What do bird flight mechanics and renewable energy technology have to do with each other? By combining ongoing Stanford research on both, researchers hope to cut down on the number of birds and bats that collide with wind turbines’ spinning blades.
This and nine other interdisciplinary projects focused on developing environmental solutions will receive funding from the Stanford Woods Institute for the Environment’s 2017 Environmental Venture Projects (EVP) and Realizing Environmental Innovation Program (REIP) grants. Teams from across campus will collaborate on research aimed at developing innovations ranging from coral-safe sunscreen to a smartphone app that motivates pro-environmental behavior change.
The same type of machine learning technology that can automatically organize Google photos might someday be used to prevent sudden death.
That’s the vision of Vic Gundotra, chief executive officer of medical technology start-up AliveCor and a former executive at Google. Gundotra announced on Wednesday that his company is teaming up with the Mayo Clinic to develop tools to screen for a heart rhythm condition called Long QT that causes thousands of deaths per year.
AliveCor is also announcing its third round of investment from the Mayo Clinic in the past nine months, bringing its total funding from all of its investors to just more than $45 million. The company declined to comment on the details of its funding from Mayo.
ON JULY 16th, Roger Federer (pictured) triumphed over Marin Cilic to win the men’s Wimbledon tennis championship for the eighth time. It was an expected end to an otherwise unpredictable two weeks, with many top seeds exiting the tournament earlier than anticipated. Despite—or perhaps because of—the unlikely results, punters flocked to the betting windows. Paddy Power Betfair, one of the world’s largest betting groups, saw nearly £1bn ($1.3bn) traded on Wimbledon this year. But it is not just ordinary gamblers who are showing renewed interest in sports betting. In recent years finance and technology types have also been increasingly drawn to the gambling industry: former quantitative traders at investment banks have migrated to the world of sports; job ads asking for machine-learning know-how are not uncommon on bookmakers’ websites. What have complex algorithms got to do with one of the oldest pastimes in the world?
The Whole Foods acquisition by Amazon weeks ago was only the latest milepost in the latter’s inexorable march to the top of retail. The company sold $136 billion worth of products in 2016 – more than any other online retailer (and just over a third of what Wal-Mart did).
And we find that Amazon is big and gaining on Google for product-related searches (be they early or late in the purchase journey). This means that more and more purchase journeys will start on Amazon’s home page, and proceed via search to a variety of product pages all the way to a sale.
This changes a lot of things, but the upshot – to my mind – will be a massive outlay in product content and product-related experiences by manufacturers.
Amazon’s plans for Whole Foods are the subject of plenty of speculation. But one thing is certain: The Mackey marketing playbook — bringing labor to the fore in order to assure customers that they are A-O.K. and in good hands — has little to offer Amazon, which can and does regard labor as a simple commodity. In contrast to Whole Foods, which focuses in its marketing on fair-trade and locally sourced offerings, Amazon is made up of a dizzying array of supply chains that are effectively invisible: not hidden, just easily omitted from the consumer experience.
I didn’t lay eyes on a single Amazon employee during Prime Day, except for the home-shopping-marathon talent, leaving the onus on me to wonder what was going on behind the scenes
Goldman Sachs wants to become the Google of Wall Street, and now it is staffing up the unit that could help it achieve that goal.
The New York-based financial services giant is hiring for Marquee, a platform that provides clients access to the bank’s analytics, data, content and execution capabilities via a browser or an API.
Today we think that essentially every galaxy contains a supermassive black hole at its centre, and something like 1 per cent of them are accreting mass fast enough to be seen as luminous quasars. The supermassive black hole at the centre of our own galaxy is essentially ‘off’. On rare occasions, though, such a black hole rapidly turns itself ‘on’. The most fascinating cause is a so-called ‘tidal disruption event’ in which a star like the Sun passes too close to the black hole and is ripped apart by the black hole’s tides. Some of the debris then falls into the black hole to power a transient flare. These tidal disruption events are far rarer than supernovae, occurring only about once every 10,000 years in any particular galaxy. In the distant Universe, the study of variability is essentially the study of black holes and supernovae.
This gives you some sense of the remarkable astronomical zoo of variable and transient objects. The challenge for the professional astronomer is to find and characterise all these different sources not only for how they work individually, but also to determine their overall demographics and statistics. To find large numbers of them, you need a big telescope that can detect the much more numerous distant, faint objects. In general, however, bigger telescopes see only smaller pieces of the sky. This frustrating rule can be bent only by spending large sums of money.
Although artificial intelligence—or AI—has taken center stage in nearly all technology discussions, it’s unclear that every business needs an “AI strategy.”
Initially, most attendees of a Fortune Brainstorm Tech conference panel in Aspen, Colo. on Tuesday agreed with the assertion that all companies need an AI game plan. But that consensus withered after just a little while.
For small companies, in particular, it probably makes no sense to dedicate limited resources to hire an AI expert, even if there were one available. It was also unclear how a mom-and-pop business, say a tailor shop, could benefit from AI, although Zachary Bogue, co-managing partner of the Data Collective, an early-stage tech venture fund, said that robots can already handle and sew fabric.
Intelligent X is using machine learning algorithms to perfect the process of brewing craft beers. Coming up with new brews and refining them using customer feedback, they’re hoping to stay one step ahead of tastes. Globalive Chairman Anthony Lacavera sits down with Intelligent Layer founder Rob McInerney.
Whether it’s done consciously or subconsciously, racial discrimination continues to have a serious, measurable impact on the choices our society makes about criminal justice, law enforcement, hiring and financial lending. It might be tempting, then, to feel encouraged as more and more companies and government agencies turn to seemingly dispassionate technologies for help with some of these complicated decisions, which are often influenced by bias. Rather than relying on human judgment alone, organizations are increasingly asking algorithms to weigh in on questions that have profound social ramifications, like whether to recruit someone for a job, give them a loan, identify them as a suspect in a crime, send them to prison or grant them parole.
But an increasing body of research and criticism suggests that algorithms and artificial intelligence aren’t necessarily a panacea for ending prejudice, and they can have disproportionate impacts on groups that are already socially disadvantaged, particularly people of color. Instead of offering a workaround for human biases, the tools we designed to help us predict the future may be dooming us to repeat the past by replicating and even amplifying societal inequalities that already exist.
Women go through menopause long before the end of their expected lifespan. Researchers have long hypothesized that menopause and long post-reproductive lifespan provide an evolutionary advantage; that is, they increase the chances of a woman passing on her genes. However, the precise nature of this advantage is still up for debate.
To investigate the evolutionary advantage of menopause, Carla Aimé and colleagues at the Institute of Evolutionary Sciences of Montpellier developed computer simulations of human populations using artificial neural networks. Then they tested which conditions were required for menopause to emerge in the simulated populations.
Funding cuts will undoubtedly “increase the risk of a devastating pandemic and the risk of a successful bioterrorism attack,” says Elizabeth Cameron, senior director at the Nuclear Threat Initiative. Decreased investment will also lead to economic and political instability in “regions of increased conflict and migration”. If this happens, she continues, “a major cross-border epidemic could cause political collapse and create new safe havens for terrorist groups”.
Amazon.com Inc. acquired a Santa Barbara data analysis and search engine start-up in May to help improve its Alexa virtual assistant and other services, according to four sources familiar with the deal but unauthorized to discuss it.
The previously unreported acquisition of Graphiq Inc. and its more than 100 employees has given Amazon a new Southern California outpost. It recently began looking to hire additional software developers and data associates in Santa Barbara to work on Alexa.
Hedge fund manager Desmond Lun’s 21 percent average return over the last four years springs from an unlikely source — a petri dish of algae.
Lun, 37, is a new kind of quant, combining AI wizardry with old-school biology to trade futures. Although his Taaffeite Capital Management is small, Lun makes a big claim: His research into one of the natural world’s most byzantine systems — the biological cell — has given him an edge in untangling the secrets of financial markets.
Computational biologists like Lun are late to the quant wave that’s upending hedge funds. Physicists and mathematicians were the first disruptors, who found that their statistical models, neural networks and machine-learning tools have had as many stumbles as triumphs. Now comes Lun, with artificial intelligence derived from algorithms he developed to figure out the mysteries of cells.
Artificial intelligence promises to make decisions better and faster than humans can — even smart humans. AI’s superiority is clear when the choice is “Which road should I take home?” or “How should I organize distribution chains?” But in life-or-death situations, can AI deliver?
As a former tour manager for Bob Dylan and The Band, Jonathan Taplin isn’t your typical academic. Lately, though, he’s been busy writing somber tomes about market shares, monopolies, and online platforms. His conclusion: Amazon.com, Facebook, and Google have become too big and too powerful and, if not stopped, may need to be broken up.
Crazy? Maybe not. Taplin, 70, author of Move Fast and Break Things: How Facebook, Google, and Amazon Cornered Culture and Undermined Democracy, knows digital media, having run the Annenberg Innovation Lab at the University of Southern California. Ten years before YouTube, he founded one of the first video-on-demand streaming services. He also knows media M&A as a former Merrill Lynch investment banker in the 1980s. He says Google is as close to a monopoly as the Bell telephone system was in 1956.
Purdue University will begin offering a major in data science, beginning in the fall semester of 2017.
The new major is a collaboration between the Department of Computer Science and the Department of Statistics, and is also a part of the university-wide Purdue Moves initiative, which was launched by President Mitch Daniels in 2013.
“In this competition, you are tasked with developing a controller to enable a physiologically-based human model to navigate a complex obstacle course as quickly as possible. You are provided with a human musculoskeletal model and a physics-based simulation environment where you can synthesize physically and physiologically accurate motion.” Challenge deadline will be in early-September.
“Machine Learning is that eight-course dinner, in a catered wedding of the Prince of whales with 200,000 guests. Would you want to cook all that yourself(CPU) or use a service? (Hint: gpu.js is that service.)”
I don’t think the data structures used in machine learning are significantly different than those used in other areas of software development. Because of the size and difficulty of many of the problems, however, having a really solid handle on the basics is essential.
Also, because machine learning is a very mathematical field, one should have in mind how data structures can be used to solve mathematical problems and as mathematical objects in their own right.
Enrico Bertini and Moritz Stefaner, Data Stories podcast
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“In this episode we have famous cartoonist and comics theorist Scott McCloud. Scott wrote the popular books Understanding Comics (1993), Reinventing Comics (2000), and Making Comics (2006), which explain the theory and practice behind making comics and telling stories visually.” [audio, 39:20]
Do you have big, monolith web applications or services that are hard to manage, hard to change, and hard to scale? Maybe breaking them into microservices would give you many more options to evolve and grow that app.
This week, we meet up again with Miguel Grinberg to discuss the trades offs and advantages of microservices. [audio, 1:05:12]
Modern artificial intelligence makes many benefits available to business, bringing cognitive abilities to machines at scale. As a field of computer science, AI is moving at an unprecedented rate: the time you must wait for a research result in an academic paper to translate into production-ready code can now be measured in mere months. However, with this velocity comes a corresponding level of confusion for newcomers to the field. As well as developing familiarity with AI techniques, practitioners must choose their technology platforms wisely. This post surveys today’s foremost options for AI in the form of deep learning, examining each toolkit’s primary advantages as well as their respective industry supporters.
Last year we introduced Open Images, a collaborative release of ~9 million images annotated with labels spanning over 6000 object categories, designed to be a useful dataset for machine learning research. The initial release featured image-level labels automatically produced by a computer vision model similar to Google Cloud Vision API, for all 9M images in the training set, and a validation set of 167K images with 1.2M human-verified image-level labels.
Today, we introduce an update to Open Images, which contains the addition of a total of ~2M bounding-boxes to the existing dataset, along with several million additional image-level labels.
