Artificial intelligence (AI) is the ability of machines to mimic human capabilities in a way that we would consider'smart'. You most likely have come across – or are aware of – AI applications such as self-driving cars, facial recognition, chess or go players, security systems, or speech/voice recognition (for example, those used in an intelligent virtual assistant). In conventional computing, a programmer writes a computer program that precisely instructs a computer what to do to solve a particular problem. With AI, however, the programmer instead writes a program that allows the computer to learn to solve a problem by itself. That sounds like overdoing it, but this is really the way we do things.
Let us start with an easy example, say you are teaching a kid to differentiate dogs from cats. How would you do it? You may show him/her a dog and say "here is a dog" and when you encounter a cat you would point it out as a cat. When you show the kid enough dogs and cats, he may learn to differentiate between them. If he is trained well, he may be able to recognise different breeds of dogs which he hasn't even seen. Similarly, in Supervised Learning, we have two sets of variables.
Learning is a critical skill in software development. Engineers have to learn new technologies, master existing ones, and get familiar with APIs and codebases. It is crucial to have a proper strategy for skill acquisition and overall professional development. There is a high chance of getting stuck at the same level or wasting a lot of time on things that do not matter much. The Dreyfus brothers looked at highly skilled professionals, including airline pilots, chess players, and military commanders.
Artificial intelligence is quickly becoming an indispensable tool in the classroom. From helping students with homework to collaborating with classmates on projects, teachers are finding uses for AI beyond just checking homework assignments and reading texts aloud. With AI, teachers can tailor lessons to students' interests, create engaging activities, and even create virtual lessons to help students learn in more challenging aspects of a subject. We explore the pros and cons of the AI-only classroom, explore the pros and cons of teaching students with AI, and explore the best ways to integrate AI into education. Artificial intelligence has been around since the mid-twentieth century when it was used to help with tasks like word searching and chess playing.
Early AI began with a variety of tasks such as checkers and chess, speech recognition, language translation, and solving word problems. Over the years it has progressed to give us automated vacuum cleaners, robot dogs, Siri and Alexa, image recognizers, Chess and Go world masters, self-driving cars, and self-guided drones. These technologies have powerful impacts on Naval operations and warfighting as well. AI has the potential to revolutionize military technology, capability and operations. The possibilities have raised many speculations about what AI is capable of and whether it can be trusted.
General AI (Artificial Intelligence) is coming closer thanks to combining neural networks, narrow AI and symbolic AI. Yves Mulkers, Data strategist and founder of 7wData talked to Wouter Denayer, Chief Technology Officer at IBM Belgium, to share his enlightening insights on where we are and where we are going with Artificial Intelligence. Join us in our chat with Wouter. Yves Mulkers Hi and welcome, today we're together with Wouter Denayer, Chief Technology Officer at IBM. Wouter, you're kind of authority in Belgium and I think outside the borders of Belgium as well on artificial intelligence. Can you tell me a bit more about what you're doing at IBM and What keeps you busy? Wouter Denayer Yeah, Yves, thank you, and thanks for having me. Of course, if you call me an authority already, I think if you call yourself an authority, then something is wrong. It's almost impossible to follow everything that's going on in AI, the progress is actually amazing. I do love to follow everything that's going on as much as possible, especially focussing on what IBM Research is doing, we can come back to that later. In my role as CTO for IBM Belgium, I communicate a lot with C-level people in our strategic clients. Sometimes global clients that really want to know what's coming, what is this AI thing. People understand more or less.
Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.
Deep reinforcement learning has gathered much attention recently. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to solve difficult problems. They have learned to fly model helicopters and perform aerobatic manoeuvers such as loops and rolls. In some applications they have even become better than the best humans, such as in Atari, Go, poker and StarCraft. The way in which deep reinforcement learning explores complex environments reminds us of how children learn, by playfully trying out things, getting feedback, and trying again. The computer seems to truly possess aspects of human learning; this goes to the heart of the dream of artificial intelligence. The successes in research have not gone unnoticed by educators, and universities have started to offer courses on the subject. The aim of this book is to provide a comprehensive overview of the field of deep reinforcement learning. The book is written for graduate students of artificial intelligence, and for researchers and practitioners who wish to better understand deep reinforcement learning methods and their challenges. We assume an undergraduate-level of understanding of computer science and artificial intelligence; the programming language of this book is Python. We describe the foundations, the algorithms and the applications of deep reinforcement learning. We cover the established model-free and model-based methods that form the basis of the field. Developments go quickly, and we also cover advanced topics: deep multi-agent reinforcement learning, deep hierarchical reinforcement learning, and deep meta learning.
Depending on the people you talk to, architects approach artificial intelligence (AI) with a range of anticipation, skepticism, or dread. Some say algorithms will handle drudge work and free designers to focus on the more creative aspects of their jobs. Others assert that AI won't live up to its hype--at least not in the near future--and will make only marginal improvements in the profession. And a third group worries that software that learns on its own will put a lot of architects out of work. Science fiction writers have been imagining robots that think like human beings for more than 100 years.
Many predictions to the outcome of the humans and artificial intelligence take an either/or approach. Skynet is determined to end the human race in The Terminator. In The Matrix, the machines have learned to farm humans for battery power. Then, there are the reports that AI beats the best Go and Chess players in the world, and there is no shot for a human victory. These views make it seem that in order to win, humans must exist free of computer control. A more likely outcome--a better picture of success--is to say, "We found our peace through AI/human augmentation."