As I write this article I'm travelling on an Airbus A380-800, one of the most advanced passenger planes in the world but despite the fact we have the technology to operate it fully autonomously airlines still provide pilots and Airbus still build a cockpit. Because they know we are all more comfortable having a human in the loop – even despite the fact that, ironically, real world tests at Heathrow have shown that fully autonomous aircraft are safer and more efficient than their 20th century equivalents. On the other hand UAV manufacturers in the defence industry have no such compulsion about removing the human. Having a human in the loop gives us comfort and a feigned assurance that, despite everything we are still in control. However, in the next twenty years there are going to be fewer and fewer humans in the loop as technologies from Artificial Intelligence to Autonomous Vehicles kick us to the curb.

Betty is part of the 7.2 million EU-funded STRANDS project where robots are learning how to act intelligently and independently in real-world environments while understanding 3D space. Although Betty's creators say the programmes are designed to create robots which will'clean floors in MacDonalds' or stocktake experts have warned that jobs will be lost because of AI. Richard Susskind, an Oxford professor who has advised governments around the world, recently told the Hay Festival that computers will soon outperform even the best professionals. He said: "It's no longer science fiction. In our view a lot of these technologies will be coming through in the 2020s. "We're not saying that doctors are going to be replaced overnight, we're not even saying in the 2020s, but we are saying that the practical impact of artificial intelligence research coming out of research laboratories is remarkable.

It's not all that easy to call KnuEdge a startup. Created a decade ago by Daniel Goldin, the former head of the National Aeronautics and Space Administration, KnuEdge is only now coming out of stealth mode. It has already raised 100 million in funding to build a "neural chip" that Goldin says will make data centers more efficient in a hyperscale age. Goldin, who founded the San Diego, California-based company with the former chief technology officer of NASA, said he believes the company's brain-like chip will be far more cost and power efficient than current chips based on the computer design popularised by computer architect John von Neumann. In von Neumann machines, memory and processor are separated and linked via a data pathway known as a bus.

Microsoft (NASDAQ:MSFT) co-founder Bill Gates recently called artificial intelligence "the holy grail that anyone in computer science has been thinking about" during Vox Media's Code Conference. Gates discussed the rapid progress of speech recognition and computer vision technologies over the past five years, and noted that "the dream is finally arriving." If that dream arrives, tech investors should recognize the major trends and players in this market. To get started, let's examine 10 fascinating facts about the AI industry. Research firm Markets and Markets estimates that the AI market will grow from 420 million in 2014 to 5.05 billion by 2020, thanks to the rising adoption of machine learning and natural language processing technologies in the media, advertising, retail, finance, and healthcare industries.

Machine learning algorithms are optimized to model statistical properties of the training data. If the input data reflects stereotypes and biases of the broader society, then the output of the learning algorithm also captures these stereotypes. In this paper, we initiate the study of gender stereotypes in {\em word embedding}, a popular framework to represent text data. As their use becomes increasingly common, applications can inadvertently amplify unwanted stereotypes. We show across multiple datasets that the embeddings contain significant gender stereotypes, especially with regard to professions. We created a novel gender analogy task and combined it with crowdsourcing to systematically quantify the gender bias in a given embedding. We developed an efficient algorithm that reduces gender stereotype using just a handful of training examples while preserving the useful geometric properties of the embedding. We evaluated our algorithm on several metrics. While we focus on male/female stereotypes, our framework may be applicable to other types of embedding biases.

We present a framework that couples the syntax and semantics of natural language sentences in a generative model, in order to develop a semantic parser that jointly infers the syntactic, morphological, and semantic representations of a given sentence under the guidance of background knowledge. To generate a sentence in our framework, a semantic statement is first sampled from a prior, such as from a set of beliefs in a knowledge base. Given this semantic statement, a grammar probabilistically generates the output sentence. A joint semantic-syntactic parser is derived that returns the $k$-best semantic and syntactic parses for a given sentence. The semantic prior is flexible, and can be used to incorporate background knowledge during parsing, in ways unlike previous semantic parsing approaches. For example, semantic statements corresponding to beliefs in a knowledge base can be given higher prior probability, type-correct statements can be given somewhat lower probability, and beliefs outside the knowledge base can be given lower probability. The construction of our grammar invokes a novel application of hierarchical Dirichlet processes (HDPs), which in turn, requires a novel and efficient inference approach. We present experimental results showing, for a simple grammar, that our parser outperforms a state-of-the-art CCG semantic parser and scales to knowledge bases with millions of beliefs.

This paper presents a novel kernel-based generative classifier which is defined in a distortion subspace using polynomial series expansion, named Kernel-Distortion (KD) classifier. An iterative kernel selection algorithm is developed to steadily improve classification performance by repeatedly removing and adding kernels. The experimental results on character recognition application not only show that the proposed generative classifier performs better than many existing classifiers, but also illustrate that it has different recognition capability compared to the state-of-the-art discriminative classifier - deep belief network. The recognition diversity indicates that a hybrid combination of the proposed generative classifier and the discriminative classifier could further improve the classification performance. Two hybrid combination methods, cascading and stacking, have been implemented to verify the diversity and the improvement of the proposed classifier. Keywords: Distortion feature space, kernel-based generative classifier, hybrid classification, deep belief nets, character recognition 1. Introduction Learning and inference are two important aspects for any machine learning application.

In this paper, we present a new wrapper feature selection approach based on Jensen-Shannon (JS) divergence, termed feature selection with maximum JS-divergence (FSMJ), for text categorization. Unlike most existing feature selection approaches, the proposed FSMJ approach is based on real-valued features which provide more information for discrimination than binary-valued features used in conventional approaches. We show that the FSMJ is a greedy approach and the JS-divergence monotonically increases when more features are selected. We conduct several experiments on real-life data sets, compared with the state-of-the-art feature selection approaches for text categorization. The superior performance of the proposed FSMJ approach demonstrates its effectiveness and further indicates its wide potential applications on data mining.

In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. Compared to state-of-the-art models such as Neural Tensor Network and Holographic Embeddings, our approach based on complex embeddings is arguably simpler, as it only uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors. Our approach is scalable to large datasets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.

We explore two techniques which use color to make sense of statistical text models. One method uses in-text annotations to illustrate a model's view of particular tokens in particular documents. Another uses a high-level, "words-as-pixels" graphic to display an entire corpus. Together, these methods offer both zoomed-in and zoomed-out perspectives into a model's understanding of text. We show how these interconnected methods help diagnose a classifier's poor performance on Twitter slang, and make sense of a topic model on historical political texts.