Synechron the global financial services consulting and technology services provider, has announced the launch of its AI Data Science Accelerators for Financial Services, Banking and Insurance (BFSI) firms. These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad.

Pedestrians walk under a surveillance camera, which is part of a facial recognition technology test in Berlin, Germany. Machine learning shines at tasks like this because it can recognize patterns and predict threats in massive data sets, all at machine speed. There is a temptation to hail major advances in technology as cure-alls for the challenges facing organizations and society today. The fanfare usually ends in disappointment, as the latest superhero technology doesn't live up to its expectations. Not surprisingly, machine learning, a domain within the broader field of artificial intelligence, has been hailed as the current be-all end-all answer in cybersecurity. As a result, it is currently at the peak of inflated expectations in Gartner's most recent Hype Cycle for Emerging Technologies.

About me: Dinis Guarda: author, CEO and founder Working / collaborating / advising the likes of Advisor: Founder board member: Books 3. What is the biggest challenge humanity faces now? by @DinisGuarda 4. 4 What is the DNA of our time? What happens when we can hack this code? As we digitise all society, ourselves and datify our own data ... we are / will leapfrog the very system of our human identity and society. Organic and digital DNA are merging. Scientists and technologists as they have access to its engineering have and are using DNA conventions to store books, recordings, GIFs, and planning things such as an Amazon gift card.

A recent Wall Street Journal article by Manhattan Institute writer Oren Cass bears the title quoted above. It echoes futurist Martin Ford's 2015 book title Rise of the Robots, and invites you to consider their overall effects on the future of work. How are you reckoning with the rise of robots in the future of your work? Below are some questions to consider. Over 1,000 participants from 16 countries demonstrated state-of-the-art robotics in competitions such as soccer, rescue and services.

Self-driving car services could be on the streets of London within three years under a partnership between the private hire firm Addison Lee and the British driverless car pioneers Oxbotica. The companies have signed a deal to develop and deploy autonomous vehicles in the city by 2021. Oxbotica will start mapping more than 250,000 miles of public roads in and around London from next month, using its technology to create a comprehensive map of every traffic feature. While the link-up could eventually allow Addison Lee's fleet of black Mercedes and Prius cabs to be driven autonomously, the 5,000 drivers in London will remain employed, the firm says. However, it could also offer a cheaper, autonomous ride-sharing version of its hire service.

This article is from today's FT Opinion email. Sign up to receive a daily digest of the big issues straight to your inbox. The giant US retailer Sears, the "everything store", offers a cautionary tale about how companies across the sector have failed to keep ahead of their customers' needs and shopping behaviour. For Andrew Edgecliffe-Johnson, who explores the company's bankruptcy filing in a column this week, it is not just a sad tale of a once-great American business fallen on hard times. The demise of the "19th-century offline Amazon", as Andrew describes it, is a warning to those other companies who have failed to come up with adequate strategies in response to the online retail threat.

State space models (SSMs) are a flexible approach to modeling complex time series. However, inference in SSMs is often computationally prohibitive for long time series. Stochastic gradient MCMC (SGMCMC) is a popular method for scalable Bayesian inference for large independent data. Unfortunately when applied to dependent data, such as in SSMs, SGMCMC's stochastic gradient estimates are biased as they break crucial temporal dependencies. To alleviate this, we propose stochastic gradient estimators that control this bias by performing additional computation in a `buffer' to reduce breaking dependencies. Furthermore, we derive error bounds for this bias and show a geometric decay under mild conditions. Using these estimators, we develop novel SGMCMC samplers for discrete, continuous and mixed-type SSMs. Our experiments on real and synthetic data demonstrate the effectiveness of our SGMCMC algorithms compared to batch MCMC, allowing us to scale inference to long time series with millions of time points.

In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring which is based on an array of event-triggered seismic sensors with advanced signal processing and characterization capabilities for a novel co-detection technique. On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly when it matters most and not wasting precious resources when nothing can be observed. On the other hand we use machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones. The sensors' response time and memory requirement is substantially improved by pipelining the inference of a convolutional neural network. In this way, convolutional neural networks that would not run unmodified on a memory constrained device can be executed in real-time and at scale on low-power embedded devices.

Rejection Sampling is a fundamental Monte-Carlo method. It is used to sample from distributions admitting a probability density function which can be evaluated exactly at any given point, albeit at a high computational cost. However, without proper tuning, this technique implies a high rejection rate. Several methods have been explored to cope with this problem, based on the principle of adaptively estimating the density by a simpler function, using the information of the previous samples. Most of them either rely on strong assumptions on the form of the density, or do not offer any theoretical performance guarantee. We give the first theoretical lower bound for the problem of adaptive rejection sampling and introduce a new algorithm which guarantees a near-optimal rejection rate in a minimax sense.

Solving partial differential equations (PDEs) underlies much of applied mathematics and engineering, ranging from computer graphics and financial pricing, to civil engineering and weather prediction. Conventional approaches to prediction in PDE models rely on numerical solvers and require substantial computing resources in the model-application phase. While in some application domains, such as structural engineering, the longer run-times may be acceptable, in domains with rapid decay of value of the prediction, such as weather forecasting, the run-time of the solver is of paramount importance. In many such applications, the ability to generate large volumes of data facilitates the use of surrogate or reduced-order models [1] obtained using deep artificial neural networks [2]. Although the observation that artificial neural networks could be applied to physical models is not new [3, 4, 5, 6, 7, 8, 9, 5, 10], and indeed, it is seen as one of the key trends [11, 12, 13] on the interface of applied mathematics, data science, and deep learning, their applications did not reach the level of success observed in the field of the image classification, speech recognition, machine translation, and other problems processing unstructured high-dimensional data, yet. A key issue faced by applications of deep-learning techniques to physical models is their scalability. Even very recent research on deep-learning for physical models [14, 15, 16] uses a solver for PDEs to obtain hundreds of thousands of outputs. The deep learning can then be seen as means of nonlinear regression between the inputs and outputs.