A little background reading for this post:

First, a quick Wikipedia definition of MOOC: ‘A massive open online course is an online course aimed at unlimited participation and open access via the web. In addition to traditional course materials such as filmed lectures, readings, and problem sets, many MOOCs provide interactive user forums to support community interactions among students, professors, and teaching assistants (TAs). MOOCs are a recent and widely researched development in distance education which were first introduced in 2006 and emerged as a popular mode of learning in 2012.’ To see some of the courses offered, go to Open Culture’s “1300 Free Online Courses from Top Universities” web page.

Next, a quick overview of Artificial Intelligence (AI) in modern life. Here’s a quick collection of ways you might be experiencing AI in 2017: Smart vehicles: self driving cars will be available in the near future; driverless busses are already in operation.  Perhaps your car already has an AI parking system? Surveillance:  AI can be trained to take input from security cameras and warn human security officers to investigate further. Fraud services: financial, computer, and other services use AI to detect unusual activity on your account to block potential fraud. Have you been asked to authorize a financial transaction, or informed that your email account was signed into on another device?  Writing, essays, news: AI is being used to write simple stories, like financial news summaries, sports recaps, property descriptions, and other data-driven stories.

Customer Service: have you used ‘live chat’ for customer support on a web site?  This is one of the more ubiquitous applications of AI today! Many of these chat bots are automated responders, but some are able to extract information from the site and present it to you on request.  Video Games: many modern games are written with performance based modifiers involved. Have you played a game with a memory of your interactions and objectives?  Predictive Purchasing: does a merchant offer you coupons, rewards or special deals based on your purchasing history? Do any of your computer apps offer you recommendations for radio, music or movies?  Smart homes: do you have any management systems in your home which learn from your behaviour, and automatically take action, for instance, turning down the heat at night?  Virtual assistants: do you make use of Siri, Cortana, or Alexa? Do you use Google maps to find the quickest way for you to get from A to B?  Have you flown on a commercial flight that uses autopilot for takeoff and landing? Does your email program include an efficient spam filter? Social Media: do you use a site that can recognize and tag faces of your friends, and personalize your news feed, offering you targeted advertisements? To read much more, see Wikipedia’s AI page.

The first MOOC I signed up for was Harvard’s CS50, David Malan’s 2012 enormously successful online Introduction to Computer Science course. It is one of the 50 most popular MOOCs offered to date. You can find the original course at this link on the Harvard site, and the updated course at this link at the EdX site.  The course is ‘Harvard University’s introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. As of Fall 2016, the on-campus version of CS50x, CS50, was Harvard’s largest course.’

A post on Open Culture in May 2017 alerted me to another course I plan to take, Artificial Intelligence, offered by MIT.  ‘This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.’

If you read my previous posts about AI (Animating Still LifeTransforming the meaning of evidence and truth) you won’t be surprised at my interest in the role AI is playing in our perception of truth and fact. I think I need to understand AI a little better.

Near the beginning of the video below,  Prof. Winston introduces the course, saying ‘Well, it must have something to do with thinking. So let’s start up here, a definition of artificial intelligence, by saying that it’s about thinking, whatever that is. My definition of artificial intelligence has to be rather broad. So we’re going to say it’s not only about thinking. It’s also about perception, and it’s about action. And if this were a philosophy class, then I’d stop right there and just say, in this subject we’re going to talk about problems involving thinking, perception, and action…’

‘But this is not a philosophy class. It’s an engineering school class. It’s an MIT class. So we need more than that. And therefore we’re going to talk about models that are targeted at thinking, perception, and action…That’s what we do at MIT. We build the models using differential equations. We build models using probabilities. We build models using physical and computational simulations. Whatever we do, we build models. Even in humanities class, MIT approach is to make models that we can use to explain the past, predict the future, understand the subject, and control the world.’

‘That’s what MIT is about. And that’s what this subject is about, too. And now, our models are models of thinking. So you might say, if I take this class will I get smarter?  And the answer is yes. You will get smarter. Because you’ll have better models of your own thinking, not just the subject matter of the subject, but better models of your own thinking. So models targeted at thinking, perception, and action…’ The transcripts of the MIT videos are at this link.

The group that brought you the fake faces I wrote about in Transforming the meaning of evidence and truth  Nivdia, this month (December 2017) has revealed that they have developed ‘an unsupervised learning method for computers which allows for sweeping changes to video content it’s fed. By using the new method, they were able to produce startling results.’ (link) The system can change day into night, winter into summer, and house cats into cheetahs and cougars, Corgis into German Shepherds and Huskies, with minimal training materials. The Nivdia white paper is available at this link and a summary is in this post at The Verge. All the videos that accompany the paper are at this link.

I made the image below with three simple programs on my Mac: a web browser, Seahorse and ComicLife. The landscape is found here, the face is a screen shot from this page. You can probably tell that I made the composite image, but would you identify the face and landscape as being computer generated?