commercializes thermally activated delayed fluorescence (TADF) OLED display and lighting technology. The Molecular Space Shuttle is an artificial intelligence platform designed by Alán Aspuru-Guzik's group at Harvard's chemistry and chemical biology department, where Aspuru-Guzik is a professor.

FANUC, the world's largest maker of industrial robots, plans to start connecting 400,000 of their installed systems by the end of this year. The goal is to collect data about their operations and, through the use of deep learning, improve performance. Similarly, Kuka is building a deep-learning AI network for their industrial robots. FANUC is now moving forward to connect all its manufacturing robots. The system proactively detects and informs of a potential equipment or process problem before unexpected downtime occurs.

Stellaris 1.2.5 hotfix patch released, with AI fixes Calling all coders: is AI coming for your job?

I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simple your model is to the quality of the results you get out of it blows past your expectations, and this was one of those times. What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I've in fact reached the opposite conclusion). Fast forward about a year: I'm training RNNs all the time and I've witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me. This post is about sharing some of that magic with you. We'll train RNNs to generate text character by character and ponder the question "how is that even possible?" By the way, together with this post I am also releasing code on Github that allows you to train character-level language models based on multi-layer LSTMs. You give it a large chunk of text and it will learn to generate text like it one character at a time. You can also use it to reproduce my experiments below.

Will Content Marketing Die To Robotic AI?

Intel Scalable Systems Framework (Intel SSF) reduces confusion given the wealth of new technologies now available to HPC customers, and offers guidance for the right mix of balanced and validated hardware and software technologies. Intel SSF incorporates a host of software and hardware technologies including Intel Omni-Path Architecture (Intel OPA), Intel Optane SSDs built on 3D XPoint technology, and new Intel Silicon Photonics – plus it incorporates Intel's compute and storage products, including Intel Xeon processors, Intel Xeon Phi processors, and Intel Enterprise Edition for Lustre* software. Benchmarks show that a combination of Intel SSF technologies (Intel Xeon Phi and Intel OPA) provide significantly better scaling and performance when training deep learning neural networks than GPU-based products on well-known benchmarks such as AlexNet and GoogleNet [1]. These and other deep-learning benchmarks can be viewed on the Intel machine learning portal. Intel Xeon Phi processors deliver superior neural networking training performance using up to seventy two (72) processing cores per processor where each core contains two Intel AVX-512 vector processing units.

By beating humans at games of Go and Jeopardy, artificial intelligence engines like Google's DeepMind and IBM's Watson have captured attention for their promise of solving bigger human problems. Watson, for example, is being enlisted to help doctors predict cancer in patients. The American internet pioneer Douglas Engelbart suggests that AI's grandest promise is the amplification of human ability. Whether it's automating rote cognitive tasks like tagging people in photos or assisting in complex work flows like cancer treatment, the human-augmentation promise feels almost inevitable in every product and domain. Self-driving cars rely on massive amounts of data collected over several years from efforts like Google's people-powered street canvassing, which provides the ability to "see" roads.

Benchmarks are an important tool for measuring performance, but in a rapidly evolving field it can be difficult to keep up with the state of the art. Recently Intel published some incorrect "facts" about their long promised Xeon Phi processors. Few fields are moving faster right now than deep learning. Today's neural networks are 6x deeper and more powerful than just a few years ago. There are new techniques in multi-GPU scaling that offer even faster training performance.

Ford outlined a few of the ways it's aiming to ship driverless cars by 2021, and part of the plan involves acquisitions. CEO Mark Fields revealed at a press event in Palo Alto today that the automaker acquired SAIPS, an Israeli company focusing on machine learning and computer vision. It's also partnering exclusively with Nirenberg Neuroscience, to bring more "humanlike intelligence" to machine learning components of driverless car systems. SAIPS' technology brings image and video processing algorithms, as well as deep learning tech focused on processing and classifying input signals, all key ingredients in the special sauce that makes up autonomous vehicle tech. This company's expertise should help with on-board interpretation of data captured by sensors on Ford's self-driving cars, and turning that data into usable info for the car's virtual driver system.