IDC sees widespread adoption of cognitive systems and artificial intelligence (AI) across a broad range of industries will drive worldwide revenues from nearly $8.0 billion in 2016 to more than $47 billion in 2020. According to a new Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide from International Data Corporation (IDC), the market for cognitive/AI solutions will experience a compound annual growth rate (CAGR) of 55.1% over the 2016-2020 forecast period. "Near-term opportunities for cognitive systems are in industries such as banking, securities and investments, and manufacturing," said Jessica Goepfert, program director, Customer Insights and Analysis at IDC. "In these segments, we find a wealth of unstructured data, a desire to harness insights from this information, and an openness to innovative technologies. For instance, cognitive technologies are being used in the banking industry to detect and combat fraud – consistently a top industry pain point. Meanwhile, in manufacturing, executives cite improving product quality as a top initiative. In this case, cognitive systems recognize and know how to respond to dynamic fluctuations in product specs by adapting the production to stay within quality targets."

AI has forever changed digital advertising. As marketers, it already allows us to decide how to best engage potential customers and markets like never before. But there's room to grow. Deep learning tools are the next major area of AI-based research, and it will spark a wave of future innovation in every industry – bringing a new era of marketing which both advertisers and end-users will benefit from. Our interfaces have already adapted to fit a user's interests on a personal level, matching industry insights and behaviours with display advertising – or personalization.

Artificial intelligence (AI) researchers have a long history of going back in time to explore old ideas, and now researchers at OpenAI, which is backed by Elon Musk, have revisited "Neuroevolution," a field that has been around since the 1980s, and they've achieved state of the art results. The group, which was led by OpenAI's research director Ilya Sutskever, explored the use of a set of algorithms called "Evolution strategies," which are aimed at solving "optimisation" problems. Optimisation problems are just like they sound, think of something that needs optimising, such as your route to work, a flight plan, or even a healthcare treatment and optimise it. On an abstract level, the technique the team used works by letting successful algorithms to pass their characteristics on to future generations – in short, each successive generation gets better and better at whatever tasks they've been assigned. However, coming back into the present day, the researchers took these algorithms and reworked them so they'd work better with today's deep neural networks and run better on large scale distributed computing systems.

Powerful machine-learning methods have taken the tech world by storm in recent years, vastly improving voice and image recognition, machine translation, and many other things. Now these techniques are poised to upend countless other industries, including the world of finance. But progress may be stymied by a significant problem: it's often impossible to explain how these "deep learning" algorithms reach a decision. Adam Wenchel, vice president of machine learning and data innovation at Capital One, says the company would like to use deep learning for all sorts of functions, including deciding who is granted a credit card. But it cannot do that because the law requires companies to explain the reason for any such decision to a prospective customer.

There's currently a shortage of over seven million physicians, nurses and other health workers worldwide, and the gap is widening. Doctors are stretched thin -- especially in underserved areas -- to respond to the growing needs of the population. Meanwhile, training physicians and health workers is historically an arduous process that requires years of education and experience. TNW Conference won best European Event 2016 for our festival vibe. See what's in store for 2017.

Deep learning involves'training' a computational system to'understand' natural language, so inferring complex meaning rather than just understanding the surface meaning. The computer is then quizzed on the information and goes back to learn from its mistakes. These multiple layers help systems analyse and make decisions about data more independently, "Such as whether or not an email is spam, to use a simple example", explains Rob Speer, chief science officer at Luminoso, a Massachusetts-based text analysis and artificial intelligence company. "For many companies, a major reason to turn to deep learning over machine learning is that there are fewer steps of human intervention required to train the system before it can work with data." This significantly cuts staff effort and reduces the burden on the poor workies.

The European Robotics Forum in Edinburgh late March brought together over 800 people from industry, academia and government. The 3-day event packed it's lot of talks, workshops, and panel discussions. So much to see, meet and listen at #ERF2017 @eu_Robotics pic.twitter.com/b640q25IIK The event kicked off with a dive into deep learning and its applications in robotics with keynotes by Senior Research Scientist at DeepMind Raia Hadsell, CEO of FiveAI Stan Boland, and Member of the Scottish Parliament Keith Brown. The theme of the forum this year was "living and working with robots".

As a highlighting research topic in the multimedia area, cross-media retrieval aims to capture the complex correlations among multiple media types. Learning better shared representation and distance metric for multimedia data is important to boost the cross-media retrieval. Motivated by the strong ability of deep neural network in feature representation and comparison functions learning, we propose the Unified Network for Cross-media Similarity Metric (UNCSM) to associate cross-media shared representation learning with distance metric in a unified framework. First, we design a two-pathway deep network pretrained with contrastive loss, and employ double triplet similarity loss for fine-tuning to learn the shared representation for each media type by modeling the relative semantic similarity. Second, the metric network is designed for effectively calculating the cross-media similarity of the shared representation, by modeling the pairwise similar and dissimilar constraints. Compared to the existing methods which mostly ignore the dissimilar constraints and only use sample distance metric as Euclidean distance separately, our UNCSM approach unifies the representation learning and distance metric to preserve the relative similarity as well as embrace more complex similarity functions for further improving the cross-media retrieval accuracy. The experimental results show that our UNCSM approach outperforms 8 state-of-the-art methods on 4 widely-used cross-media datasets.

The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of {24,752} labeled images on fashion conversations, containing visual and textual cues, available for the research community.

Predicting the biological function of molecules, be it proteins or drug-like compounds, from their atomic structure is an important and long-standing problem. Function is dictated by structure, since it is by spatial interactions that molecules interact with each other, both in terms of steric complementarity, as well as intermolecular forces. Thus, the electron density field and electrostatic potential field of a molecule contain the "raw fingerprint" of how this molecule can fit to binding partners. In this paper, we show that deep learning can predict biological function of molecules directly from their raw 3D approximated electron density and electrostatic potential fields. Protein function based on EC numbers is predicted from the approximated electron density field. In another experiment, the activity of small molecules is predicted with quality comparable to state-of-the-art descriptor-based methods. We propose several alternative computational models for the GPU with different memory and runtime requirements for different sizes of molecules and of databases. We also propose application-specific multi-channel data representations. With future improvements of training datasets and neural network settings in combination with complementary information sources (sequence, genomic context, expression level), deep learning can be expected to show its generalization power and revolutionize the field of molecular function prediction.