Many great ideas in artificial intelligence languish in textbooks for decades because we don’t have the computational power to apply them. That’s what happened with neural networks, a technique inspired by our brains’ wiring that has recently succeeded in translating languages and driving cars. Now, another old idea—improving neural networks not through teaching, but through evolution—is revealing its potential. Five new papers from Uber in San Francisco, California, demonstrate the power of so-called neuroevolution to play video games, solve mazes, and even make a simulated robot walk.
The robot is being tested in one corner of a vast, heavily automated fulfillment center operated by Ocado, an online-only grocery store based in the U.K. Ocado led a seven-million-euro project, called SecondHands, to develop the underlying technology. The hardware and the dialogue system were developed in the lab of Tamim Asfour at the Karlsruhe Institute of Technology in Germany. The robot’s vision system comes from Sapienza University in Rome and University College London (UCL).
If you ever hear that implementing algorithmic decision-making tools to enable social services or other high stakes government decision-making will increase efficiency or reduce the cost to taxpayers, know that you’re being lied to. When implemented ethically, these systems cost more. And they should.
Whether we’re talking about judicial decision making (e.g., “risk assessment scoring”) or modeling who is at risk for homelessness, algorithmic systems don’t simply cost money to implement. They cost money to maintain. They cost money to audit. They cost money to evolve with the domain that they’re designed to serve. They cost money to train their users to use the data responsibly. Above all, they make visible the brutal pain points and root causes in existing systems that require an increase of services.
Otherwise, all that these systems are doing is helping divert taxpayer money from direct services, to lining the pockets of for-profit entities under the illusion of helping people. Worse, they’re helping usher in a diversion of liability because time and time again, those in powerful positions blame the algorithms.
The universities will join Edinburgh, Oxford, Warwick, Leeds, Manchester and others in supporting the institute’s efforts to position the UK as a world leader in data science and artificial intelligence (AI) research.
The institute has selected Birmingham and Exeter to join its list of collaborators based on the data science and AI research both organisations have previously produced, along with their ability to recruit new researchers.
Last year we open sourced Prophet, a forecasting procedure designed to help scale high quality forecasts across many people and problems. Since the release, we’ve been overwhelmed by the enthusiastic response from the community — with lots of new contributors helping us improve the project and new users contacting us with interesting applications. We now use Prophet internally at Facebook.
The model underlying the Prophet procedure is implemented using Stan, an open-source platform for statistical modeling developed by the excellent Stan Development Team. The Stan community has blossomed over the last few years and we are proud to be among the early adopters using it in production.
The ability to quantify the extent of kidney damage and predict the life remaining in the kidney, using an image obtained at the time when a patient visits the hospital for a kidney biopsy, now is possible using a computer model based on artificial intelligence (AI).
The findings, which appear in the journal Kidney International Reports, can help make predictions at the point-of-care and assist clinical decision-making.
Today, at least 45 start-ups are working on chips that can power tasks like speech and self-driving cars, and at least five of them have raised more than $100 million from investors. Venture capitalists invested more than $1.5 billion in chip start-ups last year, nearly doubling the investments made two years ago, according to the research firm CB Insights.
Nissan has extended a prior agreement with NASA to collaborate on technology for managing fleets of driverless vehicles, the automaker announced today.
The two companies have been working together under a five-year partnership to advance autonomous vehicle systems. A year ago, Nissan introduced a platform for integrating autonomous vehicles based on technology from NASA. Dubbed “Nissan Seamless Autonomous Mobility,” or SAM for short, the platform combines artificial intelligence inside the vehicle with remote human support to help driverless cars navigate difficult situations on the road.
Chirrp is a conversational platform that harnesses the power of artificial intelligence to create engaging interactions with customers. Through Chirrp, companies can rapidly offer intelligent conversation that is engaging, personalized and targeted.
Georgia Institute of Technology, College of Computing
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Georgia Tech’s College of Computing has launched the Constellations Center for Equity in Computing with the goal of democratizing computer science education. The mission of the new center is to ensure that all students—especially students of color, women, and others underserved in K-12 and post-secondary institutions—have access to quality computer science education, a fundamental life skill in the 21st century.
Constellations is dedicated to challenging and improving the national computer science (CS) educational ecosystem through the provision of curricular content, educational policy assessment, and development of strategic institutional partnerships. According to Senior Director Kamau Bobb, democratizing computing requires a “real reckoning with the race and class divisions of contemporary American life.”
Amazon is on the hunt for as much as 1 million square feet of office space in Boston, adding a new level of intrigue to the retail giant’s plans as it whittles down the list of the cities competing for its second headquarters.
The Seattle company is negotiating with a Seaport developer to lease an entire office building, and possibly two, to continue its expansion in the city, according to real estate industry executives with knowledge of the talks.
Amazon employs more than 1,000 people in Boston and Cambridge — mostly software engineers and developers — with offices open or under construction in Kendall Square, the Back Bay, and Fort Point. It expects to nearly double that number over the next few years, and could grow even larger here with more space in the Seaport.
For someone who’s not a Sherlock superfan, cognitive neuroscientist Janice Chen knows the BBC’s hit detective drama better than most. With the help of a brain scanner, she spies on what happens inside viewers’ heads when they watch the first episode of the series and then describe the plot.
Chen, a researcher at Johns Hopkins University in Baltimore, Maryland, has heard all sorts of variations on an early scene, when a woman flirts with the famously aloof detective in a morgue. Some people find Sherlock Holmes rude while others think he is oblivious to the woman’s nervous advances. But Chen and her colleagues found something odd when they scanned viewers’ brains: as different people retold their own versions of the same scene, their brains produced remarkably similar patterns of activity.
Chen is among a growing number of researchers using brain imaging to identify the activity patterns involved in creating and recalling a specific memory. Powerful technological innovations in human and animal neuroscience in the past decade are enabling researchers to uncover fundamental rules about how individual memories form, organize and interact with each other. Using techniques for labelling active neurons, for example, teams have located circuits associated with the memory of a painful stimulus in rodents and successfully reactivated those pathways to trigger the memory. And in humans, studies have identified the signatures of particular recollections, which reveal some of the ways that the brain organizes and links memories to aid recollection. Such findings could one day help to reveal why memories fail in old age or disease, or how false memories creep into eyewitness testimony. These insights might also lead to strategies for improved learning and memory.
“Applications are now open for the Communicating Science 2018 workshop, to be held in Boston, MA on June 14-16, 2018!” The application deadline is March 1st.
“Coming to CDS was an experiment. Indeed, the CDS itself is an experiment, and its scientific mission currently funded by a joint grant provided by the Moore and Sloan foundations, along with two other institutes at UC Berkeley and UW Seattle. I came to CDS with the goal of learning as much as I can about how other fields deal with data. I did that, too, occasionally, but the learning experiences I value most have nothing at all to do with that. They’re in no particular order of importance, and credit for some of this post goes to David Hogg for some illuminating discussions along the way.”
“1) What’s a data scientist, anyway? After there years at the Center for Data Science, I’m still no closer to understanding what a “data scientist” actually is. But that’s okay, I’m not sure anyone really does (yet?). I sort of call myself that occasionally, in an awkward attempt to try and explain what I do and why I think it’s useful (despite the occasional heckling from other scientists about how it’s all hype and not really science, anyway).”
Journal of the American Medical Informatics Association; Xiaoling Chen et al.
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The biomedical and health CAre Data Discovery Index Ecosystem (bioCADDIE) project, funded by the National Institutes of Health via the Big Data to Knowledge program, is focused on the discovery of biomedical datasets. Since its start, researchers, service providers, and knowledge experts around the globe have participated in various aspects of bioCADDIE, such as working groups, pilot projects, and dataset retrieval challenges (https://biocaddie.org). To instantiate the concepts and recommendations developed by this large community, bioCADDIE developed a prototype data discovery index (DDI) named DataMed, which collects and indexes metadata from broad types of biomedical datasets of interest from heterogeneous sources and makes them searchable through a web-based interface.
“I’ve compiled this list of over 600 such free online courses that you can start this month. For this, I leveraged Class Central’s database of over 9,000 courses. I’ve also included each course’s average rating.”
arXiv, Computer Science > Computation and Language; Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, Matt Post
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“We describe Sockeye (version 1.12), an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). Sockeye is a production-ready framework for training and applying models as well as an experimental platform for researchers. Written in Python and built on MXNet, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural networks, self-attentional transformers, and fully convolutional networks.”
“Knowledge sharing is an important driving force behind scientific progress. In an open-science approach, EPFL’s Blue Brain Project has created and open sourced Blue Brain Nexus that allows the building of data integration platforms. Blue Brain Nexus enables data-driven science through searching, integrating and tracking large-scale data and models.”