A hilarious new video reveals the clumsy progress of AI'parkour,' as scientists work to teach computer systems how to navigate'challenging terrains and obstacles.' DeepMind researchers have trained a number of simulated bodies, including a headless'walker,' a four-legged'ant,' and a 3D humanoid, to learn more complex behaviours as they carry out different locomotion tasks. The results, while comical, show how these systems can learn to improve their own techniques as they interact with the different environments, eventually allowing them to run, jump, crouch and turn as needed. Footage from the study offers a hilarious look into the trial-and-error process. As the team explains in the paper, the environments presented to the simulated bodies are of varying levels of difficulty.

Deep learning made the headlines when the UK's AlphaGo team beat Lee Sedol, holder of 18 international titles, in the Go board game. Go is more complex than other games, such as Chess, where machines have previously crushed famous players. The number of potential moves explodes exponentially so it wasn't possible for computers to use the same techniques used in Chess. In learning Go, the computer would have to create millions of games, competing against itself and discovering new strategies that humans may never have considered. Deep learning itself isn't that new, and researchers have been working on algorithms for many years, refining the approach and developing new algorithms.

Artificial Intelligence presents an important paradigm shift for science. Science is traditionally founded on theories and models, most often formalized with mathematical formulas handcrafted by theoretical scientists and refined through experiments. Machine learning, an important branch of modern Artificial Intelligence, focuses on learning from data. This leads to a fundamentally different approach to model-building: we step back and focus on the design of algorithms capable of building models from data, but the models themselves are not designed by humans. This is even more true with deep learning, which requires little engineering by hand and is responsible for many of Artificial Intelligence's spectacular successes. In contrast to logic systems, knowledge from a deep learning model is difficult to understand, reuse, and may involve up to a billion parameters.

Reinforcement learning (RL) is the practice of teaching and guiding behavior by using a reward system. Desirable behavior produces rewards; undesirable behavior does not. It's a common tool used in machine learning, and now the the Alphabet team has used it to teach the DeepMind AI to successfully navigate a parkour course. The team wanted to see if simple rewards would work in a complex environment. They set up a virtual parkour course with drops, hurdles and ledges and set a reward for forward progress.

At GTC, we unveiled Volta, our greatest generational leap since the invention of CUDA. It incorporates 21 billion transistors. It includes the fastest HBM memories from Samsung. Volta features a new numeric format and CUDA instruction that perform 4 4 matrix operations–an elemental deep learning operation–at super-high speeds. Each Volta GPU is 120 teraflops.

Google is developing a new system designed to prevent artificial intelligence from going rogue and clashing with humans. It's an idea that has been explored by a multitude of sci-fi films, and has grown into a genuine fear for a number of people. Google is now hoping to tackle the issue by encouraging machines to work in a certain way. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar. Japan's On-Art Corp's CEO Kazuya Kanemaru poses with his company's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' and other robots during a demonstration in Tokyo, Japan Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' performs during its unveiling in Tokyo, Japan Singulato Motors co-founder and CEO Shen Haiyin poses in his company's concept car Tigercar P0 at a workshop in Beijing, China A picture shows Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China Connected company president Shigeki Tomoyama addresses a press briefing as he elaborates on Toyota's "connected strategy" in Tokyo.

If you live in a city and own a car, you're familiar with the calculus of illegal parking. How can we maximize our time in an illegal parking spot while minimizing the chances of getting caught, and, thus, ticketed or towed? Let's just ignore this LOADING ZONE sign, it will only be a minute. Or let's let the meter run out, it will be fine. Or maybe I didn't even see the fire hydrant.

The newest release by Salesforce is the Einstein Platform Services and APIs. This new offering includes Einstein Intent, Einstein Sentiment and Einstein Object Detection. The Einstein platform was introduced last year in September and it is an AI offering designed especially for customer relationship management (CRM). This platform is of particular use to developers as it provides APIs and other tools to help them create CRM apps that use machine learning capabilities like NLP (natural language processing) and image recognition. Furthermore, developers can use the integrated REST APIs which are Heroku and Apex to train and design bespoke deep learning models.

The big conceptual difference between deep learning and traditional machine learning is that deep learning is the first, and currently the only learning method that is capable of training directly on the raw data (e.g., the pixels in our face recognition example), without any need for feature extraction. When applying traditional machine learning, it is necessary to first convert the computer files from raw bytes to a list of features (e.g., important API calls, etc), and only then is this list of features fed into the machine learning module. Additionally, unlike traditional machine learning, which reaches a performance ceiling as the number of files it is trained on increases, deep learning can effectively improve as the datasets grow, to the extent of hundreds of millions of malicious and legitimate files. The results of benchmarks that compare the performance of deep learning vs traditional machine learning in cybersecurity show that deep learning results in a considerably higher detection rate and a lower false positive rate. As malware developers use more advanced methods to create new malware, the gap between the detection rates of deep learning vs traditional machine learning will grow wider; and in coming years it will be critical to rely on deep learning in order to have a realistic chance of foiling the most sophisticated attacks.

NVIDIA and Baidu announced a broad partnership to bring the world's leading artificial intelligence technology j cloud computing, self-driving vehicles and AI home assistants. Speaking in the keynote at Baidu's AI developer conference in Beijing, Baidu president and COO Qi Lu described his company's plans to work with NVIDIA to bring next-generation NVIDIA Volta GPUs to Baidu Cloud, providing cloud customers with the world's leading deep learning platform. This partnership will adopt NVIDIA's DRIVE PX platform for Baidu's self-driving car initiative, and develop self-driving cars with major Chinese carmakers. Optimize Baidu's PaddlePaddle open source deep learning framework for NVIDIA Volta GPUs and make it widely available to academics and researchers. "NVIDIA and Baidu have pioneered significant advances in deep learning and AI," said Ian Buck, NVIDIA vice president and general manager of accelerated computing.