Learning Artificial Intelligence

After billions of dollars and 50 years of effort, researchers are finally cracking the code on artificial intelligence. As society stands on the cusp of unprecedented change, Jerry Kaplan unpacks the latest advances in robotics, machine learning, and perception powering systems that rival or exceed human capabilities. Driverless cars, robotic helpers, and intelligent agents that promote our interests have the potential to usher in a new age of affluence and leisure.

In this audiobook, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general listener, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context.

Artificial intelligence has become an inherent part of our everyday life, and whether we admit it or not, at times, it seems to know too much about us. You are about to find out and be amazed at how well companies have integrated AI in all their services in an attempt to make your life more comfortable. But artificial intelligence is responsible for so much more than just adding comfort to your everyday life.

Unleash the power of Generative AI in less than 30 days. Turn tech curiosity into a practical skillset even if you're a beginner. Don't let your perception of what's difficult hold you back. This book is designed specifically with simplicity in mind, tailored for those who may be stepping into the world of AI for the first time. It is packed with interactive, up-to-date exercises and real-world applications, that will guide you every step of the way, ensuring that you stay connected to the most relevant and useful topics in AI.

We live in an incredible period in history. The computer revolution may be even more life-changing than the Industrial Revolution. We can do things with computers that could never be done before, and computers can do things for us that could never be done before. But our love of computers should not cloud our thinking about their limitations. The AI Delusion explains why we should not be intimidated into thinking that computers are infallible, that data-mining is knowledge discovery, and that black boxes should be trusted.

Would you like to learn how to use Python to generate machine learning models but you think it would be too difficult? Or perhaps you want to automate simple things with your computer but you don’t know how to do it? Here's the deal.... As a beginner you might think that programming is complex.... Learning artificial intelligence coding can take months, and the possibility to give up before mastering it could be high.

In AI for Good: Applications in Sustainability, Humanitarian Action, and Health, a team of veteran Microsoft AI researchers delivers an insightful and fascinating discussion of how one of the world's most recognizable software companies is tackling intractable social problems with the power of artificial intelligence (AI). In the book, you'll see real in-the-field examples of researchers using AI with replicable methods and reusable AI code to inspire your own uses.

We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning.

Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us - and to make decisions on our behalf. But alarm bells are ringing. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole - and appear to assess black and white defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And autonomous vehicles on our streets can injure or kill.

Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning.

Even though you have no prior knowledge about AI, this guide will let you explore the world of AI and how you can leverage the technology to improve decision-making, enhance efficiency, and boost sales—all while reaching out to more customers.

Yes, you can succeed in an AI-driven world by unleashing your potential in machine learning! A working knowledge of AI and ML is no longer a “nice-to-have” but an “essential” in today's job market. But what if you feel lost amid the tech jargon? What if terms like “algorithms”, “neural networks”, or “data preprocessing” sound like a foreign language to you?

Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a truly robust artificial intelligence. Taking inspiration from the human mind, professors Gary Marcus and Ernest Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won't need to worry about a future of machine overlords. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better.

What does it mean to be smart? To be human? What do we really want from life and the intelligence we have, or might create? With deep and exclusive reporting, across hundreds of interviews, New York Times Silicon Valley journalist Cade Metz brings you into the rooms where these questions are being answered. Where an extraordinarily powerful new artificial intelligence has been built into our biggest companies, our social discourse, and our daily lives, with few of us even noticing.

This book is an introduction to basic machine learning and artificial intelligence. It gives you a list of applications and also a few examples of the different types of machine learning.

To most of us, it seems like recent developments in artificial intelligence emerged out of nowhere to pose unprecedented threats to humankind. But to Dr. Joy Buolamwini, who has been at the forefront of AI research, this moment has been a long time in the making.

Either if you want to start your own AI enterprise, to empower your business or to work in the greatest and most innovative companies, Artificial Intelligence is the future, and neural networks programming is the skill you want to have. The good news is that there is no exclusive club, you can easily (if you commit, of course) learn how to program and use neural networks, and to do that Neural Networks for Beginners is the perfect way.

Under the aegis of machine learning in our data-driven machine age, computers are programming themselves and learning about - and solving - an extraordinary range of problems, from the mundane to the most daunting. Today it is machine learning programs that enable Amazon and Netflix to predict what users will like, Apple to power Siri's ability to understand voices, and Google to pilot cars.

If you read (or listen to) nothing else on artificial intelligence and machine learning, listen to these ten articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand the future direction of AI, bring your AI initiatives to scale, and use AI to transform your organization.

Elon Musk, Stephen Hawking, and Bill Gates have proclaimed Artificial Intelligence as a possible threat in the future unless humans learn to use it wisely. Could this be true? Is it possible for us to create artificial intelligence programming so that we'll soon have thinking and feeling AI? Is it even possible for us to have a human-looking AI run for president in the future? All of these questions and thoughts are addressed in Artificial Intelligence: Understanding A.I. and the Implications of Machine Learning.