The big news from Google’s I/O conference was Google’s “AI-first” strategy. This isn’t entirely new: Sundar Pichai has been talking about AI first since last year. But what exactly does AI first mean?
In a Quora response, Peter Norvig explains Google’s “AI first” direction by saying that it’s a transition from information retrieval to informing and assisting users. Google’s future isn’t about enabling people to look things up; it’s about anticipating our needs, and helping us with them.
Music on my mind. From Oliver Sacks‘ book Musicophilia to a range of studies over the past three years, there is a growing interest in the neurological impacts of music on the brain. The Sync Project in Boston is investigating the use of music as precision medicine following studies that showed music has a measurable impact on brain chemistry, including cortisol and dopamine levels. If it sounds good, do it.
Elsewhere in precision medicine, it was inevitable that someone would design a Potbot. It was less clear what it would be trained to do. The Potbot from Potbotics reads the medical literature and sorts out which strains of marijuana are most likely to alleviate particular symptoms and ailments.
Data science and cows: fascinating. Image analysis can help farmers get those cows milked and fed, singling out cows who may need extra attention to stay healthy. Herd models have also demonstrated that grazing herds have two groups: fast eaters and slow eaters. Each cow has to make a tough choice about which group best suits their needs. When the milk commercials refer to happy cows out munching on naturally grown grass? They are oversimplifying. Some of those cows are likely feeling rushed.
Artificial intelligence is being used to improve banking, marketing, the legal field — and now to find which one of the more than 30,000 strains of medical marijuana is best for you.
Potbot uses AI to “read” through peer-reviewed medical journals to find studies on cannabinoids, the active compounds in marijuana. Using the research, it pairs 37 symptoms like insomnia, asthma and cancer with branded marijuana strains to find which type of weed is best suited to treat each one.
This VW hookup is one part of a triple dose of automotive-themed news updates from Nvidia today.
Separately, it announced that Volvo and Autoliv have committed to sell self-driving cars powered by its technology by 2021. Nvidia also signed up auto suppliers ZF and Hella to build additional safety standards into its autonomous vehicle platform.
Whether it was the social media boom, the smart phone “revolution”, or the commercialisation of the world wide web, if there’s money to be made, entrepreneurs will try and make it. And they should. For better or worse, economic prosperity and stability depends on what brilliance can be conjured up by scientific minds.
But that’s only one side of the coin. The flipside is that prosperity and stability can only be maintained if equally brilliant minds work together to ensure we have durable ways to govern these technologies, legally, ethically, and for the social good. In some cases, this might mean agreeing that there are simply certain things we should not do with AI; some things that profit should not be derived from. We might call this “conscious capitalism” – but it is, in fact, now a societal imperative.
The U.S. Air Force Research Laboratory (AFRL) and IBM are collaborating on a brain-inspired supercomputing system powered by a 64-chip array. The laboratory is investigating applications for the system in embedded, mobile, autonomous settings where limiting factors today include size, weight and power.
As an end-to-end software ecosystem, the scalable platform would enable deep neural-network learning and information discovery. Its advanced pattern recognition and sensory processing power would be the equivalent of 64 million neurons and 16 billion synapses; however, the processor component only will consume approximately 10 watts, the equivalent of a dim light bulb.
NVIDIA topped the latest edition of MIT Technology Review’s list of 50 Smartest Companies. The magazine’s 2017 list, released Tuesday, ranks companies that combine innovative technology with an effective business model.
“The companies on the list combine a high level of technology innovation with a business model that will help them make the most of it,” said Nanette Byrnes, business senior editor of MIT Technology Review. “These are the ones that competitors must follow.”
Baidu SVAIL extended DeepBench to include support for inference as well as expanded training kernels. Also of interest are new capabilities to benchmark at lower precision—something Baidu systems researcher, Sharan Narang tells The Next Platform is increasingly important for their own research and production models.
Narang’s focus is on making training and inference faster for the company’s wide-ranging deep learning models for speech, image, and other application areas. This includes looking at different techniques for ultra-efficient low-precision training. “Basically anything that can a model smaller and faster,” he says.
Sooner or later, our children will be raised by robots, so it’s natural that Disney, purveyor of both robots and child-related goods, would want to get ahead of that trend. A trio of studies from its Research division aim at understanding and improving how kids converse with and otherwise interact with robots and other reasonably smart machines.
The three studies were executed at once as a whole, with each part documented separately in papers posted today. The kids in the study (about 80 of them) proceeded through a series of short activities generally associated with storytelling and spoken interaction, their progress carefully recorded by the experimenters.
The idea of a Chinese-U.S. arms race for artificial intelligence conjures up images of an army of swarmbots defeating self-driving tanks on a smoldering, depopulated hellscape. It’s an idea so captivating that Sen. John Cornyn, R-Texas, wants to make it harder for the Chinese to invest in U.S. technology development, including in companies developing artificial intelligence, out of fear that Beijing will use small investment positions in Silicon Valley firms to erode U.S. national security and technological advantage. But tech entrepreneurs, academics in the field, and former senior officials in the White House and Pentagon think the proposal would do more harm than good.
Cornyn pitched his idea at a Council on Foreign Relations event on Thursday, citing a 2016 Defense Department report that explored how various Chinese investment activities might affect U.S. national security. The report, produced under former Defense Secretary Ash Carter and sometimes referred to as “the DIUx paper,” is not classified but has not been made available to the public.
Based on what little I have read so far… A piece of widely used tax software — one used by the Ukrainian government — did its usual “phone home” to check for updates. Instead of getting back a few hundred bytes of acknowledgement, it got a viral payload. Basically, this tax software served as a means of auto-updating the virus to thousands of targets. The result is not just accounting systems down, though. It’s gas stations and point of sale systems in grocery stores.
This kind of thing basically makes me wonder how long we’ll have the Internet.
If the casting director of the TV show “Silicon Valley” were asked to produce a canonical example of an applicant to Y Combinator’s incubator program, she may well have come up with the guy strolling to the front of a basement auditorium at Stanford on a mid-April day this year. He’s a goateed bro in his mid-twenties, rocking a grey pullover hoodie, a brown stocking cap, and an eagerness to share his killer idea—Airbnb for parking! He’s come to the Gates Computer Science Building to pitch it to the two interlocutors holding “Office Hours,” where savvy veterans of the startup process dispense wisdom to aspiring Mark Zuckerbergs. The bro is clearly happy when both of them—Sam Altman, the head of Y Combinator, and Yuri Sagalov, a startup CEO who completed YC’s startup boot camp in 2010 and now is a part-time partner there—express excitement at the concept.
But Altman, a wiry 32-year-old who himself is wearing a zip-up hoodie, hits the brakes on the lovefest. “When is this going to launch?” he asks. The founder says the app is six months out.
Real-time data will be integrated from a number of sources including courtside statisticians, chair umpires, radar guns, ball position, player location and will also include Twitter for social comment on how the match is playing out.
Sam Seddon, Wimbledon Client & Programme Executive, IBM said of the innovations for 2017: “Cognitive computing is the next revolution in sports technology and working with us, Wimbledon is exposed to the foremost frontier of what technology can do, as we work together to achieve the best possible outcome for the brand and the event.”
Cornell and IBM announced a joint research project June 23 that will use genetic sequencing and big-data analyses to help keep the global milk supply safe.
Foodborne disease outbreaks and food spoilage are an ongoing global dilemma. With the application of metagenomics and analytics to food safety, the partnership aims to minimize the chance that hazardous food will reach consumers, prevent food fraud and reduce spoilage.
Greg Walden recently was riding comfortably in his Subaru Outback, the cruise control guiding his car, when a “big black bird” — a crow, he suspects — swooped down in front of him. “The car braked on its own,” the Oregon congressman recalled. “Of course, my wife woke up.” Startled in the passenger seat beside him, she asked if he was tired. She didn’t believe him when he said no.
To Walden, though, the minor incident illustrated a point. Compared to his old Dodge van, “it reacted before I reacted,” he told Recode in an interview. Braking assistance is hardly some new, gasp-inducing feature in sport-utility vehicles, but Walden said it helped crystallize for him how more-advanced technology — fully self-driving cars — might someday prevent more harrowing traffic incidents.
The de Blasio Administration today announced, as part of its New York Works plan to create 100,000 good jobs, the selection of New York University Tandon School of Engineering to develop and operate a hub for virtual reality and augmented reality (VR/AR) at the Brooklyn Navy Yard with a workforce development center at CUNY Lehman College in the Bronx. The lab will directly create over 500 jobs over the next ten years, and further position New York City as a global leader in the VR/AR industry.
Fueled by $6 million investment by the New York City Economic Development Corporation (NYCEDC) and the Mayor’s Office of Media and Entertainment (MOME), the lab will be the first publicly-funded VR/AR facility in the country to support startups, talent development, and research and innovation.
The Computing Community Consortium (CCC) has been working hard on various white papers over the past couple of months and slowly releasing them. You can see all of them here.
Today, we highlight another paper, called Big Data, Data Science, and Civil Rights by Solon Barocas, Elizabeth Bradley, Vasant Honavar, and Foster Provost.
Government, academia, and the private sector have increasingly recognized that the use of big data and data science in decisions has important implications for civil rights. However, a coherent research agenda for addressing these topics is only beginning to emerge and the need for such an agenda is critical and timely.
Mary Ann Liebert Publishing, Big Data journal; Brian d'Alessandro, Cathy O'Neil, and Tom LaGatta
Recent research has helped to cultivate growing awareness that machine-learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively address these concerns. This article introduces issues of discrimination to the data science community on its own terms. In it, we tour the familiar data-mining process while providing a taxonomy of common practices that have the potential to produce unintended discrimination. We also survey how discrimination is commonly measured, and suggest how familiar development processes can be augmented to mitigate systems’ discriminatory potential. We advocate that data scientists should be intentional about modeling and reducing discriminatory outcomes. Without doing so, their efforts will result in perpetuating any systemic discrimination that may exist, but under a misleading veil of data-driven objectivity. [full text]
Deep neural networks have learnt to do an amazing array of tasks – from recognising and reasoning about objects in images to playing Atari and Go at super-human levels. As these tasks and network architectures become more complex, the solutions that neural networks learn become more difficult to understand.