The AI Course

Peter Brooks, Stuyvesant High School, NYC



Main Reference book:
  • Artificial Intelligence: A Modern Approach (4th ed.) by Stuart Russell and Peter Norvig
  • This is the bible of the field for AI. Very expensive!
  • Exceptional chapters 1, 27 and 28 on definition of AI and history and philosophy of AI (if you're also interested in these topics).
  • on paper: try to find the version printed in South Asia (much cheaper)
  • online PDF (I do not know about the legality of this...):
  • Instructors' page for the book (extensive resources)
Main book

Machines That Think by Toby Walsh

Alternate book Humans Need Not Apply by Jerry Kaplan (book)

AI Advances:

AI advances in applications in computer science, general science, health care, the military, commerce, transportation, agriculture, recreation, arts, etc. will be covered by student reports.


Issues -- Essays:

Machine vs. Human Thought
Security / Surveillance
Military uses
On Deep/Machine Learning
Ethics of AI / Legal Issues
AI Newsletter An excellent weekly newsletter on AI news and issues from O'Reilly:  requires signing up (free)
On Machine Thought The Mind's I is a superb book of essays, fiction, and commentary on human and machine consciousness, whose chapters I have been assigning and strongly recommending.  They include the most famous essays on the philosophy of machine thought:
- "Computing Machinery and Intelligence" by Alan Turing that introduced the "Turing Test"
- "Mind's, Brains and Programs" by John Searle, introducing the Chinese Room Argument opposing the usefulness of the Turing Test
- "What is it Like to be a Bat?" by Thomas Nagel on the deep problem of consciousness.
...and many other great essays and short stories...
Here is a copy (possibly scanned, with occasional typos) of the whole book.


Issues -- Internet Resources:

Online: AI Introductions
Online: learning machine learning (ML) courses
(expect to spend 5-20 hours/week on these multi-week courses)
Online: learning ML (other resouces)
Learning ML via matchboxes
A.I. Creativity
Realtime Reasoning
Particular AI techniques tutorials
CounterCulture Efforts
Security / Surveillance / Privacy
Employment Issues
Watching Deep Learning learn
Data Bias
Military Uses
Adversarial attacks on image classifiers and audio assistants
Critiques of AI techniques
Possible Futures
Historical Perspectives


AI and economic development Kai-Fu Lee, Chairman and CEO of Sinovation Ventures and author of "AI Superpowers: China, Silicon Valley and the New World Order," reports of the devastating impacts artificial intelligence could have on the developing world. An anonymous reader shares the report from Bloomberg:In recent decades, China and India have presented the world with two different models for how such countries can climb the development ladder. In the China model, a nation leverages its large population and low costs to build a base of blue-collar manufacturing. It then steadily works its way up the value chain by producing better and more technology-intensive goods. In the India model, a country combines a large English-speaking population with low costs to become a hub for outsourcing of low-end, white-collar jobs in fields such as business-process outsourcing and software testing. If successful, these relatively low-skilled jobs can be slowly upgraded to more advanced white-collar industries. Both models are based on a country's cost advantages in the performance of repetitive, non-social and largely uncreative work -- whether manual labor in factories or cognitive labor in call centers. Unfortunately for emerging economies, AI thrives at performing precisely this kind of work. 

Without a cost incentive to locate in the developing world, corporations will bring many of these functions back to the countries where they're based. That will leave emerging economies, unable to grasp the bottom rungs of the development ladder, in a dangerous position: The large pool of young and relatively unskilled workers that once formed their greatest comparative advantage will become a liability -- a potentially explosive one. Increasing desperation in the developing world will contrast with a massive accumulation of wealth among the AI superpowers. AI runs on data and that dependence leads to a self-perpetuating cycle of consolidation in industries: The more data you have, the better your product. The better your product, the more users you gain. The more users you gain, the more data you have.
Lee says the best thing emerging economies can do is to "recognize that the traditional paths to economic development -- the China and India models -- are no longer viable." Countries with "less-educated workers" are advised to build up human-centered service industries. 

"At the same time, developing countries need to carve out their own niches within the AI landscape," Lee writes. "... governments need to fund the AI education of their best and brightest students, with the goal of building local companies that employ AI."