Gatwick first trialled facial-recognition-based checks at some of its departure gates last year
Gatwick has become the UK’s first airport to confirm it will use facial-recognition cameras on a permanent basis for ID checks before passengers board planes.
It follows a self-boarding trial carried out in partnership with EasyJet last year.
The London airport said the technology should reduce queuing times but travellers would still need to carry passports.
Sidewalk Labs says it will spend $1.3 billion on the project in the hopes of spurring $38 billion in private sector investment by 2040
Sidewalk Labs, Alphabet’s smart city subsidiary, released its massive plan Monday to transform a slice of Toronto’s waterfront into a high-tech utopia. Eighteen months in the making and clocking in at 1,524 pages, the plan represents Alphabet’s first, high-stakes effort to realize Alphabet CEO Larry Page’s long-held dream of a city within a city to experiment with innovations like self-driving cars, public Wi-Fi, new health care delivery solutions, and other city planning advances that modern technology makes possible.
Previously, Sidewalk Labs called it “a neighborhood built from the internet up.” But on Monday, Sidewalk Labs CEO Dan Doctoroff went a step further to describe it as “the most innovative district in the world.”
‘Being smart is about working in a smarter way with different partners and empowering citizens’
Stockholm is one of the world’s most connected cities, and a beacon for innovators and international talent. We are also a forward-looking city, leading the environmental and smart city agendas. By 2040, we have the ambition to be both carbon neutral and the smartest city in the world.
It has become perhaps the most important guiding principle of today’s world of data science: “data is truth.” The statisticians, programmers and machine learning experts that acquire and analyze the vast oceans of data that power modern society are seen as uncovering undeniable underlying “truths” about human society through the power of unbiased data and unerring algorithms. Unfortunately, data scientists themselves too often conflate their work with the search for truth and fail to ask whether the data they are analyzing can actually answer the questions they ask of it. Why can’t data scientists be more like those of the physical sciences that see not “universal truths” but rather “current consensus understanding?”
Given the sheer density of statisticians in the data sciences, it is remarkable how poorly the field adheres to statistical best practices like normalization and characterizing data before analyzing it. Programmers in the data sciences, too, tend to lack the deep numerical methods and scientific computing backgrounds of their predecessors, making them dangerously unaware of the myriad traps that await numerically-intensive codes.
Most importantly, however, somewhere along the way data science became about pursuing “truth” rather than “evidence.”
Are you taking in too much information every day? If so, be on the lookout for this potentially dangerous new condition.
Obesity and dramatic overweight are a huge global problem, costing an estimated $450 billion per year in the U.S. alone, where more than two-thirds of people are overweight and an estimated 35.7% are considered obese. But that’s just physical obesity. The exact same processes that companies use to trick us into wanting to eat and eat are also being used to get us to spend more and more time online.
Nine more have ended veteran homelessness. It’s part of a national program called Built for Zero that uses a data-based approach to help officials figure out exactly who needs what services. Now it’s accelerating its work in 50 more cities.
In late February, the city of Abilene, Texas, made an announcement: It had ended local veteran homelessness. It was the first community in the state and the ninth in the country to reach that goal, as part of a national program called Built for Zero. Now, through the same program, Abilene is working to end chronic homelessness. While homelessness might often be seen as an intractable problem because of its complexity–or one that costs more to solve than communities can afford–the program is proving that is not the case.
“By ending homelessness, we mean getting to a place where it’s rare, brief, and it gets solved correctly and quickly when it does happen,” says Rosanne Haggerty, president of Community Solutions, the nonprofit that leads the Built for Zero program. “That’s a completely achievable end state, we now see.” The nonprofit, which calls this goal “functional zero,” announced today that it is accelerating its work in 50 communities.
Garbage in is garbage out. There’s no saying more true in computer science, and especially is the case with artificial intelligence. Machine learning algorithms are very dependent on accurate, clean, and well-labeled training data to learn from so that they can produce accurate results. If you train your machine learning models with garbage, it’s no surprise you’ll get garbage results. It’s for this reason that the vast majority of the time spent during AI projects are during the data collection, cleaning, preparation, and labeling phases.
According to a recent report from AI research and advisory firm Cognilytica, over 80% of the time spent in AI projects are spent dealing with and wrangling data. Even more importantly, and perhaps surprisingly, is how human-intensive much of this data preparation work is. In order for supervised forms of machine learning to work, especially the multi-layered deep learning neural network approaches, they must be fed large volumes of examples of correct data that is appropriately annotated, or “labeled”, with the desired output result. For example, if you’re trying to get your machine learning algorithm to correctly identify cats inside of images, you need to feed that algorithm thousands of images of cats, appropriately labeled as cats, with the images not having any extraneous or incorrect data that will throw the algorithm off as you build the model. (Disclosure: I’m a principal analyst with Cognilytica)
“Data is the new oil” is one of those deceptively simple mantras for the modern world. Whether in The New York Times, The Economist, or WIRED, the wildcatting nature of oil exploration, plus the extractive exploitation of a trapped asset, seems like an apt metaphor for the boom in monetized data.
Antonio García Martínez (@antoniogm) is an Ideas contributor for WIRED. Previously he worked on Facebook’s early monetization team, where he headed its targeting efforts. His 2016 memoir, Chaos Monkeys, was a New York Times best seller and NPR Best Book of the Year.
The metaphor has even assumed political implications. Newly installed California governor Gavin Newsom recently proposed an ambitious “data dividend” plan, whereby companies like Facebook or Google would pay their users a fraction of the revenue derived from the users’ data. Facebook cofounder Chris Hughes laid out a similar idea in a Guardian op-ed, and compared it to the Alaskan Permanent Fund, which doles out annual payments to Alaskans based on the state’s petroleum revenue. As in Alaska, the average Google or Facebook user is conceived as standing on a vast substratum of personal data whose extraction they’re entitled to profit from.
Facebook has been in the news quite a bit for its ad targeting over the past year, most notably with reports that the now-defunct Cambridge Analytica used improperly obtained data to develop “personality” profiles on U.S. voters and target ads toward them during the 2016 U.S. presidential election. But many users are still unaware what information Facebook actually collects for ad targeting purposes.
A new survey out this morning from Pew Research found that 74 percent of Facebook users surveyed did not know there was a “your ad preferences page” where they could see which ad categories Facebook had placed them into, based on interests and information they’ve shared with the service. Pew surveyed 963 U.S. adults with Facebook accounts between September 4 and October 1, 2018.
How new developments in automation, machine deception, hardware, and more will shape AI.
Here are key AI trends business leaders and practitioners should watch in the months ahead.
We will start to see technologies enable partial automation of a variety of tasks.
Automation occurs in stages. While full automation might still be a ways off, there are many workflows and tasks that lend themselves to partial automation. In fact, McKinsey estimates that “fewer than 5% of occupations can be entirely automated using current technology. However, about 60% of occupations could have 30% or more of their constituent activities automated.”
We have already seen some interesting products and services that rely on computer vision and speech technologies, and we expect to see even more in 2019. Look for additional improvements in language models and robotics that will result in solutions that target text and physical tasks. Rather than waiting for a complete automation model, competition will drive organizations to implement partial automation solutions—and the success of those partial automation projects will spur further development.
Microsoft and Kroger are taking a leaf out of Amazon’s book by building futuristic “connected” grocery stores.
As part of a pilot project, Kroger, the largest supermarket in the U.S. by revenue, and Microsoft have transformed two retail stores, one near each of their respective headquarters — in Monroe, Ohio and Redmond, Washington — using technology powered by connected sensors and Microsoft’s Azure cloud platform.
The first fruit of the partnership is a digital shelving system, which was actually announced last year and is in the process of rolling out to dozens of Kroger stores across the U.S. Called EDGE (Enhanced Display for Grocery Environment), it bypasses paper price tags for digital shelf displays that can be changed in real time from anywhere, and it also can display promotions, dietary information, and more.
But the test stores are where Kroger and Microsoft are taking things to the next level. In addition to EDGE shelving, the system will include a new guided shopping experience, personalized ads, and something the partners are calling “pick-to-light.”
