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Books on AI Topics
Machine Learning by Call Number: Q 325.5 .A47 2016A concise overview of machine learning-computer programs that learn from data-which underlies applications that include recommendation systems, face recognition, and driverless cars.
A Human's Guide to Machine Intelligence by Call Number: Q 335 .H675 2019Through the technology embedded in almost every major tech platform and web-enabled device, algorithms make a staggering number of everyday decisions for us. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives.
You Look Like a Thing and I Love You by Call Number: Q 335 .S463 2021ISBN: 0316525227Publication Date: 2021-03-23In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these machines learn, fail, and adapt--and how they reflect both the best and worst of humanity.
Algorithms for the People by Call Number: Q 335 .S56 2023Drawing on his experience as a research fellow at Harvard University, a visiting research scientist on Facebook's Responsible AI team, and a policy advisor to the UK's Labour Party, Simons gets under the hood of predictive technologies, offering an accessible account of how they work, why they matter, and how to regulate the institutions that build and use them
The Age of AI by Call Number: Q 335 .K577 2022Three of the world's most accomplished and deep thinkers come together to explore Artificial Intelligence (AI) and the way it is transforming human society--and what this technology means for us all.
What Makes Us Human by Call Number: BD 435 .W275 2022Publication Date: 2022To create What Makes Us Human?, internationally bestselling poet Iain S. Thomas and globally recognized prodigious researcher and innovator Jasmine Wang prompted the world's most advanced AI, GPT-3, with a wealth of humanity's most cherished works. Then they asked GPT-3 our most pressing questions about life.
The Digital Mindset by Call Number: HD 45 .L434 2022Publication Date: 2022"Award-winning researchers and professors Paul Leonardi and Tsedal Neeley will show you how to do it and let you in on the surprising and welcome secret: developing a digital mindset isn't as hard as you think. Most people can become digitally savvy if they follow the "30 percent rule"--the minimum threshold that gives us enough digital literacy to understand and take advantage of the digital threads woven into the fabric of our world."
Weapons of Math Destruction by Call Number: QA 76.9 .B45 O64 2017Publication Date: 2017...Algorithms are not neutral, free from human prejudice and fallibility; instead, those biases and failings are encoded into the systems. Data scientist O'Neil, who blogs at mathbabe.org, explores this premise in-depth and chillingly describes the extent to which data-driven, algorithm-based decision-making in such areas as hiring, policing, lending, education, and health care actually increases inequality.
The Future of Health: how digital technology will make care accessible, sustainable, and human by Call Number: R 855.3 .A83 2022Publication Date: 2021The Future of Health is an insightful and comprehensive overview of the past, present, and future of digital health. Accomplished health innovation leader Roberto Ascione delivers a practical exploration of how the latest digital technologies are transforming the practice of medicine and redefining health itself by making it more accessible, sustainable, and human.
Invisible Women by Call Number: HQ 1237 .C75 2019Publication Date: 2019Data is fundamental to the modern world. From economic development, to healthcare, to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems.
Algorithms of Oppression by Call Number: ZA 4230 .N63 2018Publication Date: 2018eBook VersionA revealing look at how negative biases against women of color are embedded in search engine results and algorithms
