The Institute for Systems Biology and Arivale, Inc. announced today results from their Pioneer 100 Wellness Project (P100), a nine-month study of 108 individuals, demonstrated that combining personal, dense, dynamic data clouds with tailored behavioral coaching can optimize wellness for individuals. These data clouds also can identify early transitions into disease states and facilitate the reversal of some disease states back to wellness. The new approach tested in this study represents the foundation for the launch of a new industry – Scientific Wellness.
“Just as the Hubble Telescope provided a new view into the universe, these data sets have been transformational in providing new insights into both human biology and disease,” said Leroy Hood, MD, PhD, president and co-founder of the Institute for Systems Biology, and senior vice president & chief science officer, Providence St. Joseph Health. “We have termed this quantitative and transformational approach Scientific Wellness, which enables individuals to improve their health and wellbeing, while generating the data necessary to optimize wellness as well as avoid or slow down the transition into certain disease states.”
Tenure and promotion season is underway. Promotion committees, in the U.S. at least, use the summer to send portfolios to 3 to 10 scholars at other institutions for independent review of the candidate’s credentials. These independent assessments are a vital part of the review process, particularly for research-intensive universities. Candidates need to demonstrate that their work is impacting others in the discipline.
Advocates for improving open science and reproducibility accurately worry that the movement will fail if standards for hiring, tenure, and promotion do not change. If the likelihood of tenure and promotion is dependent exclusively on publication volume, prestige and success obtaining grants, incentives for openness will have–at best–an indirect effect on researcher’s behavior through journals and funders. Successful nudging of the culture of incentives requires that institutions likewise reward scholars for conducting open, rigorous, reproducible research.
MIT associate professor of electrical engineering and computer science Vivienne Sze and colleagues unveiled a new, energy-efficient computer chip optimized for neural networks, which could enable powerful artificial-intelligence systems to run locally on mobile devices.
Diversity is not one of the tech industry’s strongest suits, but Seattle-based Code.org sees hints this could change in coming years: In 2017, a record number of women and underrepresented minorities took the Advanced Placement computer science exams.
The College Board, which runs the nationwide AP exam program, reported that the number of women taking computer science tests rose by 135 percent in just one year, while participation by underrepresented minorities was 170 percent higher.
Governments can track the movements of massive numbers of people by positioning cameras to read license plates, or by setting up facial recognition systems. Those systems need few people to operate them, automating the collection of information about people’s lives and adding that data to searchable databases. Surveillance has become cheap.
I study the law of identification and privacy, so I pay attention to that trend, and it’s worrying. The data maintained in our individual profiles can be used in making decisions about credit, employment, government benefits and more. What governments and companies think they know about us – whether or not it’s accurate – has real power over our actual lives.
University of Michigan, Michigan Institute for Data Science, MIDAS
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The new platform features a flexible, robust and scalable database environment, and a set of data pipeline tools that can ingest and process large amounts of data from sensors, mobile devices and wearables, and other sources of streaming data. The platform leverages the advanced virtualization capabilities of ARC-TS’s Yottabyte Research Cloud (YBRC) infrastructure, and is supported by U-M’s Data Science Initiative launched in 2015. YBRC was created through a partnership between Yottabyte and ARC-TS announced last fall.
Machine learning algorithms have quickly become the all-seeing shepherds of the human flock. This software connects us on the internet, monitors our email for spam or malicious content, and will soon drive our cars. To deceive them would be to shift tectonic underpinnings of the internet, and could pose even greater threats for our safety and security in the future.
Small groups of researchers—from Pennsylvania State University to Google to the U.S. military— are devising and defending against potential attacks that could be carried out on artificially intelligent systems. In theories posed in the research, an attacker could change what a driverless car sees. Or, it could activate voice recognition on any phone and make it visit a website with malware, only sounding like white noise to humans. Or let a virus travel through a firewall into a network.
American cloud and colocation provider DataBank is about to start construction of a 94,000 square foot data center in Atlanta’s Technology Square.
Most of the facility will be dedicated to high performance infrastructure operated by the Georgia Institute of Technology (Georgia Tech). The facility will support high server densities and provide access to Southern Crossroads – a high-speed fiber network built specifically for education and research, administered by a non-profit headquartered in the city.
Traditional detection algorithms use infrared heat as the main signal of a wildfire. The Firelight Detection Algorithm uses visible light instead, detecting a fire possibly a day earlier.
The bottom line: More than 1,000 vendors say their products employ AI, but many are “applying the AI label a little too indiscriminately,” Gartner says in its report. Kriti Sharma, who runs the AI team at Sage, tells Axios that a lot of companies are seeking to solve problems using AI that would be better done by humans. And what is often called AI “is just automation that you are doing,” she said.
Neuroevolution is making a comeback. Prominent artificial intelligence labs and researchers are experimenting with it, a string of new successes have bolstered enthusiasm, and new opportunities for impact in deep learning are emerging. Maybe you haven’t heard of neuroevolution in the midst of all the excitement over deep learning, but it’s been lurking just below the surface, the subject of study for a small, enthusiastic research community for decades. And it’s starting to gain more attention as people recognize its potential.
Put simply, neuroevolution is a subfield within artificial intelligence (AI) and machine learning (ML) that consists of trying to trigger an evolutionary process similar to the one that produced our brains, except inside a computer. In other words, neuroevolution seeks to develop the means of evolving neural networks through evolutionary algorithms.
Welcome to the Apple Machine Learning Journal. Here, you can read posts written by Apple engineers about their work using machine learning technologies to help build innovative products for millions of people around the world.
What kinds of operations-enhancing questions have cities asked and answered with data and analytics? The catalog below is an ongoing, regularly-updated resource for those interested in knowing what specific use cases can be addressed using more advanced data and analysis techniques.
For examples that are currently being implemented in cities across the country, you can click to expand the question to see additional information about the solution. All other examples represent potential questions that cities could work to address with data and analytics.
In 2016 the EU General Data Protection Regulation (GDPR), Europe’s new data protection framework, was approved. The new regulation will come into force across Europe – and the UK – in 2018. It has been widely and repeatedly claimed that a ‘right to explanation’ of all decisions made by automated or artificially intelligent algorithmic systemswill be legally mandated by the new regulation. This “right to explanation” is viewed as an ideal mechanism to enhance the accountability and transparency of automated algorithmic decision-making.
Such a right would enable people to ask how a specific decision (e.g. being declined insurance or being denied a promotion) was reached.
A new version of the California Academy of Sciences’ iNaturalist app uses artificial intelligence to offer immediate identifications for photos of any kind of wildlife. You can observe anywhere and ask the computer anything. I’ve been using it for a few weeks now and it seems like it mostly works. It is completely astonishing.
What can Amazon Alexa can do for people with diabetes? When Merck & Co.’s Alexa Diabetes Challenge posed that question earlier this year, contest entries turned the voice-enabled system into a mood-sensing coach, a nutrition assistant and a sleuth that detects risky behaviors.
In 1899, the world’s most powerful nations signed a treaty at The Hague that banned military use of aircraft, fearing the emerging technology’s destructive power. Five years later the moratorium was allowed to expire, and before long aircraft were helping to enable the slaughter of World War I. “Some technologies are so powerful as to be irresistible,” says Greg Allen, a fellow at the Center for New American Security, a non-partisan Washington DC think tank. “Militaries around the world have essentially come to the same conclusion with respect to artificial intelligence.”
Allen is coauthor of a 132-page new report on the effect of artificial intelligence on national security. One of its conclusions is that the impact of technologies such as autonomous robots on war and international relations could rival that of nuclear weapons.
More than anything, the new feature sound like a competitor to apps like Flipboard and Nuzzel. No service knows you or sees the internet as well as Google, so it seems plausible Google could build the best, most updated, most personalized news-reading app ever. Part of the company’s plan involves surfacing older things you’ve never seen before, things that might otherwise get buried in recency-biased search results. And if you tap on any person, place, or topic, you’ll drop right into search results, where you can learn even more.
This is the beginning of what appears to be an important new feature for Google—anything that appears on google.com matters.
New York, NY July 27 starting at 6:30 p.m., Squarespace (8 Clarkson St.). How do you effectively build and ship a machine learning product? Organized by Women in Product and Squarespace. [$$]
Minneapolis, MN July 25. An introduction to artificial intelligence and machine learning. For the second half, Pete Kane, founder of Silicon Valley AI, will give an update on SVAI’s direction and future plans in Minnesota. [free, registration required]
New York, NY August 23-25. This three-day course consists of three main themes: Bayesian inference and computation; the Stan programming language; applied statistics. [$$$$]
SSRC has offered a Transregional Research Fellowship program aimed at supporting and promoting excellence in transregional research under the rubric InterAsian Contexts and Connections. Deadline for applications is September 27.
We’re excited to be releasing v0.1 of the Ground project!
Ground is a data context service. It is a central repository for all the information surrounding the use of data in an organization. Ground concerns itself with what data an organization has, where that data is, who (both human beings and software systems) is touching that data, and how that data is being modified and described.
“Project Common Voice is a project to help make voice recognition open to everyone. Now you can donate your voice to help us build an open-source voice recognition engine that anyone can use to make innovative apps for devices and the web.”