It’s easy to think of artificial intelligence—computer systems designed to perform traditionally human tasks like visual perception, speech recognition, decision-making, and language translation—as the purview of science fiction, but these days it’s rare to find a facet of modern life that AI doesn’t impact. Ride-shares are hailed by a phone that is unlocked with your face; romances ignite thanks to matchmaking algorithms; robots roam apartments, vacuuming and mapping the floor plan as they go, or resting quietly in a corner as they wait to offer a virtual assist. Self-driving cars are already on the road in places like Palo Alto, California, and Chandler, Arizona; Facebook regularly uses AI to automatically translate text between countries and to recognize what’s in images to both filter out the “offensive” and translate them for users who are blind. As a society, we’ve grown accustomed to filtering our most intimate problems through Siri and Alexa, but the real heroes of the digital age are women—founders, inventors, researchers, and activists—who often go unnamed outside of the annals of Silicon Valley.
For years, the public face of the technology industry (particularly in the artificial intelligence sector) has been predominantly white and overwhelmingly male.
Grace DeNoya is used to getting snickers when people learn she’s majoring in marijuana.
“My friends make good-natured jokes about getting a degree in weed,” said DeNoya, one of the first students in a new four-year degree program in medicinal plant chemistry at Northern Michigan University. “I say, ‘No, it’s a serious degree, a chemistry degree first and foremost. It’s hard work. Organic chemistry is a bear.’”
As a green gold rush in legal marijuana and its non-drug cousin hemp spreads across North America, a growing number of colleges are adding cannabis to the curriculum to prepare graduates for careers cultivating, researching, analyzing and marketing the herb.
China, the United States, and Russia are each negotiating this fraught landscape differently, in ways responsive to their unique economic and military situations. Governments are motivated to pursue leadership in AI by the promise of gaining a strategic advantage. At this early stage, it’s tough to tell what sort of advantage is at stake, because we don’t know what sort of thing AI will turn out to be. Since AI is a technology, it’s natural to think of it as a mere resource that can assist in attaining one’s goals, perhaps by allowing drones to fly without supervision or increasing the efficiency of supply chains.
But computers could surpass humans in finding optimal ways of organizing and using resources. If so, they might become capable of making high-level strategic decisions. After all, there aren’t material limitations restricting the intelligence of algorithms, like those that restrict the speed of planes or range of rockets. Machines more intelligent than the smartest of humans, with more strategic savvy, are a conceptual possibility that must be reckoned with. China, Russia, and the United States are approaching this possibility in different ways. The statements and research priorities released by major powers reveal how their policymakers think AI’s developmental trajectory will unfold.
Bloomberg Wealth, Sophie Alexander and Reade Pickert
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The chances that your startup will become a billion-dollar company are almost zero. But if you do somehow manage to create a unicorn, the odds are better than 1-in-10 that you or your co-founder attended one school: Stanford.
A new study from the U.S. Geological Survey says the predicted damage from sea level rise in California triples once tides, storms and erosion are taken into account.
“There are many communities that are planning by only considering this in a bathtub and not considering the fact that that’ll be on top of these episodic storm events that’ll cause most of the short term impacts,” said the study’s lead author, Patrick Barnard, a USGS coastal geologist based in Santa Cruz.
The traces we leave on the Web and on our digital devices can give advertisers and others surprising, and sometimes disturbing, insights into our psychology
A global research team of researchers has recently demonstrated that machine learning has an unsolvable problem, and published their findings in Nature Machine Intelligence in January 2019. Researchers from Princeton University, the University of Waterloo, Technion-IIT, Tel Aviv University, and the Institute of Mathematics of the Academy of Sciences of the Czech Republic, proved that AI learnability cannot be proved nor refuted when using the standard axioms of mathematics. An axiom, or postulate, is a mathematical statement that is self-evidently true without proof.
To understand why and how the researchers arrived at this conclusion requires a retrospective look back well before the term “artificial intelligence” was even coined, in a field of study entirely different from computer science: the realm of mathematics, specifically, the continuum hypothesis.
If there were a dark horse among technology hubs, Toronto is certainly the stallion among them. In the past five years, Canada’s most populous city has shown it has more to it than its commercial and cultural attractions, and the best view of Niagara Falls. It has quietly become the world’s fastest-growing destination for technology jobs, leveraging early investments in artificial intelligence (AI) and especially machine-learning technologies at its universities, government funding and other resources for innovation, and an immigration policy that is friendly to technology talent.
Toronto has of course been a beneficiary of the Canadian prime minister Justin Trudeau’s outreach to big companies, but subsidies haven’t been a significant tool, according to Avi Goldfarb, marketing professor and the chair in artificial intelligence and healthcare at the University of Toronto’s Rotman School of Management.
Professors tend to earn more as they become older. Woolley believes that professor’s contributions take a sharp turn downward after a certain point. If this is correct and professors retire at an increasingly older age, we have a problem.
What does the research says about this? We can look at published papers as a measure of productivity.
The Center for Data Innovation spoke with Chris Mentzel, program director for the Data-Driven Discovery Initiative at the Gordon and Betty Moore Foundation in Palo Alto, California. Mentzel discussed how data-driven discovery is changing research and the challenges in emphasizing data science in academia.
Technology is advancing at such a fast rate right now that it can feel like we are well on our way to a robot apocalypse. But, if we closely observe how artists are harnessing artificial intelligence in ways that push humanity forward, we can see that our fears of a technological dystopia might never actualize. From using AI to create new and innovative genres in music, to new takes on classical nudes, and innovative ways to track anonymous warfare, art’s relationship to AI right now is illuminating humanity’s strength.
As we enter a new age of AI-powered art, here are six artists who are shaping the future of artificial intelligence.
There are good reasons to believe in Sen’s thesis that information technology won’t be the economic engine it once was — at least not for investors, and perhaps not for the broader economy, either. But the assumption that government will take tech’s place has some problems. All the big important things that government might want to do over the next decade involve a significant amount of technology.
Consider the fight against climate change. The most urgent tasks in creating a carbon-free economy, both in the U.S. and abroad, involve technology and innovation. These include producing better batteries and other forms of energy storage, transmitting electricity from place to place without big energy losses, finding ways to make cement and heat homes without releasing carbon, and developing ways to capture carbon cheaply from the air.
A new prototype version of Twitter debuted at SXSW on Tuesday, highlighting potential changes coming to the service in the near future, including updates that could change engagement and interactions between accounts.
The new prototype app—named “twttr” after the social network’s original name—allows testers to check out potential changes to Twitter, introducing new versions of the network before rolling them out to all Twitter users.
One of the biggest prototype design choices aims to encourage conversation by modifying how replies to tweets are threaded.
What will these teens expect of tech based on their current experiences? You can extrapolate by drawing converging lines from K-12 education technology and home consumer technology to see how they might overlap and intersect. Those interactions look a lot different than just evaluating consumer or edtech trends in isolation.
Las Vegas, NV June 4-7. “The Seattle company’s C Suite will be front and center at re:MARS, there will be plenty more to see. Invited guests include luminaries like Colin Angle, CEO, founder, and chair of robotics company iRobot; Aicha Evans, Intel’s former chief strategy officer and the newly appointed CEO of self-driving car startup Zoox.”
San Francisco, CA June 15. “An interdisciplinary event bringing together researchers and practitioners to address deep learning, reinforcement learning, robustness, fairness, ethics and the future of data science.” [$$$]
New Haven, CT May 8, starting at 7 a.m., Yale School of Management. “A day-long event highlighting entrepreneurial Yale faculty and students and the investable innovations coming out of Yale labs.” [$$$]
When we are talking about training artificial intelligence, it is recognised that having access to a good sized dataset appropriate for the task is beneficial, and this is why so many companies such as Google continue to provide services that allow them to have access to vast troves of information. Data mining is one way of finding data to feed the algorithms to train them, but in order to do so the researchers need to have access to data, yet this data could be proprietary.
Data can be anything that is the subject of the research: music, pictures, paintings, text, poetry, scientific literature, figures, drawings, sketches, etc. Data is not about an individual work, it is all about the accumulated reading of a collection of works. So in order to analyse this information and turn it into something useful, there has to be a process that “reads” the data. There are lots of different processes and techniques, but these require for the miner to at least copy the data temporarily.
Data fabrics can provide a catalog of consistent data services across private and public clouds. To explore data fabrics — what they are, why they are needed and how to make your own — I talked to three leading experts in the field, Anthony Lye, senior vice president and general manager of the cloud business unit at NetApp, Jack Norris, Senior Vice President, Data and Applications at MapR Technologies, and Isabelle Nuage, director of product marketing at Talend.
Lately, I’ve written a lot about data-management for unstructured data and more in general, about the relationship between data management and secondary storage. While recently attending Storage Field Day 18, I received confirmation on what is coming in the near future for date management technologies. Simplification and democratization of data management will be key for end-user adoption and success.