New bots are constantly being launched and we have covered a fair few, from this chatbot chef which plans meals for users via emoji, to a messenger bot that helps out in emergency situations. New innovation, Wonder, is designed to help users store and recall the information they need from their gym locker password, their insurance provider, right through to the type of ink cartridges their printer uses. Customers first enter their phone number on Wonder's website. They then text Wonder the information they want to remember at a later date. The app stores that information, and when the customer is trying to recall the details, they can ask Wonder directly via text message: "When's the next company meeting?"

Information contained on this page is provided by an independent third-party content provider. If you are affiliated with this page and would like it removed please contact pressreleases@franklyinc.com BellHawk Systems Corporation announces the availability of a new white paper "The Artificial Intelligence Revolution in Manufacturing Operations Management." This white paper is available for download from the front page News section of www.BellHawk.com. This white paper describes how real-time Artificial Intelligence (AI) techniques originally developed for the USAF and NASA are being applied to manufacturing organizations to enable managers to run their manufacturing plants with less stress and much smaller management teams. It gives examples of how even small manufacturing organizations are able to use these methods to automate their planning and scheduling and for managers to be alerted whenever problems arise.

Summary: Sensors that know how you feel? Sensors that want to change the way you feel? When did that happen and better yet how? We're getting used to sensors finding out what we're doing. Apparently they are now sufficiently sophisticated that they can even tell if I'm sitting up straight (yes Mom – BTW using a camera is almost cheating, you should be able to do this with just an accelerometer and a gyro).

People convey meaning by what they say as well as how they say it: Tone, word choice, and the length of a phrase are all crucial cues to understanding what's going on in someone's mind. When a psychiatrist or psychologist examines a person, they listen for these signals to get a sense of their wellbeing, drawing on past experience to guide their judgment. Researchers are now applying that same approach, with the help of machine learning, to diagnose people with mental disorders. In 2015, a team of researchers developed an AI model that correctly predicted which members of a group of young people would develop psychosis--a major feature of schizophrenia--by analyzing transcripts of their speech. This model focused on tell-tale verbal tics of psychosis: short sentences, confusing, frequent use of words like "this," "that," and "a," as well as a muddled sense of meaning from one sentence to the next.

Marco, what can we do about AI? Marco, are we doing enough on AI?" The questions all come from senior executives, desperate to harness the potential that AI promises. Yet Bressan is bemused by how the technology is talked about at board level and in the media. "Currently it denotes a vision of the future; an aspect of the sci-fi imagination; something that you still can't do. But the truth is senior financial executives have been doing AI-related work, research and deployment of products for years." At the most rudimentary level, AI involves teaching machines to learn and to interact in order to undertake cognitive tasks that were usually performed by humans. The type of AI featured in sci-fi films in which machines possess a human-like intelligence, sometimes referred to as general artificial intelligence, remains a distant and elusive prospect. The most optimistic experts, such as Google's director of engineering, Ray Kurzweil, predict that AI will be able to outsmart humans by 2029. Conservative predictions expect this to take at least 100 years, if at all. Of more immediate relevance to those working in financial services is the deployment of narrow artificial intelligence. These applications undertake specific tasks using problem solving, deduction, reasoning and natural language processing. Such programmes are being applied across financial services, from the development of customer service programmes that use natural language processing to manage and field customer queries, through to programmes that can conduct financial research and make sophisticated models of financial markets to identify trading opportunities. The potential for narrow applications has led to a boom in AI investment. Technology companies are undoubtedly leading the way. In 2015 the giants of AI – Microsoft, Google and Facebook – spent 8.5 billion on AI research, acquisitions and talent. In comparison, financial institutions have made a cautious foray into the field. A handful are making investments by hiring high-level data scientists or acquiring AI companies. The hedge fund Bridgewater Associates hired the former chief engineer behind IBM's Watson supercomputer. BlackRock has also been busy hiring some high-profile names and has announced a joint venture with Google to explore how to use AI to improve investment decision-making. Goldman Sachs has invested in a number of promising AI start-ups, including the financial research platform Kensho. Yet most financial institutions have been slow to adopt AI, even though it is likely to usher in a new type of bank, with data and technology as its heart. Failure to adapt may lead to extinction for some. As Neil Dwane, global strategist at Allianz Global Investors, explains: "Technological competence is absolutely essential for at least staying in the game.

I'd love to talk more about Flowcast, but I'm still not able to shake the image of you making a robotic submarine run by San Diego poolside (laughs). As a STEM enthusiast, I have been in awe of IBM Watson's capabilities. And I feel it's an honor to be talking to someone who has contributed to its capabilities. Now, let's come back to Flowcast. Can you share more information and shed more light on how Flowcast came about?

What comes first: the chicken or the egg? People have asked that question for ages, but nobody seems to have the answer. Which entity created the second one? How does one help the other to evolve? How do they work separately, or together? And what happens if they finally combine into one platform?

More recently, we've witnessed the encroachment of robots on the highly specialized and technical field of medicine. In 2013, the U.S. Food and Drug Administration approved a machine to administer the sedative propofol without the presence of an anesthesiologist. Computer-aided diagnosis, or CADx, has become a progressively important part of radiology, with a recent study by the Royal Society of Medicine showing that it outperformed radiologists in identifying radiolucency (the dark spots on an X-ray) by a factor of 10. More impressive -- because it marries finger dexterity with analytical prowess -- is the Smart Tissue Autonomous Robot, or STAR, which can stitch up tubular tissue, like blood vessels, with greater accuracy than its human counterpart. There's no reason why at some point in the future computers will not be able to take over other diagnostic and surgical tasks.

Researchers from Harvard and the University of Vermont have found a way to use Instagram to detect depression. Using machine learning tools, they developed a model that can predict whether a person is clinically depressed with surprising accuracy, just by looking at their Instagram photos. It's important to note right away that this model is just the beginning. The researchers themselves only go so far as to say that "these findings suggest new avenues for early screening and detection of mental illness," and "the findings reported here should not be taken as enduring facts." That said, their results are impressive.

Visualise and explore the work of 44,579 superstar Researchers from 8,437 Research Institutions in 118 countries on every sport in the Olympics 2016 Research Dashboard by wizdom.ai, the world's largest research knowledge graph powered by big data analytics, machine learning and artificial intelligence. With all eyes set on the Rio Olympics 2016, the world witnesses the greatest display of human strength, endurance, dexterity and performance by participants from across the globe. Everyday over the course of the two weeks, over 11,400 athletes compete for the gold medal in their game. They have undertaken intensive training for months and years to reach the epitome of physical fitness and to optimise their performance, making every millisecond count. Backing the Olympians that make it to the podium in every field, all along through their training there are thousands of researchers around the world who have extensively studied the games to raise the bar for human performance.