Social giant Twitter has expanded its capabilities in machine learning, a branch of artificial intelligence that seeks to teach computers how to learn, with the acquisition of London-based startup Magic Pony Technology. The US-headquartered firm has today (June 20) announced a multi-million dollar deal that sees Magic Pony become the latest tech firm to join the ever-increasing Twitter fold. According to TechCrunch, Twitter is paying 150m (around 102m) in total. Writing in a blog post, the social network's CEO and co-founder, Jack Dorsey, said the transaction builds on other investments made by Twitter in recent years, including its acquisition of image search startup Madbits in 2014 and Whetlab, another machine learning company, last year. He wrote: "Magic Pony's technology – based on research by the team to create algorithms that can understand the features of imagery – will be used to enhance our strength in live and video and opens up a whole lot of exciting creative possibilities for Twitter. "The team includes 11 PhDs with expertise across computer vision, machine learning, high-performance computing, and computational neuroscience, who are alumni of some of the top labs in the world." Mr Dorsey continued: "We are continuing to build strength into our deep learning teams with world-class talent to help Twitter be the best place to see what's happening and why it matters, first.

This free evening talk will explore the fields of Deep Learning and Computer Vision, using lie detection from video as an example. Nick studied Computer science at Imperial College before moving to the industry to work as a Data Scientist. He is active in the London startup scene and is interested in the role technology plays in our emotional wellbeing. He is an advocate of designing for happiness. If you are interested in progressing further with Machine Learning and Data Learning, learn more at the Data Science Bootcamp in Python.

This week at the International Supercomputing Conference (ISC '16) we are expecting a wave of vendors and high performance computing pros to blur the borders between traditional supercomputing and what is around the corner on the application front--artificial intelligence and machine learning. For some, merging those two areas is a stretch, but for others, particularly GPU maker, Nvidia, which just extended its supercomputing/deep learning roadmap this morning, the story is far more direct since much of the recent deep learning work has hinged on GPUs for training of neural networks and machine learning algorithms. We have written extensively over the last year about how GPUs are being used in both deep learning and in HPC separately, but we might soon arrive at a fuller merger between the two areas, at least from a systems and hardware perspective. "Deep learning is not just an application segment, it's a whole new computing model," Ian Buck, VP of Accelerated Computing, tells The Next Platform. "If you had asked me at the launch of CUDA if GPUs would be in the largest supercomputers or revolutionizing artificial intelligence, I would have said that was a vision or even a pipe dream."

Facebook has developed an engine that will enable it to better understand the context of your posts. Called DeepText, it utilizes deep neural network architecture in order to understand the text being shared. The social networking company claims that DeepText is able to understand "with near-human accuracy" the content of several thousand posts per second across 20 languages. This technology was built on ideas developed around deep learning by Ronan Collobert and Yann LeCun from Facebook's AI Research Group. Although introduced today, DeepText is already being tested across some Facebook properties, such as Messenger.

Soon you could be chatting with your computer about the morning news. An AI has learned to read and answer questions about a news article with unprecedented accuracy. Creating AI systems that can learn in the background from humanity's existing stores of information is one of the big goals of computer science. "Computers don't have the kind of general knowledge and common sense of how the world works [from reading] about things in novels or watch[ing] sitcoms," says Chris Manning at Stanford University. To get a step closer to this, last year, Google's DeepMind team used articles from the Daily Mail website and CNN to help train an algorithm to read and understand a short story.

Some of the world's fastest computers employ Nvidia's graphics processor for computer vision, deep learning and scientific calculations, and a new GPU will supercharge these applications. The Tesla P100 that plugs into the PCI-Express slots of supercomputers will speed up tasks like economic forecasting and weather modeling. The GPU is also targeted at servers, and will play a big role in helping self-driving cars, robots and drones identify objects. Further reading: Nvidia's beastly Pascal GPU is packed with cutting-edge tech and 15 billion transistors Deep-learning systems with the GPUs in data centers will improve cloud-based image recognition, classification, natural language processing and speech recognition. One system with Tesla P100 chips is Nvidia's DGX-1, which can be purchased for 129,000.

MIT researchers have developed a computer system that independently adds realistic sounds to silent videos. Although the technology is nascent, it's a step toward automating sound effects for movies. In a series of videos of drumsticks striking things - including sidewalks, grass and metal surfaces - the computer learned to pair a fitting sound effect, such as the sound of a drumstick hitting a piece of wood or rustling leaves. The findings are an example of the power of deep learning, a type of artificial intelligence whose application is trendy in tech circles. With deep learning, a computer system learns to recognize patterns in huge piles of data and applies what it learns in useful ways.

Some of the world's fastest computers employ Nvidia's graphics processor for computer vision, deep learning and scientific calculations, and a new GPU will supercharge these applications. The Tesla P100 that plugs into the PCI-Express slots of supercomputers will speed up tasks like economic forecasting and weather modeling. The GPU is also targeted at servers, and will play a big role in helping self-driving cars, robots and drones identify objects. Deep-learning systems with the GPUs in data centers will improve cloud-based image recognition, classification, natural language processing and speech recognition. One system with Tesla P100 chips is Nvidia's DGX-1, which can be purchased for US 129,000.

A video summary of the paper: Nguyen A, Yosinski J, Clune J. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. The paper is available here: http://EvolvingAI.org/fooling Special thanks to those who created the music, images, videos and software that were used to create this video.

Mar 11, 2016 AlphaGo, a computer program developed by Google DeepMind in London to play the traditional Chinese board game Go, had five matches against Se-Dol Lee, a professional Go player in Korea from March 8-15, 2016. AlphaGo won four out of the five games, a significant test result showcasing the advancement achieved in the field of general-purpose artificial intelligence (GAI), according to the company. Dr. Demis Hassabis, the Chief Executive Officer of Google DeepMind, visited KAIST on March 11, 2016 and gave an hour-long talk to students and faculty. In the lecture, which was entitled "Artificial Intelligence and the Future," he introduced an overview of GAI and some of its applications in Atari video games and Go.