Goto

Collaborating Authors

Results


Machine Learning Tutorial For Complete Beginners

#artificialintelligence

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.


Dreyfus Model of Skill Acquisition: From Novice to Expert

#artificialintelligence

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.


Is Artificial Intelligence Replacing Teachers?

#artificialintelligence

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.


"HAI 2.0" – NPS Releases Updated Artificial Intelligence Course, Video Series

#artificialintelligence

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.


The current state of Artificial Intelligence

#artificialintelligence

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.


Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence

arXiv.org Artificial Intelligence

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

arXiv.org Artificial Intelligence

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.



Modeling Strong and Human-Like Gameplay with KL-Regularized Search

arXiv.org Artificial Intelligence

We consider the task of building strong but human-like policies in multi-agent decision-making problems, given examples of human behavior. Imitation learning is effective at predicting human actions but may not match the strength of expert humans, while self-play learning and search techniques (e.g. AlphaZero) lead to strong performance but may produce policies that are difficult for humans to understand and coordinate with. We show in chess and Go that regularizing search policies based on the KL divergence from an imitation-learned policy by applying Monte Carlo tree search produces policies that have higher human prediction accuracy and are stronger than the imitation policy. We then introduce a novel regret minimization algorithm that is regularized based on the KL divergence from an imitation-learned policy, and show that applying this algorithm to no-press Diplomacy yields a policy that maintains the same human prediction accuracy as imitation learning while being substantially stronger.


Machine Learning Tutorial for Beginners

#artificialintelligence

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.