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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.

Image Credit: Dennis Lytyagin / Shutterstock.com Continue reading

Posted in Human Robots

#435172 DARPA’s New Project Is Investing ...

When Elon Musk and DARPA both hop aboard the cyborg hypetrain, you know brain-machine interfaces (BMIs) are about to achieve the impossible.

BMIs, already the stuff of science fiction, facilitate crosstalk between biological wetware with external computers, turning human users into literal cyborgs. Yet mind-controlled robotic arms, microelectrode “nerve patches”, or “memory Band-Aids” are still purely experimental medical treatments for those with nervous system impairments.

With the Next-Generation Nonsurgical Neurotechnology (N3) program, DARPA is looking to expand BMIs to the military. This month, the project tapped six academic teams to engineer radically different BMIs to hook up machines to the brains of able-bodied soldiers. The goal is to ditch surgery altogether—while minimizing any biological interventions—to link up brain and machine.

Rather than microelectrodes, which are currently surgically inserted into the brain to hijack neural communication, the project is looking to acoustic signals, electromagnetic waves, nanotechnology, genetically-enhanced neurons, and infrared beams for their next-gen BMIs.

It’s a radical departure from current protocol, with potentially thrilling—or devastating—impact. Wireless BMIs could dramatically boost bodily functions of veterans with neural damage or post-traumatic stress disorder (PTSD), or allow a single soldier to control swarms of AI-enabled drones with his or her mind. Or, similar to the Black Mirror episode Men Against Fire, it could cloud the perception of soldiers, distancing them from the emotional guilt of warfare.

When trickled down to civilian use, these new technologies are poised to revolutionize medical treatment. Or they could galvanize the transhumanist movement with an inconceivably powerful tool that fundamentally alters society—for better or worse.

Here’s what you need to know.

Radical Upgrades
The four-year N3 program focuses on two main aspects: noninvasive and “minutely” invasive neural interfaces to both read and write into the brain.

Because noninvasive technologies sit on the scalp, their sensors and stimulators will likely measure entire networks of neurons, such as those controlling movement. These systems could then allow soldiers to remotely pilot robots in the field—drones, rescue bots, or carriers like Boston Dynamics’ BigDog. The system could even boost multitasking prowess—mind-controlling multiple weapons at once—similar to how able-bodied humans can operate a third robotic arm in addition to their own two.

In contrast, minutely invasive technologies allow scientists to deliver nanotransducers without surgery: for example, an injection of a virus carrying light-sensitive sensors, or other chemical, biotech, or self-assembled nanobots that can reach individual neurons and control their activity independently without damaging sensitive tissue. The proposed use for these technologies isn’t yet well-specified, but as animal experiments have shown, controlling the activity of single neurons at multiple points is sufficient to program artificial memories of fear, desire, and experiences directly into the brain.

“A neural interface that enables fast, effective, and intuitive hands-free interaction with military systems by able-bodied warfighters is the ultimate program goal,” DARPA wrote in its funding brief, released early last year.

The only technologies that will be considered must have a viable path toward eventual use in healthy human subjects.

“Final N3 deliverables will include a complete integrated bidirectional brain-machine interface system,” the project description states. This doesn’t just include hardware, but also new algorithms tailored to these system, demonstrated in a “Department of Defense-relevant application.”

The Tools
Right off the bat, the usual tools of the BMI trade, including microelectrodes, MRI, or transcranial magnetic stimulation (TMS) are off the table. These popular technologies rely on surgery, heavy machinery, or personnel to sit very still—conditions unlikely in the real world.

The six teams will tap into three different kinds of natural phenomena for communication: magnetism, light beams, and acoustic waves.

Dr. Jacob Robinson at Rice University, for example, is combining genetic engineering, infrared laser beams, and nanomagnets for a bidirectional system. The $18 million project, MOANA (Magnetic, Optical and Acoustic Neural Access device) uses viruses to deliver two extra genes into the brain. One encodes a protein that sits on top of neurons and emits infrared light when the cell activates. Red and infrared light can penetrate through the skull. This lets a skull cap, embedded with light emitters and detectors, pick up these signals for subsequent decoding. Ultra-fast and utra-sensitvie photodetectors will further allow the cap to ignore scattered light and tease out relevant signals emanating from targeted portions of the brain, the team explained.

The other new gene helps write commands into the brain. This protein tethers iron nanoparticles to the neurons’ activation mechanism. Using magnetic coils on the headset, the team can then remotely stimulate magnetic super-neurons to fire while leaving others alone. Although the team plans to start in cell cultures and animals, their goal is to eventually transmit a visual image from one person to another. “In four years we hope to demonstrate direct, brain-to-brain communication at the speed of thought and without brain surgery,” said Robinson.

Other projects in N3 are just are ambitious.

The Carnegie Mellon team, for example, plans to use ultrasound waves to pinpoint light interaction in targeted brain regions, which can then be measured through a wearable “hat.” To write into the brain, they propose a flexible, wearable electrical mini-generator that counterbalances the noisy effect of the skull and scalp to target specific neural groups.

Similarly, a group at Johns Hopkins is also measuring light path changes in the brain to correlate them with regional brain activity to “read” wetware commands.

The Teledyne Scientific & Imaging group, in contrast, is turning to tiny light-powered “magnetometers” to detect small, localized magnetic fields that neurons generate when they fire, and match these signals to brain output.

The nonprofit Battelle team gets even fancier with their ”BrainSTORMS” nanotransducers: magnetic nanoparticles wrapped in a piezoelectric shell. The shell can convert electrical signals from neurons into magnetic ones and vice-versa. This allows external transceivers to wirelessly pick up the transformed signals and stimulate the brain through a bidirectional highway.

The magnetometers can be delivered into the brain through a nasal spray or other non-invasive methods, and magnetically guided towards targeted brain regions. When no longer needed, they can once again be steered out of the brain and into the bloodstream, where the body can excrete them without harm.

Four-Year Miracle
Mind-blown? Yeah, same. However, the challenges facing the teams are enormous.

DARPA’s stated goal is to hook up at least 16 sites in the brain with the BMI, with a lag of less than 50 milliseconds—on the scale of average human visual perception. That’s crazy high resolution for devices sitting outside the brain, both in space and time. Brain tissue, blood vessels, and the scalp and skull are all barriers that scatter and dissipate neural signals. All six teams will need to figure out the least computationally-intensive ways to fish out relevant brain signals from background noise, and triangulate them to the appropriate brain region to decipher intent.

In the long run, four years and an average $20 million per project isn’t much to potentially transform our relationship with machines—for better or worse. DARPA, to its credit, is keenly aware of potential misuse of remote brain control. The program is under the guidance of a panel of external advisors with expertise in bioethical issues. And although DARPA’s focus is on enabling able-bodied soldiers to better tackle combat challenges, it’s hard to argue that wireless, non-invasive BMIs will also benefit those most in need: veterans and other people with debilitating nerve damage. To this end, the program is heavily engaging the FDA to ensure it meets safety and efficacy regulations for human use.

Will we be there in just four years? I’m skeptical. But these electrical, optical, acoustic, magnetic, and genetic BMIs, as crazy as they sound, seem inevitable.

“DARPA is preparing for a future in which a combination of unmanned systems, AI, and cyber operations may cause conflicts to play out on timelines that are too short for humans to effectively manage with current technology alone,” said Al Emondi, the N3 program manager.

The question is, now that we know what’s in store, how should the rest of us prepare?

Image Credit: With permission from DARPA N3 project. Continue reading

Posted in Human Robots

#435161 Less Like Us: An Alternate Theory of ...

The question of whether an artificial general intelligence will be developed in the future—and, if so, when it might arrive—is controversial. One (very uncertain) estimate suggests 2070 might be the earliest we could expect to see such technology.

Some futurists point to Moore’s Law and the increasing capacity of machine learning algorithms to suggest that a more general breakthrough is just around the corner. Others suggest that extrapolating exponential improvements in hardware is unwise, and that creating narrow algorithms that can beat humans at specialized tasks brings us no closer to a “general intelligence.”

But evolution has produced minds like the human mind at least once. Surely we could create artificial intelligence simply by copying nature, either by guided evolution of simple algorithms or wholesale emulation of the human brain.

Both of these ideas are far easier to conceive of than they are to achieve. The 302 neurons of the nematode worm’s brain are still an extremely difficult engineering challenge, let alone the 86 billion in a human brain.

Leaving aside these caveats, though, many people are worried about artificial general intelligence. Nick Bostrom’s influential book on superintelligence imagines it will be an agent—an intelligence with a specific goal. Once such an agent reaches a human level of intelligence, it will improve itself—increasingly rapidly as it gets smarter—in pursuit of whatever goal it has, and this “recursive self-improvement” will lead it to become superintelligent.

This “intelligence explosion” could catch humans off guard. If the initial goal is poorly specified or malicious, or if improper safety features are in place, or if the AI decides it would prefer to do something else instead, humans may be unable to control our own creation. Bostrom gives examples of how a seemingly innocuous goal, such as “Make everyone happy,” could be misinterpreted; perhaps the AI decides to drug humanity into a happy stupor, or convert most of the world into computing infrastructure to pursue its goal.

Drexler and Comprehensive AI Services
These are increasingly familiar concerns for an AI that behaves like an agent, seeking to achieve its goal. There are dissenters to this picture of how artificial general intelligence might arise. One notable alternative point of view comes from Eric Drexler, famous for his work on molecular nanotechnology and Engines of Creation, the book that popularized it.

With respect to AI, Drexler believes our view of an artificial intelligence as a single “agent” that acts to maximize a specific goal is too narrow, almost anthropomorphizing AI, or modeling it as a more realistic route towards general intelligence. Instead, he proposes “Comprehensive AI Services” (CAIS) as an alternative route to artificial general intelligence.

What does this mean? Drexler’s argument is that we should look more closely at how machine learning and AI algorithms are actually being developed in the real world. The optimization effort is going into producing algorithms that can provide services and perform tasks like translation, music recommendations, classification, medical diagnoses, and so forth.

AI-driven improvements in technology, argues Drexler, will lead to a proliferation of different algorithms: technology and software improvement, which can automate increasingly more complicated tasks. Recursive improvement in this regime is already occurring—take the newer versions of AlphaGo, which can learn to improve themselves by playing against previous versions.

Many Smart Arms, No Smart Brain
Instead of relying on some unforeseen breakthrough, the CAIS model of AI just assumes that specialized, narrow AI will continue to improve at performing each of its tasks, and the range of tasks that machine learning algorithms will be able to perform will become wider. Ultimately, once a sufficient number of tasks have been automated, the services that an AI will provide will be so comprehensive that they will resemble a general intelligence.

One could then imagine a “general” intelligence as simply an algorithm that is extremely good at matching the task you ask it to perform to the specialized service algorithm that can perform that task. Rather than acting like a single brain that strives to achieve a particular goal, the central AI would be more like a search engine, looking through the tasks it can perform to find the closest match and calling upon a series of subroutines to achieve the goal.

For Drexler, this is inherently a safety feature. Rather than Bostrom’s single, impenetrable, conscious and superintelligent brain (which we must try to psychoanalyze in advance without really knowing what it will look like), we have a network of capabilities. If you don’t want your system to perform certain tasks, you can simply cut it off from access to those services. There is no superintelligent consciousness to outwit or “trap”: more like an extremely high-level programming language that can respond to complicated commands by calling upon one of the myriad specialized algorithms that have been developed by different groups.

This skirts the complex problem of consciousness and all of the sticky moral quandaries that arise in making minds that might be like ours. After all, if you could simulate a human mind, you could simulate it experiencing unimaginable pain. Black Mirror-esque dystopias where emulated minds have no rights and are regularly “erased” or forced to labor in dull and repetitive tasks, hove into view.

Drexler argues that, in this world, there is no need to ever build a conscious algorithm. Yet it seems likely that, at some point, humans will attempt to simulate our own brains, if only in the vain attempt to pursue immortality. This model cannot hold forever. Yet its proponents argue that any world in which we could develop general AI would probably also have developed superintelligent capabilities in a huge range of different tasks, such as computer programming, natural language understanding, and so on. In other words, CAIS arrives first.

The Future In Our Hands?
Drexler argues that his model already incorporates many of the ideas from general AI development. In the marketplace, algorithms compete all the time to perform these services: they undergo the same evolutionary pressures that lead to “higher intelligence,” but the behavior that’s considered superior is chosen by humans, and the nature of the “general intelligence” is far more shaped by human decision-making and human programmers. Development in AI services could still be rapid and disruptive.

But in Drexler’s case, the research and development capacity comes from humans and organizations driven by the desire to improve algorithms that are performing individualized and useful tasks, rather than from a conscious AI recursively reprogramming and improving itself.

In other words, this vision does not absolve us of the responsibility of making our AI safe; if anything, it gives us a greater degree of responsibility. As more and more complex “services” are automated, performing what used to be human jobs at superhuman speed, the economic disruption will be severe.

Equally, as machine learning is trusted to carry out more complex decisions, avoiding algorithmic bias becomes crucial. Shaping each of these individual decision-makers—and trying to predict the complex ways they might interact with each other—is no less daunting a task than specifying the goal for a hypothetical, superintelligent, God-like AI. Arguably, the consequences of the “misalignment” of these services algorithms are already multiplying around us.

The CAIS model bridges the gap between real-world AI, machine learning developments, and real-world safety considerations, as well as the speculative world of superintelligent agents and the safety considerations involved with controlling their behavior. We should keep our minds open as to what form AI and machine learning will take, and how it will influence our societies—and we must take care to ensure that the systems we create don’t end up forcing us all to live in a world of unintended consequences.

Image Credit: MF Production/Shutterstock.com Continue reading

Posted in Human Robots

#435152 The Futuristic Tech Disrupting Real ...

In the wake of the housing market collapse of 2008, one entrepreneur decided to dive right into the failing real estate industry. But this time, he didn’t buy any real estate to begin with. Instead, Glenn Sanford decided to launch the first-ever cloud-based real estate brokerage, eXp Realty.

Contracting virtual platform VirBELA to build out the company’s mega-campus in VR, eXp Realty demonstrates the power of a dematerialized workspace, throwing out hefty overhead costs and fundamentally redefining what ‘real estate’ really means. Ten years later, eXp Realty has an army of 14,000 agents across all 50 US states, 3 Canadian provinces, and 400 MLS market areas… all without a single physical office.

But VR is just one of many exponential technologies converging to revolutionize real estate and construction. As floating cities and driverless cars spread out your living options, AI and VR are together cutting out the middleman.

Already, the global construction industry is projected to surpass $12.9 trillion in 2022, and the total value of the US housing market alone grew to $33.3 trillion last year. Both vital for our daily lives, these industries will continue to explode in value, posing countless possibilities for disruption.

In this blog, I’ll be discussing the following trends:

New prime real estate locations;
Disintermediation of the real estate broker and search;
Materials science and 3D printing in construction.

Let’s dive in!

Location Location Location
Until today, location has been the name of the game when it comes to hunting down the best real estate. But constraints on land often drive up costs while limiting options, and urbanization is only exacerbating the problem.

Beyond the world of virtual real estate, two primary mechanisms are driving the creation of new locations.

(1) Floating Cities

Offshore habitation hubs, floating cities have long been conceived as a solution to rising sea levels, skyrocketing urban populations, and threatened ecosystems. In success, they will soon unlock an abundance of prime real estate, whether for scenic living, commerce, education, or recreation.

One pioneering model is that of Oceanix City, designed by Danish architect Bjarke Ingels and a host of other domain experts. Intended to adapt organically over time, Oceanix would consist of a galaxy of mass-produced, hexagonal floating modules, built as satellite “cities” off coastal urban centers and sustained by renewable energies.

While individual 4.5-acre platforms would each sustain 300 people, these hexagonal modules are designed to link into 75-acre tessellations sustaining up to 10,000 residents. Each anchored to the ocean floor using biorock, Oceanix cities are slated to be closed-loop systems, as external resources are continuously supplied by automated drone networks.

Electric boats or flying cars might zoom you to work, city-embedded water capture technologies would provide your water, and while vertical and outdoor farming supply your family meal, share economies would dominate goods provision.

AERIAL: Located in calm, sheltered waters, near coastal megacities, OCEANIX City will be an adaptable, sustainable, scalable, and affordable solution for human life on the ocean. Image Credit: OCEANIX/BIG-Bjarke Ingels Group.
Joined by countless government officials whose islands risk submersion at the hands of sea level rise, the UN is now getting on board. And just this year, seasteading is exiting the realm of science fiction and testing practical waters.

As French Polynesia seeks out robust solutions to sea level rise, their government has now joined forces with the San Francisco-based Seasteading Institute. With a newly designated special economic zone and 100 acres of beachfront, this joint Floating Island Project could even see up to a dozen inhabitable structures by 2020. And what better to fund the $60 million project than the team’s upcoming ICO?

But aside from creating new locations, autonomous vehicles (AVs) and flying cars are turning previously low-demand land into the prime real estate of tomorrow.

(2) Autonomous Electric Vehicles and Flying Cars

Today, the value of a location is a function of its proximity to your workplace, your city’s central business district, the best schools, or your closest friends.

But what happens when driverless cars desensitize you to distance, or Hyperloop and flying cars decimate your commute time? Historically, every time new transit methods have hit the mainstream, tolerance for distance has opened up right alongside them, further catalyzing city spread.

And just as Hyperloop and the Boring Company aim to make your commute immaterial, autonomous vehicle (AV) ridesharing services will spread out cities in two ways: (1) by drastically reducing parking spaces needed (vertical parking decks = more prime real estate); and (2) by untethering you from the steering wheel. Want an extra two hours of sleep on the way to work? Schedule a sleeper AV and nap on your route to the office. Need a car-turned-mobile-office? No problem.

Meanwhile, aerial taxis (i.e. flying cars) will allow you to escape ground congestion entirely, delivering you from bedroom to boardroom at decimated time scales.

Already working with regulators, Uber Elevate has staked ambitious plans for its UberAIR airborne taxi project. By 2023, Uber anticipates rolling out flying drones in its two first pilot cities, Los Angeles and Dallas. Flying between rooftop skyports, drones would carry passengers at a height of 1,000 to 2,000 feet at speeds between 100 to 200 mph. And while costs per ride are anticipated to resemble those of an Uber Black based on mileage, prices are projected to soon drop to those of an UberX.

But the true economic feat boils down to this: if I were to commute 50 to 100 kilometers, I could get two or three times the house for the same price. (Not to mention the extra living space offered up by my now-unneeded garage.)

All of a sudden, virtual reality, broadband, AVs, or high-speed vehicles are going to change where we live and where we work. So rather than living in a crowded, dense urban core for access to jobs and entertainment, our future of personalized, autonomous, low-cost transport opens the luxury of rural areas to all without compromising the benefits of a short commute.

Once these drivers multiply your real estate options, how will you select your next home?

Disintermediation: Say Bye to Your Broker
In a future of continuous and personalized preference-tracking, why hire a human agent who knows less about your needs and desires than a personal AI?

Just as disintermediation is cutting out bankers and insurance agents, so too is it closing in on real estate brokers. Over the next decade, as AI becomes your agent, VR will serve as your medium.

To paint a more vivid picture of how this will look, over 98 percent of your home search will be conducted from the comfort of your couch through next-generation VR headgear.

Once you’ve verbalized your primary desires for home location, finishings, size, etc. to your personal AI, it will offer you top picks, tour-able 24/7, with optional assistance by a virtual guide and constantly updated data. As a seller, this means potential buyers from two miles, or two continents, away.

Throughout each immersive VR tour, advanced eye-tracking software and a permissioned machine learning algorithm follow your gaze, further learn your likes and dislikes, and intelligently recommend other homes or commercial residences to visit.

Curious as to what the living room might look like with a fresh coat of blue paint and a white carpet? No problem! VR programs will be able to modify rendered environments instantly, changing countless variables, from furniture materials to even the sun’s orientation. Keen to input your own furniture into a VR-rendered home? Advanced AIs could one day compile all your existing furniture, electronics, clothing, decorations, and even books, virtually organizing them across any accommodating new space.

As 3D scanning technologies make extraordinary headway, VR renditions will only grow cheaper and higher resolution. One company called Immersive Media (disclosure: I’m an investor and advisor) has a platform for 360-degree video capture and distribution, and is already exploring real estate 360-degree video.

Smaller firms like Studio 216, Vieweet, Arch Virtual, ArX Solutions, and Rubicon Media can similarly capture and render models of various properties for clients and investors to view and explore. In essence, VR real estate platforms will allow you to explore any home for sale, do the remodel, and determine if it truly is the house of your dreams.

Once you’re ready to make a bid, your AI will even help estimate a bid, process and submit your offer. Real estate companies like Zillow, Trulia, Move, Redfin, ZipRealty (acquired by Realogy in 2014) and many others have already invested millions in machine learning applications to make search, valuation, consulting, and property management easier, faster, and much more accurate.

But what happens if the home you desire most means starting from scratch with new construction?

New Methods and Materials for Construction
For thousands of years, we’ve been constrained by the construction materials of nature. We built bricks from naturally abundant clay and shale, used tree limbs as our rooftops and beams, and mastered incredible structures in ancient Rome with the use of cement.

But construction is now on the cusp of a materials science revolution. Today, I’d like to focus on three key materials:

Upcycled Materials

Imagine if you could turn the world’s greatest waste products into their most essential building blocks. Thanks to UCLA researchers at CO2NCRETE, we can already do this with carbon emissions.

Today, concrete produces about five percent of all greenhouse gas (GHG) emissions. But what if concrete could instead conserve greenhouse emissions? CO2NCRETE engineers capture carbon from smokestacks and combine it with lime to create a new type of cement. The lab’s 3D printers then shape the upcycled concrete to build entirely new structures. Once conquered at scale, upcycled concrete will turn a former polluter into a future conserver.

Or what if we wanted to print new residences from local soil at hand? Marking an extraordinary convergence between robotics and 3D printing, the Institute of Advanced Architecture of Catalonia (IAAC) is already working on a solution.

In a major feat for low-cost construction in remote zones, IAAC has found a way to convert almost any soil into a building material with three times the tensile strength of industrial clay. Offering myriad benefits, including natural insulation, low GHG emissions, fire protection, air circulation, and thermal mediation, IAAC’s new 3D printed native soil can build houses on-site for as little as $1,000.

Nanomaterials

Nano- and micro-materials are ushering in a new era of smart, super-strong, and self-charging buildings. While carbon nanotubes dramatically increase the strength-to-weight ratio of skyscrapers, revolutionizing their structural flexibility, nanomaterials don’t stop here.

Several research teams are pioneering silicon nanoparticles to capture everyday light flowing through our windows. Little solar cells at the edges of windows then harvest this energy for ready use. Researchers at the US National Renewable Energy Lab have developed similar smart windows. Turning into solar panels when bathed in sunlight, these thermochromic windows will power our buildings, changing color as they do.

Self-Healing Infrastructure

The American Society of Civil Engineers estimates that the US needs to spend roughly $4.5 trillion to fix nationwide roads, bridges, dams, and common infrastructure by 2025. But what if infrastructure could fix itself?

Enter self-healing concrete. Engineers at Delft University have developed bio-concrete that can repair its own cracks. As head researcher Henk Jonkers explains, “What makes this limestone-producing bacteria so special is that they are able to survive in concrete for more than 200 years and come into play when the concrete is damaged. […] If cracks appear as a result of pressure on the concrete, the concrete will heal these cracks itself.”

But bio-concrete is only the beginning of self-healing technologies. As futurist architecture firms start printing plastic and carbon-fiber houses like the stunner seen below (using Branch Technologies’ 3D printing technology), engineers have begun tackling self-healing plastic.

And in a bid to go smart, burgeoning construction projects have started embedding sensors for preemptive detection. Beyond materials and sensors, however, construction methods are fast colliding into robotics and 3D printing.

While some startups and research institutes have leveraged robot swarm construction (namely, Harvard’s robotic termite-like swarm of programmed constructors), others have taken to large-scale autonomous robots.

One such example involves Fastbrick Robotics. After multiple iterations, the company’s Hadrian X end-to-end bricklaying robot can now autonomously build a fully livable, 180-square meter home in under 3 days. Using a laser-guided robotic attachment, the all-in-one brick-loaded truck simply drives to a construction site and directs blocks through its robotic arm in accordance with a 3D model.

Layhead. Image Credit: Fastbrick Robotics.
Meeting verified building standards, Hadrian and similar solutions hold massive promise in the long term, deployable across post-conflict refugee sites and regions recovering from natural catastrophes.

Imagine the implications. Eliminating human safety concerns and unlocking any environment, autonomous builder robots could collaboratively build massive structures in space or deep underwater habitats.

Final Thoughts
Where, how, and what we live in form a vital pillar of our everyday lives. The concept of “home” is unlikely to disappear anytime soon. At the same time, real estate and construction are two of the biggest playgrounds for technological convergence, each on the verge of revolutionary disruption.

As underlying shifts in transportation, land reclamation, and the definition of “space” (real vs. virtual) take hold, the real estate market is about to explode in value, spreading out urban centers on unprecedented scales and unlocking vast new prime “property.”

Meanwhile, converging advancements in AI and VR are fundamentally disrupting the way we design, build, and explore new residences. Just as mirror worlds create immersive, virtual real estate economies, VR tours and AI agents are absorbing both sides of the coin to entirely obliterate the middleman.

And as materials science breakthroughs meet new modes of construction, the only limits to tomorrow’s structures are those of our own imagination.

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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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