O'Really?

January 11, 2013

A joke about teaching and learning via Jason Bangbala

The MOOC

What, if anything, can stop the MOOC? Creative Commons licensed picture via Giulia Forsythe on Flickr.

The debate about Massive Open Online Courses (MOOCs) is making lots of people think harder about education and how people learn. Are we witnessing Higher Education’s Napster moment, where online services replace physical ones [1] ? Is undergraduate education over-priced [2]? How can education be improved [3]? What is the point of Education anyway? These are all interesting and mostly unanswered questions.

You might hear it said that secondary education is often delivered by excellent teachers, with questionable subject knowledge, whereas higher education is delivered by experts with excellent subject knowledge, but poorer teaching skills.

Jason Bangbala, an educational consultant, puts it another way.

What is the difference between primary, secondary and higher education?

  • In primary education, the teachers love their students.
  • In secondary education, the teachers love their subject.
  • In higher education, the teachers love themselves.

True? Hmmm, I don’t know. But it is funny…

References

  1. Moshe Vardi (2012). Will MOOCs destroy academia? Communications of the ACM, 55 (11), 5-5 DOI: 10.1145/2366316.2366317
  2. Salman Khan (2013). What college could be like Communications of the ACM, 56 (1) DOI: 10.1145/2398356.2398370
  3. Fred Martin (2012). Will massive open online courses change how we teach? Communications of the ACM, 55 (8) DOI: 10.1145/2240236.2240246

July 25, 2006

AAAI’06: Highlights and conclusions

The AAAI conference finished last Thursday, here are some highlights and papers that might be worth reading if you are interested in building and / or using a more “intelligent” (and possibly semantic) web in bioinformatics.

Here are the papers or talks I enjoyed the most and hope you might also find them useful or inspiring.

  1. Unifying Logical and Statistical AI talk given by Pedro Domingos.

    Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI (of which the semantic web is an example) has focused mainly on the former, and statistical AI (e.g. machine learning) on the latter. The two approaches can be united, with significant benefits, some of which are demonstrated by the Alchemy system

  2. Developing an intelligent personal assistant: The CALO (Cognitive Agent at that Learns and Organises) project talk given by Karen Myers.

    CALO is a desktop assistant that learns what you do in the lab / office. Sounds spooky, but involves some interesting technology and fascinating research questions.

  3. Bookmark hierarchies and collaborative recommendation by Ben Markines, Lubomira Stoilova and Filippo Menczer.

    Describes an open-source, academically-oriented social bookmarking site where you can donate your bookmarks to science at givealink

  4. Social network-based Trust in Prioritised Default Logic by Yarden Katz and Jennifer Golbeck.

    Who and how can you trust on the Web?

  5. Google vs Berners-Lee was a memorable debate. According to Jim Hendler, Tim and Peter are reconciling their differences now

Not particularly webby, but…

…entertaining nonetheless.

  1. Stephen Muggletons talk on Computational Biology and Chemical Turing Machines, went down well but unfortunately I was stuck in a parallel track, experiencing “death by ontology”.
  2. Bruce Buchanan gave a talk What Do We Know About Knowledge. A roller-coaster ride through the last 2000+ years of human attempts to understand what knowledge is, how to represent it and why it is powerful
  3. Winning the DARPA Grand Challenge with an AI Robot called Stanley talk given by Sebastian Thrun, amazing presentation on a driving a robotic car through the desert over rough terrain. However, it doesn’t take too much imagination to think of horrific applications of this. Next year they will try to drive it from San Francisco to Los Angeles on a public freeway, and Stanley hasn’t even passed its driving test yet!

Turing’s dream

Appropriately, the conference which was subtitled Celebrating 50 years of AI finished with two talks by Lenhart K. Schubert and Stuart M. Shieber about the Turing test. The first discussed Turing’s dream and the Knowledge Challenge, the second talk asked Does the Turing Test Demonstrate Intelligence or Not? Now I’m back in Manchester, where Turing once worked, I can’t help wondering, what would Alan make of the current state of AI and the semantic web? I think there are several possibilities, he could be thinking:

  • EITHER: Fifty odd years later, they’re not still wasting time working on that Turing test are they?!
  • OR: He is smugly satisifed that he devised a test, that no machine has passed, and perhaps never will, but has provided us with a satisfactory operational definition of “intelligence” ;
  • …AND What the hell is the “Semantic Web”?

We will never know what Alan Turing would make of todays efforts to make a more intelligent web. However, that won’t stop me speculating that he would be impressed by the current uses of computers (intelligent or otherwise) to drive robots through the desert, perform all sort of computations on proteins and to search for information on this massive distributed global knowledge-base we call the “Web”. Not bad for 50 years of work, here’s to the next 50…

References

  1. Alan Turing (1950) Computing Machinery and Intelligence: The Turing TestMind 59(236):433-460
  2. Stephen H. Muggleton (2006) Exceeding human limits: The Chemical Turing MachineNature 440:409-410
  3. Stephen H. Muggleton (2006) Towards Chemical Universal Turing Machines in Proceedings on the 21st National Conference on Artificial Intelligence
  4. Picture credit: Image from Steve Jurvetson
  5. This post was originally published on nodalpoint with comments

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