FinTech is a lucrative, yet quite saturated market. In order to stay competitive, businesses should keep track of the emerging trends and be able to capitalize on them before their competitors do. Artificial Intelligence is currently among the most promising FinTech trends. Leading financial brands such as Capital One, MasterCard, as well as hundreds of startups have set the pace for the adoption of virtual financial advisors. If you want to stay ahead of your competition or simply explore the opportunities for AI in fintech, this article is for you.

In this paper, we propose a novel application of Generative Adversarial Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy. Compared to natural images, cells tend to have a simpler and more geometric global structure that facilitates image generation. However, the correlation between the spatial pattern of different fluorescent proteins reflects important biological functions, and synthesized images have to capture these relationships to be relevant for biological applications. We adapt GANs to the task at hand and propose new models with casual dependencies between image channels that can generate multi-channel images, which would be impossible to obtain experimentally. We evaluate our approach using two independent techniques and compare it against sensible baselines. Finally, we demonstrate that by interpolating across the latent space we can mimic the known changes in protein localization that occur through time during the cell cycle, allowing us to predict temporal evolution from static images.

Minimum energy paths for transitions such as atomic and/or spin rearrangements in thermalized systems are the transition paths of largest statistical weight. Such paths are frequently calculated using the nudged elastic band method, where an initial path is iteratively shifted to the nearest minimum energy path. The computational effort can be large, especially when ab initio or electron density functional calculations are used to evaluate the energy and atomic forces. Here, we show how the number of such evaluations can be reduced by an order of magnitude using a Gaussian process regression approach where an approximate energy surface is generated and refined in each iteration. When the goal is to evaluate the transition rate within harmonic transition state theory, the evaluation of the Hessian matrix at the initial and final state minima can be carried out beforehand and used as input in the minimum energy path calculation, thereby improving stability and reducing the number of iterations needed for convergence. A Gaussian process model also provides an uncertainty estimate for the approximate energy surface, and this can be used to focus the calculations on the lesser-known part of the path, thereby reducing the number of needed energy and force evaluations to a half in the present calculations. The methodology is illustrated using the two-dimensional M\"uller-Brown potential surface and performance assessed on an established benchmark involving 13 rearrangement transitions of a heptamer island on a solid surface.

Concept Trees are a type of database that can organise arbitrary textual information using a very simple rule. Each tree tries to represent a single cohesive concept and the trees can link with each other for navigation and semantic purposes. The trees are therefore a type of semantic network and would benefit from having a consistent level of context for each of the nodes. The Concept Tree nodes have a mathematical basis allowing for a consistent build process. These would represent nouns or verbs in a text sentence, for example. New to the design can then be lists of descriptive elements for each of the nodes. The descriptors can also be weighted, but do not have to follow the strict counting rule of the tree nodes. With the new descriptive layers, a much richer type of knowledge can be achieved and still reasoned over automatically. The linking structure of the licas network is very relevant to building the concept trees now and forms the basis for their construction. The concept tree - symbolic neural network relation is also extended further.

The chief executive of Deutsche Bank has issued a stark warning about the impact of technology, saying a "big number" of his staff will lose their jobs as robots take over. John Cryan told an audience in Frankfurt: "In our bank we have people doing work like robots. Tomorrow we will have robots behaving like people. It doesn't matter if we as a bank will participate in these changes or not, it is going to happen." He also referred to accountants inside the bank who "spend a lot of time basically being an abacus", who would also be replaced by machines.

We stand at a watershed moment for society's vast, unknown digital future. A powerful technology, artificial intelligence (AI), has emerged from its own ashes, thanks largely to advances in neural networks modeled loosely on the human brain. AI can find patterns in massive unstructured data sets, improve performance as more data becomes available, identify objects quickly and accurately, and, make ever more and better recommendations and decision-making, while minimizing interference from complicated, political humans. This raises major questions about the degree of human choice and inclusion for the decades to come. How will humans, across all levels of power and income, be engaged and represented?

Cheaper, more capable, and more flexible technologies are accelerating the growth of fully automated production facilities. The key challenge for companies will be deciding how best to harness their power. At one Fanuc plant in Oshino, Japan, industrial robots produce industrial robots, supervised by a staff of only four workers per shift. In a Philips plant producing electric razors in the Netherlands, robots outnumber the nine production workers by more than 14 to 1. Camera maker Canon began phasing out human labor at several of its factories in 2013. This "lights out" production concept--where manufacturing activities and material flows are handled entirely automatically--is becoming an increasingly common attribute of modern manufacturing.

If you're hoping to turn up the heat in the bedroom, you may want to put on Marvin Gaye's 1983 chart-topper, Sexual Healing. A new study has shown that our sense of touch is heightened while listening to'sexy' songs. The researchers suggest that music's relation to touch may have played a key role in our evolution as a species, as song preference brought certain individuals together to mate. If you're hoping to turn up the heat in the bedroom, you may want to put on Marvin Gaye's 1983 chart-topper, Sexual Healing. A new study has shown that our sense of touch is heightened while listening to'sexy' songs (stock image) To understand the effect of sexy music, the researchers enlisted the help of a robot.

Samantha the Sex Doll is a £3,000 interactive robot designed with one specific purpose: pleasure. She is the latest development in a growing trend in Austria which has seen brothel customers preferring robots over sexual intercourse with a human. During a display at the Ars Electronica Festival in the city of Linz, Samantha shows off her'skills', which include reacting to touch, hugging and moaning - and remembering individuals she has'interacted' with. During a display at the Ars Electronica Festival in the city of Linz, Samantha shows off her'skills', which include reacting to touch, hugging and moaning - and remembering individuals she has'interacted' with The robot doll's developer Sergi Santos says Samantha can remember someone she has interacted with in the past, as she communicates according to the way she was treated by them before. The robot reacts to touch, speaks multiple languages and is even said to be able to learn new things thanks to artificial intelligence (AI) software.

Google is appealing against the record €2.4bn (£2.2bn) fine imposed by the European Union for its abuse of its dominance of the search engine market in building its shopping comparison service. The world's most popular internet search engine has launched its appeal after it was fined by the European commission for what was described as an "old school" form of illegality. The Luxembourg-based general court, Europe's second-highest, is expected to take several years before ruling on Google's appeal, which had been widely expected. The Silicon Valley giant had responded to the fine at the time of its announcement by saying that it "respectfully" disagreed with the legal argument being pursued. A spokesman for the commission said: "The commission will defend its decision in court."