One of the featured speakers at the inaugural Text By The Bay conference, held in San Francisco in April 2015, drew laughter when describing a neural network question-answering model that could beat human players in a trivia game. While such performance by computers is fairly well known to the general public, thanks to IBM's Watson cognitive computer, the speaker, natural language processing (NLP) researcher Richard Socher, said, the neural network model he described "was built by one grad student using deep learning" rather than by a large team with the resources of a global corporation behind them. Socher, now CEO of machine learning developer MetaMind, did not intend his remarks to be construed as a comparison of Watson to the academic model he and his colleagues built. As an illustration of the new technical and cultural landscape around NLP, however, the laughter Socher's comment drew was an acknowledgment that basic and applied research in language processing is no longer the exclusive province of those with either deep pockets or strictly academic intentions. Indeed, new tools and new techniques--particularly open source technologies such as Google's word2vec neural text processing tool--combined with steady increases in computing power, have broadened the potential for natural language processing far beyond the research lab or supercomputer.

Advances in deep learning have led Elon Musk and others to start preparing for the AI apocalypse. And indeed, by feeding terabytes into neural networks, computers are now able to understand voices, recognise faces and sift through data with unprecedented accuracy. And yet, advances in so-called unsupervised learning - which finds the structure or relationships in data inputs without training in the way that a child learns from experience - are almost non-existent. In recent years, Yann LeCun of Facebook, Geoffrey Hinton of Google and Yoshua Bengio from the University of Montreal have made significant advances in machine learning through their use of deep neural networks and other learning techniques. For example, Yaniv Taigman, one of my co-founders at face.com (which was acquired by Facebook in June 2012), recently published that the company achieved a 97.25 per cent accuracy rate for face recognition, just 0.25 per cent below human perception.

Advances on multiple fronts are bringing big improvements to the way computers learn, increasing the accuracy of speech and vision systems. Sign in using your ACM Web Account username and password to access premium content if you are an ACM member, Communications subscriber or Digital Library subscriber. Non-members can purchase this article or a copy of the magazine in which it appears.

Not satisfied with launching reusable rockets and designing electric supercars, Elon Musk is looking to create domestic robots to help people around the house. The billionaire entrepreneur won't be working through SpaceX or Tesla, but through another branch of his growing tech empire, collaborative artificial intelligence company Open AI. The firm, chaired by Musk and president of start-up incubator Y Combinator, Sam Altman, plans to use'off the shelf' robots rather than building them from scratch. Open AI, a non-profit founded by Elon Musk and president of start-up incubator Y Combinator, Sam Altman, plans to use'off the shelf' robots rather than building them from scratch, tweaking the robots to become mechanical maids (stock image) Open AI is a collaborative non-profit artificial intelligence company set up at the send of last year by Elon Musk and president of start-up incubator Y Combinator, Sam Altman. It received more than $1 billion in funding when it launched and sees robotics, chatbots and games as practical fields where it develop and can flex its AI muscle.

While most of us know how old we are from our birthday, there are some - like those who are orphaned at a young age - who have no idea when they were born. But now a new technique may make it possible to determine a person's age with a simple blood test. A team from Insilico Medicine in Baltimore, USA, have used artificial intelligence to create programmes that can predict a person's age from chemicals in their blood. In research published in the journal Aging, they explain how deep learning algorithms have allowed them to estimate someones age within a 10 year time frame with 83.5 per cent accuracy. By looking at biomarkers in the blood, such as glucose, urea, red blood cell count and the protein albumin, the Deep Neural Network was able to work out the age of who it came from.

It is the nearest thing to a human brain in silicon form. Lawrence Livermore National Laboratory (LLNL) and IBM have revealed a new deep learning supercomputer they say could boost AI systems. Based on a'neurosynaptic' computer chip called IBM TrueNorth, it can replicate the equivalent of 16 million neurons and 4 billion synapses - yet consumes just 2.5 watts, the energy equivalent of a hearing aid battery. TrueNorth can replicate the equivalent of 16 million neurons and 4 billion synapses - yet consumes just 2.5 watts, the energy equivalent of a hearing aid battery. A single TrueNorth processor consists of 5.4 billion transistors wired together to create an array of 1 million digital neurons that communicate with one another via 256 million electrical synapses.

Humans have been taking a beating from computers lately. The 4-1 defeat of Go grandmaster Lee Se-Dol by Google's AlphaGo artificial intelligence (AI) is only the latest in a string of pursuits in which technology has triumphed over humanity. Self-driving cars are already less accident-prone than human drivers, the TV quiz show Jeopardy! is a lost cause, and in chess humans have fallen so woefully behind computers that a recent international tournament was won by a mobile phone. Researchers from Western Sydney University two reasons why AIs are'our greatest threat. The first being they are trained with logic and heuristics.

To the uninitiated, choosing the right emoji for your instagram post can be a daunting task. Knowing just when to unleash a unicorn, or play it safe with a sad face can be bewildering - but now, a web app has come to the rescue. Emojini will do the work for you by suggesting three emojis that best describe the picture, based on its content. Emojini will do the work for you by suggesting three emojis that best describe the picture, based on its content. The web-based tool uses learning algorithms and knowledge of imagery to guess the most fitting emojis for the image you uploaded.

Google's DeepMind division is using Daily Mail and CNN articles to teach its artificial intelligence programs to read. Using the unique style of articles on the sites - with concise bullet points summarising a story at the top of a page - artificial intelligence was able to learn key facts about articles to answer queries. Ultimately, scientists hope that the study could lead to complex artificial'brains' that can read entire documents and respond to questions put to them by a human. The British-based DeepMind unit analysed almost 400,000 articles from the sites (language process shown). They were used for their unique style of bullets, text and captions. Artificial intelligence was able to learn key facts from the articles.

Uploading dozens of photos and tagging each one on Facebook can be tedious, and it appears the social network knows this. It recently created an algorithm that identifies faces'as accurately as a human' and offers tag suggestions which the user can accept, or reject. The technology - called DeepFace - was first showcased last March, but the site has now started rolling out the automatic tagging tool to select users. Facebook's DeepFace technology uses a 3D model to virtually rotate faces so that are facing the camera. DeepFace uses technology designed by an Israeli startup called face.com.