Lucky for us, choice architecture isn’t determined entirely by algorithms — but by UI designers and engineers. As such, we can encode and optimize for transparency within recommendation systems.Transparency is a broad term, let’s start with visibility and control.
MIT Sloan Management Review; Sam Ransbotham, David Kiron, Philipp Gerbert, and Martin Reeves
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Disruption from artificial intelligence (AI) is here, but many company leaders aren’t sure what to expect from AI or how it fits into their business model. Yet with change coming at breakneck speed, the time to identify your company’s AI strategy is now. MIT Sloan Management Review has partnered with The Boston Consulting Group to provide baseline information on the strategies used by companies leading in AI, the prospects for its growth, and the steps executives need to take to develop a strategy for their business.
Amazon just kicked off a location search for its second headquarters, and the retailer created a wish list to help find the perfect spot for its new campus.
In its proposal, Amazon said it’s looking for a city of more than 1 million people with an international airport, mass transit, quality higher education, an educated workforce and a solid business climate.
To help the world’s largest e-commerce company narrow down its search, CNBC looked at some of the key criteria the online retailer laid out in its pitch to cities and states.
’m thrilled to announce PERVADE (Pervasive Data Ethics for Computational Research), a collaborative, 4-year research project funded by a $3 million grant from the National Science Foundation to study how diverse stakeholders – big data researchers, platforms, regulators, and user communities – understand their ethical obligations and choices related to computational research that relies on big, pervasive data sets about people.
PERVADE brings together a multi-disciplinary team with expertise in computational science, research ethics, data practices, law and policy, health information, social computing, qualitative and quantitative research methods, and data privacy.
It’s been nearly ninety years since that Pravda article about Lysenko was published, helping to launch him on his dismal career. It’s been over fifty years since he fell at last. When you hear this story, you may think, “Well, that’s appalling, but it happened a long time ago, and in a faraway place. It has no meaning to us today in the United States in 2017.”
I disagree. The things we are discussing today at this meeting — democracy, science, and journalism — are three valuable institutions that have made life in this country far better than it would be without them. They are worth defending, and worth keeping free of corruption.
We can look back over history to see how, in different places and different times, each of these pillars cracked and sometimes even fell. We should not be smug when we look back at these episodes.
Earlier this month, Aaron Crews departed as Walmart’s global head of e-discovery to join TextIQ — in his own words “a little AI startup” — where he is general counsel and vice president of strategy.
To explain why he is leaving a company that reported $485.8 billion in revenue last year to join one that claims to have millions in revenue, Crews said he believes several changes are imminent in terms of how is data managed.
Right now, at most companies, discovery costs are still rising, he said. But that’s only because data volumes are growing so fast, Crews added.
Building a neural network is hard. Most developers working on artificial intelligence projects are forced to commit early to a framework developed by experts in the field, and if they later realize they needed to go in a slightly different direction, it’s much harder to switch. Microsoft and Facebook have decided to make that easier.
The two companies announced the creation of the Open Neural Network Exchange (ONNX) format Thursday. The format allows AI developers to switch between open-source frameworks like Facebook’s Caffe2, Microsoft’s Cognitive Toolkit, and PyTorch, the three frameworks supported at the moment.
Facebook and Microsoft are today introducing Open Neural Network Exchange (ONNX) format, a standard for representing deep learning models that enables models to be transferred between frameworks. ONNX is the first step toward an open ecosystem where AI developers can easily move between state-of-the-art tools and choose the combination that is best for them.
When developing learning models, engineers and researchers have many AI frameworks to choose from. At the outset of a project, developers have to choose features and commit to a framework. Many times, the features chosen when experimenting during research and development are different than the features desired for shipping to production. Many organizations are left without a good way to bridge the gap between these operating modes and have resorted to a range of creative workarounds to cope, such as requiring researchers work in the production system or translating models by hand.
Three Equifax Inc. senior executives sold shares worth almost $1.8 million in the days after the company discovered a security breach that may have compromised information on about 143 million U.S. consumers.
The trio had not yet been informed of the incident, the company said late Thursday.
The credit-reporting service said earlier in a statement that it discovered the intrusion on July 29. Regulatory filings show that on Aug. 1, Chief Financial Officer John Gamble sold shares worth $946,374 and Joseph Loughran, president of U.S. information solutions, exercised options to dispose of stock worth $584,099. Rodolfo Ploder, president of workforce solutions, sold $250,458 of stock on Aug. 2. None of the filings lists the transactions as being part of 10b5-1 scheduled trading plans.
Every time students take a writing exercise on Quill.org–a writing instruction platform for schools–their responses are logged by computers and analyzed for patterns. Algorithms take account of every false word they type, every misplaced comma, every inappropriate conjunction, deepening a sense of where the nation’s kids are succeeding in sentence-construction and where they need extra help.
The algorithms substitute for human intervention. Instead of teachers having to correct errors late at night with a red pen, the system does it automatically, suggesting corrections and concepts on its own. The goal, says Peter Gault, who founded Quill three years ago, is to reach more students than traditional teaching methods, including those who need support the most. About 400,000 students in 2,000 schools have used the (mostly free) writing-instruction platform so far.
SLAC and Stanford researchers demonstrate that brain-mimicking ‘neural networks’ can revolutionize the way astrophysicists analyze their most complex data, including extreme distortions in spacetime that are crucial for our understanding of the universe.
With 30,200 active companies employing roughly 4.6 million, MIT is hugely important — sadly, it’s falling behind when it comes to attracting venture capital. The problem? Since DEC peaked in the 1980s, its spinoffs have been leaders in smaller markets.
Despite the “America First” rhetoric of Donald Trump, Obama’s directive protecting foreign privacy has survived. Trump’s director of national intelligence, Dan Coates, has said he agrees that Obama’s post-Snowden reforms are necessary to ensure a smooth transatlantic flow of personal data.
Congress must decide by the end of this year whether to renew the NSA’s power to engage in surveillance of communications that transit switches and servers inside the United States using a secret court order. The intelligence community has revealed that over 100,000 targets were under such surveillance in 2016, for reasons well beyond terrorism. While the government may not single out Americans as targets, it may search the database for information about Americans who may be communication with foreigners. It did so more than 30,000 times last year.
IBM and MIT today announced that IBM plans to make a 10-year, $240 million investment to create the MIT–IBM Watson AI Lab in partnership with MIT. The lab will carry out fundamental artificial intelligence (AI) research and seek to propel scientific breakthroughs that unlock the potential of AI. The collaboration aims to advance AI hardware, software, and algorithms related to deep learning and other areas; increase AI’s impact on industries, such as health care and cybersecurity; and explore the economic and ethical implications of AI on society. IBM’s $240 million investment in the lab will support research by IBM and MIT scientists.
The new lab will be one of the largest long-term university-industry AI collaborations to date, mobilizing the talent of more than 100 AI scientists, professors, and students to pursue joint research at IBM’s Research Lab in Cambridge, Massachusetts — co-located with the IBM Watson Health and IBM Security headquarters in Kendall Square — and on the neighboring MIT campus.
USA Today News, Brad Heath,Fredreka Schouten,Steve Reilly,Nick Penzenstadler and Aamer Madhani
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Dozens of lobbyists, contractors and others who make their living influencing the government pay President Trump’s companies for membership in his private golf clubs, a status that can put them in close contact with the president, a USA TODAY investigation found.
Members of the clubs Trump has visited most often as president — in Florida, New Jersey and Virginia — include at least 50 executives whose companies hold federal contracts and 21 lobbyists and trade group officials. Two-thirds played on one of the 58 days the president was there, according to scores they posted online.
Because membership lists at Trump’s clubs are secret, the public has until now been unable to assess the conflicts they could create. USA TODAY found the names of 4,500 members by reviewing social media and a public website golfers use to track their handicaps, then researched and contacted hundreds to determine whether they had business with the government.
Department heads at universities in the United States and Canada should start preparing applications to nominate fellows now. The submission period is October 2–16, 2017.
Teams of three or four students are instructed to ask questions (for science) or define problems (for engineering), and then construct explanations (for science) or design solutions (for engineering) based on identified problems in their community. Students compete for State, Regional, and National Awards. Deadline to register is December 13.
Language models are a fundamental part of many systems that attempt to solve natural language processing tasks such as machine translation and speech recognition. Currently, all state of the art language models are neural networks.
The first part of this post presents a simple feedforward neural network that solves this task. In the second part of the post, we will improve the simple model by adding to it a recurrent neural network (RNN). The final part will discuss two recently proposed regularization techniques for improving RNN based language models.
