At first I liked tinkering with computers and learn computer programming languages, after graduating high school I started to develop the concept of work on data processing and I've completed it. More recently the IT world the term Deep Learning (DL) number of campuses or institutions have been developing this concept, and many experts of computer data or data processing experts began to talk about it. I do not know that it is actually a concept I have done resemblance to Deep Learning or part of Deep Learning but once I learned it was different, DL they mean is to show something of what they are looking for based on the data input as much as possible so that what they the purpose is to learn to structure the deepest and provide advisory or decision, but it relates to the search engine or internet network application using algorithms, meaning that when it is applied in the world of the stock market as Wall Street, the working concept Deep Learning will detect fraud there is. Deep Learning systems work similar to the concept of the brain where the objects are visible to the eye to be delivered to specific parts to be stored and studied by contrasting the existing data and the use of certain alogritma method to render a decision as well as a warning signal. Deep Learning tend to use super computers or computer large capacity for looking at the use of data (big data), big data here can mean pictures, numbers, files, chat, text, web pages, maps of the world, the code algoritmatik, core decision made deep learning is seen in a comparison of all the data held (such as scanned photos) means more data entry means more comparisons, and if more and more comparisons, the decision is getting better, so that a deficiency also that deep learning must wear a large-capacity computers.

The featured guest for episode 148 is Paul Roetzer, Founder & CEO of PR 20/20, a well-known marketing agency specializing in inbound marketing strategies. Paul is the author of two popular books: The Marketing Agency Blueprint and The Marketing Performance Blueprint. In November 2016, Paul launched the Marketing Artificial Intelligence Institute (MAII). On this episode, we dive deep into what MAII is, why it exists, and why you should care. If you're not familiar with Artificial Intelligence (AI), Paul describes it as "the umbrella of the tools and technologies that are designed to make machines smarter."

On the Stanford University campus, you could practically throw a rock and hit 100 graduate students who are building apps that enable people to communicate more effectively. But Terry Winograd is particularly enthusiastic about the app one of his graduate students, Catalin Voss, is working on. Voss, a native of Germany who completed his bachelor's and master's degrees last June at the age of 21, is working on an app that deploys Google Glass, linked to a smartphone, to help autistic children recognize human emotions through facial expressions. Venture capitalists weren't interested, even though Voss had created and sold a startup that used eye-tracking technology to monitor attentiveness to a Toyota subsidiary while still a freshman. But Terry Winograd was interested. "It runs, it has AI [artificial intelligence]," says Winograd, who 20-odd years ago advised another graduate student on the then nascent field of searching the World Wide Web. "It's at a stage where we've actually put 30 devices into homes. Our goal is to have 100 in the trial." Voss says his objective is to build a medical product that insurers will be willing to pay for. "We want to prove the investors wrong, who didn't believe in it, and build an aid for people with autism, and other mental disorders as well," he says. "We believe we've built a fairly holistic system for mental health." Winograd was Voss's first choice for an advisor even though the 70-year-old professor retired from teaching three years ago.

Great idea, big potential, but few applications so far and a lot to learn. This sums up the outlook for how public services could make use of artificial intelligence (AI), the technology that is stirring up hopes and fears, and is already surrounded by an aura of inevitability. There is some debate about its definition, but it is generally seen as a stream of computing developed to carry out tasks usually requiring human intelligence, and to learn from what it takes in. It came in for a new round of attention last week when the Government's chief scientific adviser, Sir Mark Walport, delivered a Turing Institute lecture on the potential. It was notable for emphasising the overall significance rather than much precision on how AI could be used: Walport spoke of applications in justice, welfare, education and medicine, but largely in broad terms.

Education has mostly followed the same structure for centuries -- e.g., the "sage on a stage" and "assembly line" models. As AI continues to disrupt industries like consumer electronics, ecommerce, media, transportation, and healthcare, is education the next big opportunity? Given that education is the foundation that prepares people to pursue advancements in all the other fields, it has the potential to be the most impactful application of AI. The three segments of the education market -- K-12, higher education, and corporate training -- are going through transitions. In the K-12 market, we are seeing the effect of the newer, more rigorous academic standards (Common Core, Next Generation Science Standards) shifting the focus toward measuring students' critical thinking and problem-solving skills and preparing them for college and career success in the 21st century.

It's all well and good to ask if androids dream of electric sheep, but science fact has evolved to a point where it's beginning to coincide with science fiction. No, we don't have autonomous androids struggling with existential crises -- yet -- but we are getting ever closer to what people tend to call "artificial intelligence." Machine Learning is a sub-set of artificial intelligence where computer algorithms are used to autonomously learn from data and information. In machine learning computers don't have to be explicitly programmed but can change and improve their algorithms by themselves. Today, machine learning algorithms enable computers to communicate with humans, autonomously drive cars, write and publish sport match reports, and find terrorist suspects.

Students learn concepts and skills more quickly when they receive one-on-one instruction. Stottler Henke develops intelligent tutoring systems that provide the benefits of one-on-one training -- automatically and cost-effectively. These systems encode the subject matter and teaching expertise of experienced instructors, using artificial intelligence (AI) software technologies and cognitive psychology models. We have developed numerous systems that provide practice-based learning for K-12 education, corporate training and professional development, and military training. For additional information, read Intelligent Tutoring Systems: The What and the How, Intelligent Tutoring Systems Technologies for Military Training, or the Powerpoint presentation: Intelligent Training Systems.

Using analogical model formulation with sketches to solve Bennett Mechanical Comprehension Test problems.

A week after Donald Trump's election, a thirty-year-old cognitive scientist named Maya Shankar purchased a plane ticket to Flint, Michigan. Shankar held one of the more unorthodox jobs in the Obama White House, running the Social and Behavioral Sciences Team, also known as the President's "nudge unit." When she launched the team, in early 2014, it felt, Shankar recalls, "like a startup in my parents' basement"--no budget, no mandate, no bona-fide employees. Within two years, the small group of scientists had become a staff of dozens--including an agricultural economist, an industrial psychologist, and "human-centered designers"--working with more than twenty federal agencies on seventy projects, from fixing gaps in veterans' health care to relieving student debt. Usually, the initiatives had, at their core, one question: Could the growing body of knowledge about the quirks of the human brain be used to improve public policy? For months, Shankar had been thinking about how to bring behavioral science to bear on the problems in Flint, where a crisis stemming from lead contamination of the drinking water had stretched on for almost two years. She wondered if lessons from the beleaguered city could inform the Administration's approach to the broader threat posed by lead across America--in pipes, in paint, in dust, and in soil. "Flint is not the only place poisoning kids," Shankar said. In recent years, behavioral science has become a voguish field. In 2002, the Israeli psychologist Daniel Kahneman won a Nobel Prize in Economic Sciences for his work with a colleague, Amos Tversky, exploring the peculiarities of human decision-making in the face of uncertainty. A basic premise of the discipline they'd helped to create was that people's cognition is bias-prone, and susceptible to the cognitive equivalent of optical illusions. As a result, small tweaks of presentation or circumstance could make a major difference: if a judge rendered a decision about granting parole just before a meal, the inmate's odds for a favorable outcome dipped to near zero; just after the judge ate, the chances rose to around sixty-five per cent. Grocers had learned that they could sell double the amount of soup if they placed a sign above their cans reading "limit of 12 per person." But, for all the field's potential, its advances seemed mostly to have served the private sector. A prominent exception was the "nudge," a notion advanced by the legal scholar Cass R. Sunstein, now at Harvard Law School, and the University of Chicago behavioral economist Richard Thaler, in their 2008 best-seller "Nudge: Improving Decisions About Health, Wealth, and Happiness."

Good communication is vital in health care, both among health care professionals, and between health care professionals and their patients. And well-written documents, describing and/or explaining the information in structured databases may be easier to comprehend, more edifying, and even more convincing than the structured data, even when presented in tabular or graphic form. Documents may be automatically generated from structured data, using techniques from the field of natural language generation. These techniques are concerned with how the content, organization and language used in a document can be dynamically selected, depending on the audience and context. They have been used to generate health education materials, explanations and critiques in decision support systems, and medical reports and progress notes. Effective communication is vital in health care, both between health care providers and their patients and among health care providers themselves. Different participants in the health care process--consultants, nurses, general practitioners, medical researchers, patients, their relatives, and even accountants and administrators--must all be able to obtain and communicate relevant information on patients and their treatment. But there are many obstacles in the way of effective communication: Participants may use different terms to describe the same thing--a particular problem for patients who do not understand medical terminology. Different participants frequently have different information needs and little time to filter information, so that no single report is truly adequate for all.