The Greeks shared their beliefs with the Egyptians on how to interpret good and bad dreams, and the idea of incubating dreams. In that century, other cultures influenced Greeks to develop the belief that souls left the sleeping body.

A dream is successions of images, ideas, emotions, and sensations that usually occurs involuntarily in the mind during certain stages of sleep.[1] The content and purpose of dreams are not definitively understood, though they have been a topic of scientific speculation, as well as a subject of philosophical and religious interest, throughout recorded history. The scientific study of dreams is called oneirology.[2] Dreams mainly occur in the rapid-eye movement (REM) stage of sleep--when brain activity is high and resembles that of being awake. REM sleep is revealed by continuous movements of the eyes during sleep. At times, dreams may occur during other stages of sleep. However, these dreams tend to be much less vivid or memorable.[3] The length of a dream can vary; they may last for a few seconds, or approximately 20–30 minutes.[3] People are more likely to remember the dream if they are awakened during the REM phase. The average person has three to five dreams per night, and some may have up to seven;[4] however, most dreams are immediately or quickly forgotten.[5] Dreams tend to last longer as the night progresses. During a full eight-hour night sleep, most dreams occur in the typical two hours of REM.[6] In modern times, dreams have been seen as a connection to the unconscious mind. They range from normal and ordinary to overly surreal and bizarre. Dreams can have varying natures, such as being frightening, exciting, magical, melancholic, adventurous, or sexual. The events in dreams are generally outside the control of the dreamer, with the exception of lucid dreaming, where the dreamer is self-aware.[7]

A man has used thought alone to control nanorobots inside a living creature for the first time. The technology released a drug inside cockroaches in response to the man's brain activity – a technique that may be useful for treating brain disorders such as schizophrenia and ADHD. Getting drugs to where they need to be exactly when you want them is a challenge. Most drugs diffuse through the blood stream over time – and you're stuck with the side effects until the drug wears off. Now, a team at the Interdisciplinary Center, in Herzliya, and Bar Ilan University, in Ramat Gan, both in Israel, have developed a system that allows precise control over when a drug is active in the body.

Johnson, 25, was the sniper who targeted the officers at the conclusion of a peaceful march July 7 in downtown Dallas, where demonstrators were protesting fatal police shootings in Minnesota and Louisiana. Armed with an assault rifle, he took multiple positions as he fired. Hours later, authorities used a bomb-carrying robot to kill him.

Rana El Kaliouby is cofounder and CEO of Affectiva, the pioneer in emotion-aware technology--the next frontier of artificial intelligence. Rana invented the company's award-winning emotion recognition technology, built on an emotion AI science platform that uses deep learning and the world's largest emotion data repository of nearly 4 million faces analyzed from 75 countries, amounting to more than 40 billion emotion data points. Prior to founding Affectiva, as a research scientist at MIT Media Lab, Rana spearheaded the application of emotion technology in a variety of fields, including mental health and autism research. Her work has appeared in numerous publications including the New Yorker, Wired, Forbes, Fast Company, the Wall Street Journal, the New York Times, CNN, CBS, Time magazine, Fortune, and Reddit. A TED speaker, Rana was recognized by Entrepreneur as one of the seven most powerful women to watch In 2014, inducted into the Women in Engineering Hall of Fame, recognized as a 2012 Technology Review top 35 innovators under 35, listed on Ad Age's 40 under 40, and given Smithsonian magazine's 2015 American Ingenuity Award for Technology.

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.