When people talk about the effects of automation and artificial intelligence on the economy, they often fixate on the quantity of human workers. Will robots take our jobs? Others focus instead on threats to the quality of employment—the replacement of middle-class occupations with lower-skill, lower-wage ones; the steady elimination of human discretion as algorithms order around warehouse pickers, ride-hailing drivers, and other workers.
What’s less understood is that artificial intelligence will transform higher-skill positions, too—in ways that demand more human judgment rather than less. And that could be a problem. As AI gets better at performing the routine tasks traditionally done by humans, only the hardest ones will be left for us to do. But wrestling with only difficult decisions all day long is stressful and unpleasant. Being able to make at least some easy calls, such as allowing Santorini onto Kickstarter, can be deeply satisfying.
While the courts may have endorsed facial recognition technology, the British public do not. A new survey by the Ada Lovelace Institute found that 55 per cent of people wanted government restrictions on police use of the technology. Respondents were also uncomfortable with its commercial use — only 17 per cent wanted to see facial recognition technology used for age verification in supermarkets, 7 per cent approved of its use for tracking shoppers and 4 per cent thought it was appropriate for screening job candidates.
When considering facial recognition technology, we need to debate not only what is lawful, but also what is ethical. In order to have that conversation, we should press pause on the further rollout of the technology. Companies should adopt a voluntary moratorium on using it. There is an impressive precedent for this in the insurance industry.
A Nature analysis finds that several fields of science are moving away from male-dominated conferences and panels — but it’s easy to slip back into old habits.
Cornell Tech and UCSF are partnering with several mHealth groups to develop an Android version of the Apple Health Records app, potentially giving millions of people access to their health data on mobile devices.
The social networking site last week debuted Facebook Dating, a service that lets adult Facebook users opt into a separate “Dating” profile bearing only their first name and age, in the United States. The product operates in 19 other countries, including Canada, Mexico and the Philippines, and will roll out to Europe by early next year.
Users can decide whether they want to share information like photos, occupation and gender identity, and current Facebook friends aren’t suggested as matches or able to see a user’s dating profile, according to the company. Matches are suggested based on “preferences, interests and other things you do on Facebook.” An Instagram-integrated “Secret Crush” feature also lets users carry a torch for people they know, only notifying the other party if there’s mutual interest.
Apple (AAPL) on Tuesday announced that it’s launching three research studies that will assess the Apple Watch’s capabilities in monitoring women’s medical conditions, hearing health, and mobility signals like heart rate and walking pace.
It’s the latest sign of Apple’s rising profile and ambition as a player in medical research — centered around a consumer gadget that has yet to demonstrate that it offers widespread health benefits for individuals or the population at large.
Apple will partner on the studies with the National Institutes of Health and the American Heart Association, as well as leading academic medical institutions including the Harvard T.H. Chan School of Public Health and Brigham and Women’s Hospital in Boston, and the University of Michigan.
Mayo Clinic, one of medicine’s most prestigious brands, announced Tuesday that it has struck a sweeping partnership with Google to store patient data in the cloud and build products using artificial intelligence and other technologies to improve care.
The 10-year partnership is a testament to Google’s expanding role in the U.S. health care system and gives Mayo greater access to the engineering talent and computing resources it needs to embed its expertise in algorithms and commercial devices.
Even as humans are getting better at recognizing bots—social media personas that are just software disguised as people—so these bots are growing more sophisticated and human-like. A new study by researchers at the University of Southern California tracks how—and suggests ramifications for public opinion and the 2020 election.
The study, published in the journal First Monday, looked at 244,699 Twitter accounts that tweeted about politics or the election in both 2016 and 2018. Using Indiana University’s Botometer tool, the researchers determined that 12.6 percent—about 31,000 accounts—were bots, a percentage that aligns with previous research.
A look at the bots’ tweets showed that most of their 2016 activity was, well, bot-like, as in rhythmically mechanical and largely composed of retweets. But in 2018, “bots better aligned with humans’ activity trends, suggesting the hypothesis that some bots have grown more sophisticated.” Moreover, the bots did a lot less retweeting.
Tl;dr: Transcribing is a pain, recent progress in speech recognition software has helped, but it is still a challenge. Furthermore, how can you be sure that your person-identifiable interview data is not going to be listened to and transcribed by someone who wasn’t on your consent forms. The bigger disruptor is the ability to annotate video and audio files.
A pioneer in artificial intelligence says conventional companies can still distinguish themselves in A.I. despite worries that tech giants like Google and Amazon have already won.
Andrew Ng, a prominent Silicon Valley executive and investor who previously led some of the biggest A.I. projects at Google and its Chinese rival Baidu, says the next wave of A.I. will be in industries in which the tech giants aren’t firmly rooted. Think manufacturing, agriculture, and healthcare.
Official Google Webmaster Central Blog, Danny Sullivan and Gary Illyes
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Nearly 15 years ago, the nofollow attribute was introduced as a means to help fight comment spam. It also quickly became one of Google’s recommended methods for flagging advertising-related or sponsored links. The web has evolved since nofollow was introduced in 2005 and it’s time for nofollow to evolve as well.
Today, we’re announcing two new link attributes that provide webmasters with additional ways to identify to Google Search the nature of particular links.
The Partnership on AI (PAI) today published a paper to address the challenges experienced by the global artificial intelligence and machine learning (AI/ML) community in obtaining visas. Based on input from PAI Partners, AI practitioners, and PAI’s own research, this paper offers recommendations that will enable multidisciplinary AI/ML experts to collaborate with international counterparts. PAI encourages individuals, organizations, and policymakers to implement these policy recommendations in order to benefit from diverse perspectives offered by the global AI/ML community.
Today’s visa application systems have a disproportionate effect on those without the resources to hire experts or employ teams of people to navigate complex global visa processes – enhancing inequity seen across many dimensions of the technology sector and the AI/ML field. While multinational companies and well-funded universities are able to hire visa experts, resource constrained startups, students, and less affluent applicants lack the finances to successfully obtain visas.
Harvard Business Review, Brooks Holtom and David Allen
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Companies know that employee turnover is expensive and disruptive. And they know that retaining their best and brightest employees helps them not only save money but also preserve competitive advantages and protect intellectual capital.
Most retention efforts, however, rely on two retrospective tools. First, exit interviews are conducted to better understand why people chose to leave, though by this point, it is usually too late to keep them. Second, annual employee surveys are used to assess engagement. These survey results are later compared to people who left the organization, in the hopes that they will yield any relevant predictors of departures. The problem is that these data don’t give managers a real-time picture of who might be considering leaving.
Our latest research has focused on using big data and machine-learning algorithms to develop a turnover propensity index for individuals – a real-time indicator of who is likely thinking about quitting. We grounded the development of these predictive models in academic research on turnover and then conducted a series of studies. Our results demonstrate that it is possible to develop indices that predict in real time the likelihood of a person to consider an outside offer and to eventually leave the firm.
A Wright State University affiliate has been selected to lead a first-of-its-kind program that aims to enhance partnerships between the Air Force and academia.
Wright State Applied Research Corp., a nonprofit that provides contracting, security and research administration services for Wright State Research Institute, the university and the state of Ohio, will head the new U.S. Air Force Academic Partnership and Engagement Experiment (APEX) program.
The five-year, $49 million APEX project will focus on enhancing academic engagement nationwide in support of the Air Force Small Business program, National Defense Strategy and the Air Force’s 2030 Strategy.
The term “big data” has permeated society without much clarity for many about what it means.
A proposed master’s program in data science at the University of Nebraska at Omaha will cover the topic and capitalize on the need for big data experts in the workforce.
Alex Miller, Ryan Senior, Mario Aquino, Nick Cowan, Bridget Hillyer, Crystal Martin
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St. Louis, MO September 12-14. “Strange Loop is a multi-disciplinary conference that brings together the developers and thinkers building tomorrow’s technology in fields such as emerging languages, alternative databases, concurrency, distributed systems, security, and the web.” [$$$]
Washington, DC September 12, starting at 8 a.m. “The theme of this event is the future of government: how federal employees, private industry, and their partners are implementing new technology to enhance and transform government missions.” [rsvp required]
Boston, MA November 6-8. “The third meeting in a series on this topic, this community workshop is designed to gather a broad collection of astronomers, educators, and engineers to discuss the technical, institutional, and sociological barriers preventing any astronomer leveraging the scientific potential of these petabyte-scale datasets, and to develop a community roadmap designed to address these challenges and enable a data inclusion revolution in astronomy.” [signup required]
Portland, OR March 26-27, 2020. “We are now accepting submissions for presentations, workshops, and more for SCIENCE TALK ‘20. Here’s your chance to share your expertise with other science communication practitioners, facilitators, and professionals.” Deadline for Presentations and Workshops submissions is September 20.
How many of you out there have this problem? You build your application in an IDE, like VSCode, then you deploy your app in a Kubernetes cluster using kubectl, and then you proceed to use a bunch of other CLI’s or GUI-based consoles for various other tasks like monitoring, analyzing and troubleshooting. We live with this process every day, but it’s clunky and, frankly, takes too long.
To address this problem, we created and released an open-source project called Kui, which we believe makes life easier for cloud-native developers. We use it to seamlessly interact with multiple tools in order to minimize context switching and get more done in a single place. Some tasks that we find are especially easy with Kui include navigating through the hierarchy of resources, creating and deleting resources, and finding logs.
This library provides high-performance components leveraging the hardware acceleration support and automatic differentiation of TensorFlow. The library will provide TensorFlow support for foundational mathematical methods, mid-level methods, and specific pricing models. The coverage is being rapidly expanded over the next few months.