Fortnightly Mailing

Peter Norvig's TED talk reflecting on creating and running the online AI course

[Added 9 July 2012: The discussion on Hacker News prompted by this post by Colin Wright is well worth reading.]

Peter Norvig is Google's Director of Research. Here, finally, is a recording of Peter Norvig's February 2012  six minute TED talk about his and Sebastian Thrun's 2011 mass free online computer science course about artificial intelligence (in which I took part as a student and from which I wrote weekly reports throughout - to find them scroll down here). It is interesting in its own right. It also shows how much can be conveyed in only six minutes (though post-talk questions and discussion would have added a great deal).

Note Norvig's conclusion:

"....the most exciting part of it is the data that we're gathering. We're gathering thousands of interactions per student per class, billions of interactions altogether, and now we can start analyzing that, and when we learn from that, do experimentations, that's when the real revolution will come."

[Below the video I have pasted the transcript of the talk.]

Taking the red pill: Sebastian Thrun's candid reflection on the AI course

After a horribly embarrassing introduction, this 25 minute talk yesterday by Sebastian Thrun gives Thrun's own candid and personal reflection on last Autumn's AI course, which had 160,000 sign-ups (nearly 100,000 of whom were on the advanced track), 46,000 submitters of the first homework, 23,000 submitters of the mid-term exam, and 20,000 who completed the final exam.

Highlights of the talk:

• the large drop-out rate from the lectures on the same course at Stanford, with students preferring to use the free video-based version;
• a volunteer army of ~2000 translators;
• individual feedback from students in terrible places in the world or under big social pressure who completed the course;
• Thrun's own epiphany concerning the wrongness of "weeder" classes;
• Thrun's decision not to teach by lecture at Stanford again and instead to concentrate his efforts on a private venture-capital funded initiative called Udacity, whose online courses will be free.

Udacity aims to enrol 500,000 students on its first two courses: CS101- Building a search engine; CS373 - Programming a robotic car.

My own and others' reports from the AI course.

Interviews about "remote learning" with Dan Cardinali, Paul Mitiguy, and Peter Norvig

Here are links to three November 2011 "canteen" interviews (audio and, in two cases helpful text transcripts) with Peter Norvig (Director of Research at Google), Dan Cardinali (President of Communities in Schools) and Paul Mitiguy (Senior Consulting Professor of Mechanical Engineering at Stanford University). The interviews form part of the resources of an undergraduate project by Deniz Kahramaner, Jon Rodriguez and Ben Kallman about "the challenges and ethical implications of implementing large-scale distance education platforms". 

The interview with Peter Norvig draws on his contemporaneous experience with the Stanford/KnowLabs Introduction to Artificial Intelligence course [November 2011 article in ALT News Online; my weekly reports from the course] and contains several perceptive and diverse insights, ranging over:

• course design (and the advantages of combining the deadline driven "discipline" of face-to-face learning with the "anytime anyplace" nature of asynchronous learning);
• the advantages (from a learner's point of view) of video, and challenges (from a course design point of view) of using it;
• the need for design/production environments for teachers and for experimentation/exploration environments for learners;
• the value, from a learning point of view, of ambiguity in presentations and open-endedness in questions;
• recruitment (and a possible business model for providers of free remote education);
• the possible motivations for teachers to contribute to the production of free courses;
• the need for collegiality and collaboration between the start-ups that are getting involved in online education.

Unexpected - how an open course's conventional exam can prompt subsequent learning

Graph by automagic using code by pedrosorio.
Other posts tagged ai-course.

The AI course finished this week. Here is a link to a discussion thread involving some very mathematically able people discussing, after the exam results were in, a "disputed" mark concerning an exam question about a statistical technique called Laplace smoothing.

I'm not making a point about the substance of the discussion (a lot of which is over my head), or about the underlying investigation (which is even more so).

But the interesting thing is the way that this open course, with its underlying conventional, assessed structure, has resulted in a great deal of very high level collaborative research, investigation, and discussion, which has gone far beyond its (first year US undergraduate) boundaries.

Even if the subject matter of the post is outside your ken, it is worth taking in the kinds of reflections that contributors are making, for example:

"This is really the sort of thing and the sort of thinking and exploring that I believe professors really want to see result from efforts to teach stuff in class (whether in K-12 or undergraduate or graduate school). This one little question has generated more serious inquiry than anything else I have seen so far from our class...... But THANK YOU for all of the serious and thoughtful responses to this question which it has been a pleasure to read."

 

Sitting in a bar with a really smart friend. The ai-class: Notes from a Lab Rat

Guest Contribution by Rob Rambusch

Picture by Ressaure (CC licensed)

Me: Test Subject

Rob Rambusch is a Project Manager for software development and implementation based in New York City. This was his first exposure to any class on Artificial Intelligence. He can be contacted at robrambusch [AT] gmail.com, or through Google+.

It: Experiment

"A bold experiment in distributed education, "Introduction to Artificial Intelligence" will be offered free and online to students worldwide from October 10th to December 18th 2011. The course will include feedback on progress and a statement of accomplishment. Taught by Sebastian Thrun and Peter Norvig, the curriculum draws from that used in Stanford's introductory Artificial Intelligence course. The instructors will offer similar materials, assignments, and exams."

Them: Experimenters

"Peter Norvig is Director of Research at Google Inc.  He is also a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Norvig is co-author of the popular textbook Artificial Intelligence: A Modern Approach. Prior to joining Google he was the head of the Computation Sciences Division at NASA Ames Research Center."

"Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning."

 One Month Before: Preparation

I started working on the prerequisites in the month before the course began. I prepared for the class by learning Probability and Linear Algebra from Salman Khan at the Khan Academy website. I followed up by watching lectures on Linear Algebra by Gilbert Strang at the MIT OCW website. So even before the class began I had exposure to two common online teaching styles, the filmed lecture and the video tutorial.

Final report from the Norvig/Thrun/Stanford/Know Labs Artificial Intelligence course

Word-cloud by Sea Otter

(Other posts tagged ai-course. Post originally published on 20/12/2011, with paragraph one updated to incorporate completion numbers kindly provided by David Stavens of Know Labs, and a new concluding sentence to the final paragraph.)

Along with just over 20,000 others (some 3,000 fewer than had taken the midterm exam) I completed the final examination for the free online Introduction to Artificial Intelligence course taught by Sebastian Thrun and Peter Norvig. Here is my final participant's  report from the course.

1. The final section of the course concerned Natural Language Processing. I've had an interest in machine translation for some years [e.g.]: and it was this interest that initially made me aware of Peter Norvig's work.) So for me this meant that the best part of the course came last, and if you want to gain an underlying appreciation of the science of natural language processing, it will take you a couple of hours to work through the courses 42 short videos about NLP, starting here. It is probably worth doing despite a certain amount of dependency on earlier sections of the AI course.

2. The course has been mercifully free from programming assignments: being capable of completion using pen and paper, a calculator (and on a couple of occasions a slide rule unused since 1973).  To conclude the NLP unit there were two optional programming problems, both of which could be tackled without programming. I did the second of the problems (recovering a message from a shredded version) using scissors and adhesive tape:

Apples, screwdrivers and desks: a comparative review of three Stanford free online computer science courses

Guest Contribution by Gundega Dekena

[Update posted by Seb Schmoller on 12 July 2012. Note that Gundega now works for Udacity, the company that developed from the AI course. Read how she became part of the Udacity team on the Udacity blog.]

Gundega Dekena is a self taught Linux administrator and web programmer, based in Riga, Latvia. She has been studying all three of the October to December Stanford online computer science courses in parallel - Introduction to Artificial Intelligence (AI), Introduction to Machine Learning (ML), and Introduction to Databases (DB) - putting her in a good position to compare and contrast them. Gundega can contacted at gundega.dekena [AT] gmail.com, or through Google+.

Overview

Comparing these three courses feels a bit like comparing apples, screwdrivers and desks, yet I see a lot of students doing that, usually without much thought about the differences. So, this is my look (from the perspective of a student) atthe things that can be compared, and that can be learned from all three courses, for the benefit of the next batch of courses that is going to come from Stanford next year.

Tenth report from the Norvig/Thrun/Stanford/Know Labs Artificial Intelligence course

(Other posts tagged ai-course.)

No report this week. Instead, here is a Guest Contribution by Gundega Dekena, which provides a thorough comparison between Stanford's three current free mass online computer science courses.

Food for thought. Video recording of Q&A session between Sal Khan, Peter Norvig and Sebastian Thrun

There is plenty of food for thought and a great deal to identify with - from a very diverse set of angles - in yesterday's 45 minute "Google+ Hangout" discussion between Sal Khan, Peter Norvig and Sebastian Thrun. Examples include:

• the authenticity and hence superiority of unscripted, tentative explanations, with "low" production values;
• how lectures empower lecturers not students;
• data analysis as an "underpinning process" for real personalisation;
• why their is such a mismatch between the value that a university thinks its courses provide what students think they provide;
• the challenge of combining individual study with group interaction.

 

Ninth report from the Norvig/Thrun/Stanford/Know Labs Artificial Intelligence course

Screen shot of Filip Wasilewski's Chrome plug-in explained in paragraph 7 below.

(Other posts tagged ai-course.)

Here is my ninth participant's  report from the Stanford Introduction to Artificial Intelligence course.

1. I wrote last week that the units on Games, Game Theory, and Advanced Planning had been hard work; and that the associated homework had felt demanding and been very time-consuming. Securing an adequate mark on that homework despite too many  "stupid errors" means I probably learned more than I had realised. But I also know that I was answering some of the questions from the "stored fat" of knowledge I already had, rather than from what the course had been teaching me.