College students last year enrolled in introductory AI and machine learning classes in record numbers, the number of academic papers on the topic shot up and officials mentioned the technology in more than 70 meetings of the U.S. Congress, according to Stanford University analysis of transcripts.
AI technology has matured in recent years, as more companies have started using predictive algorithms and other automated techniques across myriad disciplines. In the fourth quarter, the largest AI deal in the U.S. was a $400 million funding round raised by synthetic biology startup Zymergen Inc. Zymergen uses AI-powered robots to genetically engineer microbes to create new materials like flexible glass and improve existing ones like paint that resists radar detection.
University at Buffalo engineers have been awarded a $1 million Defense Advanced Research Projects Agency grant to combine physics-based models with conventional, data-driven AI methods.
The goal is to provide AI systems, which work within specific frameworks and lack tools to explain their reasoning process, with a broader foundation of knowledge through physics. In theory, this will allow for more streamlined, efficient and adaptable AI systems — ideal traits for defense systems, such as unmanned aerial vehicles (UAVs), which operate in uncontrolled environments.
As the cold sets in, and snow turns the western mountain ranges of the United States into winter’s playground, thousands of deer, elk, moose, bighorn sheep, bison and mountain goats begin their seasonal trek to lower elevations. The herd mammals follow historic routes, passed along by mother to young from year to year until they are instinctually ingrained in the animals. Some of the routes can take decades to become second nature.
These wildlife migration routes make up a series of distinct pathways that animals follow to survive in highly seasonal climates, like the mountains of Wyoming, says Matthew Kauffman, a professor of zoology and physiology at the University of Wyoming and director of the Wyoming Migration Initiative.
Rattlesnakes, bears, hurricanes, and freezing weather haven’t stopped ecologist Jeff Atkins from taking weekly hikes into Virginia’s Shenandoah National Park for the past 8 years to collect water samples from remote streams. But Atkins is now facing an insurmountable obstacle: the partial shutdown of the U.S. government, in its third week.
Harvard Business Review, Bobby Gibbs and Nick Harrison
Giant travel search engines such as TripAdvisor, Expedia, Kayak, and Google Flights have all but replaced travel agents as most consumers’ travel advisors. Soon, independent curating engines like these could trigger the next wave of disruption in retail. The first stage of the digital shopping revolution saved consumers time and money by letting them buy things they already wanted without having to go to a traditional retail store. A major part of the second stage will likely be a dramatic refinement of technologies that tailor recommendations and then scour the internet for the best deal.
Some established retailers already offer services to help customers find the most suitable products among those they supply. Amazon collects user reviews and makes customized suggestions based on learning algorithms. In the United Kingdom, womenswear retailer Topshop and department store John Lewis partner with online engine provider Dressipi to create personalized outfit recommendations based on initial profiling followed by machine learning applied to preferences.
A new generation of retail choice engines will work more clearly on behalf of customers by offering transparency, neutrality, and an unlimited catalog. Just as flight intermediaries such as Google Flights, Hopper and Skyscanner find the lowest possible prices, agnostic digital retail curators could direct consumers to the retailer offering the best deals — or advise them to delay a purchase when a promotion is likely. In the same way that Expedia makes bookings directly with hotel chains, these digital curators could negotiate terms directly with manufacturers.
Mindful perhaps of continuing opposition among researchers, Nature has made the first issue of Nature Machine Intelligence available at no cost, and plans to keep the journal free through the remainder of 2019.
The world’s highest-valued artificial intelligence startup SenseTime has set foot in Japan. The Beijing-based firm announced on Friday that it just opened a self-driving facility in Joso, a historic city 50 kilometers away from Tokyo, where it plans to conduct R&D and road test driverless vehicles.
The initiative follows its agreement with Japanese auto giant Honda in 2017 to jointly work on autonomous driving technology. SenseTime, which is backed by Alibaba and was last valued at more than $4.5 billion, is best known for object recognition technologies that have been deployed in China widely across retail, healthcare and public security. Bloomberg reported this week that the AI upstart is raising $2 billion in fresh funding.
Can Microsoft and Kroger upgrade the grocery experience better than Amazon and Whole Foods can?
That’s the unstated subplot in the news this morning that Microsoft and Kroger are testing new technologies to streamline the process of finding and purchasing items in traditional grocery stores.
The companies today are unveiling new pilot stores near their respective headquarters in Redmond, Wash., and Monroe, Ohio, featuring smart shelves with digital displays that update prices dynamically and show personalized icons to help shoppers find items they’ve put on their shopping lists. The shelves will help also help workers identify items to fulfill curbside pickup orders.
University of Oxford, Future of Humanity Institute
A report published by the Center for the Governance of AI (GovAI), housed in the Future of Humanity Institute, surveys Americans’ attitudes on artificial intelligence. The impact of artificial intelligence technology on society is likely to be large. While the technology industry and governments currently predominate policy conversations on AI, the authors expect the public to become more influential over time. Understanding the public’s views on artificial intelligence will, therefore, be vital to future AI governance. The survey, carried out by Baobao Zhang and Allan Dafoe, is one of the most comprehensive surveys focusing on the American public’s opinions on artificial intelligence to date, including 2000 participants using the survey firm YouGov.
Key findings from our report include:
Americans express mixed support for the development of AI. After reading a short explanation, a substantial minority (41%) somewhat support or strongly support the development of AI, while a smaller minority (22%) somewhat or strongly opposes it.
Philip Benfey, Ph.D, a Duke biology professor and co-founder of HFG, told the Biotech Center in an earlier interview that “We’re in the process of developing a fully integrated seed company. Our goal is to sell conventional corn seed. We’re not doing genetic modification. It’s data driven and exploits our intellectual property around root traits.”
Spencer Maughan, Ph.D., co-founder of HFG and Finistere Ventures, said in a news release: “In agriculture, the most consolidated point of value and technology is genetics because the only thing a farmer actually has to buy to be in business is a seed.”
When we created Social Science One to facilitate access for the world’s social scientific community to social media data, we promised to release periodic updates noting our progress and describing the challenges we confront. In this post, we describe the substantial work accomplished over the past several months and highlight the remaining obstacles we face. We describe additional datasets to be made available in the coming months, and plans to announce the first group of researchers who will be granted (privacy-preserving) access to Facebook data and foundation funding through our partnership with the Social Science Research Council. We also detail the important legal, technical, organizational, computational, privacy, and security challenges that have occupied our work to date. Despite these challenges, we believe we are building a firm foundation for a multi-year effort to investigate fundamental questions of social media’s impact on democracy around the world, which we hope can be expanded to other critical areas of research.
Non-profit RESOLVE’s* new TrailGuard AI* camera uses Intel-powered artificial intelligence (AI) technology to detect poachers entering Africa’s wildlife reserves and alert park rangers in near real-time so poachers can be stopped before killing endangered animals. TrailGuard AI builds on anti-poaching prototypes funded by Leonardo DiCaprio Foundation and National Geographic Society.
Online “WiDS Datathon 2019 will last from mid-to-late-January until February 28, 2019. Join the WiDS mailing list to be notified when the WiDS Datathon launches in January. Winners will be announced at the WiDS Conference at Stanford University on March 4, 2019.”
“STGlobal is a vibrant science and technology studies/science, technology and policy conference produced by students and faculty from Virginia Tech, Arizona State, Drexel University, George Washington University, Georgetown University, and the University of Virginia. This year’s conference will take place on March 29 & 30, 2019 at the National Academies of Sciences, Engineering, and Medicine in Washington, DC.” Deadline for proposals is January 15.
“The Intelligence Advanced Research Projects Activity (IARPA) is seeking information on research efforts in the area of machine learning with a particular focus on deep learning. This request for information (RFI)is issued solely for information gathering and planning purposes; this RFI does not constitute a formal solicitation for proposals. The following sections of this announcement contain details of the scope of technical efforts of interest, along with instructions for the submission of responses.” Deadline for submissions is January 17.
“The Undergraduate Summer Internship is our headline program enabling undergraduate students to collaborate with our researchers, as well as their own peers, at Harvard’s FAS Center for Systems Biology and the Department of Systems Biology at Harvard Medical School.” Deadline for applications is February 1.
“The Idea Competition is designed to solicit ideas and proposals from the CTSA community for a future DREAM Challenge. The winner of the CD2H Idea Competition will be awarded a subcontract for up to $100K to support activities associated with the development and execution of a challenge, that include (a) data extraction and curation (b) algorithm development and assessment, and (c) domain expert support.” Deadline for submissions is February 15.
I have long maintained that one of the most significant barriers to Twitter research and archiving are Twitter’s Developer Policies. This barrier takes the form of not only the restrictions contained in the policies, but the ambiguity of the documents themselves.
In addition to just being poorly worded, my read of the policies is that they are written primarily for Twitter’s business partners. As such, it is unclear how or if Twitter intends them to apply to research and archiving.
In my work supporting research and archiving using the Twitter API, my approach is to try to suss out the spirit of the policies (assuming Twitter’s good intentions), and balance it against the best interests of Twitter, the societal value of the research and archiving, and the agency of the content creators. It is my experience that colleagues in the research, library, and archives community do the same, though as you see below, the results can be quite varied.
It seems like industry doesn’t really use any of the recent advancements that come fresh from research, including things like capsule networks or advances in Neural Architecture search?
Is there a gulf between the release of cutting edge research and its commercialization? Couldn’t many of these models be commercialized faster and be made available as enterprise products? [12 comments]