‘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.
Continue reading… “How Stockholm is leading the way for smart cities”
The internet is growing, but old information continues to disappear daily.
Many MySpace users were dismayed to discover earlier this year that the social media platform lost 50 million files uploaded between 2003 and 2015.
The failure of MySpace to care for and preserve its users’ content should serve as a reminder that relying on free third-party services can be risky.
MySpace has probably preserved the users’ data; it just lost their content. The data was valuable to MySpace; the users’ content less so.
Continue reading… “Your internet data is rotting”
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.”
Continue reading… “Data Isn’t ‘Truth’”
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.
Continue reading… “How Tech is creating data “cravability,” to make us digitally obese”
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.
Continue reading… “3 cities in the U.S. have ended chronic homelessness: Here’s how they did it”
AI & Big Data
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)
Continue reading… “The Achilles’ Heel of AI”
“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.
Continue reading… “No, Data is not the new oil”
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.
Continue reading… “Pew: 74% of users don’t know Facebook records their ad preferences”
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.
Continue reading… “9 AI trends on our radar”
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.”
Continue reading… “Microsoft and Kroger to create data-driven connected grocery stores”
One can intuitively surmise artificial intelligence (AI) is today’s hot commodity, gaining traction in businesses, academia and government in recent years. Now, there is data — all in one place — that documents growth across many indicators, including startups, venture capital, job openings and academic programs. These bellwethers were captured in the AI Index, produced under the auspices of was conceived within Stanford University’s Human-Centered AI Institute and the One Hundred Year Study on AI (AI100).
One key measure of AI development is startups and venture capital funding. From January 2015 to January 2018, active AI startups increased 2.1x, while all active startups increased 1.3x, the report states. “For the most part, growth in all active startups has remained relatively steady, while the number of AI startups has seen exponential growth,” the report’s authors add. The trickle of venture capital into AI startups, another bellwether, also turned into a torrent. VC funding for AI startups in the US increased 4.5x from 2013 to 2017. Meanwhile, VC funding for all active startups increased 2.08x.
Another key measure, job openings, accelerated in AI. While machine learning is the largest skill cited as a requirement, deep learning is growing at the fastest rate — from 2015 to 2017 the number of job openings requiring deep learning increased 35x, the report’s authors state.
Continue reading… “How fast is artificial intelligence growing? Look at the key bellwethers”
Nvidia’s new AI represents a major leap forward in graphics generation based on neural networks.
Crafting an interactive virtual world of the kind found in many modern video games is a labor-intensive process that can require years of work, hundreds of people, and millions of dollars. Soon, some of that work may be done by machines.
Computer hardware company Nvidia, which specializes in graphics cards, announced on Monday that it developed a new AI model that can take video of the real world and use it to generate a realistic and interactive virtual world. According to Nvidia, its new AI could be used to drastically lower the cost of generating virtual environments, which will be particularly useful in the video game and film industries.
Continue reading… “AI can generate interactive virtual worlds based on simple videos”
By delving into the futuring techniques of Futurist Thomas Frey, you’ll embark on an enlightening journey.
Learn More about this exciting program.