John McClurg, Vice President in the Office of Security and Trust (OST), Cylance, told us, "CylancePROTECT is a truly advanced threat prevention solution. It sits on each endpoint within the organization, whether it's a desktop, laptop, mobile device, server, or virtual machine. By applying artificial intelligence, machine learning, and mathematic techniques, it instantly identifies and prevents malware and cyberattacks from executing. Basically, it protects from every threat known and yet-to-be-known, including system- and memory-based attacks, malicious documents, zero-day malware, privilege escalations, scripts, and potentially unwanted programs. The solution boosts the efficiency of your IT resources and reduces user impact throughout your organization. The endpoint security product uses little memory, less than 1% of CPU. It requires no Internet connection or signature updates and is engineered to run with minimal updates and fewer system resources. In addition, it works with Windows and Mac OS, easily integrates into existing security platforms, and is available in OEM and embedded versions for technology partners. It operates in every environment, whether it's 1,000, 10,000, or 100,000 endpoints. James Lantrip, Segment Head, Security, Siemens Industry, Inc., told us, "Siemens has a very customer-centric view.

One of the world's fastest-growing trading shops doesn't have any traders. XTX Markets Ltd. has emerged as a foreign-exchange powerhouse, relying on programmers and mathematicians to fuel its rise into the global top five earlier this year. Now, after becoming a formidable player in currencies, XTX has its sights set on growing in stocks, commodities and bonds markets. But in a world where the difference between profit and loss can be tiny fractions of a second, XTX says it relies more on smarts than speed. Instead of building microwave networks to ferret out prices a microsecond before anyone else, XTX uses mathematical models that are tuned with massive data sets.

Affiliate marketing company Impact Radius has locked its sights on artificial intelligence and machine-learning techniques, with aspirations of integrating the technology into attribution modeling and ad fraud detection to monitor campaigns across devices. Overtime programmatic technology combined with unified measurement and optimization will increasingly automate processes, according to Per Pettersen, CEO and co-founder at Impact Radius. "At this point you're really competing on the merit of margin and lifetime value," he said. When asked how artificial intelligence will change affiliate marketing and search, Pettersen said computers will analyze the data and make specific recommendations. "In theory, as systems get more sophisticated, the technology will make buying decisions," he said, without the marketing having to cap the bid.

It's a Wednesday night in North East London and upstairs at the Vortex Jazz Club the machines are calling the shots. The human spectators are jiggling happily in their seats, and the musicians are undeniably flesh-and-blood, sweating and straining at their instruments. But the music itself is the product of electronic brains -- trained to soak up the music of great artists and strain out new melodies. This is "the first concert consisting almost entirely of music composed by artificial intelligence" says professor Geraint Wiggins of Queen Mary's University at the beginning of the evening. In about a few minutes we'll be listening to Medieval chants, Baroque chorales, and jazz and pop -- all made by artificial intelligence with the help of computer scientists who programmed the evening's "composers."

Multilingual Text Classification (MLTC) is a text classification task in which documents are written each in one among a set L of natural languages, and in which all documents must be classified under the same classification scheme, irrespective of language. There are two main variants of MLTC, namely Cross-Lingual Text Classification (CLTC) and Polylingual Text Classification (PLTC). In PLTC, which is the focus of this paper, we assume (differently from CLTC) that for each language in L there is a representative set of training documents; PLTC consists of improving the accuracy of each of the |L| monolingual classifiers by also leveraging the training documents written in the other (|L| − 1) languages. The obvious solution, consisting of generating a single polylingual classifier from the juxtaposed monolingual vector spaces, is usually infeasible, since the dimensionality of the resulting vector space is roughly |L| times that of a monolingual one, and is thus often unmanageable. As a response, the use of machine translation tools or multilingual dictionaries has been proposed. However, these resources are not always available, or are not always free to use. One machine-translation-free and dictionary-free method that, to the best of our knowledge, has never been applied to PLTC before, is Random Indexing (RI). We analyse RI in terms of space and time efficiency, and propose a particular configuration of it (that we dub Lightweight Random Indexing LRI). By running experiments on two well known public benchmarks, Reuters RCV1/RCV2 (a comparable corpus) and JRC-Acquis (a parallel one), we show LRI to outperform (both in terms of effectiveness and efficiency) a number of previously proposed machine-translation-free and dictionary-free PLTC methods that we use as baselines.

Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) provide powerful tools for constraining procedural models in computer graphics, but they require many samples to produce desirable results. In this paper, we show how to create procedural models which learn how to satisfy constraints. We augment procedural models with neural networks which control how the model makes random choices based on the output it has generated thus far. We call such models neurally-guided procedural models. As a pre-computation, we train these models to maximize the likelihood of example outputs generated via SMC. They are then used as efficient SMC importance samplers, generating high-quality results with very few samples. We evaluate our method on L-system-like models with image-based constraints. Given a desired quality threshold, neurally-guided models can generate satisfactory results up to 10x faster than unguided models.

The study of neurocognitive tasks requiring accurate localisation of activity often rely on functional Magnetic Resonance Imaging, a widely adopted technique that makes use of a pipeline of data processing modules, each involving a variety of parameters. These parameters are frequently set according to the local goal of each specific module, not accounting for the rest of the pipeline. Given recent success of neural network research in many different domains, we propose to convert the whole data pipeline into a deep neural network, where the parameters involved are jointly optimised by the network to best serve a common global goal. As a proof of concept, we develop a module able to adaptively apply the most suitable spatial smoothing to every brain volume for each specific neuroimaging task, and we validate its results in a standard brain decoding experiment.

We introduce the localized Lasso, which is suited for learning models that are both interpretable and have a high predictive power in problems with high dimensionality $d$ and small sample size $n$. More specifically, we consider a function defined by local sparse models, one at each data point. We introduce sample-wise network regularization to borrow strength across the models, and sample-wise exclusive group sparsity (a.k.a., $\ell_{1,2}$ norm) to introduce diversity into the choice of feature sets in the local models. The local models are interpretable in terms of similarity of their sparsity patterns. The cost function is convex, and thus has a globally optimal solution. Moreover, we propose a simple yet efficient iterative least-squares based optimization procedure for the localized Lasso, which does not need a tuning parameter, and is guaranteed to converge to a globally optimal solution. The solution is empirically shown to outperform alternatives for both simulated and genomic personalized medicine data.

A UK parliamentary committee has urged the government to act proactively -- and to act now -- to tackle "a host of social, ethical and legal questions" arising from growing usage of autonomous technologies such as artificial intelligence. "While it is too soon to set down sector-wide regulations for this nascent field, it is vital that careful scrutiny of the ethical, legal and societal dimensions of artificially intelligent systems begins now," says the committee. "Not only would this help to ensure that the UK remains focused on developing'socially beneficial' AI systems, it would also represent an important step towards fostering public dialogue about, and trust in, such systems over time." The committee kicked off an enquiry into AI and robotics this March, going on to take 67 written submissions and hear from 12 witnesses in person, in addition to visiting Google DeepMind's London office. Publishing its report into robotics and AI today, the Science and Technology committee flags up several issues that it says need "serious, ongoing consideration" -- including: "[W]itnesses were clear that the ethical and legal matters raised by AI deserved attention now and that suitable governance frameworks were needed," it notes in the report.

This story was delivered to BI Intelligence Apps and Platforms Briefing subscribers. To learn more and subscribe, please click here. Using Twitter as a mode of business to consumer (B2C) communication could result in up to 20% more consumer spend in the future, according to a recent joint study from Twitter and Applied Marketing. The survey examined interactions between more than 3,100 Twitter users with a variety of industries like pizza delivery chains and airlines. The glowing results from the study could be an attempt by Twitter to remind businesses of its capability as a customer service channel, which is likely being threatened by the uptick in interest regarding chatbots, according to Marketing Dive.