What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference
eBook
English language
Published Sept. 24, 2024 by Princeton University Press.
This is written for people who aren't keeping a close eye on the AI space. The issue is that the only people who will likely discover this book are those interested in the AI space.
This is a pretty good primer on a few different AI technologies by experts in the field who are generally positive about AI development. It provided a good contrast to the (well-researched) generally negative videos and articles I had seen on the topic so far.
The best part of the book is the extended metaphor in the introduction, comparing the term "AI" to the term "vehicle". Vehicle is a category covering everything from bikes to trucks to space rockets to boats, but if we didn't have separate terms for all those things, we couldn't have meaningful conversations about them, especially their benefits and harms. This is one of the big problems we are running into with discussing "AI" now.
I found the section on predictive AI excellent. It's essentially the core of the book and was really informative since I knew next to nothing about that.
On the other hand, …
This is a pretty good primer on a few different AI technologies by experts in the field who are generally positive about AI development. It provided a good contrast to the (well-researched) generally negative videos and articles I had seen on the topic so far.
The best part of the book is the extended metaphor in the introduction, comparing the term "AI" to the term "vehicle". Vehicle is a category covering everything from bikes to trucks to space rockets to boats, but if we didn't have separate terms for all those things, we couldn't have meaningful conversations about them, especially their benefits and harms. This is one of the big problems we are running into with discussing "AI" now.
I found the section on predictive AI excellent. It's essentially the core of the book and was really informative since I knew next to nothing about that.
On the other hand, because I already had learned a lot about generative AI (language models, image generators, etc.), that part of the book wasn't too helpful to me and I skimmed a lot of it.
The book provides a good introduction to many different aspects of AI, an umbrella term for very different techniques. They cover 3 different domains: predictive AI, generative AI and AI for content moderation. There are many examples and plenty to agree with. In particular, people should treat the claims from predictive AI with the same degree of skepticism as any "traditional" solution: the vendor must prove first that the system works before one considers it. This seems like the least to ask but as soon as the word AI is sprinkled over a program, it seems that all critical thinking goes out of the window. AI is not special and this is also the stance of the FTC as well: even if it's AI-based, it is still subject to laws regarding false advertising. Despite their critics and their exposition of the limits of AI, the authors are still weirdly (and …
The book provides a good introduction to many different aspects of AI, an umbrella term for very different techniques. They cover 3 different domains: predictive AI, generative AI and AI for content moderation. There are many examples and plenty to agree with. In particular, people should treat the claims from predictive AI with the same degree of skepticism as any "traditional" solution: the vendor must prove first that the system works before one considers it. This seems like the least to ask but as soon as the word AI is sprinkled over a program, it seems that all critical thinking goes out of the window. AI is not special and this is also the stance of the FTC as well: even if it's AI-based, it is still subject to laws regarding false advertising. Despite their critics and their exposition of the limits of AI, the authors are still weirdly (and disappointingly) very positive about generative AI, seeing even more potential in it than in predictive AI. This is not going age well. The chapter about content moderation is fairly interesting and the argument that it is actually a good thing that this issue cannot be solved with any technology, including AI, is a convincing one. Those are political and moral decisions and they require an informed debate. They certainly can't be left to private companies or software engineers. (Incidently, their characterisation of content moderation on the Fediverse completely misses the mark ("worst of both worlds"); the point of moderation there is to protect and nurture a community/instance, not to fix the rest of the network in one fell swoop. Scaling is not the point.)
For a more informed and biting review of the book, see Edward Ongweso Jr's review in The New Republic (21 Nov. 2024).
As educators, we constantly strive to equip our students with the tools they need to navigate an increasingly complex world. One of the most significant challenges facing us today is the rise of Artificial Intelligence (AI) and its pervasive influence across various aspects of life. It's crucial that we, as teachers, not only understand AI ourselves but also empower our students to critically evaluate its promises and pitfalls.
The book AI Snake Oil by Arvind Narayanan and Sayash Kapoor provides a valuable framework for approaching this challenge. It resonates with the principles of effective teaching by breaking down complex AI concepts into digestible pieces. Just as we guide our students through intricate subjects step-by-step, this book demystifies AI, enabling readers to grasp its true capabilities and limitations at their own pace.
Here's why I believe this book is relevant for educators:
● Exposing the Hype: The book debunks the exaggerated …
As educators, we constantly strive to equip our students with the tools they need to navigate an increasingly complex world. One of the most significant challenges facing us today is the rise of Artificial Intelligence (AI) and its pervasive influence across various aspects of life. It's crucial that we, as teachers, not only understand AI ourselves but also empower our students to critically evaluate its promises and pitfalls.
The book AI Snake Oil by Arvind Narayanan and Sayash Kapoor provides a valuable framework for approaching this challenge. It resonates with the principles of effective teaching by breaking down complex AI concepts into digestible pieces. Just as we guide our students through intricate subjects step-by-step, this book demystifies AI, enabling readers to grasp its true capabilities and limitations at their own pace.
Here's why I believe this book is relevant for educators:
● Exposing the Hype: The book debunks the exaggerated claims surrounding AI, similar to how we address misconceptions in the classroom. By distinguishing genuine advancements from misleading hype, it encourages a critical lens, much like we encourage our students to question and analyze information.
● Practical Applications and Ethical Implications: "AI Snake Oil" explores the real-world applications of AI in fields like education, medicine, and criminal justice. It also sheds light on the ethical dilemmas associated with AI, such as bias and privacy concerns. This aligns with our role as educators to prepare students for the ethical challenges they'll face in an AI-driven world.
● Empowering Informed Decision-Making: Just as we aim to equip our students with the knowledge to make sound decisions, the book advocates for a nuanced understanding of AI. It encourages readers to move beyond the surface-level promises and consider the potential risks and limitations. This resonates with the critical thinking skills we strive to cultivate in our students.
Incorporating the insights from AI Snake Oil into our teaching can help us foster AI literacy among our students. By equipping them with the ability to discern fact from fiction, analyze ethical implications, and make informed decisions about AI, we can empower them to navigate the future with confidence and responsibility.
