The woman startled at the blessing. In English: "I have called you by name; you are mine!" It may not have been the words themselves that caused her to jump. The blessing, taken from the biblical book of Isaiah, has comforted many for thousands of years. It may have been the source of the benediction: A robot built on the body of an ATM machine, whose plastic fingers sprung open and palms lit up as it raised its mechanic hands in blessing, brightening an otherwise gray, rainy day in mid-June.

One of the charges against Socrates was that his arguments were like robots. As the Greek philosopher approached his own trial, Euthyphro told Socrates, "You are like Daedalus." He meant that just as Daedalus made automata that moved on their own in Greek myth, Socrates' arguments were so persuasive that his ideas seemed to move under their own power. Even 2,500 years ago, automata inspired both fascination and fear. I recently speculated about whether a machine could have a mystical experience.

There has never been a more exciting time in the banking industry. As technology's onward rush continues nonstop it provides institutions with opportunities that expand well beyond traditional financial services. More than expanding the services offered, financial institutions will be able to process data and engage with consumers faster than ever, as the rollout of 5G networks becomes a reality. These superfast networks will provide the opportunity to instantaneously and intelligently meet any consumer need, at any time, on any channel. Achieving this will require new computing and storage strategies, advanced analytics, enhanced cybersecurity capabilities and a brand new perspective on how banking services can be delivered.

The major objective of the Cognitive Systems & Artificial Intelligence In BFSI market report is to help the user understand the market in terms of its definition, segmentation, market potential, influential trends, and the challenges that the market is facing. This research is conducted to understand the current landscape of the market, especially in 2019 up-to the forecast year 2025. The readers will find this report very helpful in understanding the Cognitive Systems & Artificial Intelligence In BFSI market in depth. The data and the information regarding the market are taken from reliable sources such as websites, annual reports of the companies, journals, and others and were checked and validated by the industry experts. The facts and data are represented in the report using diagrams, graphs, pie charts, and other pictorial representations.

Last Saturday we took a look at some of the most-read Singularity Hub articles from 2019. As opposed to short pieces about what's happening, these are long reads about why it matters and what's coming next. Some of them make the news while others frame the news, go deep on big ideas, go behind the scenes, or explore the human side of technological progress. We hope you find them as fascinating, inspiring, and illuminating as we did. DeepMind and Google: The Battle to Control Artificial Intelligence Hal Hodson 1843 "[DeepMind cofounder and CEO Demis] Hassabis thought DeepMind would be a hybrid: it would have the drive of a startup, the brains of the greatest universities, and the deep pockets of one of the world's most valuable companies. Every element was in place to hasten the arrival of [artificial general intelligence] and solve the causes of human misery."

AN ARTIFICIAL INTELLIGENCE programme has been developed which can detect breast cancer from mammograms better than experts, a study found. A new study has found that an AI system developed by Google Health can identify cancer in breast screening mammograms with fewer false positives, and fewer false negatives than radiologists. The programme was developed in collaboration with DeepMind, Cancer Research UK Imperial Centre, Northwestern University, and Royal Surrey County Hospital. Researchers said that the AI model was trained and tuned on anonymised mammograms from more than 76,000 women in the UK and more than 15,000 women in the US to see if it could learn to spot signs of breast cancer. It was then tested on a separate data selection of more than 25,000 women in the UK and over 3,000 women in the US.

You can't keep the big tech companies out of the news, can you? Smart home devices is one of the niches where they have been battling to gain supremacy -- Amazon's Echo, Google Home or Apple HomePod are all popular smart home devices. Despite the involvement of these tech giants among other major players, Smart home tech adoption has been slow so far. In my opinion, one of the biggest hindrances in this regard has been the privacy concerns emanating from the use of such smart home technology, like smart speakers. Amazon, Google & Apple -- all three have been previously embroiled in controversies where they were involved in privacy breach of the customers using their devices.

Spiking Neural Networks have revealed themselves as one of the most successful approaches to model the behavior and learning potential of the brain, and exploit them to undertake practical online learning tasks [1]. In Stream Learning (SL), aka data stream mining or machine learning for data streams, applications (such as mobile phones, sensor networks, industrial process controls and intelligent user interfaces, among others) generate huge amounts of data in the form of fast streams, acquiring special relevance with the advent of the Big Data and IoT era. In these scenarios, algorithms cannot explicitly access all historical data because the storage capacity needed for this purpose becomes unmanageable. Indeed, data streams are fast and large (potentially, infinite), so information must be extracted from them in real-time, being therefore necessary to learn in an online manner [2]. Besides, some of these scenarios produce non-stationary data streams which are becoming increasingly prevalent, and where the process generating the data may change over time, producing changes in the patterns to be modeled (concept drift).

In the year ahead, we see the cloud, AI, and data management as the megaforces of the data and analytics agenda. And so, picking up where Big on Data bro Andrew Brust left off last week, we're looking at some of the underlying issues that are shaping adoption. In the world of data and analytics, you can't start a conversation today without bringing in cloud and AI. Yesterday in Part I, we hit the cloud checkbox: we explored how the upcoming generation change in enterprise applications will in turn shift the context of how enterprises are going to be evaluating cloud deployment. Today we turn our attention to the core building block – what's happening in databases, and what we expect to become the sleeper issue this year in AI.

Cyclists, whether they be food delivery riders or MAMILs, are infamous for doing everything they can to conserve their hard-won speed, even if it means running a red light or careering into the way of pedestrians on the footpath. But new work from IBM Research Australia and RMIT's Exertion Games Lab, however, is looking to avoid tiresome stops or dangerous behaviour by using artificial intelligence (AI) to catch the'green wave' of traffic signals. It's well known many cyclists jump traffic signals or make legally questionable deviations to maintain momentum getting from A to B. If you're an underpaid international student under Dickensian food delivery conditions, there's simply no other way. That could be about to change. In a project dubbed'Ari the e-bike,' the researchers used traffic data and'green wave' modelling from VicRoads and internet of things (IoT) technologies to help the rider regulate their speed to match cycles of green traffic lights.