Palo Alto, California-based Nauto uses deep learning to enable dashboard-mounted cameras and other image sensors to alert drivers about oncoming traffic, lights, and other hazards. Deep learning also enables Nauto's cameras to recognize when a collision is imminent, so they can record the events. Images and data about the accident are stored in the cloud for sharing with a fleet manager, insurance company, and/or the police via Nauto's mobile app. Founded in 2015 by Stefan Heck and Fredrick Soo, Nauto raised 12 million in Series A funding in April and has received additional investments from Toyota, BMW, and Allianz. Many more are injured and maimed.

Alibaba will be among 13 businesses working with the Hangzhou government on a'brain' for the city and will work with the National Astronomical Observatory of China (NAOC) on deep space exploration projects, it announced at its annual Computing Conference this week. According to the retail and cloud computing giant, it will be supplying a range of its tech services such as AI, deep learning and data storage. The B2B technology supply side to the Alibaba business is growing fast and puts it very much in battle with Amazon on a global playing field. The Hangzhou City Brain project is a new government initiative to address its urban city living issues, such as traffic congestion. It will use Alibaba Cloud's AI program "ET" and big data analytics capabilities to perform real-time traffic prediction by using its video and image recognition technologies.

The DeepMind artificial intelligence (AI) being developed by Google's parent company, Alphabet, can now intelligently build on what's already inside its memory, the system's programmers have announced. Their new hybrid system – called a Differential Neural Computer (DNC) – pairs a neural network with the vast data storage of conventional computers, and the AI is smart enough to navigate and learn from this external data bank. What the DNC is doing is effectively combining external memory (like the external hard drive where all your photos get stored) with the neural network approach of AI, where a massive number of interconnected nodes work dynamically to simulate a brain. "These models... can learn from examples like neural networks, but they can also store complex data like computers," write DeepMind researchers Alexander Graves and Greg Wayne in a blog post. At the heart of the DNC is a controller that constantly optimises its responses, comparing its results with the desired and correct ones.

Huawei will provide a US 1 million fund to UC Berkeley for research into many subjects of interest in AI, including deep learning, reinforcement learning, machine learning, natural language processing and computer vision. Through close cooperation, the Research and Development (R&D) teams of Huawei and the Berkeley Artificial Intelligence Research (BAIR) Lab will strive to make significant breakthroughs in AI theories and key technologies. The two parties believe that this strategic partnership will fuel the advancement of AI technology and create completely new experiences for people, thus contributing greatly to society at large. As one of the world's leading higher education institutes, UC Berkeley has profound expertise in machine learning and other AI domains. Its newly founded BAIR Lab brings together UC Berkeley researchers across the areas of computer vision, machine learning, natural language processing, robotics, and research planning.

Some of the work in the thesis was previously presented in [Gal, 2015; Gal and Ghahramani, 2015a,b,c,d; Gal et al., 2016], but the thesis contains many new pieces of work as well. There are two factors at play when visualising uncertainty in dropout Bayesian neural networks: the dropout masks and the dropout probability of the first layer. Uncertainty depictions in my previous blog posts drew new dropout masks for each test point--which is equivalent to drawing a new prediction from the predictive distribution for each test point -2 \leq \x \leq 2 . More specifically, for each test point \x_i we drew a set of network parameters from the dropout approximate posterior \boh_{i} \sim q_\theta(\bo), and conditioned on these parameters we drew a prediction from the likelihood \y_i \sim p(\y \x_i, \boh_{i}) . Another important factor affecting visualisation is the dropout probability of the first layer.

With the advances of modern data storage technology, chips the size of your fingernail are capable of storing an entire library's worth of knowledge, so one thing you might think computers do better than people is remember things. But according to Google Inc.'s DeepMind team, the artificial intelligence research group that developed AlphaGo, that is not entirely true. In a new paper published in the journal Nature, DeepMind has outlined a process where it trained a neural network to have human-like memory, giving it not only the ability to store data, but also to recall that information and use it to solve novel problems. "Neural networks excel at pattern recognition and quick, reactive decision-making, but we are only just beginning to build neural networks that can think slowly – that is, deliberate or reason using knowledge," the DeepMind team wrote in a recent blog post. "For example, how could a neural network store memories for facts like the connections in a transport network and then logically reason about its pieces of knowledge to answer questions?" DeepMind calls its new method differentiable neural computers, and the team demonstrated its capabilities using the London Underground, one of the largest public transit systems in the world.

Artificial-intelligence (AI) systems known as neural networks can recognize images, translate languages and even master the ancient game of Go. But their limited ability to represent complex relationships between data or variables has prevented them from conquering tasks that require logic and reasoning. In a paper published in Nature on October 12, the Google-owned company DeepMind in London reveals that it has taken a step towards overcoming this hurdle by creating a neural network with an external memory. The combination allows the neural network not only to learn, but to use memory to store and recall facts to make inferences like a conventional algorithm. This in turn enables it to tackle problems such as navigating the London Underground without any prior knowledge and solving logic puzzles.

The Internet of Things (IoT) has been touted as the next Industrial Revolution, with pervasive connectivity and the insights it can generate offering a new digital lens for viewing and managing the physical world. But in addition to the tangible process efficiencies and quality of life improvements expected from the IoT, it's also a stepping stone to perhaps the greatest achievement in human history: artificial intelligence (AI). In many ways the technological progression of AI and the IoT are intertwined. IoT will provide the information that fuels our data-driven economy, while AI is the engine that will consume it. Though both paradigms are still in their infancy, each's success is contingent upon the other's: The IoT can never reach its potential without a mechanism for autonomously processing large heterogeneous data sets, just as AI is incapable of expanding without being fed massive amounts of data.

Google's DeepMind artificial intelligence lab does more than just develop computer programs capable of beating the world's best human players in the ancient game of Go. The DeepMind unit has also been working on the next generation of deep learning software that combines the ability to recognize data patterns with the memory required to decipher more complex relationships within the data. Deep learning is the latest buzz word for artificial intelligence algorithms called neural networks that can learn over time by filtering huge amounts of relevant data through many "deep" layers. The brain-inspired neural network layers consist of nodes (also known as neurons). Tech giants such as Google, Facebook, Amazon, and Microsoft have been training neural networks to learn how to better handle tasks such as recognizing images of dogs or making better Chinese-to-English translations. These AI capabilities have already benefited millions of people using Google Translate and other online services.

These systems don't just require more information than humans to understand concepts or recognize features, they require hundreds of thousands times more, says Neil Lawrence, a professor of machine learning at the University of Sheffield and part of Amazon's AI team. Once they've been trained, they can be incredibly efficient at tasks like recognizing cats or playing Atari games, says Google DeepMind research scientist Raia Hadsell. A solution to this might be something called progressive neural networks -- this means connecting separate deep learning systems together so that they can pass on certain bits of information. One way of doing this is revisiting an old, unfashionable strand of artificial intelligence known as symbolic AI or Good Old-Fashioned Artificial Intelligence (GOFAI), says Murray Shanahan, a professor of cognitive robotics at Imperial College London (and also the scientific advisor on Ex Machina).