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Machine learning versus AI: what’s the difference?

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.

Source: Machine learning versus AI: what’s the difference?

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We need open models, not just open data – O’Reilly Radar • ©[radar.oreilly.com]

We need open models, not just open data - O'Reilly Radar

… 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.

via We need open models, not just open data – O’Reilly Radar.

Why e-learning should be in perpetual beta – [clive-shepherd.blogspot.com.es/]

Why e-learning should be in perpetual beta

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.

via Why e-learning should be in perpetual beta.

Moving Beyond MOOCs: Experiments in Non-traditional Product Education by Julia Wilkowski [learningsolutionsmag.com]

The engineering education team’s staff meeting on May 2, 2012 began like any other: reports of new engineer orientation, computer science outreach efforts, and an updated mission statement: “To provide Google engineers and the world with relevant and timely technical content, learning resources, and tools.”

With five minutes remaining in the meeting, the director announced that she was recruiting team members who were willing to tackle an audacious goal: create an online course for ten million people in eight weeks. Many of us left the room that day with more questions than answers: Could we really create a course from scratch for that many people? In only eight weeks? What would we teach, and why? How would we know if we were successful?

via Moving Beyond MOOCs: Experiments in Non-traditional Product Education by Julia Wilkowski : Learning Solutions Magazine.

If a MOOC instructor moves, who keeps the intellectual property rights? [insidehighered.com]

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.

via If a MOOC instructor moves, who keeps the intellectual property rights? | Inside Higher Ed.

Data Mining Exposes Embarrassing Problems for Massive Open Online Courses [technologyreview.com]

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.

via Data Mining Exposes Embarrassing Problems for Massive Open Online Courses | MIT Technology Review.

Learning at the Speed of Links and Conversations [blog.changeagentsworldwide.com]

The use of search engines, databases, platforms, and spaces for collaborative exploration and exchange has exploded into our personal and collective world. Both real-time and asynchronous connection combined with effective technologies for compressing the bits that drive/carry audio and video have enabled inexpensive and effective telepresence. We’re transitioning into an era of “conversations” from which we extract useful information and knowledge, whilst time and space are being altered in front of our faces.

Arguably the capabilities offered by these new tools and the conditions they generate are having deep impact upon how, why, where, and when we learn. I think it’s “how” we learn that is the most important focus or issue for these early days of a new set of conditions rapidly becoming ubiquitous.

via Learning at the Speed of Links and Conversations.

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