Intel today unveiled new hardware and software targeting the artificial intelligence (AI) market, which has emerged as a focus of investment for the largest data center operators. The chipmaker introduced an FPGA accelerator that offers more horsepower for companies developing new AI-powered services. The Intel Deep Learning Inference Accelerator (DLIA) combines traditional Intel CPUs with field programmable gate arrays (FPGAs), semiconductors that can be reprogrammed to perform specialized computing tasks. FPGAs allow users to tailor compute power to specific workloads or applications. The DLIA is the first hardware product emerging from Intel's $16 billion acquisition of Altera last year.

We are at an incredible moment in technology history. Thanks to cloud computing power, more advanced algorithms and the availability of massive amounts of data, the artificial intelligence (AI) field has exploded – allowing computer scientists to create technology many of us only dreamed about just a few years ago. Using deep learning, computers today can recognize the words in a conversation about as well as a person does and provide real-time translation. Thanks to advances in fields such as reinforcement learning, we are making tangible progress in the effort to build systems that have true artificial intelligence. At Microsoft, we believe everyone deserves to be able to take advantage of these breakthroughs, in both their work and personal lives.

OpenAI, the nonprofit artificial intelligence research organization co-founded by tech visionaries Elon Musk, Sam Altman, Greg Brockman, and Ilya Sutskever, announced Tuesday that it was partnering with Microsoft to advance its work in AI. As part of the deal, announced via a blog post, Microsoft Azure will act as the primary cloud platform for OpenAI. In the post, Microsoft said that it is "committed to democratizing AI and making it accessible to everyone," and that is a mission that they share with OpenAI. As part of the deal, Microsoft isn't just providing infrastructure, it will also help OpenAI "advance their research and create new tools and technologies that are only possible with the cloud," the post said. The reasoning behind OpenAI's decision to partner with Microsoft, according to the post, is due to Microsoft's focus on deep learning, open source technologies, and the capabilities available in tools such as Azure Batch, Azure Machine Learning, and the Microsoft Cognitive Toolkit.

For millennia, we surpassed the other intelligent species with which we share our planet--dolphins, porpoises, orangutans, and the like--in almost all skills, bar swimming and tree-climbing. In recent years, though, our species has created new forms of intelligence, able to outperform us in other ways. One of the most famous of these artificial intelligences (AIs) is AlphaGo, developed by Deepmind. In just a few years, it has learned to play the 4,000-year-old strategy game, Go, beating two of the world's strongest players. Other software developed by Deepmind has learned to play classic eight-bit video games, notably Breakout, in which players must use a bat to hit a ball at a wall, knocking bricks out of it.

For millennia, we surpassed the other intelligent species with which we share our planet -- dolphins, porpoises, orangutans, and the like -- in almost all skills, bar swimming and tree-climbing. In recent years, though, our species has created new forms of intelligence, able to outperform us in other ways. One of the most famous of these artificial intelligences (AIs) is AlphaGo, developed by Deepmind. In just a few years, it has learned to play the 4,000-year-old strategy game, Go, beating two of the world's strongest players. Other software developed by Deepmind has learned to play classic eight-bit video games, notably Breakout, in which players must use a bat to hit a ball at a wall, knocking bricks out of it.

Elon Musk's AI firm joins forces with Microsoft to develop a'cloud brain' OpenAI will run large-scale experiments on Microsoft's Azure service Microsoft will collaborate with company on creating new tools OpenAI will run large-scale experiments on Microsoft's Azure service OpenAI will use Azure for its experiments in deep learning and AI, and Microsoft will collaborate with the company on advancing research and creating new tools and technologies. Electric vehicles safety crackdown: New rules will force... Impressive footage shows cockatoos... Watch the US Army's real-life PHASER GUN in action: Weapon... Electric vehicles safety crackdown: New rules will force... Impressive footage shows cockatoos... Watch the US Army's real-life PHASER GUN in action: Weapon... ELON MUSK'S AI FIRM: HOW DOES OPENAI WORK? DGX-1 was designed with the sole purpose of deep learning, which will help AI researchers train other systems much faster with more data. To understand the speed of this supercomputer, a conventional computer's computations take 250 hours compared to the 10 hours on the DGX-1 Man discovers wife is cheating on him following her with drone Mob storm police station and lynch suspected paedophile Victoria Fritz hides her baby bump moments before giving birth Ivanka Trump gives glimpse of her father's $100m penthouse Protestor at an anti-Trump rally at Ohio State gets slammed 100 special police agents protect suspected paedophile from mob Chili's manager snatches veteran's free meal after complaint Is this the creepy moment the corpse of a girl OPENS her eyes? Ivanka Trump gives glimpse of her father's $100m penthouse Is this the creepy moment the corpse of a girl OPENS her eyes?

We examine the effect of the Group Lasso (gLasso) regularizer in selecting the salient nodes of Deep Neural Network (DNN) hidden layers by applying a DNN-HMM hybrid speech recognizer to TED Talks speech data. We test two types of gLasso regularization, one for outgoing weight vectors and another for incoming weight vectors, as well as two sizes of DNNs: 2048 hidden layer nodes and 4096 nodes. Furthermore, we compare gLasso and L2 regularizers. Our experiment results demonstrate that our DNN training, in which the gLasso regularizer was embedded, successfully selected the hidden layer nodes that are necessary and sufficient for achieving high classification power.

Any observer can sense the artistic style of painting, even if it takes training to articulate it. To an art historian, the artistic style is the primary means of classifying the painting [10]. However, artistic style is not well defined, and may be loosely described as ".. a distinctive manner which permits the grouping of works into related categories" [1]. Algorithmically determining the artistic style of an artwork is a challenging problem which may include analysis of features such as the painting's color, its texture, and its subject matter, or none of those at all. Detecting the style of a digitized image of a painting poses additional challenges raised by the digitization process, which itself has consequences that may affect the ability of a machine to correctly detect artistic style; for instance, textures may be affected by the resolution of the digitization. Despite these challenges, intelligent systems for detecting artistic style would be useful for identification and retrieval of images of a similar style. In this paper we investigate several methods based on recent advances in convolutional neural networks for large-scale determination of artistic style. In particular, we adapt the neural-style algorithm introduced in [2] for large-scale style classification, showing performance that is competitive with other deep convolutional neural network based approaches. 1 Figure 1: Original image on the left, after application of the'neural-style' algorithm (style image'Starry Night', by Van Gogh) on the right.

V ariational autoencoders (VAEs), that are built upon deep neural networks have emerged as popular generative models in computer vision. Most of the work towards improving variational autoencoders has focused mainly on making the approximations to the posterior flexible and accurate, leading to tremendous progress. However, there have been limited efforts to replace pixel-wise reconstruction, which have known shortcomings. In this work, we use real-valued non-volume preserving transformations (real NVP) to exactly compute the conditional likelihood of the data given the latent distribution. W e show that a simple VAE with this form of reconstruction is competitive with complicated VAE structures, on image modeling tasks. As part of our model, we develop powerful conditional coupling layers that enable real NVP to learn with fewer intermediate layers.

OpenAI, the artificial intelligence research non-profit backed by Tesla's Elon Musk, Y Combinator's Sam Altman, a Donald Trump fan called Peter Thiel, and numerous other tech luminaries, is partnering with Microsoft to tackle the next set of challenges in the still-nascent field. OpenAI will also make Microsoft Azure its preferred cloud platform, in part because of its existing support for AI workloads with the help of Azure Batch and Azure Machine Learning, as well as Microsoft's work on its recently rebranded Cognitive Toolkit. Microsoft also offers developers access to a high-powered GPU-centric virtual machine for these kind of machine learning workloads. These N-Series machines are still in beta, but OpenAI has been an early adopter of them and Microsoft says they will become generally available in December. Amazon already offers a similar kind of GPU-focused virtual machine, though oddly enough, Google has lagged behind and -- at least for the time being -- doesn't offer this kind of machine type yet.