Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before. They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs.
The UK has a new AI centre – so when robots kill, we know who to blame The UK has a new AI centre – so when robots kill, we know who to blameArtificial Intelligence 12 Oct 2016.
But while AI and machine learning are very much related, they are not quite the same thing. AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while Stanford University defines machine learning as “the science of getting computers to act without being explicitly programmed”. You need AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent.
… if you’re not careful, modelling has a nasty way of enshrining prejudice with a veneer of “science” and “math.”Cathy has consistently made another point that’s a corollary of her argument about enshrining prejudice. At O’Reilly, we talk a lot about open data. But it’s not just the data that has to be open: it’s also the models. (There are too many must-read articles on Cathy’s blog to link to; you’ll have to find the rest on your own.)
You can have all the crime data you want, all the real estate data you want, all the student performance data you want, all the medical data you want, but if you don’t know what models are being used to generate results, you don’t have much.
I once asked the CEO of a major e-learning company how much of their work was maintenance of existing content, thinking that this would be a substantial revenue earner. I was surprised to find that hardly anyone maintains their content. They just wait four or five years for the content to become obsolete, then they start all over again.
A right first time approach works if you are building skyscrapers or making Hollywood movies. The safety considerations or the cost of re-work simply demand it. And if you are sending out physical product, like printed books, it is clearly uneconomic to keep printing and distributing new versions.
But in an era in which software apps and web content are updated almost constantly and usually painlessly, there is simply no argument for treating e-learning content as if we were making $100m movies or printing books.
Agile development of learning content is a process of successive approximation – getting closer and closer to what is right for the user.
When faculty members move from one institution to the next, so do their courses, but after having spent hundreds of thousands of dollars to prepare those courses to a massive audience, are universities entitled to a share of the rights?
The question has so far gone unanswered (though not undiscussed) even at some of the earliest entrants into the massive open online course market, including Harvard University and the Massachusetts Institute of Technology. Since MOOC providers have gotten out of the intellectual property rights debate by saying they will honor whatever policy their institutional partners have in place, it falls on the universities to settle the matter.
It wasn’t so long ago that the excitement surrounding online education reached fever pitch. Various researchers offering free online versions of their university classes found they could attract vast audiences of high quality students from all over the world. The obvious next step was to offer far more of these online classes.
That started a rapid trend and various organisations sprung up to offer online versions of university-level courses that anyone with an Internet connection could sign up for. The highest profile of these are organisations such as Coursera, Udacity, and edX.
But this new golden age of education has rapidly lost its lustre.