The AI community has been abuzz today on Twitter after one of its own was elevated in an unusual fashion. Stanford University grad Alexandre Robicquet is a researcher in machine vision who works in the lab of Google X founder Sebastian Thrun. But he’s also helping front a new Yves Saint Laurent ad campaign, with his face — and profession — popping up in posters and photoshoots. Artificial intelligence? It’s so hot right now.
Simons Foundation, Center for Computational Astrophysics
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The Simons Foundation is pleased to announce a new research group within the Flatiron Institute’s Center for Computational Astrophysics (CCA). Led by astrophysicist Yuri Levin, the Compact Objects group will explore the physics underlying gravitational waves and relatively compact astronomical objects, such as neutron stars and supermassive black holes.
Human Dx is one of many AI systems being tested in health care. The IBM Watson Health unit is perhaps the most prominent, with the company for the past several years claiming that its AI is assisting major medical centers and hospitals in tasks such as genetically sequencing brain tumors and matching cancer patients to clinical trials. Studies have shown AI can help predict which patients will suffer from heart attacks or strokes in 10 years or even forecast which will die within five. Tech giants such as Google have joined start-ups in developing AI that can diagnose cancer from medical images. Still, AI in medicine is in its early days and its true value remains to be seen. Watson appears to have been a success at Memorial Sloan Kettering Cancer Center, yet it floundered at The University of Texas M. D. Anderson Cancer Center, although it is unclear whether the problems resulted from the technology or its implementation and management.
YouTube has always been useful; since its founding in 2005, it has been a pillar of the internet. But over the past year or so, for me anyway, YouTube had started to seem weirdly good. The site had begun to predict with eerie accuracy what clips I might be interested in — much better than it ever had before. So what changed?
“But over the past year or so, for me anyway, YouTube had started to seem weirdly good”
Over the course of 12 years, YouTube has transformed itself from a site driven by search to a destination in its own right. Getting there required hundreds of experiments, a handful of redesigns, and some great leaps forward in the field of artificial intelligence. But what really elevated YouTube was its evolution into a feed.
How do you teach your robot to learn? This is the question that Sergey Levine, an assistant professor in the department of electrical engineering and computer science at UC Berkeley is trying to answer.
“One of the most important things is that you have to somehow communicate to the robot what it means to succeed,” Levine said in a conversation with AI Podcast host Michael Copeland. “That’s one of the most basic things …You need to tell it what it should be doing.”
During Levine’s research, he explored reinforcement learning, in which robots learn what functions are desired to fulfill a particular task. He’s also quick to point out that it’s important that the robots don’t just repeat what they learn in training, but understand why a task requires certain actions. [audio, 24:08]
Entrepreneur Reid Hoffman explains what automated services may be available in the future and describes the ideal relationship between humans and artificial intelligence. [video, 8:55]
Russian President Vladimir Putin says that whoever reaches a breakthrough in developing artificial intelligence will come to dominate the world.
Putin, speaking Friday at a meeting with students, said the development of AI raises “colossal opportunities and threats that are difficult to predict now.”
He warned that “the one who becomes the leader in this sphere will be the ruler of the world.”
Data innovation and technology are a much discussed but rarely successfully implemented in large financial services firms. Despite $480 Billion spent globally in 2016 on financial services IT, the pace of financial innovation from incumbents lags behind FinTech which received a comparatively puny $17 Billion in investment in 2016. What lies behind the discrepancy?
We provide a unique vantage point, having pushed for enterprise-wide innovation from inside Credit Suisse and having worked closely with a dozen major financial institutions to develop and train their big data and innovation talent at The Data Incubator. Drawing on that experience, we have identified four consistent obstacles to adoption of data and innovation. These obstacles are: organizational structure, constrained budgets, data talent gap, and legacy cash-cow businesses.
Machine intelligence startups are the black sheep of the startup world. The new kids on the block are challenging investors to do their technical homework and differentiate themselves in intentional ways. Y Combinator joined a growing list of investors offering exclusive services to these companies in a specialized AI track for its latest S17 batch of startups.
In the competitive world of investing, Y Combinator has to work to convince top startups to apply to the program. Today, many startups that fit the bill are working to solve challenging AI problems. And with the amount of money sloshing around for AI startups, the sense of urgency isn’t always there for prominent researchers who have their choice of financial partners.
Daniel Gross, a Partner at Y Combinator and the brains behind the AI track, explained to me that his aim was to offer founders desirable data sets, compute resources and technical mentors, among other things.
What, exactly, constitutes harm when it comes to A.I.? I suggest a more concrete basis for avoiding A.I. harm, based on three rules of my own.
First, an A.I. system must be subject to the full gamut of laws that apply to its human operator. This rule would cover private, corporate and government systems. We don’t want A.I. to e
My first question here is why did these software first companies assume that they could just turn around and build cars? And why have we all assumed all that matters in this push is the actual development of the hardware/self-driving car? We forget there are a lot of aspects to the technology that is required to make that future happen. It’s never made sense that these companies would build cars.
The Institute for Foundations in Data Science, which will be part of the Wisconsin Institutes for Discovery, will re-examine the core mathematics, statistics and computer science that make big data science possible. The ultimate mission will be to come up with new ways to more efficiently and effectively use big sets of data.
Stephen Wright, a professor of computer science with the university, is leading the 14-member faculty behind the new initiative. He said that big data faces some looming challenges that make revisiting the fundamentals necessary.
The atmosphere that blankets our planet contains around 5,600 trillion tons of air. It can blast the ground below with lightning, torrential rain, heat waves, and tornadoes, or caress it with a light breeze or dusting of snowflakes. As the past few days have reminded us, it’s no small feat to make predictions about what this vast, seething mass of wind and water will do.
But our forecasting prowess—at least when it comes to predicting how hot the coming days will be—has been making impressive strides. High-temperature predictions have improved significantly over the past 12 years, according to a new report from ForecastWatch, a Columbus, Ohio-based company that assesses the accuracy of weather forecasts. In fact, our ability to pin down the next day’s high temperature has improved by almost a degree Fahrenheit, says founder Eric Floehr.
Digital Biomarkers: Which digital tools do you see as having the most promise for rigor or the most promise for accelerating drug development?
Robert Califf: Well, I would point to several areas as being really exciting right now. One is just the ability to measure multiple biomarkers at the same time and apply integrative analytics to come up with a more comprehensive view of what’s happening with biology. It’s been so limited by measuring one thing at a time, and then we get cognitively focused as if a stick diagram could describe biology – you change Biomarker A, and if it goes up or down that tells you whether the drug is going to work. That’s not the way biology works. So I think, the integration of combinations of biomarkers with good analytics is a big space.
Second is continuous measurement. As I said about glucose or blood pressure, those are great biomarkers, but we don’t know that much about continuous measurement over periods of time, and I think there’s going to be a great amount of discovery as we analyze temporal patterns.
Third, I think social biomarkers like the tone of your voice, the way you ask questions on search, your physical location, and the people with whom you associate – those are going to be great biomarkers. We obviously have work to do there in terms of confidentiality and security, but it’s no question, those will be tremendous biomarkers of human health.
In a dining hall at Yale University, the portrait of an avid proponent of slavery has been replaced with a shield depicting a heraldic dolphin.
On Tuesday, beneath the dolphin’s fearsome eye, Yale’s president and the Navy’s chief of operations will make speeches, a chaplain will offer a blessing, and a secret ceremonial object will be unveiled.
With that, Yale’s Calhoun College, named for John C. Calhoun — a vice president, senator from South Carolina, and founding forefather of the Civil War — will recede further into the New Haven university’s past. The gothic stone building, one of the 14 residential colleges where undergraduates live and eat, will be dedicated as Hopper College, after Rear Adm. Grace Murray Hopper, a boundary-smashing computer pioneer and naval officer. The dolphin on the Hopper College shield is a nod to her maritime career.
Never read the comments. People are not always their best selves there. To find out exactly how bad the bad behavior is, we partnered with Disqus, an online commenting platform (disclosure: WIRED.com uses it) to quantify the problem. Cofounder Daniel Ha says toxic posts have been an issue from day one, and he sees it as a human problem, not a technological one: “It’s never really going to go away.” The company analyzed 92 million comments over a 16-month period, written by almost 2 million authors on more than 7,000 forums that use the software. (So sites like Infowars and the Wirecutter are included, but Facebook and Twitter are not.) The numbers reveal everything from the trolliest time of day to the nastiest state in the union.
Oracle Corp. is hiring another 5,000 employees for its cloud software business as it fights Salesforce.com Inc. for market share in the fast-growing industry.
The hiring surge aims to beef up what’s already Oracle’s fastest-growing business, increasing revenue by 58 percent in the quarter it reported June 21 compared with a year earlier.
Bobby Braun, the new dean of the University of Colorado’s College of Engineering and Applied Science, thinks his school is the best kept secret on the Boulder campus — and he’s doing everything short of screaming from the hilltops to get the word out.
Friday, the engineering school is launching a set of goals separate from the university’s own framework — goals so ambitious, Braun admits maybe they won’t be met — designed to lead the college through a physical and mission-based metamorphosis.
Within five years, the College of Engineering intends to be the school first of its kind to achieve an undergraduate population that’s 50 percent women. In 2016, around 26 percent of undergraduate engineering students at CU were women.
New York, NY Teams of NYU students, faculty, researchers, and staff will compete to win over $10,000 in total prizes. Phase I: Healthcare Innovation Challenges Announcement & Team-Hunt will be on September 14. [rsvp required]
I created Semiotic, a framework designed to quickly deploy bar charts and line charts but not lock us into using those charts due to requirements of configuration and data processing. It doesn’t limit us in our data visualization methods as a cost of convenience, but it also doesn’t force us to write new custom D3 code for every minor iteration. It embodies the principle that data visualization methods should not be limited by how we initially format data or the initial guess at how that data should be displayed. It draws its name from the study of symbols and meaning making, topics I feel are integral to data visualization, which is fundamentally a communication and design problem, not an engineering problem.
arXiv, Computer Science > Artificial Intelligence; Niels Justesen, Philip Bontrager, Julian Togelius, Sebastian Risi
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In this paper we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games or real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards.