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#435505 This Week’s Awesome Stories From ...

AUGMENTED REALITY
This Is the Computer You’ll Wear on Your Face in 10 Years
Mark Sullivan | Fast Company
“[Snap’s new Spectacles 3] foreshadow a device that many of us may wear as our primary personal computing device in about 10 years. Based on what I’ve learned by talking AR with technologists in companies big and small, here is what such a device might look like and do.”

ROBOTICS
These Robo-Shorts Are the Precursor to a True Robotic Exoskeleton
Devin Coldewey | TechCrunch
“The whole idea, then, is to leave behind the idea of an exosuit as a big mechanical thing for heavy industry or work, and bring in the idea that one could help an elderly person stand up from a chair, or someone recovering from an accident walk farther without fatigue.”

ENVIRONMENT
Artificial Tree Promises to Suck Up as Much Air Pollution as a Small Forest
Luke Dormehl | Digital Trends
“The company has developed an artificial tree that it claims is capable of sucking up the equivalent amount of air pollution as 368 living trees. That’s not only a saving on growing time, but also on the space needed to accommodate them.”

FUTURE
The Anthropocene Is a Joke
Peter Brannen | The Atlantic
“Unless we fast learn how to endure on this planet, and on a scale far beyond anything we’ve yet proved ourselves capable of, the detritus of civilization will be quickly devoured by the maw of deep time.”

ARTIFICIAL INTELLIGENCE
DeepMind’s Losses and the Future of Artificial Intelligence
Gary Marcus | Wired
“Still, the rising magnitude of DeepMind’s losses is worth considering: $154 million in 2016, $341 million in 2017, $572 million in 2018. In my view, there are three central questions: Is DeepMind on the right track scientifically? Are investments of this magnitude sound from Alphabet’s perspective? And how will the losses affect AI in general?”

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#435308 Brain-Machine Interfaces Are Getting ...

Elon Musk grabbed a lot of attention with his July 16 announcement that his company Neuralink plans to implant electrodes into the brains of people with paralysis by next year. Their first goal is to create assistive technology to help people who can’t move or are unable to communicate.

If you haven’t been paying attention, brain-machine interfaces (BMIs) that allow people to control robotic arms with their thoughts might sound like science fiction. But science and engineering efforts have already turned it into reality.

In a few research labs around the world, scientists and physicians have been implanting devices into the brains of people who have lost the ability to control their arms or hands for over a decade. In our own research group at the University of Pittsburgh, we’ve enabled people with paralyzed arms and hands to control robotic arms that allow them to grasp and move objects with relative ease. They can even experience touch-like sensations from their own hand when the robot grasps objects.

At its core, a BMI is pretty straightforward. In your brain, microscopic cells called neurons are sending signals back and forth to each other all the time. Everything you think, do and feel as you interact with the world around you is the result of the activity of these 80 billion or so neurons.

If you implant a tiny wire very close to one of these neurons, you can record the electrical activity it generates and send it to a computer. Record enough of these signals from the right area of the brain and it becomes possible to control computers, robots, or anything else you might want, simply by thinking about moving. But doing this comes with tremendous technical challenges, especially if you want to record from hundreds or thousands of neurons.

What Neuralink Is Bringing to the Table
Elon Musk founded Neuralink in 2017, aiming to address these challenges and raise the bar for implanted neural interfaces.

Perhaps the most impressive aspect of Neuralink’s system is the breadth and depth of their approach. Building a BMI is inherently interdisciplinary, requiring expertise in electrode design and microfabrication, implantable materials, surgical methods, electronics, packaging, neuroscience, algorithms, medicine, regulatory issues, and more. Neuralink has created a team that spans most, if not all, of these areas.

With all of this expertise, Neuralink is undoubtedly moving the field forward, and improving their technology rapidly. Individually, many of the components of their system represent significant progress along predictable paths. For example, their electrodes, that they call threads, are very small and flexible; many researchers have tried to harness those properties to minimize the chance the brain’s immune response would reject the electrodes after insertion. Neuralink has also developed high-performance miniature electronics, another focus area for labs working on BMIs.

Often overlooked in academic settings, however, is how an entire system would be efficiently implanted in a brain.

Neuralink’s BMI requires brain surgery. This is because implanted electrodes that are in intimate contact with neurons will always outperform non-invasive electrodes where neurons are far away from the electrodes sitting outside the skull. So, a critical question becomes how to minimize the surgical challenges around getting the device into a brain.

Maybe the most impressive aspect of Neuralink’s announcement was that they created a 3,000-electrode neural interface where electrodes could be implanted at a rate of between 30 and 200 per minute. Each thread of electrodes is implanted by a sophisticated surgical robot that essentially acts like a sewing machine. This all happens while specifically avoiding blood vessels that blanket the surface of the brain. The robotics and imaging that enable this feat, with tight integration to the entire device, is striking.

Neuralink has thought through the challenge of developing a clinically viable BMI from beginning to end in a way that few groups have done, though they acknowledge that many challenges remain as they work towards getting this technology into human patients in the clinic.

Figuring Out What More Electrodes Gets You
The quest for implantable devices with thousands of electrodes is not only the domain of private companies. DARPA, the NIH BRAIN Initiative, and international consortiums are working on neurotechnologies for recording and stimulating in the brain with goals of tens of thousands of electrodes. But what might scientists do with the information from 1,000, 3,000, or maybe even 100,000 neurons?

At some level, devices with more electrodes might not actually be necessary to have a meaningful impact in people’s lives. Effective control of computers for access and communication, of robotic limbs to grasp and move objects as well as of paralyzed muscles is already happening—in people. And it has been for a number of years.

Since the 1990s, the Utah Array, which has just 100 electrodes and is manufactured by Blackrock Microsystems, has been a critical device in neuroscience and clinical research. This electrode array is FDA-cleared for temporary neural recording. Several research groups, including our own, have implanted Utah Arrays in people that lasted multiple years.

Currently, the biggest constraints are related to connectors, electronics, and system-level engineering, not the implanted electrode itself—although increasing the electrodes’ lifespan to more than five years would represent a significant advance. As those technical capabilities improve, it might turn out that the ability to accurately control computers and robots is limited more by scientists’ understanding of what the neurons are saying—that is, the neural code—than by the number of electrodes on the device.

Even the most capable implanted system, and maybe the most capable devices researchers can reasonably imagine, might fall short of the goal of actually augmenting skilled human performance. Nevertheless, Neuralink’s goal of creating better BMIs has the potential to improve the lives of people who can’t move or are unable to communicate. Right now, Musk’s vision of using BMIs to meld physical brains and intelligence with artificial ones is no more than a dream.

So, what does the future look like for Neuralink and other groups creating implantable BMIs? Devices with more electrodes that last longer and are connected to smaller and more powerful wireless electronics are essential. Better devices themselves, however, are insufficient. Continued public and private investment in companies and academic research labs, as well as innovative ways for these groups to work together to share technologies and data, will be necessary to truly advance scientists’ understanding of the brain and deliver on the promise of BMIs to improve peoples’ lives.

While researchers need to keep the future societal implications of advanced neurotechnologies in mind—there’s an essential role for ethicists and regulation—BMIs could be truly transformative as they help more people overcome limitations caused by injury or disease in the brain and body.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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#435224 Can AI Save the Internet from Fake News?

There’s an old proverb that says “seeing is believing.” But in the age of artificial intelligence, it’s becoming increasingly difficult to take anything at face value—literally.

The rise of so-called “deepfakes,” in which different types of AI-based techniques are used to manipulate video content, has reached the point where Congress held its first hearing last month on the potential abuses of the technology. The congressional investigation coincided with the release of a doctored video of Facebook CEO Mark Zuckerberg delivering what appeared to be a sinister speech.

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Scientists are scrambling for solutions on how to combat deepfakes, while at the same time others are continuing to refine the techniques for less nefarious purposes, such as automating video content for the film industry.

At one end of the spectrum, for example, researchers at New York University’s Tandon School of Engineering have proposed implanting a type of digital watermark using a neural network that can spot manipulated photos and videos.

The idea is to embed the system directly into a digital camera. Many smartphone cameras and other digital devices already use AI to boost image quality and make other corrections. The authors of the study out of NYU say their prototype platform increased the chances of detecting manipulation from about 45 percent to more than 90 percent without sacrificing image quality.

On the other hand, researchers at Carnegie Mellon University recently hit on a technique for automatically and rapidly converting large amounts of video content from one source into the style of another. In one example, the scientists transferred the facial expressions of comedian John Oliver onto the bespectacled face of late night show host Stephen Colbert.

The CMU team says the method could be a boon to the movie industry, such as by converting black and white films to color, though it also conceded that the technology could be used to develop deepfakes.

Words Matter with Fake News
While the current spotlight is on how to combat video and image manipulation, a prolonged trench warfare on fake news is being fought by academia, nonprofits, and the tech industry.

This isn’t the fake news that some have come to use as a knee-jerk reaction to fact-based information that might be less than flattering to the subject of the report. Rather, fake news is deliberately-created misinformation that is spread via the internet.

In a recent Pew Research Center poll, Americans said fake news is a bigger problem than violent crime, racism, and terrorism. Fortunately, many of the linguistic tools that have been applied to determine when people are being deliberately deceitful can be baked into algorithms for spotting fake news.

That’s the approach taken by a team at the University of Michigan (U-M) to develop an algorithm that was better than humans at identifying fake news—76 percent versus 70 percent—by focusing on linguistic cues like grammatical structure, word choice, and punctuation.

For example, fake news tends to be filled with hyperbole and exaggeration, using terms like “overwhelming” or “extraordinary.”

“I think that’s a way to make up for the fact that the news is not quite true, so trying to compensate with the language that’s being used,” Rada Mihalcea, a computer science and engineering professor at U-M, told Singularity Hub.

The paper “Automatic Detection of Fake News” was based on the team’s previous studies on how people lie in general, without necessarily having the intention of spreading fake news, she said.

“Deception is a complicated and complex phenomenon that requires brain power,” Mihalcea noted. “That often results in simpler language, where you have shorter sentences or shorter documents.”

AI Versus AI
While most fake news is still churned out by humans with identifiable patterns of lying, according to Mihalcea, other researchers are already anticipating how to detect misinformation manufactured by machines.

A group led by Yejin Choi, with the Allen Institute of Artificial Intelligence and the University of Washington in Seattle, is one such team. The researchers recently introduced the world to Grover, an AI platform that is particularly good at catching autonomously-generated fake news because it’s equally good at creating it.

“This is due to a finding that is perhaps counterintuitive: strong generators for neural fake news are themselves strong detectors of it,” wrote Rowan Zellers, a PhD student and team member, in a Medium blog post. “A generator of fake news will be most familiar with its own peculiarities, such as using overly common or predictable words, as well as the peculiarities of similar generators.”

The team found that the best current discriminators can classify neural fake news from real, human-created text with 73 percent accuracy. Grover clocks in with 92 percent accuracy based on a training set of 5,000 neural network-generated fake news samples. Zellers wrote that Grover got better at scale, identifying 97.5 percent of made-up machine mumbo jumbo when trained on 80,000 articles.

It performed almost as well against fake news created by a powerful new text-generation system called GPT-2 built by OpenAI, a nonprofit research lab founded by Elon Musk, classifying 96.1 percent of the machine-written articles.

OpenAI had so feared that the platform could be abused that it has only released limited versions of the software. The public can play with a scaled-down version posted by a machine learning engineer named Adam King, where the user types in a short prompt and GPT-2 bangs out a short story or poem based on the snippet of text.

No Silver AI Bullet
While real progress is being made against fake news, the challenges of using AI to detect and correct misinformation are abundant, according to Hugo Williams, outreach manager for Logically, a UK-based startup that is developing different detectors using elements of deep learning and natural language processing, among others. He explained that the Logically models analyze information based on a three-pronged approach.

Publisher metadata: Is the article from a known, reliable, and trustworthy publisher with a history of credible journalism?
Network behavior: Is the article proliferating through social platforms and networks in ways typically associated with misinformation?
Content: The AI scans articles for hundreds of known indicators typically found in misinformation.

“There is no single algorithm which is capable of doing this,” Williams wrote in an email to Singularity Hub. “Even when you have a collection of different algorithms which—when combined—can give you relatively decent indications of what is unreliable or outright false, there will always need to be a human layer in the pipeline.”

The company released a consumer app in India back in February just before that country’s election cycle that was a “great testing ground” to refine its technology for the next app release, which is scheduled in the UK later this year. Users can submit articles for further scrutiny by a real person.

“We see our technology not as replacing traditional verification work, but as a method of simplifying and streamlining a very manual process,” Williams said. “In doing so, we’re able to publish more fact checks at a far quicker pace than other organizations.”

“With heightened analysis and the addition of more contextual information around the stories that our users are reading, we are not telling our users what they should or should not believe, but encouraging critical thinking based upon reliable, credible, and verified content,” he added.

AI may never be able to detect fake news entirely on its own, but it can help us be smarter about what we read on the internet.

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#435098 Coming of Age in the Age of AI: The ...

The first generation to grow up entirely in the 21st century will never remember a time before smartphones or smart assistants. They will likely be the first children to ride in self-driving cars, as well as the first whose healthcare and education could be increasingly turned over to artificially intelligent machines.

Futurists, demographers, and marketers have yet to agree on the specifics of what defines the next wave of humanity to follow Generation Z. That hasn’t stopped some, like Australian futurist Mark McCrindle, from coining the term Generation Alpha, denoting a sort of reboot of society in a fully-realized digital age.

“In the past, the individual had no power, really,” McCrindle told Business Insider. “Now, the individual has great control of their lives through being able to leverage this world. Technology, in a sense, transformed the expectations of our interactions.”

No doubt technology may impart Marvel superhero-like powers to Generation Alpha that even tech-savvy Millennials never envisioned over cups of chai latte. But the powers of machine learning, computer vision, and other disciplines under the broad category of artificial intelligence will shape this yet unformed generation more definitively than any before it.

What will it be like to come of age in the Age of AI?

The AI Doctor Will See You Now
Perhaps no other industry is adopting and using AI as much as healthcare. The term “artificial intelligence” appears in nearly 90,000 publications from biomedical literature and research on the PubMed database.

AI is already transforming healthcare and longevity research. Machines are helping to design drugs faster and detect disease earlier. And AI may soon influence not only how we diagnose and treat illness in children, but perhaps how we choose which children will be born in the first place.

A study published earlier this month in NPJ Digital Medicine by scientists from Weill Cornell Medicine used 12,000 photos of human embryos taken five days after fertilization to train an AI algorithm on how to tell which in vitro fertilized embryo had the best chance of a successful pregnancy based on its quality.

Investigators assigned each embryo a grade based on various aspects of its appearance. A statistical analysis then correlated that grade with the probability of success. The algorithm, dubbed Stork, was able to classify the quality of a new set of images with 97 percent accuracy.

“Our algorithm will help embryologists maximize the chances that their patients will have a single healthy pregnancy,” said Dr. Olivier Elemento, director of the Caryl and Israel Englander Institute for Precision Medicine at Weill Cornell Medicine, in a press release. “The IVF procedure will remain the same, but we’ll be able to improve outcomes by harnessing the power of artificial intelligence.”

Other medical researchers see potential in applying AI to detect possible developmental issues in newborns. Scientists in Europe, working with a Finnish AI startup that creates seizure monitoring technology, have developed a technique for detecting movement patterns that might indicate conditions like cerebral palsy.

Published last month in the journal Acta Pediatrica, the study relied on an algorithm to extract the movements from a newborn, turning it into a simplified “stick figure” that medical experts could use to more easily detect clinically relevant data.

The researchers are continuing to improve the datasets, including using 3D video recordings, and are now developing an AI-based method for determining if a child’s motor maturity aligns with its true age. Meanwhile, a study published in February in Nature Medicine discussed the potential of using AI to diagnose pediatric disease.

AI Gets Classy
After being weaned on algorithms, Generation Alpha will hit the books—about machine learning.

China is famously trying to win the proverbial AI arms race by spending billions on new technologies, with one Chinese city alone pledging nearly $16 billion to build a smart economy based on artificial intelligence.

To reach dominance by its stated goal of 2030, Chinese cities are also incorporating AI education into their school curriculum. Last year, China published its first high school textbook on AI, according to the South China Morning Post. More than 40 schools are participating in a pilot program that involves SenseTime, one of the country’s biggest AI companies.

In the US, where it seems every child has access to their own AI assistant, researchers are just beginning to understand how the ubiquity of intelligent machines will influence the ways children learn and interact with their highly digitized environments.

Sandra Chang-Kredl, associate professor of the department of education at Concordia University, told The Globe and Mail that AI could have detrimental effects on learning creativity or emotional connectedness.

Similar concerns inspired Stefania Druga, a member of the Personal Robots group at the MIT Media Lab (and former Education Teaching Fellow at SU), to study interactions between children and artificial intelligence devices in order to encourage positive interactions.

Toward that goal, Druga created Cognimates, a platform that enables children to program and customize their own smart devices such as Alexa or even a smart, functional robot. The kids can also use Cognimates to train their own AI models or even build a machine learning version of Rock Paper Scissors that gets better over time.

“I believe it’s important to also introduce young people to the concepts of AI and machine learning through hands-on projects so they can make more informed and critical use of these technologies,” Druga wrote in a Medium blog post.

Druga is also the founder of Hackidemia, an international organization that sponsors workshops and labs around the world to introduce kids to emerging technologies at an early age.

“I think we are in an arms race in education with the advancement of technology, and we need to start thinking about AI literacy before patterns of behaviors for children and their families settle in place,” she wrote.

AI Goes Back to School
It also turns out that AI has as much to learn from kids. More and more researchers are interested in understanding how children grasp basic concepts that still elude the most advanced machine minds.

For example, developmental psychologist Alison Gopnik has written and lectured extensively about how studying the minds of children can provide computer scientists clues on how to improve machine learning techniques.

In an interview on Vox, she described that while DeepMind’s AlpahZero was trained to be a chessmaster, it struggles with even the simplest changes in the rules, such as allowing the bishop to move horizontally instead of vertically.

“A human chess player, even a kid, will immediately understand how to transfer that new rule to their playing of the game,” she noted. “Flexibility and generalization are something that even human one-year-olds can do but that the best machine learning systems have a much harder time with.”

Last year, the federal defense agency DARPA announced a new program aimed at improving AI by teaching it “common sense.” One of the chief strategies is to develop systems for “teaching machines through experience, mimicking the way babies grow to understand the world.”

Such an approach is also the basis of a new AI program at MIT called the MIT Quest for Intelligence.

The research leverages cognitive science to understand human intelligence, according to an article on the project in MIT Technology Review, such as exploring how young children visualize the world using their own innate 3D models.

“Children’s play is really serious business,” said Josh Tenenbaum, who leads the Computational Cognitive Science lab at MIT and his head of the new program. “They’re experiments. And that’s what makes humans the smartest learners in the known universe.”

In a world increasingly driven by smart technologies, it’s good to know the next generation will be able to keep up.

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#435080 12 Ways Big Tech Can Take Big Action on ...

Bill Gates and Mark Zuckerberg have invested $1 billion in Breakthrough Energy to fund next-generation solutions to tackle climate. But there is a huge risk that any successful innovation will only reach the market as the world approaches 2030 at the earliest.

We now know that reducing the risk of dangerous climate change means halving global greenhouse gas emissions by that date—in just 11 years. Perhaps Gates, Zuckerberg, and all the tech giants should invest equally in innovations to do with how their own platforms —search, social media, eCommerce—can support societal behavior changes to drive down emissions.

After all, the tech giants influence the decisions of four billion consumers every day. It is time for a social contract between tech and society.

Recently myself and collaborator Johan Falk published a report during the World Economic Forum in Davos outlining 12 ways the tech sector can contribute to supporting societal goals to stabilize Earth’s climate.

Become genuine climate guardians

Tech giants go to great lengths to show how serious they are about reducing their emissions. But I smell cognitive dissonance. Google and Microsoft are working in partnership with oil companies to develop AI tools to help maximize oil recovery. This is not the behavior of companies working flat-out to stabilize Earth’s climate. Indeed, few major tech firms have visions that indicate a stable and resilient planet might be a good goal, yet AI alone has the potential to slash greenhouse gas emissions by four percent by 2030—equivalent to the emissions of Australia, Canada, and Japan combined.

We are now developing a playbook, which we plan to publish later this year at the UN climate summit, about making it as simple as possible for a CEO to become a climate guardian.

Hey Alexa, do you care about the stability of Earth’s climate?

Increasingly, consumers are delegating their decisions to narrow artificial intelligence like Alexa and Siri. Welcome to a world of zero-click purchases.

Should algorithms and information architecture be designed to nudge consumer behavior towards low-carbon choices, for example by making these options the default? We think so. People don’t mind being nudged; in fact, they welcome efforts to make their lives better. For instance, if I want to lose weight, I know I will need all the help I can get. Let’s ‘nudge for good’ and experiment with supporting societal goals.

Use social media for good

Facebook’s goal is to bring the world closer together. With 2.2 billion users on the platform, CEO Mark Zuckerberg can reasonably claim this goal is possible. But social media has changed the flow of information in the world, creating a lucrative industry around a toxic brown-cloud of confusion and anger, with frankly terrifying implications for democracy. This has been linked to the rise of nationalism and populism, and to the election of leaders who shun international cooperation, dismiss scientific knowledge, and reverse climate action at a moment when we need it more than ever.

Social media tools need re-engineering to help people make sense of the world, support democratic processes, and build communities around societal goals. Make this your mission.

Design for a future on Earth

Almost everything is designed with computer software, from buildings to mobile phones to consumer packaging. It is time to make zero-carbon design the new default and design products for sharing, re-use and disassembly.

The future is circular

Halving emissions in a decade will require all companies to adopt circular business models to reduce material use. Some tech companies are leading the charge. Apple has committed to becoming 100 percent circular as soon as possible. Great.

While big tech companies strive to be market leaders here, many other companies lack essential knowledge. Tech companies can support rapid adoption in different economic sectors, not least because they have the know-how to scale innovations exponentially. It makes business sense. If economies of scale drive the price of recycled steel and aluminium down, everyone wins.

Reward low-carbon consumption

eCommerce platforms can create incentives for low-carbon consumption. The world’s largest experiment in greening consumer behavior is Ant Forest, set up by Chinese fintech giant Ant Financial.

An estimated 300 million customers—similar to the population of the United States—gain points for making low-carbon choices such as walking to work, using public transport, or paying bills online. Virtual points are eventually converted into real trees. Sure, big questions remain about its true influence on emissions, but this is a space for rapid experimentation for big impact.

Make information more useful

Science is our tool for defining reality. Scientific consensus is how we attain reliable knowledge. Even after the information revolution, reliable knowledge about the world remains fragmented and unstructured. Build the next generation of search engines to genuinely make the world’s knowledge useful for supporting societal goals.

We need to put these tools towards supporting shared world views of the state of the planet based on the best science. New AI tools being developed by startups like Iris.ai can help see through the fog. From Alexa to Google Home and Siri, the future is “Voice”, but who chooses the information source? The highest bidder? Again, the implications for climate are huge.

Create new standards for digital advertising and marketing

Half of global ad revenue will soon be online, and largely going to a small handful of companies. How about creating a novel ethical standard on what is advertised and where? Companies could consider promoting sustainable choices and healthy lifestyles and limiting advertising of high-emissions products such as cheap flights.

We are what we eat

It is no secret that tech is about to disrupt grocery. The supermarkets of the future will be built on personal consumer data. With about two billion people either obese or overweight, revolutions in choice architecture could support positive diet choices, reduce meat consumption, halve food waste and, into the bargain, slash greenhouse gas emissions.

The future of transport is not cars, it’s data

The 2020s look set to be the biggest disruption of the automobile industry since Henry Ford unveiled the Model T. Two seismic shifts are on their way.

First, electric cars now compete favorably with petrol engines on range. Growth will reach an inflection point within a year or two once prices reach parity. The death of the internal combustion engine in Europe and Asia is assured with end dates announced by China, India, France, the UK, and most of Scandinavia. Dates range from 2025 (Norway) to 2040 (UK and China).

Tech giants can accelerate the demise. Uber recently announced a passenger surcharge to help London drivers save around $1,500 a year towards the cost of an electric car.

Second, driverless cars can shift the transport economic model from ownership to service and ride sharing. A complete shift away from privately-owned vehicles is around the corner, with large implications for emissions.

Clean-energy living and working

Most buildings are barely used and inefficiently heated and cooled. Digitization can slash this waste and its corresponding emissions through measurement, monitoring, and new business models to use office space. While, just a few unicorns are currently in this space, the potential is enormous. Buildings are one of the five biggest sources of emissions, yet have the potential to become clean energy producers in a distributed energy network.

Creating liveable cities

More cities are setting ambitious climate targets to halve emissions in a decade or even less. Tech companies can support this transition by driving demand for low-carbon services for their workforces and offices, but also by providing tools to help monitor emissions and act to reduce them. Google, for example, is collecting travel and other data from across cities to estimate emissions in real time. This is possible through technologies like artificial intelligence and the internet of things. But beware of smart cities that turn out to be not so smart. Efficiencies can reduce resilience when cities face crises.

It’s a Start
Of course, it will take more than tech to solve the climate crisis. But tech is a wildcard. The actions of the current tech giants and their acolytes could serve to destabilize the climate further or bring it under control.

We need a new social contract between tech companies and society to achieve societal goals. The alternative is unthinkable. Without drastic action now, climate chaos threatens to engulf us all. As this future approaches, regulators will be forced to take ever more draconian action to rein in the problem. Acting now will reduce that risk.

Note: A version of this article was originally published on World Economic Forum

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