More than one-in-four UK business leaders believe a lack of data skills within their organisation (27%) is impacting their ability to deliver engaging, personalised experiences for customers. Adobe's Mind the Data Gap Report surveyed 750 business leaders. Adobe found 22% felt their ability to develop personalised customer experiences was held back by lack of a data-centric culture. Twenty-four percent identified the absence of a unified technology platform as the biggest obstacle. Adobe's Mind the Data Gap fieldwork was conducted by London Research, between June and July 2019. The company surveyed 750 European business leaders, 250 each in the UK, France and Germany.

Global Artificial Intelligence in BFSI Market size in 2017 is 2.50 million US$ and it is expected to reach 19.80 million US$ by the end of 2026 with a CAGR of 29.52% during 2017 -2026. Artificial intelligence (AI) in BFSI refers to the simulation of human intelligence into machines with the help of sophisticated machine learning, deep learning, chat-bots, cognitive computing, and natural language processing algorithms that help in customer relationship management, communication, and recruitment & wealth management. Artificial intelligence in BFSI is driven mainly by digital data .Artificial Intelligence (AI) is fast evolving as the go-to technology for banks across the world to personalize experience for individuals. Positive rise of AI-based application in BFSI such as customer support, fraud detection, improving employee efficiency, reduce fraud and security risks. Growing adoption of smart devices and growing penetration of internet services across the globe is fuelling amount of the data. A number of financial services institution are already generating value from artificial intelligence.

Strong writing can determine whether a Hollywood show turns out like Game of Thrones or Pee-Wee Herman's Big Adventure. That's why Ron Howard and Brian Grazer's Imagine Entertainment has opened an incubator for writers called Imagine Impact. The film and TV industries employ 2.6 million people in the U.S. alone, and those businesses generate $177 billion a year in wages. But surfacing new writers can be a haphazard process. Imagine Impact, the division of a film production company with dozens of Oscar-nominated films, wants to create a pipeline of strong writers.

Aegis AI, an artificial intelligence startup that builds software which employs computer vision to automatically detect weapons in security camera feeds, today announced that it's rebranding as Actuate and launched new AI threat-detection features. Actuate was founded in early 2018 by University of Chicago MBAs Sonny Tai and Ben Ziomek. Tai is a former Marine Corps captain who spent his formative years in Johannesburg, South Africa, where gun violence rates are some of the highest in the world, while Ziomek brings deep data science and AI expertise gained from his time as a program manager at Microsoft. The New York City-based Aegis Systems is a venture capital-backed AI startup that provides computer vision software to turn any security camera into a threat-detecting smart camera. Aegis AI system automatically detects firearms in existing security camera feeds, providing early warning and dramatically improving law enforcement response.

For the last several years, Russia has been steadily improving its ground combat robots. Just last year, Kalashnikov, the maker of the famous AK-47 rifle, announced it would build "a range of products based on neural networks," including a "fully automated combat module" that promises to identify and shoot at targets. According to Bendett, Russia delivered a white paper to the UN saying that from Moscow's perspective, it would be "inadmissible" to leave UAS without any human oversight. In other words, Russia always wants a human in the loop and to be the one to push the final button to fire that weapon. Worth noting: "A lot of these are still kind of far-out applications," Bendett said.

Artificial Intelligence in retail market is expected to grow at CAGR of 38.5% during the forecast period, 2019–2026. AI has become a cardinal element across various industry verticals for digitalization, especially in the retail segment. According to the World Economic Forum, e-commerce is on the verge of becoming the most important retail channel, driving 42% of consumption growth and 90% of the growth from mobile e-commerce. Thus, implementing advanced technologies in e-commerce, such as artificial intelligence, would offer better prospects for the retail industry in the coming years. AI is predicted to unleash a digital disruption in retail with prominent industry players ramping-up their businesses. AI-powered solutions are increasingly becoming a priority for the food & beverage industry and retailers.

In this paper, we proposed a general framework for data poisoning attacks to graph-based semi-supervised learning (G-SSL). In this framework, we first unify different tasks, goals, and constraints into a single formula for data poisoning attack in G-SSL, then we propose two specialized algorithms to efficiently solve two important cases --- poisoning regression tasks under $\ell_2$-norm constraint and classification tasks under $\ell_0$-norm constraint. In the former case, we transform it into a non-convex trust region problem and show that our gradient-based algorithm with delicate initialization and update scheme finds the (globally) optimal perturbation. For the latter case, although it is an NP-hard integer programming problem, we propose a probabilistic solver that works much better than the classical greedy method. Lastly, we test our framework on real datasets and evaluate the robustness of G-SSL algorithms. For instance, on the MNIST binary classification problem (50000 training data with 50 labeled), flipping two labeled data is enough to make the model perform like random guess (around 50\% error).

We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence. The proposed contrastive attention mechanism accommodates two categories of attention: one is the conventional attention that attends to relevant parts of the source sentence, the other is the opponent attention that attends to irrelevant or less relevant parts of the source sentence. Both attentions are trained in an opposite way so that the contribution from the conventional attention is encouraged and the contribution from the opponent attention is discouraged through a novel softmax and softmin functionality. Experiments on benchmark datasets show that, the proposed contrastive attention mechanism is more focused on the relevant parts for the summary than the conventional attention mechanism, and greatly advances the state-of-the-art performance on the abstractive sentence summarization task. We release the code at https://github.com/travel-go/

It is only natural that businesses look within their own territory or market to learn about new technology. But chatbots are a global phenomenon, adopted far faster outside the west that most others, so taking a look at their growth, use and adoption globally provides some valuable insights. Both countries have populations of over 1.3 billion people, or over a third of the world's population. That makes them the global focus for customer service automation to battle the sheer volume of calls that even a small public business will receive. Banks, airlines and hotels are adopting chatbots at high speed to cope with the demand, with the likes of WeChat dominating platform usage with its 700 million users across Asia.

Recent innovations in agile aerospace have created unique offerings in high cadence satellite imagery. While this is of immense interest to imagery analysts, a significant portion of GIS professionals and geo-data scientists work less with raster data (AKA imagery) and more with point and vector data. Planet operates the world's largest constellation of earth observation satellites providing near-daily coverage of the entirety of Earth's landmass. Over the past couple of years, we have been working on bringing computer vision and spatiotemporal analysis to market to enable access and data transformations on this rich imagery archive. We recently announced the general availability of our analytic feeds and the launch of our building change detection analytics in private beta (sign up for info here).