Courses:

6.xxx is designed to help you learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. Thus, 6.xxx complements 6.034, because 6.xxx focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability.

• You should take 6.xxx if you want to learn about the enterprise of explaining intelligence from a computational point of view. When you have finished the subject, you will understand the powerful ideas behind an optimistic view of what will be discovered in the next decade.
• You should take 6.xxx if you want to develop a foundation for making personal contributions toward reaching the goal of understanding intelligence. When you have finished the subject, you will know about intriguing ideas begging for extension. 
• You should take 6.xxx if you want to learn how to dig the salient ideas out of a research paper without distraction by minutiae. When you have finished the subject, you will have learned to identify big ideas and ignore detritus. 
• You should take 6.xxx if you want to learn to present complex ideas effectively, as if you were presenting a thesis, delivering a job talk, chatting with a high-ranking official at breakfast, or making a presentation to a potential customer or venture capitalist. When you have finished the subject, you will have learned about heuristics that will improve your ability to do all these.

• You should avoid 6.xxx unless you can commit to on-time attendance and committed reading.

Because of the emphasis on reading and discussion, and the limitation on enrollment, regular attendance is obligatory, along with commitment to reading the papers. If you cannot picture yourself in class twice a week, you should not register, so as to make room for others who would otherwise be excluded because of the enrollment limitation. A corollary is that you probably should not register for 6.xxx if you are taking five subjects or course equivalents, such as UROP. You definitely should not register you are involved in a startup or you are taking six or more subjects or subject equivalents.

• You should avoid 6.xxx if you are not interested in understanding human intelligence from a computational point of view.

Believing that both mind-stretching and near-miss learning are educationally useful, some of the papers I have selected are boring, stupid, or nearly unintelligible. One goal of the subject is to develop the skill of gleaning useful ideas from such papers, but if you have little or no interest in understanding human intelligence, you should not subject yourself to the necessary reading. For more detail on what you will need to read, have a look at the previous year's schedule.

• You should avoid 6.xxx if you already know everything you need to know about communication.

About one-third of the subject is devoted to discussing how to package ideas orally and in writing. You need to be enthusiastic about practicing the skills taught with a positive attitude. For more detail on what will be covered in the communication dimension, have a look at the previous year's schedule.

• You should avoid 6.xxx if you are enrolled in another limited-enrollment AI subject or an AI subject whose enrollment should be limited.

Alas, advanced AI subjects are scarce, and fairness dictates that they should be offered as broadly as possible. This fairness goal must be balanced, however, against the need to keep some of them small. If you are just generally interested in AI, you should take one of the graduate lecture-based subjects.

6.803 is the undergraduate version of 6.xxx, and 6.833 is the graduate version. The two differ in that 6.833 may require you to attend some extra classes and will require you to complete a substantial term project. Both meet together ordinarily.

The graduate, H-level subject forms a bridge between 6.034 and design/project/thesis work in Artificial Intelligence.
Overall, the level is bounded as follows:

• Bounded below by 6.034. 
• Bounded above by having already written an MEng proposal or equivalent.

The content of 6.xxx is largely based on papers identified in an informal survey of representative AI leaders, who were asked what has most influenced the way they think about human intelligence. The papers mentioned tend to fall into the following categories, ranked by frequency:

• Visionary thinking by the giants 
• Computational models of perception and cognition 
• Powerful computational ideas 
• Neuroscience and human behavior

• You read parts or all of one or two papers for each class.
• You discuss the content of those papers in class, occasionally with the authors.

The following mechanisms are used to ensure that you read the papers and absorb the material:

• Homework, consisting of either short answers to questions about the papers or the preparation of abstracts, slide shows, and other forms of communication.
• Verbal questions, often asked of random students during class.

Because of the emphasis on reading, discussion, and presentation, enrollment is limited.

Doing a substantial project is required for graduate H credit. See projects link on subject's home page.

6.803 The Human Intelligence Enterprise

(Subject meets with 6.833)
Prereq.: 6.034
U (Spring)
3-0-9

Analyzes seminal work directed at the development of a computational understanding of human intelligence, such as work on object tracking, object recognition, change representation, language evolution, and the role of symbols in learning and communication. Reviews visionary ideas of Turing, Minsky, and other influential thinkers. Examines role of brain scanning, systems neuroscience, and cognitive psychology. Emphasis on discussion and analysis of original papers. Meets with graduate subject 6.833, but assignments differ. Enrollment limited.

6.833 The Human Intelligence Enterprise

(Subject meets with 6.803)
Prereq.: 6.034
G (Spring)
3-0-9 H-LEVEL Grad Credit

Meets with undergraduate subject 6.803. Intended, in part, to prepare students for MEng thesis work in the AI concentration. Requires completion of supplementary exercises and a substantial term project. Enrollment limited.