VAILL Guides: AI Bootcamp for 1Ls

In AI Snake Oil, computer scientists Arvind Narayanan and Sayash Kapoor cut through the confusion to give you an essential understanding of how AI works and why it often doesn't, where it might be useful or harmful, and when you should suspect that companies are using AI hype to sell AI snake oil--products that don't work, and probably never will. By revealing AI's limits and real risks, AI Snake Oil will help you make better decisions about whether and how to use AI at work and home.

From the founder of Khan Academy, the first book on the AI revolution in education, its implications for parenting, and how we can best harness its power for good. Khan emphasizes that embracing AI in education is not about replacing human interaction but enhancing it with customized and accessible learning tools that encourage creative problem-solving skills and prepare students for an increasingly digital world.

Mollick urges us to engage with AI as co-worker, co-teacher, and coach. He assesses its profound impact on business and education, using dozens of real-time examples of AI in action. Co-Intelligence shows what it means to think and work together with smart machines, and why it's imperative that we master that skill. Mollick challenges us to utilize AI's enormous power without losing our identity, to learn from it without being misled, and to harness its gifts to create a better human future. Wide ranging, hugely thought-provoking, optimistic, and lucid, Co-Intelligence reveals the promise and power of this new era.

How generative AI systems capture a core function of language Looking at the emergence of generative AI, Language Machines presents a new theory of meaning in language and computation, arguing that humanistic scholarship misconstrues how large language models (LLMs) function. Seeing LLMs as a convergence of computation and language, Leif Weatherby contends that AI does not simulate cognition, as widely believed, but rather creates culture. This evolution in language, he finds, is one that we are ill-prepared to evaluate, as what he terms "remainder humanism" counterproductively divides the human from the machine without drawing on established theories of representation that include both.

When most of us think about Artificial Intelligence, our minds go straight to cyborgs, robots, and sci-fi thrillers where machines take over the world. But the truth is that Artificial Intelligence is already among us. It exists in our smartphones, fitness trackers, and refrigerators that tell us when the milk will expire. In some ways, the future people dreamed of at the World's Fair in the 1960s is already here. We're teaching our machines how to think like humans, and they're learning at an incredible rate. In Thinking Machines, technology journalist Luke Dormehl takes you through the history of AI and how it makes up the foundations of the machines that think for us today. This remarkable book will invite you to marvel at what now seems commonplace and to dream about a future in which the scope of humanity may need to widen to include intelligent machines.

Unmasking AI goes beyond the headlines about existential risks produced by Big Tech. It is the remarkable story of how Buolamwini uncovered what she calls "the coded gaze"--the evidence of encoded discrimination and exclusion in tech products--and how she galvanized the movement to prevent AI harms by founding the Algorithmic Justice League. Applying an intersectional lens to both the tech industry and the research sector, she shows how racism, sexism, colorism, and ableism can overlap and render broad swaths of humanity "excoded" and therefore vulnerable in a world rapidly adopting AI tools. Computers, she reminds us, are reflections of both the aspirations and the limitations of the people who create them.