The next time you open the door to find the pizza you ordered (via an app of course), you may not find a delivery person standing on your doorstep. Expect to see a drone dropping off your dinner. And perhaps that dinner was by prepped, at least in part, by robots. Eventually, some of your pizza's toppings may be lab grown-- but in the meantime, even those fresh-off-the-farm ingredients have a good chance of reaching your fork via robotics. Today, automation is shaping up to be our generation's food revolution.

Artificial intelligence is expected to transform a wide range of industries, as simple tasks are automated and carried out by machines. The IT sector is no different, with machine learning algorithms increasingly being targeted at automating and improving data centre operations. A notable example has been Google, which recently revealed that it is using its own DeepMind technology to manage power consumption at its huge server farms, reducing the amount of electricity needed by 40 percent. There is also potential for AI technology to automate functions carried out by IT operations teams. Machine learning offers a way to manage infrastructure and react quickly to faults without human intervention.

A team of researchers in the UK have developed an artificial intelligence (AI) program that can predict the outcome of human rights cases involving torture, degrading treatment, and privacy. The AI -- developed by researchers at University College London (UCL) and the University of Sheffield, alongside Dr Daniel Preo?iuc-Pietro from the University of Pennsylvania -- successfully predicted the verdicts for 79% of 584 cases at the European Court of Human Rights (ECtHR). In order to reach a decision, the AI analysed case text using a machine learning algorithm, the researchers said. The algorithm looked for patterns in the text and was able to classify each case either as a "violation" or a "non-violation". To prevent bias and mislearning, the team selected an equal number of violation and non-violation cases.

Professor Christopher Bartneck never believed his research paper, written by Apple's iOS autocomplete, would be accepted for a nuclear physics conference An academic who jokingly wrote a research paper written entirely by Apple's iOS autocomplete - and was subsequently filled with nonsense - has been accepted to present his findings at a nuclear physics conference. Christopher Bartneck, an associate professor at the University of Canterbury's Human Interface Technology laboratory in New Zealand, was stunned to discover he had been successful in securing a place at the conference, which takes place in America next month. 'I started a sentence with'Atomic' or'Nuclear' and then randomly hit the autocomplete suggestions,' wrote Bartneck in a blog post on Thursday. 'The text really does not make any sense.' Bartneck's mischievous side was fired up after receiving an invitation from the International Conference on Atomic and Nuclear Physics, which will be held in Atlanta in November.

An AI judge has accurately predicted most verdicts of the European Court of Human Rights, and might soon be making important decisions about cases. Scientists built an artificially intelligence computer that was able to look at legal evidence as well as considering ethical questions to decide how a case should be decided. And it predicted those with 79 per cent accuracy, according to its creators. The algorithm looked at data sets made up 584 cases relating to torture and degrading treatment, fair trials and privacy. The computer was able to look through that information and make its own decision – which lined up with those made by Europe's most senior judges in almost every case.

Advances in robotics and Artificial Intelligence (AI) hold the potential to fundamentally reshape the way we live and work, yet the government does not yet have a strategy for developing skills, a report by the Science and Technology Committee has concluded. The report states that AI systems are starting to have transformational impacts on everyday life: from driverless cars and supercomputers that can assist doctors with medical diagnoses, to intelligent tutoring systems that can tailor lessons to meet a student's individual cognitive needs. Such breakthroughs raise a host of questions for society, including ethical issues about the transparency of AI decision-making as well as privacy and safety. The Committee is calling for a Commission on Artificial Intelligence to be established at the Alan Turing Institute to examine the social, ethical and legal implications of recent and potential developments in AI. The UK is well-placed to provide this type of intellectual leadership, it adds.

Imagine receiving a phone call from your aging mother seeking your help because she has forgotten her banking password. The voice on the other end of the phone call just sounds deceptively like her. It is actually a computer-synthesized voice, a tour-de-force of artificial intelligence technology that has been crafted to make it possible for someone to masquerade via the telephone. Such a situation is still science fiction -- but just barely. It is also the future of crime.

In his best-selling 2011 book Thinking, Fast and Slow, Nobel Prize-winning economist Daniel Kahneman hypothesized that thinking could be broken down into two distinct processes -- aptly named fast and slow thought. The former is all about your gut, the initial automatic responses you have to things, while the later is calculated, reflective and time-consuming. A new algorithm from DeepMind is beginning to show us that so-called "slow" thinking may soon be within the reach of machine learning. In a new paper published in Nature, the Google subsidiary DeepMind explained a new approach to machine learning that uses something called a differentiable neural computer. Neural networks operate using what essentially amounts to a very sophisticated trial and error process, eventually arriving at an answer.

Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets. Addressing this goal, we develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy. Our implementation and experiments demonstrate that we can train deep neural networks with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.

Vector representations of words have heralded a transformational approach to classical problems in NLP; the most popular example is word2vec. However, a single vector does not suffice to model the polysemous nature of many (frequent) words, i.e., words with multiple meanings. In this paper, we propose a three-fold approach for unsupervised polysemy modeling: (a) context representations, (b) sense induction and disambiguation and (c) lexeme (as a word and sense pair) representations. A key feature of our work is the finding that a sentence containing a target word is well represented by a low rank subspace, instead of a point in a vector space. We then show that the subspaces associated with a particular sense of the target word tend to intersect over a line (one-dimensional subspace), which we use to disambiguate senses using a clustering algorithm that harnesses the Grassmannian geometry of the representations. The disambiguation algorithm, which we call $K$-Grassmeans, leads to a procedure to label the different senses of the target word in the corpus -- yielding lexeme vector representations, all in an unsupervised manner starting from a large (Wikipedia) corpus in English. Apart from several prototypical target (word,sense) examples and a host of empirical studies to intuit and justify the various geometric representations, we validate our algorithms on standard sense induction and disambiguation datasets and present new state-of-the-art results.