… USA TODAY Sports examined what popular fitness-tracking hardware and app companies such as Apple, Fitbit and Strava state in those privacy statements and terms of service. Some share information with third parties.
Fitbit, for example, said it “may share non-personal information that is aggregated or de-identified so that it cannot reasonably be used to identify an individual.”
“We may disclose such information publicly and to third parties, for example, in public reports about exercise and activity, to partners under agreement with us, or as part of the community benchmarking information we provide to users of our subscription services,” Fitbit said in a statement to USA TODAY Sports. “We never sell personal data, and we do not share customer personal information except in the limited circumstances described in our privacy policy.”
Humanyze is hardly alone—employee-tracking technology is big business. Steelcase, a company that manufactures office furniture and helps design workplaces, offers employers an online platform called Workplace Advisor that, it says, “uses strategically placed sensors and gateways to track precise, real-time space usage.” Along with calendar data, e-mails and other digital inputs, Workplace Advisor also employs heat sensors to determine how many people are inside a room. “If the space is designed to have six users in it, does it?” asks Brandon Buckingham, director of a Steelcase unit called Smart + Connected. If people are not using rooms the way they were intended, he says, it might be time for a company to rethink its office design. Beyond sophisticated equipment such as heat sensors, companies from McDonald’s to Amazon employ algorithms and passive tracking software to watch their employees, all with an eye to increasing efficiency and productivity.
It might seem intuitive that gathering such information will lead to insights that ultimately boost productivity. But researchers who study organizations are not necessarily convinced that this is so.
Bates College has received a National Science Foundation grant of $3.97 million to create a groundbreaking Visual Experience Database to support research in fields that rely on the analysis and recognition of images, such as neuroscience, cognitive science, and artificial intelligence.
The largest-ever federal grant awarded to Bates, the four-year award will fuel the creation of a vast gallery of videos that depict what, and how, people see as they go about daily activities. Bates developed the grant proposal collaboratively with researchers at North Dakota State University and the University of Nevada, Reno.
Last month, R users from across the world gathered in Toulouse, France to discuss new developments at the useR! conference, the language’s premier international gathering. At nearly every talk I attended, the name Hadley Wickham was mentioned. Wickham is the language’s most important developer. Over the past decade, along with his collaborators, Wickham built a set of popular data analysis and visualization libraries (also known as packages) called the “tidyverse,” which has almost become its own language. Wickham’s libraries are among the most popular in R, and have become the standard for new learners. (R is free to use.) … In Toulouse, I spoke with Wickham about the current state of R and what he sees for the future of the language. The conversation has been edited and condensed.
Stock market investors often rely on financial risk theories that help them maximize returns while minimizing financial loss due to market fluctuations. These theories help investors maintain a balanced portfolio to ensure they’ll never lose more money than they’re willing to part with at any given time.
Inspired by those theories, MIT researchers in collaboration with Microsoft have developed a “risk-aware” mathematical model that could improve the performance of cloud-computing networks across the globe. Notably, cloud infrastructure is extremely expensive and consumes a lot of the world’s energy.
Their model takes into account failure probabilities of links between data centers worldwide—akin to predicting the volatility of stocks. Then, it runs an optimization engine to allocate traffic through optimal paths to minimize loss, while maximizing overall usage of the network.
The model could help major cloud-service providers—such as Microsoft, Amazon, and Google—better utilize their infrastructure.
WIRED, Opinion; Olaf J. Groth, Mark J. Nitzberg, Stuart J. Russell
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Intelligent systems at scale need regulation because they are an unprecedented force multiplier for the promotion of the interests of an individual or a group. For the first time in history, a single person can customize a message for billions and share it with them within a matter of days. A software engineer can create an army of AI-powered bots, each pretending to be a different person, promoting content on behalf of political or commercial interests. Unlike broadcast propaganda or direct marketing, this approach also uses the self-reinforcing qualities of the algorithm to learn what works best to persuade and nudge each individual.
Manipulating user preferences and using bot armies to leverage widespread deceit has disrupted societal cohesion and democratic processes. To protect the cognitive autonomy of individuals and the political health of society at large, we need to make the function and application of algorithms transparent, and the FDA provides a useful model.
In the long unfolding arc of technology innovation, artificial intelligence (AI) looms as immense. In its quest to mimic human behavior, the technology touches energy, agriculture, manufacturing, logistics, healthcare, construction, transportation and nearly every other imaginable industry – a defining role that promises to fast track the fourth Industrial Revolution. And if the industry oracles have it right, AI growth will be nothing shy of explosive.
“The gains these days are not incremental,” Ajit Manocha, SEMI president and CEO, said to a gathering in July of the Chinese American Semiconductor Professional Association (CASPA) for its Summer Symposium at SEMI’s headquarters in Milpitas. “They are hockey stick – exponential – with AI semiconductors growing in market size from $4 billion this year to $70 billion in 2025.”
It is early July, almost 30C outside, but Mihkel Jäätma is thinking about Christmas. In a co-working space in Soho, the 39-year-old founder and CEO of Realeyes, an “emotion AI” startup which uses eye-tracking and facial expression to analyse mood, scrolls through a list of 20 festive ads from 2018. He settles on The Boy and the Piano, the offering from John Lewis that tells the life story of Elton John backwards, from megastardom to the gift of a piano from his parents as a child, accompanied by his timeless heartstring-puller Your Song. The ad was well received, but Jäätma is clearly unconvinced.
He hits play, and the ad starts, but this time two lines – one grey (negative reactions), the other red (positive) – are traced across the action. These follow the second-by-second responses of a 200-person sample audience who watched the ad and allowed Realeyes to record them through the camera of their computer or smartphone. Realeyes then used its AI technology to analyse each individual’s facial expression and body language. The company did this with all of Jäätma’s list of 20 Christmas ads from 2018, watching 4,000 people, before rating each commercial for attention, emotion, sentiment and finally giving it a mark out of 10.
What is wrong with The Boy and the Piano, then? “So these are the metrics we measure: are you happy? Confused? Sad? Disgusted?” explains Jäätma, as the video plays. “If you look at the grey line, the negative emotions, you see that the UK audience is not that excited about the Elton John parts. The negativity goes up, people are tired about this promotion of the celebrity, they have had enough of this Elton John stuff.” Only when Elton as a child makes an appearance is there a spike of red. “Now when it goes into family and kids and it’s not the celebrity any more,” Jäätma goes on, “that’s where the positivity goes up.”
Heat pumps are already incredibly efficient. Researchers in Switzerland say they can push efficiencies even further using artificial intelligence.
A research team led by Jürg Alexander Schiffmann at the L’Ecole Polytechnique Fédérale de Lausanne (Swiss Federal Institute of Technology Lausanne, or EPFL) is using AI to design compressors that slash heat pumps’ electricity consumption by around 25 percent.
Unlike conventional furnaces or boilers, which combust fuels to generate heat, heat pumps use electricity to move heat from one place to another. Employing a compressor and refrigerant, heat pumps expel heat from the indoors to the outside during the cooling season, or capture heat outdoors from the ground or air and draw it indoors in winter.
In Chandler, Arizona, a suburb of Phoenix, Waymo’s fleet of 600 minivans shuttling people from place to place. Ordering one feels almost exactly like calling a Lyft or Uber, except for one thing: the vans drive themselves. Alphabet’s Waymo has been testing self-driving vehicles in Arizona since 2017 and we got a look at what it’s like. [video, 13:01]
June (as shown at the top of this post) is when zero-click searches in browsers passed 50%, but the pie chart above shows that even before that, Google was sending a huge portion of search clicks to their own properties (~6% of queries and ~12% of clicks). Those properties include YouTube, Maps, Android, Google’s blog, subdomains of Google.com, and a dozen or so others (full list here).
Maybe Google’s websites are ranking exclusively because they’re the best result, but if Congress is asking questions about whether a monopoly is potentially abusing its market dominance in one field to unfairly compete in another, I’ve got something else they’ll want to see. It’s a chart of where searches happened on major web properties in Q2, and as you can see, there’s no competition.
While voice has been touted as an emerging technology with the ability to lower the bar to entry, industry players are now starting to warn of privacy gaps. Amazon Alexa and Google Home devices are now becoming a frequent household item, used for everything from ordering a new wardrobe to helping with homework.
But when used in the medical industry, the technology needs to be administered differently than in the consumer world.
“When it comes to healthcare and voice design, we have several challenges we face every day,” Freddie Feldman, voice design director at Wolters Kluwer Health, said at The Voice of Healthcare Summit at Harvard Medical School last week. “HIPAA is a big topic on everyone’s mind nowadays, and it is one we take seriously. The first thing most people think about when they hear HIPAA is securing servers platforms, but there is more to it. We have to consider things like the unintended audience for a call.”
The UK National Audit Office recently noted that data are often not seen as a priority in government, and that this hampers policymaking. Despite the fact that so many policy challenges — from obesity to climate change — are cross-departmental, the government does not think about data in a joined-up way.
With the 2021 census fast approaching, the Office for National Statistics is going to rely more than ever on piecing together data from other departments. The biggest concern the Statistics Authority, the regulator of official statistics, has about the census is that the ONS may not be able to obtain data from departments when it is needed.
This seems a failure of leadership: we have the data, we just cannot get the incentives and controls right to share it properly. And yet, while there are many excited conversations about AI in the civil service, few seem keen to discuss the difficulties posed by data sharing without which AI applications will not have the raw materials to learn from.
Our voices may convey subtle clues about our mood and psychological state. Now, scientists are using artificial intelligence to pick up these clues, with the aim of building voice-analyzing technologies that can identify individuals in need of mental-health care. But others caution they could do more harm than good.
At the University of Alberta, computing science PhD student Mashrura Tasnim has developed a machine-learning model that can recognize the speech qualities of people with depression. Her goal is to create a smartphone application that would monitor users’ conversations and alert their emergency contacts or mental-health professionals when it detects depression.
Her work, described in a paper presented in May at the Canadian Conference on Artificial Intelligence, was spurred by tragedy, Ms. Tasnim said. A few years ago, she was working as a lecturer at a university, where a student, who was under the care of a psychosocial counsellor friend of hers, unexpectedly took his own life.
Google this week started rolling out a revamped version of a mobile learning app, called Socratic, that the tech giant acquired last year. The updated app, with new machine learning-powered features, coincides with the start of the school year, as well as other Google for Education initiatives.
Socratic aims to help both high school and university students in their studies outside of the classroom. If students need help answering a study question, they can now use the Socratic app to ask a question with their voice, or to take a picture of a question in their study materials. The app will then find relevant material from across the web.
Vancouver, BC, Canada December 2-6. “IVADO and Mila are partnering with the UBC Data Science Institute to offer this training in Vancouver from December 2 to 6, 2019.” [$$$$]
Washington, DC October 10, starting at 5 p.m., National Academies of Sciences, Engineering, and Medicine
Keck Center (500 5th St., NW). [registration required]
Anyone who is a member of the community, has attended a Carpentry workshop, has gone through instructor training or collaborated with community members in one way or another (on GitHub, in conferences/Hackathons/symposia, etc.) is invited to offer their opinion and contribute to the formulation of community values in The Carpentries by answering these two questions:
Envision people you think of as representative of The Carpentries community. What words would you use to describe these people? There is no need to identify them, just briefly outline their characteristics.
Workshops, calls, interactions, and guidelines led by The Carpentries are but a few things that make them who they are as a community. With this in mind, in two to three sentences, how would you describe The Carpentries community culture?
“Welcome to the Learn to Move: Walk Around challenge, one of the official challenges in the NeurIPS 2019 Competition Track. Your task is to develop a controller for a physiologically plausible 3D human model to walk or run following velocity commands with minimum effort. You are provided with a human musculoskeletal model and a physics-based simulation environment, OpenSim.” Round 1 competition ends on October 13.
Artificial intelligence (AI) applications are inherently data-intensive, with multiple reads and writes to the file system. And, at the outset, the AI algorithm absorbs tremendous amounts of training data as it learns the parameters of its job.
Once that is done, your AI system then diligently performs its task, but it has to output the results somewhere. And, as AI applications scale, they can encounter storage-related bottlenecks that can harm performance.
So, at every stage in the deployment, training and operation of AI systems, storage is a big consideration. In this article, we look at AI/machine learning and the storage needed to support it, which increasingly means NVMe flash.
With technological innovation comes intense speculation: will machines soon take over the world? Will your Roomba turn on you? We may not be quite there yet, but the common sci-fi theme of humans vs. machines is still quickly becoming relevant in natural language processing. CDS’ Nikita Nangia and Samuel R. Bowman, also of NYU’s Department of Linguistics and Department of Computer Science, presented research to redefine the target performance standard for GLUE (General Language Understanding Evaluation). The GLUE benchmark aims to train, evaluate, and analyze performance in NLU (Natural Language Understanding), using nine distinct NLU tasks. These tasks are diverse and include natural language inference, sentiment analysis, acceptability judgment, sentence similarity, and common sense reasoning. The objective of GLUE is to drive development of robust systems that perform well on multiple NLU tasks without additional training with massive amounts of data.