Cornell Tech and the Jacobs Technion-Cornell Institute today announced that Dr. Fernando Gómez-Baquero, an award-winning nanomaterials engineer and entrepreneur lauded for bringing energy technologies from academia to market, joined as the new Director of Runway and Spinouts. The institute also announced four new startup postdocs joining the Jacobs Runway Startup Postdoctoral Program, tackling cybersecurity issues, computer network performance, digital drug discovery development and educational technologies.
… Even the very first advances in civilization had this cyborg quality. The marriage of humans with technology is what made us the masters of other species, giving us weapons and tools harder and sharper than the claws of any animal, projecting our strength at greater and greater distance until we could bring down even the greatest of beasts in the hunt, not to mention engineer new crops that produce far more food than their wild forebears, and domesticate animals to make us stronger and faster.
In short, there are two types of augmentation, physical and mental, in a complex dance. One frontier of augmentation is the addition of sensors to the physical world, allowing data to be collected and analyzed at a previously unthinkable scale. That is the real key to understanding what is often called the “Internet of Things.” Things that once required guesswork are now knowable. (Insurance may well be the native business model of the “Internet of Things” in the same way that advertising became the native business model of the internet, because of the data-driven elimination of uncertainty.) It isn’t simply a matter of smart, connected devices like the Nest thermostat or the Amazon Echo, the Fitbit and the Apple Watch, or even self-driving cars. It’s about the data these devices provide. The possibilities of the future cascade in unexpected ways.
The smart city instead has had modest ambitions. It could best be seen as a campaign for incremental, iterative improvements to twentieth-century urban infrastructure designs that have failed to meet the burdens placed on them by the scale and speed of twenty-first-century urbanization.
This version of the smart city, cementing private-sector operators in key positions over an indefinite period, has unsurprisingly led to ominously, though not often overly, corporatist views of the future city. These visions have typically overlooked structural inconsistencies in global capitalism and the exclusions that it has created in cities— income inequality; inadequate housing supply and the corresponding problems in affordability; inequitable environmental risks; and so on.
The information-architectural consequences of this frame are profoundly dystopian. They celebrate and consolidate a massive centralization of all the key pieces of urban digital systems—from sensors that record events to the networks that transmit them and the computing capacity that analyzes and stores the data gathered. By and large, then, the idea of the smart city has evolved as a simulacrum of the larger global internet, in that its economic logic is rapidly centralizing information and power. The smart city, in computer metaphor terms, is a mainframe. Not only is this not seen as undesirable—it is a vision and design strategy actively pushed by the corporations involved in the creation of smart cities—it is largely not seen or understood by urban policymakers.
Artificial intelligence and machine learning permeate many aspects of your everyday life. Now they’re creeping into music production, performance, and DJing, and making the formerly impossible possible. We grilled the experts on what machine learning can and will do to change the game of computer music. Should DJs and music producers welcome it as a liberator, or fear it as a usurper of your job behind the decks? Spoiler alert: You’re going to want a better laptop.
Penn researchers just received a five-year, $27 million government grant to develop a team of autonomous, specialized and resilient robots for the United States military.
One of the researchers, Electrical and Systems Engineering Chair George Pappas, said what distinguishes this research from other similar projects is its focus on specialized teams of robots, rather than individual ones. These robots will also be able to learn from each another in unknown environments.
If Blade Runner had a classroom scene, it might look something like the promotional video by BrainCo, Inc. Students sit at desks wearing electronic headbands that report EEG data back to a teacher’s dashboard, and that information purports to measure students’ attention levels. The video’s narrator explains: “School administrators can use big data analysis to determine when students are better able to concentrate.”
BrainCo just scored $15 million in venture funding from Chinese investors, and has welcomed a prominent Harvard education dean, who will serve as an adviser. The company says it has a working prototype and is in conversations with a Long Island school to pilot the headset.
In the new book Life 3.0: Being Human in the Age of Artificial Intelligence, MIT cosmologist and co-founder of the Future of Life Institute Max Tegmark offers a sweeping exploration of a wide range of issues related to artificial intelligence—from how AI could affect the job market and how we fight wars, to whether a super-smart computer program could threaten our collective survival. I talked to Tegmark about the threats and possibilities of AI over Skype.
There are seven species of sea turtles around the world, each with a unique biology and habitat that faces different threats. Even small differences between populations and species can change how well they recover, says Margaret Lamont, a biologist with the United States Geological Survey. Lamont is also a researcher at the University of Florida’s Archie Carr Center for Sea Turtle Research, and has studied several species of sea turtles. She says that understanding which conservation efforts work and don’t work is particularly challenging, because the animals live for so long.
Oceans Deeply spoke with Lamont about why some species of turtles are recovering at different rates than others and why good data on these animals is so challenging to collect.
Adobe Systems has awarded Drexel University and 14 other universities a total of $750,000 in data science research grants.
Some of the other universities included in this series of grants are New York University, Massachusetts Institute of Technology and the University of Maryland. With each grant maximum set at $50,000, Adobe has decided to award this amount to 15 different universities, creating a record $750,000 donation.
This grant allows the 15 various universities to help encourage the understanding of data science as a field. The end goal for this research, Adobe hopes, is to create new and innovative advances to the area of marketing.
Government Data Science News
The FDA regulates rugs and medical devices to ensure patient protection. This regulatory apparatus is a clunky obstruction to bringing algorithmic-diagnoses into widespread use. We have read in this newsletter about algorithms matching or beating doctors on diagnoses from skin cancer to detecting heart arrhythmias in EKGs to diagnosing asthma from the sound wave patterns of a cough. Of course the FDA has a responsibility to protect patients, but their current workflows will need to be reconfigured for precision medicine.
NASA is looking for a private company (or very wealthy individual) to take over the Spitzer telescope in 2019 or 2020. The scope costs NASA $14 million to run on an annual basis and is being joined by a much larger telescope, the James Webb Space Telescope. Spitzer’s hardware is in great shape, but it is getting farther and farther from Earth, reducing its effectiveness as a communication tool. It’s fascinating what happens to space infrastructure at the end of its lifetime.
The Republican’s tax plan contains a few provisions that will directly impact higher education if passed. First, the bill imposes a new 1.44% excise tax on private universities endowed at $100,000 or more per student. Student loan repayment would no longer be subject to any tax breaks and people whose companies cover their tuition would be taxed on that money as if it were income. The three existing direct higher education tax credits would be rolled into one program (I do like the efficiency gain) and would credit families for the first $2000 spent on tuition, books and supplies and provide an additional 25% tax credit for the next $2,000 for up to four years. Since so many students take five years to graduate, there is a smaller credit available in the fifth year: $500.
Vector Institute headed by AI founding father Geoff Hinton in Toronto has doubled its staff, hiring ten new machine learning, deep learning, and AI researchers. This continues Canada’s development of top data science institutes, universities, and companies. A Canadian government official, Navdeep Bains noted, “We’ve launched the PanCanadian AI Strategy to attract top academic talent and increase the number of graduates and researchers studying artificial intelligence and deep learning in Canada.” Indeed, it seems to be working.
History tells us that when technology squeezes people out of jobs, they revolt. Industrialization in 19th-century England, for example, gave rise to Luddite activism. Unfortunately, history also suggests that protests of the marginalized don’t solve the underlying problem. The British Army suppressed the Luddites; the government passed laws to protect factory equipment and industrialization marched on. As Marx went on to theorize, in a capitalist society, the government is co-opted by the wealthy classes.
What happens, though, when that skilled upper class is itself put out of a job? That’s the question that mass AI-based unemployment would pose. What would happen when well-educated lawyers, journalists, bureaucrats, corporate managers and other creative-class knowledge workers can’t find work? Could the rise of AI lead to a white-collar rebellion?
Gone is the era of elaborate cartographical sketches and oil paintings of salamanders, and of salted old-timer politicians drawing up their “contributions to modern art” armed with markers and heads full of electoral smarts. Today, political mapmaking is a multimillion-dollar enterprise, with dozens of high-profile paid consultants, armies of lawyers, terabytes worth of voting data, advanced software, and even a supercomputer or two. Redistricting is the great game of modern politics, and the arms race for the next decade’s maps promises to be the most extensive—and most expensive—of all time.
Republicans certainly maintain the advantage in that game right now. They began the escalation over seven years ago, with the creation of the groundbreaking REDMAP initiative.
Oxford, England Monday, November 4 at Oxford’s L3 Mathematical Institute. “This interactive seminar and workshop will bring together publishers, academics, journalists and technologists for a hands-on insight into the creation and use of interactive visualisations.” [free, registration required]
New York, NY Saturday, November 4, starting at 9:30 p.m., CAVEAT (21 A Clinton Street). “In this long-running monthly show, two short, entrancing science lectures inspire sets of hilarious, never-to-be-seen-again improv comedy. Featuring veteran improv team Thank You, Robot.” [$]
“Let’s make the conference center pretty together! You create neural art – We will render the best 50 pieces in high resolution and print them on posters for the conference center. At the conference, we’ll have a vote for the best poster. The winner will be invited to the Competition Track Dinner on Friday, Dec. 8.”
The Harvard University Data Science Initiative is seeking applications for its Harvard Data Science Postdoctoral Fellows Program for the 2018-2019 academic year. Deadline for applications is December 4.
NYU Center for Data Science News
DS had a pretty good run at the Conference of the International Society for Music Information Retrieval (ISMIR-17) last week in Suzhou, China
The best paper award went to a paper co-authored by Kyunghyun Cho, based on work partially done when the first author was visiting CDS last year:
arXiv, Computer Science > Social and Information Networks; Jason Anastasopoulos, Jake Ryland Williams
We create a computational framework for understanding social action and demonstrate how this framework can be used to build an open-source event detection tool with scalable statistical machine learning algorithms and a subsampled database of over 600 million geo-tagged Tweets from around the world. These Tweets were collected between April 1st, 2014 and April 30th, 2015, most notably when the Black Lives Matter movement began. We demonstrate how these methods can be used diagnostically-by researchers, government officials and the public-to understand peaceful and violent collective action at very fine-grained levels of time and geography.
“A great way to see your own work afresh (to read it like a reader) is to deliberately reread your own stuff in slow motion; intensely aware of the order in which the words arrive, and of what they are making happen in your head. This isn’t, quite, reading – it’s paying attention to yourself reading. It’s watching yourself read. In doing this, you will be much better able to see those moments where the words fail to deliver the necessary information in the right order, and thus cause a break in the flow of internal mental pictures.”
After almost a decade of writing the Corner Office column, this will be my final one — and from all the interviews, and the five million words of transcripts from those conversations, I have learned valuable leadership lessons and heard some great stories. Here are some standouts.