1988
Human-level control through deep reinforcement learning : Nature : Nature Publishing Group
The theory of reinforcement learning provides a normative account1, deeply rooted in psychological2 and neuroscientific3 perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems4, 5, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms3. While reinforcement learning agents have achieved some successes in a variety of domains6, 7, 8, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks9, 10, 11 to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games12. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
machinelearning  google  artificial_intelligence 
5 days ago
Why Silicon Valley has a chance to dominate the auto industry - Vox
A 1985 article in the New York Times wondered what had happened to the fad of laptop computers, which had (the article claimed) been relatively common in the early 1980s but had since dropped in popularity. "People don't want to lug a computer with them to the beach or on a train to while away hours they would rather spend reading the sports or business section of the newspaper," the Times wrote. A 1980 article about early online news services made a similar point: "you cannot tuck a computer under your arm as you head for the subway."

The recent swirl of speculation around Apple making a car has provided many opportunities for people to make a similar mistake — this time about autonomous cars. When they think about the future of self-driving vehicles a couple of decades from now, too many people envision a world that looks mostly like our world today except that drivers can activate a sort of super-cruise control that takes over the steering wheel. The Economist, for example, cites a research report predicting that the market for cars with self-driving capabilities will never exceed 25 percent of the market.
artificial_intelligence  siliconvalley  public_discourse 
7 days ago
Is teaching undergraduates central to the mission…? — Crooked Timber
Megan McArdle quite reasonably takes me to task for a seemingly (but not actually) throw-away phrase in my post about the recent dispute over the mission of my university. I’m very much in sympathy with the direction of her piece, so I thought I’d explain what I meant. One caveat—she very clearly specifies that she is talking about public flagship universities like mine, and I shall stick with that, so neither of us should be interpreted as implying anything about any other kind of institution (she takes her main example from an Ivy league school, but that example could just as easily have been at Madison).
higher_ed  college  public_discourse  moocs 
7 days ago
Hoping Google’s Lab Is a Rainmaker - NYTimes.com
Google’s research arm, Google X, is called the company’s Moonshot Factory. One reason the company picked the word “Moonshot” was to remind people to tackle big problems that may well blow up in their faces.

Last month, after years of promotion, Google ended a test trial of its Internet-connected glasses, called Glass. While the device seemed to have promising commercial applications in hospitals or on factory floors, its first pass at the consumer world was unsuccessful.

The very public failure of Glass points to a bigger question. After patiently abiding a steep increase in research and development spending on efforts that range from biology to space exploration, Wall Street is starting to wonder when — and if — Google’s science projects will pay off.
google  research 
7 days ago
How long can Google keep investing in outlandish science projects? - Vox
America's largest companies are investing way less in science than they used to. Decades ago, firms like AT&T and IBM ran massive labs where scientists could dream big and pursue the sort of research that won Nobel Prizes — even if it didn’t translate immediately into new products. (AT&T’s Bell Labs famously helped invent the laser and the transistor.) But that’s increasingly a rare sight.

And to see why, just look at Google.
google  research 
7 days ago
Expected contents of log files? - Google Groups
Dav Clark of Berkeley hasmore questions on the edx data
edx  openedx  forums  learning_analytics 
9 days ago
The reluctant king of the hidden internet – Henry Farrell – Aeon
This created an obvious vulnerability – indeed, an existential threat to Ulbricht’s business. If any reasonably successful dealer leaked the contact details for users en masse, customers would flee and the site would collapse. And so, when a Silk Road user with the pseudonym FriendlyChemist threatened to do just that, Ulbricht did not invoke Silk Road’s internal rules or rely on impersonal market forces. Instead, he tried to use the final argument of kings: physical violence. He paid $150,000 to someone whom he believed to be a senior member of the Hells Angels to arrange for the murder of his blackmailer, later paying another $500,000 to have associates of FriendlyChemist murdered too.

It is unclear if anyone was, in fact, killed by anyone else. Indeed, it seems most likely that the whole affair was a scam in which FriendlyChemist and his purported assassin were associates (or possibly the same person). Still, it marked the final stage in an extraordinary transformation. Ulbricht began as an idealist, setting out to build a market free from what he described as the ‘thieving murderous mits’ of the state. He ended up paying muscle to protect the bureaucratic system that he had created.
research 
9 days ago
EdX Production Setup - Google Groups
Anagha,

I've updated https://github.com/edx/configuration/wiki/Hosting-edX-in-Production with an outline of EdX.org's configuration.

If you keep an eye on this mailing list, we'll soon be open sourcing our loadtest framework, which will give you an idea of how many simultaneous users you can run on a given configuration.   Our basic method is to use a tool like New Relic to identify website performance and to scale up capacity to keep website performance at a usable level.
edx  openedx  forums 
11 days ago
DANCE: Discussion Affordances for Natural Collaborative Exchange
Drawing from two decades of research in Computer Supported Collaborative Learning (CSCL), we are working to design an extension of the edX platform to enhance instructionally beneficial discussion opportunities available to students. Our work will initially focus on EdX in particular, but in the long run we seek to provide these capabilities to Massive Open Online Courses (MOOCs) and other online learning platforms more generally. In particular, this working group will partner with edX as a satellite collaborative, seeking to involve researchers and developers from multiple universities, foundations, and industrial organizations.

Currently we are organizing this working group in order to identify who is interested in contributing and in what ways. We are also seeking to build a common vision regarding what kinds of research people would be valuable to the community once such a platform extension was in place to support it. There will be a wide variety of roles to play once this working group is formed, including but not limited to vision setting, prototyping and more industrial strength software development, integration, etc. The long term vision is to produce a production quality extension to the platform.

Our foundational work is beginning with specific interventions designed to offer synchronous collaboration activities supported by intelligent conversational agents and enhancements to threaded discussions to support more intensive help exchange by leveraging social recommendation technology. However, our goals are much broader than this, seeking to leverage insights and methodologies from the field of Human-Computer Interaction and encompassing both synchronous and asynchronous communication very broadly. Our vision includes text, speech, and video based interactions, instrumented with all sorts of intelligent support powered by state-of-the-art analytics and leveraging language technologies and artificial intelligence more broadly in order to offer contextually appropriate support. We will coordinate this effort with regular online meetings and occasional in person working meetings.

Please join our working group by filling in this form.
edx  openedx  cmu  learning_research 
14 days ago
Developer Postings for Michigan's New Digital Innovation Greenhouse - Google Groups
Greetings,

Over the last four years, innovators at the University of Michigan have developed many applications which use learning analytics data to improve teaching and learning. Making these data-driven tools available to everyone requires a new kind of support - technical expertise from designers and developers, communities of practice around tool adoption, and hands-on help for early adopters.

In November, a group of us received support from the Third Century Initiative to build DIG, the Digital Innovation Greenhouse. DIG will bring together innovators, developers, HCI and instructional designers, ITS infrastructure experts, and CRLT supported user communities to grow digital engagement tools to scale. 

DIG will begin by expanding the application of Student Explorer, making E2Coach available to a much broader array of users, and building ART 2.0, a new tool which will help everyone on campus answer questions about teaching and learning.

At the moment, we're building the DIG team by searching for four or five software developers. Ideal candidates would be eager to improve education with technology, excited about working with Michigan's creative research community, and enterprising enough to join us in this new endeavor. 

Here are the links to the job postings:
Lead Developer for the Digital Innovation Greenhouse
DIG development team members
Many thanks for your help!

Cheers,
Tim 
moocs  learning_analytics  forums  michigan 
14 days ago
Most Popular Coding Languages of 2015 — CodeEval
Python is still the top but R is rising.

An interesting thing to note is the rise of R language which gaining in popularity because of the big data trend.  On another major index, TIBOE R language has jumped in ranking from 44 to 18 in a single year. It's a new language that we've just started supporting 2 months ago so we won't have the same data to support it's rise but we're already seeing it's impact and expect it experience high growth this year. 
code  coding  computer_science  data_science  public_discourse 
15 days ago
Data can tell you how to up your online dating game - Vox
Has a lot of papers on online dating research, and then they've translated those results into recommendations.
data_science  public_discourse  okcupid  dating 
16 days ago
Book Review: ‘To Explain the World’ by Steven Weinberg - WSJ by Steven Shapin
Plato was “silly”; Aristotle was “tedious” and “wrong about the laws of nature”; Galileo’s emphasis on geometry over algebra “was somewhat behind the times”; Bacon was “overrated” and, despite the enthusiastic embrace of Baconianism by generations of thinkers, “It is not clear to me that anyone’s scientific work was actually changed for the better by Bacon’s writing.” Descartes was overrated too: “it is remarkable how wrong Descartes was about so many aspects of nature,” and his assertion that animals are machines, lacking consciousness, is refuted “on the basis of observation of several lovable pet cats.” Newton escapes whipping: His achievements “provided the paradigm that all subsequent science” (botany? seismology?) “has followed, as it became modern.”
research 
16 days ago
Harvard and M.I.T. Are Sued Over Lack of Closed Captions - NYTimes.com
The complaints say Harvard and M.I.T. violated both the Americans With Disabilities Act and the Rehabilitation Act of 1973, and seek a permanent injunction requiring them to include closed captioning, which provides a text version of the words being spoken, in their online materials. Despite repeated requests by the association, the complaints say, the two universities provide captioning in only a fraction of the materials, “and even then, inadequately.”

The lawsuits, filed by the National Association of the Deaf, which is seeking class-action status, say the universities have “largely denied access to this content to the approximately 48 million — nearly one out of five — Americans who are deaf or hard of hearing.”
moocs  public_discourse  mit  harvard 
18 days ago
Google Maps Tenth Anniversary | Re/code
A history of google maps -- and how it made its way into google.
google  history  technology  platformization  public_discourse 
19 days ago
Uncovering Security Flaws in Digital Education Products for Schoolchildren - NYTimes.com
On security problems in online learning. Mentions Coursera.

“A lot of education sites have glaring security problems,” said Mr. Porterfield, the principal engineer at a software start-up in Los Altos, Calif. “A big part of the problem is that there’s not even any consensus of what ‘good security’ means for an educational website or app.”
coursera  platformization  security  public_discourse 
21 days ago
A Prickly Partnership for Uber and Google - NYTimes.com
When Google’s venture capital arm poured more than $250 million into Uber in 2013, it looked like a match made in tech heaven.

Google, with billions of dollars in the bank and house-by-house maps of most of the planet, seemed like the perfect partner for Uber, the hugely popular ride-hailing service.

Bliss between the Internet titan and the hot young company, however, has proved fleeting.

Uber recently announced plans to develop self-driving cars, a longtime pet project at Google. It is also adding engineers who are experts on mapping technology. And the company, based in San Francisco, has been in talks with Google’s advertising archrival, Facebook, to find ways to work together.

Not to be outdone, Google has been experimenting with a ride-sharing app similar to Uber’s, according to Bloomberg. And both companies have long toyed with the idea of offering same-day delivery of items like groceries and other staples.

Continue reading the main story
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Add it all up and the business partners, just two years after that major investment, are “going to war,” as the Bloomberg report put it.
google  uber  platformization  public_discourse 
22 days ago
questionable hypothesizing | orgtheory.net
Heads turn whenever accusations are made about academic impropriety, but it is especially provocative when one of the researchers involved in a study makes accusations about his/her own research. A forthcoming article in the Journal of Management Inquiry written by an anonymous senior scholar in organizational behavior does exactly this. The author, who remains anonymous to protect the identity of his/her coauthors, claims that research in organizational behavior routinely violates norms of scientific hypothesis testing.

I want to be clear: I never fudged data, but it did seem like I was fudging the framing of the work, by playing a little fast and loose with the rules of the game—as I thought I understood how the game should be played according to the rules of the scientific method. So, I must admit, it was not unusual for me to discover unforeseen results in the analysis phase, and I often would then create post hoc hypotheses in my mind to describe these unanticipated results. And yes, I would then write up the paper as if these hypotheses were truly a priori. In one way of thinking, the theory espoused (about the proper way of doing research) became the theory-in-practice (about how organizational research actually gets done).
abtesting  platformization  public_discourse 
22 days ago
Starting a new thread - Google Groups
More questions from Dav Clark of Berkeley.. continuing his earlier thread about course sturcture
edx  openedx  forums  moocs 
23 days ago
A.I. is getting smarter | Computerworld
Today, A.I. is on an upswing fueled by academic research labs at institutions like Carnegie Mellon University, WPI and MIT. It's also getting a boost in the tech industry, with companies like Google and Microsoft throwing their financial and intellectual might behind A.I. efforts.

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"Right now, we're in more optimistic times," said Parker. "There have been a lot of advances in robotics, with [IBM's] Watson and natural language processing and speech recognition with technologies like Apple's Siri."

So what advances are just ahead of us?

Major gains are being made, or are about to be made, in natural language processing, speech recognition, object recognition, computer vision, machine translation and neural networks.

Many of those technologies will be used to build robots that move more fluidly, like humans. They also will help scientists integrate multiple capabilities into one robotic system.
artificial_intelligence  public_discourse  machinelearning 
23 days ago
The computer-generated cookbook: just the thing for a hungry digital age | Books | The Guardian
IBM’s experts began by “training” Watson - which is named after IBM founder Thomas Watson - on tens of thousands of recipes, and on the chemical composition of foods, moving on to the flavours and ingredients which complement each other - such as rosemary and potatoes, or olives and gin. The computer then used three different metrics to analyse ingredients, rating them for surprise (suggesting ingredients which are rarely found together), pleasantness (“researchers have carried out studies on the flavours that give people pleasure at a molecular level,” says the book), and synergy (“studies indicate that foods sharing common chemical flavour compounds taste good together”).
artificial_intelligence  machinelearning  watson  ibm  public_discourse 
23 days ago
Why Google Glass Broke - NYTimes.com
At the time, unknown to anyone outside X, an impassioned split was forming between X engineers about the most basic functions of Google Glass. One faction argued that it should be worn all day, like a “fashionable device,” while others thought it should be worn only for specific utilitarian functions. Still, nearly everyone at X was in agreement that the current prototype was just that: a prototype, with major kinks to be worked out.

Continue reading the main story
There was one notable dissenter. Mr. Brin knew Google Glass wasn’t a finished product and that it needed work, but he wanted that to take place in public, not in a top-secret lab. Mr. Brin argued that X should release Glass to consumers and use their feedback to iterate and improve the design.
agile  google  software 
25 days ago
What does the other university should do if it wants to use Edx system for its pupose? - Google Groups
I think you’re asking what a university is required to do in order to install and use the Open edX software. Is that right, or are you asking something else?

The Open edX software is available under an AGPL license. That means that you are free to use the software for whatever purpose you want, and you don’t need to ask permission or pay edX anything in order to do so. However, there are still some restrictions, and the biggest one is that you must make your copy of the software open source as well, including any changes you make to it. If you run a website based on the Open edX software, and you build a new feature into your site, change the HTML pages, or make any change at all to the source code, then you are legally required to make the source code of your website available to anyone who uses your website in any capacity. (If you run a public-facing website, as many people do, then your source code must be visible to everyone in the world.)

I should also mention that the Open edX software is freely available under the AGPL, but the courses running on edx.org are not. Those courses are owned by the universities that produced those courses, and they are not required to share them. Similarly, you can use your own copy of the Open edX software to build your own course, and you would not be required to share the contents of that course.

Does all of that make sense, and do you have any other questions? If so, I suggest that you ask them on the edx-code mailing list. The openedx-ops mailing list is supposed to be for technical questions around getting the software up and running.

David Baumgold
Developer Advocate, edX
edx  openedx  debates  moocs 
25 days ago
Open edX Community Survey - Google Groups
Open-edx just released a survey

Hi! I'm the community coordinator at Open edX. I'm with the Open Source team, and we recently put together a short survey. We're looking to get an idea of who is in the community and how open open edX is used. Please fill it out and pass it around.

https://docs.google.com/forms/d/1xpxf_T-v3ntFt9Xcdk07no1e_92sTjBdU7KCzYeENNg/viewform?usp=send_form

Cheers,
Molly
openedx  edx  moocs 
25 days ago
Putting the Computer Science in Computing Education Research | February 2015 | Communications of the ACM
We can look in history for two examples: engineering education and computational biology. Preparation of physics, math, and science K–12 education largely happens in education departments or schools because these core subjects are required in K–12. Engineering K–12 education, on the other hand, has found a place in the college of engineering as well as education. Research on college-level instruction occurs in cognate departments. In all solutions, the cognate field is a large part of the research. Computational biology is another example. Initially, both biology and computer science departments were reluctant to hire in this area. Computer science departments felt biology was an application of current algorithms. The few departments who saw the promise of computational biology have made transformative discoveries in mapping the human genome and driving new computer science areas such as data mining. The same will hold true for computing education research. Computer scientists need to lead not only to make advances in computing education, but also to find the problems that will drive computer science research.
moocs  computer_science  public_discourse  platformization 
28 days ago
Do-It-Yourself Textbook Publishing | February 2015 | Communications of the ACM
Although we did not know it when we started the book (mid-2011), we were about to be offered the chance to adapt the first half of the campus course to a MOOC (Massive Open Online Course). MOOCs turned out to play a major role in the textbook's development. We accelerated our writing in order to have an "alpha edition" consisting of half of the content, that is, the chapters that would be most useful to the MOOC students. Indeed, based on advice from colleagues who had offered MOOCs, we were already structuring the MOOC as short video segments interspersed with self-check questions and assignments; we decided to mirror that structure in the book, with each section in a chapter mapping to a topical segment in the MOOC. While the book was only recommended and not required for the MOOC, the MOOC was instrumental in increasing the book's visibility. It also gave us class testing on steroids, as we got bug reports and comments from thousands of MOOC learners. Clearly, the MOOC helps with marketing, since faculty and practitioners enroll in MOOCs and they supply reviews on Amazon.
moocs  public_discourse 
28 days ago
MOOCs deliver robots for everyone - Computerworld
The courses have been designed for undergraduate engineers but QUT said they are suitable for anyone with a strong interest in robotics. They are the first ever developed for people with undergraduate STEM knowledge, and the first robotics and vision MOOCs to be offered globally, according to the school.

“While the MOOCs might attract some high school STEM stars and skilled armchair roboticists, I expect most of the students will be undergraduates, perhaps studying engineering or computer science at a university that doesn't itself have a strong robotics program,” said Professor Peter Corke, who created the course and is the director of the ARC Centre of Excellence for Robotic Vision.
moocs  public_discourse 
28 days ago
Ratings Now Cut Both Ways, So Don’t Sass Your Uber Driver - NYTimes.com
EBay pulled back on allowing sellers to review customers in 2008. Buying on eBay is a straightforward transaction with little personal interaction between seller and buyer, and so the reviews got in the way. Now eBay allows sellers to make only positive comments about buyers.

But the new platforms let reviews go both ways, and vary in their transparency about the process. Yelp is straightforward: Businesses can post replies to critical customers. On Lyft, the second-biggest of the new cab companies, passengers are vaguely warned that “a low star rating” means requests for rides may not be accepted. Uber does not mention passenger ratings at all in its user agreement but noted in a blog post that “an Uber trip should be a good experience for drivers too.”
uber  airbnb  lyft  tripadvisor  platformization  public_discourse 
28 days ago
MOOCs Aim To Strengthen Computer Science And Physics Teaching In Middle And High Schools
How do we spark interest and nurture ability in CS and physics in these underrepresented groups? One way to build interest is to introduce students to these subjects earlier in their education, in the K–12 pipeline. Research shows that early exposure to science and math classes increases the likelihood that students will pursue STEM fields in college.

Yet many middle and high schools, especially those serving students from underrepresented populations, don’t have the resources or training to offer CS. Only about 10% of U.S. K–12 schools offer classes in computer science and just 5% of U.S. high schools were certified to teach AP CS in 2012–13.

To help fill this gap in K–12 STEM education, Harvey Mudd created its first MOOC for middle and high school teachers. Middle Years Computer Science (MyCS) walks a teacher through the lesson plans, activities and exercises of a curriculum developed to appeal to students with a broad range of interests and no prior CS experience. Schools that have been using it have found it to be easy to use, accessible and engaging for their students.
moocs  public_discourse 
28 days ago
Scientists and investors warn on AI - FT.com
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Dozens of scientists, entrepreneurs and investors involved in the field of artificial intelligence, including Stephen Hawking and Elon Musk, have signed an open letter warning that greater focus is needed on its safety and social benefits.
The letter and an accompanying paper from the Future of Life Institute, which suggests research priorities for “robust and beneficial” artificial intelligence, come amid growing nervousness about the impact on jobs or even humanity’s long-term survival from machines whose intelligence and capabilities could exceed those of the people who created them.
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“Because of the great potential of AI, it is important to research how to reap its benefits while avoiding potential pitfalls,” the FLI’s letter says. “Our AI systems must do what we want them to do.”
The FLI was founded last year by volunteers including Jaan Tallinn, a co-founder of Skype, to stimulate research into “optimistic visions of the future” and to “mitigate existential risks facing humanity”, with a focus on those arising from the development of human-level artificial intelligence.
Mr Musk, the co-founder of SpaceX and Tesla, who sits on the FLI’s scientific advisory board alongside actor Morgan Freeman and cosmologist Stephen Hawking, has said that he believes uncontrolled artificial intelligence is “potentially more dangerous than nukes”.
Other signatories to the FLI’s letter include Luke Muehlhauser, executive director of Machine Intelligence Research Institute, Frank Wilczek, professor of physics at the Massachusetts Institute of Technology and a Nobel laureate, and the entrepreneurs behind artificial intelligence companies DeepMind and Vicarious, as well as several employees at Google, IBM and Microsoft.
Rather than fear-mongering, the letter is careful to highlight both the positive and negative effects of artificial intelligence.
“There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase,” the letter reads. “The potential benefits are huge, since everything that civilisation has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty are not unfathomable.”
Because of the great potential of AI, it is important to research how to reap its benefits while avoiding potential pitfalls. Our AI systems must do what we want them to do
- Future of Life Institute letter
Benefits from artificial intelligence research that are already coming into use include speech and image recognition, and self-driving vehicles. Some in Silicon Valley have estimated that more than 150 start-ups are working on artificial intelligence today.
As the field draws in more investment and entrepreneurs and companies such as Google eye huge rewards from creating computers that can think for themselves, the FLI warns that greater focus on the social ramifications would be “timely”, drawing not only on computer science but economics, law and IT security.
“Many economists and computer scientists agree that there is valuable research to be done on how to maximise the economic benefits of AI while mitigating adverse effects, which could include increased inequality and unemployment,” the FLI writes in the paper.
artificial_intelligence  machinelearning  platformization  public_discourse 
28 days ago
No need to panic – artificial intelligence has yet to create a doomsday machine
Riedl’s Lovelace 2.0 test requires the AI to create an artifact matching a plausible, but arbitrarily complex, set of design constraints. The constraints, set by an evaluator who also judges its success, should be chosen so that meeting them would be deemed as evidence of creative thinking in a person, and so by extension in an AI.

For example the evaluator might ask the machine to (as per Riedl’s example): “create a story in which a boy falls in love with a girl, aliens abduct the boy and the girl saves the world with the help of a talking cat”. A crucial difference from the Turing test is that we are not testing the output of the machine against that of a person. Creativity, and by implication intelligence, is judged by experts. Riedl suggests we leave aside aesthetics, judging only whether the output meets the constraints. So, if the machine constructs a suitable science fiction tale in which Jack, Jill and Bagpuss, repel ET and save Earth, then that’s a pass – even thought the result is somewhat unoriginal as a work of childrens’ fiction.

I like the idea of testing creativity – there are talents that underlie human inventiveness that AI developers have not even begun to fathom. But the essence of Riedl’s test appears to be constraint satisfaction – problem solving. Challenging, perhaps, but not everyone’s idea of creativity. And by dropping the competitive element of Turing’s verbal tennis match, judging Lovelace 2.0 is left too much in the eye of the beholder.
artificial_intelligence  machinelearning  data_science  public_discourse  platformization 
28 days ago
The Hype is Dead, but MOOCs Are Marching On
Interview with Koller. Also in data. “The previous hype was completely unmerited because it was based on the presumption that MOOCs were going to put universities out of business.”
coursera  moocs  public_discourse 
28 days ago
New Stanford course brings Silicon Valley to the humanities classroom
Students from computer science and the humanities join forces to create literary websites and mobile apps, combining their strengths to launch literature into the 21st century.
moocs  stanford  public_discourse 
28 days ago
MOOCs Aren’t Revolutionizing College, but They’re Not a Failure | MIT Technology Review
Education researchers are still just beginning to mine all the data that MOOCs generate about how students respond to the material. Researchers like Pritchard can track every step of every student through a MOOC; he says that for him to study his traditional students that way, “they’d have to carry a head-cam 24-7.” Eventually, such data should yield insights about the best ways to present, sequence, and assess particular subjects. Kevin Carey, who has researched MOOCs as director of education policy at the New America Foundation, points out that today’s MOOCs haven’t even begun to make serious use of artificial intelligence to personalize courses according to each student’s strengths and weaknesses (a surprise considering that pioneers like Thrun and Coursera’s Daphne Koller came from AI backgrounds).
moocs  public_discourse 
28 days ago
The Responsive Enterprise: Embracing the Hacker Way | December 2014 | Communications of the ACM
Crazy article on the software-ization of everything -- worth looking at. also in the edx/data folder

there are lines like: software is unstoppable... etc.
platformization  public_discourse 
28 days ago
On the Significance of Turing's Test | December 2014 | Communications of the ACM
Moshe Y. Vardi was correct in his Editor's Letter "Would Turing Have Passed the Turing Test?" (Sept. 2014) when he suggested that Turing's "imitation game" should be regarded as nothing more than a game. I would go further. Computer scientists have wasted far too much time and resources trying to answer "big" but vague philosophical questions (such as "Can a machine be intelligent?"). Their effort would be better spent on answering "little" questions about specific issues (such as "Can a computing machine be trusted to park a car?"). Discussion of such practical matters would be far more useful than endless debates about the Turing test and who or what might pass it.
artificial_intelligence  machinelearning  public_discourse  platformization 
28 days ago
At Silk Road Trial, Lawyers Fight to Include Evidence They Call Vital: Emoji - NYTimes.com
Interesting article on the ongoing case against the guy who allegedly ran Silk Road
research  law 
28 days ago
Here Comes Professor Everybody - The Chronicle Review - The Chronicle of Higher Education
Jeff Young writes about Udemy. The whole article is worth a look but especially this by Eric Schmidt. I have seen other computer scientists say things like this about "markets" figuring out who's a good teacher

The participation in Mooc.org by Google—one of the biggest tech players—seemed to me like a major endorsement of the idea. I was curious about why the company is creating an open, global schoolhouse. So last year when Google’s chairman, Eric Schmidt, spoke at an event at Tufts University while promoting his book The New Digital Age, I asked him during a public question-and-answer session.

"We really want to democratize the access to education, and the access to teaching, and then let the marketplace figure it out," he said. "You’ll discover that teaching is an art. That there are people who are gifted at it, and because of the way the Internet works, eventually the very most talented teachers will emerge, from everywhere. It’s a great thing."

His tone suggested that letting the marketplace "figure out" teaching was the most common-sense plan in the world. But having covered colleges for more than 15 years, I found his comment subversive, even aggressive. Because in the typical college system, the marketplace doesn’t decide the best teaching, at least not in any direct way. Professors are typically hired and promoted based on the quality of their research, and most undergraduates choose a college based not on a specific instructor but on a range of other factors, from geographic location to the quality of grub in the food court.

Udemy’s chief executive, Dennis Yang, takes Schmidt’s argument even further. "What we have is competition among teachers" on the platform, he says. "It’s one of the few environments where teachers and instructors have to compete with each other." Someone considering Walter’s iPhone course is shown how many stars previous students rated it, along with a list of other, similar courses, many of them cheaper.

That strikes some academics as a nightmare scenario. After all, when it comes to learning, the customer isn’t always right. Students might rate challenging professors more harshly simply because they are more difficult. Yet students may learn more in a challenging class. Or they may simply not know whether the information they learned is up to date—or even accurate.
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28 days ago
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