Conversational User Interfaces (CUI) are at the heart of the current wave of AI development. Although many applications and products out there are simply "Mechanical Turks" -- which means machines that pretend to be automatized while a hidden person is actually doing all the work -- there have been many interesting advancements in speech recognition from the symbolic or statistical learning approaches. In particular, deep learning is drastically augmenting the abilities of the bots with respect to traditional NLP (i.e., bag-of-words clustering, TF-IDF, etc.) and is creating the concept of "conversation-as-a-platform", which is disrupting the apps market. Our smartphone currently represents the most expensive area to be purchased per squared centimeter (even more expensive than the square meters price of houses in Beverly Hills), and it is not hard to envision that having a bot as unique interfaces will make this area worth almost zero. None of these would be possible though without heavily investing in speech recognition research. Deep Reinforcement Learning (DFL) has been the boss in town for the past few years and it has been fed by human feedbacks.

We're working to put the world's fastest GPU into the hands of the world's smartest AI researchers. Last month in Honolulu, NVIDIA shocked top AI researchers, giving them the world's first NVIDIA Tesla V100 GPU accelerators. Last night, in Sydney, we struck again, handing out 15 NVIDIA Tesla V100 GPU accelerators. "I think it's fantastic," said Sergey Levine, an assistant professor at the University of California, Berkeley, who is known for his work at the intersection of deep learning and robotics. Given out at a meetup for participants in our NVIDIA AI Labs program at the International Conference on Machine Learning, and signed by NVIDIA founder and CEO Jensen Huang, the V100s are the world's most powerful GPUs, offering more than 100 teraflops of deep learning performance.

Deep Learning allows us to solve more complex problems and perform different tasks with great efficiency, constructing such technology is rather a critical task for data scientists and engineers. Instead, an expanding range of frameworks makes it easier to construct deep learning solutions of some complexity. Every framework is different and unique to its own kind, built with a different prospect and offers a unique range of feature.PyTorch and TensorFlow are amongst the most popular deep learning framework available. TensorFlow provides with an open source software library which is used for numerical computation through data flow graphs. It was developed by Google Brain Team for the conducting machine learning and deep neural networks research.

Major tech companies have actively reoriented themselves around AI and machine learning: Google is now "AI-first," Uber has ML running through its veins, and internal AI research labs keep popping up. They're pouring resources and attention into convincing the world that the machine intelligence revolution is arriving now. They tout deep learning, in particular, as the breakthrough driving this transformation and powering new self-driving cars, virtual assistants, and more. Despite this hype around the state of the art, the state of the practice is less futuristic. Software engineers and data scientists working with machine learning still use many of the same algorithms and engineering tools as they did years ago.

Almost everyone loves to spend their leisure time to watch movies with their family and friends. We all have the same experience when we sit on our couch to choose a movie that we are going to watch and spend the next two hours but can't even find one after 20 minutes. We definitely need a computer agent to provide movie recommendation to us when we need to choose a movie and save our time. Apparently, a movie recommendation agent has already become an essential part of our life.. According to Data Science Central "Although hard data is difficult to come by, many informed sources estimate that, for the major ecommerce platforms like Amazon and Netflix, that recommenders may be responsible for as much as 10% to 25% of incremental revenue."

When Jason Matheny joined the U.S. Intelligence Advanced Research Projects Activity (IARPA) as a program manager in 2009, he made a habit of chatting to the organization's research analysts. "What do you need?" he would ask, and the answer was always the same: a way to make more accurate predictions. "What if we made you an artificially intelligent computer model that forecasts real-world events such as political instability, weapons tests and disease outbreaks?" The analysts' response was enthusiastic, except for one crucial caveat. "It came down to whether they could explain the model to a decision maker--like the secretary of Defense," says Matheny, who is now IARPA's director.

Think only a large enterprise has the resources to deploy artificial intelligence technology? Intel aims to make AI more affordable and accessible, especially to smaller companies and entrepreneurs. Last month, the company introduced the Movidius Neural Compute Stick, which it billed as "the world's first USB-based deep learning inference kit and self-contained" AI accelerator. The $79 USB stick delivers "dedicated deep neural network processing capabilities to a wide range of host devices at the edge," Intel says. With the USB stick, Intel suggests that product developers, researchers and makers will be able to add AI capabilities to their devices and develop, tune and deploy AI-based applications far more easily.

Google, apparently tired of trouncing human players at Go with its DeepMind AI, set its computer intelligence up with Blizzard's video game Starcraft II last fall. It seems that was more than a stunt: Today, Google announced it has built a whole research environment around training its AI to play the space-age strategy game -- and it's publicly available. Anyone who wants can tinker with DeepMind's new toolset, SC2LE, to facilitate their own AI research. The bundle includes a pair of kits up on GitHub: first, Blizzard's machine learning API, which has Linux tools for the first time, and then an open source version of the DeepMind toolset called PySC2. Blizzard also provided some extra goodies, like a dataset of anonymized 1v1 game replays for programmers to parse through, along with sample code and some sample bots.

Center for Healthy Aging at the University of Copenhagen has announced a research collaboration with a company specializing in artificial intelligence (AI) to develop solutions for preventing early aging. The aim of this partnership is to develop medicines to prevent and cure a broad range of diseases associated with aging such as Alzheimer's, Parkinson's and cardiovascular diseases. Alzheimer's, Parkinson's and cardiovascular diseases are strongly associated with aging and share many characteristics on the molecular level. Experts in the genetics of aging at the Department of Cellular and Molecular Medicine partnered with the Baltimore-based company, Insilico Medicine, specializing in AI to find molecules that can be developed into drugs to cure and prevent these diseases. The objective of this collaboration is to increase health span for everyone on the planet.

Two Minute Papers reviews how DeepMind learned how to play the Atari classic "Break Out" simply by observing the game being played from a video feed. The original paper "Imagination-Augmented Agents for Deep Reinforcement Learning" is online at https://arxiv.org/abs/1707.06203