I recently got involved in the chess scene, before I understood the game of chess I wasn't very compelled to learn things about the game and how it worked. I thought it was just some ridiculously complicated thing that you needed to be very smart to play but that is not the case at all its ALL ABOUT THE PATTERNS. You may be wondering how this all ties into AI. A game on a board connects with AI? In 1992 at the dawn of the internet something called online chess was created.

"Will AI take over jobs?" is a very controversial and interesting question that has been around for many years, and yet it will be questioned even more in the upcoming years, as artificial intelligence rapidly develops. Some people believe that AI will create more jobs than it destroys. They argue that as AI automates certain tasks, it will free up workers to do other, more creative or complex tasks. For instance, a bank teller whose job is automated by AI may be able to use their freed-up time to provide financial planning services to customers. Similarly, a manufacturing worker whose job is taken over by a robot may be able to move into maintenance or quality control. Others believe that AI will destroy more jobs than it creates.

This is an interview with Professor Emily Mower Provost that was first published by The Michigan Engineer News Center. Using machine learning to decode the unpredictable world of human emotion might seem like an unusual choice. But in the ambiguity of human expression, U-M computer science and engineering associate professor Emily Mower Provost has discovered a rich trove of data waiting to be analyzed. Mower Provost uses machine learning to help measure emotion, mood, and other aspects of human behavior; for example, she has developed a smartphone app that analyzes the speech of patients with bipolar disorder to track their mood, with the ultimate goal of helping them more effectively manage their health. How do you quantify something as ambiguous as emotion in a field where, traditionally, ambiguity is the enemy?

Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference. Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider "smart".

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning--and the need for it. In 2007 Fei-Fei Li, the head of Stanford's Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers. By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.1 1.Fei-Fei Li, "How we're teaching computers to understand pictures," TED, March 2015, ted.com. Last November, Li's team unveiled a program that identifies the visual elements of any picture with a high degree of accuracy.

Microsoft's quest to build computing systems that understand the world around them doesn't end with the company's Project Oxford machine-learning technology. Researchers at the Redmond, Wash., software maker are also developing systems that mimic how humans pull information from the things they see. "When a person is asked about something in a photo, they're taking in a lot of details--a lot of words--to answer questions about it," blogged Microsoft spokesperson Athima Chansanchai. "Now, a team of Microsoft researchers, together with colleagues from Carnegie Mellon University, has created a system that uses computer vision, deep learning and language understanding to analyze images and answer questions the same way humans would." Together, the researchers created a model that "applies multi-step reasoning to answer questions about pictures," said Chansanchai.

Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference. Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world. In short, the best answer is that: Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider "smart".

Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference. Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider "smart".

Writing programs that can create programs have been an elusive goal of artificial intelligence(AI) research for many years. As a matter of fact, the idea that AI agents can create their own programs if often seem as one of the differentiators of general AI vs. narrow AI. So important is this goal, that AI researchers have created a specific area of research known as Program Synthesis that focuses on addressing those challenges. The idea behind program synthesis is to create AI agents that can generate programs that match a given specification. We often use primitive versions of this technique when we take advantage of, for instance, the Flash Fill feature in Microsoft Excel.

Games have long been used as testbeds and benchmarks for artificial intelligence, and there has been no shortage of achievements in recent months. Google DeepMind's AlphaGo and poker bot Libratus from Carnegie Mellon University have both beaten human experts at games that have traditionally been hard for AI – some 20 years after IBM's DeepBlue achieved the same feat in chess. Games like these have the attraction of clearly defined rules; they are relatively simple and cheap for AI researchers to work with, and they provide a variety of cognitive challenges at any desired level of difficulty. By inventing algorithms that play them well, researchers hope to gain insights into the mechanisms needed to function autonomously. With the arrival of the latest techniques in AI and machine learning, attention is now shifting to visually detailed computer games – including the 3D shooter Doom, various 2D Atari games such as Pong and Space Invaders, and the real-time strategy game StarCraft.