Tag Archives: black

#435196 Avatar Love? New ‘Black Mirror’ ...

This week, the widely-anticipated fifth season of the dystopian series Black Mirror was released on Netflix. The storylines this season are less focused on far-out scenarios and increasingly aligned with current issues. With only three episodes, this season raises more questions than it answers, often leaving audiences bewildered.

The episode Smithereens explores our society’s crippling addiction to social media platforms and the monopoly they hold over our data. In Rachel, Jack and Ashley Too, we see the disruptive impact of technologies on the music and entertainment industry, and the price of fame for artists in the digital world. Like most Black Mirror episodes, these explore the sometimes disturbing implications of tech advancements on humanity.

But once again, in the midst of all the doom and gloom, the creators of the series leave us with a glimmer of hope. Aligned with Pride month, the episode Striking Vipers explores the impact of virtual reality on love, relationships, and sexual fluidity.

*The review contains a few spoilers.*

Striking Vipers
The first episode of the season, Striking Vipers may be one of the most thought-provoking episodes in Black Mirror history. Reminiscent of previous episodes San Junipero and Hang the DJ, the writers explore the potential for technology to transform human intimacy.

The episode tells the story of two old friends, Danny and Karl, whose friendship is reignited in an unconventional way. Karl unexpectedly appears at Danny’s 38th birthday and reintroduces him to the VR version of a game they used to play years before. In the game Striking Vipers X, each of the players is represented by an avatar of their choice in an uncanny digital reality. Following old tradition, Karl chooses to become the female fighter, Roxanne, and Danny takes on the role of the male fighter, Lance. The state-of-the-art VR headsets appear to use an advanced form of brain-machine interface to allow each player to be fully immersed in the virtual world, emulating all physical sensations.

To their surprise (and confusion), Danny and Karl find themselves transitioning from fist-fighting to kissing. Over the course of many games, they continue to explore a sexual and romantic relationship in the virtual world, leaving them confused and distant in the real world. The virtual and physical realities begin to blur, and so do the identities of the players with their avatars. Danny, who is married (in a heterosexual relationship) and is a father, begins to carry guilt and confusion in the real world. They both wonder if there would be any spark between them in real life.

The brain-machine interface (BMI) depicted in the episode is still science fiction, but that hasn’t stopped innovators from pushing the technology forward. Experts today are designing more intricate BMI systems while programming better algorithms to interpret the neural signals they capture. Scientists have already succeeded in enabling paralyzed patients to type with their minds, and are even allowing people to communicate with one another purely through brainwaves.

The convergence of BMIs with virtual reality and artificial intelligence could make the experience of such immersive digital realities possible. Virtual reality, too, is decreasing exponentially in cost and increasing in quality.

The narrative provides meaningful commentary on another tech area—gaming. It highlights video games not necessarily as addictive distractions, but rather as a platform for connecting with others in a deeper way. This is already very relevant. Video games like Final Fantasy are often a tool for meaningful digital connections for their players.

The Implications of Virtual Reality on Love and Relationships
The narrative of Striking Vipers raises many novel questions about the implications of immersive technologies on relationships: could the virtual world allow us a safe space to explore suppressed desires? Can virtual avatars make it easier for us to show affection to those we care about? Can a sexual or romantic encounter in the digital world be considered infidelity?

Above all, the episode explores the therapeutic possibilities of such technologies. While many fears about virtual reality had been raised in previous seasons of Black Mirror, this episode was focused on its potential. This includes the potential of immersive technology to be a source of liberation, meaningful connections, and self-exploration, as well as a tool for realizing our true identities and desires.

Once again, this is aligned with emerging trends in VR. We are seeing the rise of social VR applications and platforms that allow you to hang out with your friends and family as avatars in the virtual space. The technology is allowing for animation movies, such as Coco VR, to become an increasingly social and interactive experience. Considering that meaningful social interaction can alleviate depression and anxiety, such applications could contribute to well-being.

Techno-philosopher and National Geographic host Jason Silva points out that immersive media technologies can be “engines of empathy.” VR allows us to enter virtual spaces that mimic someone else’s state of mind, allowing us to empathize with the way they view the world. Silva said, “Imagine the intimacy that becomes possible when people meet and they say, ‘Hey, do you want to come visit my world? Do you want to see what it’s like to be inside my head?’”

What is most fascinating about Striking Vipers is that it explores how we may redefine love with virtual reality; we are introduced to love between virtual avatars. While this kind of love may seem confusing to audiences, it may be one of the complex implications of virtual reality on human relationships.

In many ways, the title Black Mirror couldn’t be more appropriate, as each episode serves as a mirror to the most disturbing aspects of our psyches as they get amplified through technology. However, what we see in uplifting and thought-provoking plots like Striking Vipers, San Junipero, and Hang The DJ is that technology could also amplify the most positive aspects of our humanity. This includes our powerful capacity to love.

Image Credit: Arsgera / 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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Image Credit: OCEANIX/BIG-Bjarke Ingels Group. Continue reading

Posted in Human Robots

#434837 In Defense of Black Box AI

Deep learning is powering some amazing new capabilities, but we find it hard to scrutinize the workings of these algorithms. Lack of interpretability in AI is a common concern and many are trying to fix it, but is it really always necessary to know what’s going on inside these “black boxes”?

In a recent perspective piece for Science, Elizabeth Holm, a professor of materials science and engineering at Carnegie Mellon University, argued in defense of the black box algorithm. I caught up with her last week to find out more.

Edd Gent: What’s your experience with black box algorithms?

Elizabeth Holm: I got a dual PhD in materials science and engineering and scientific computing. I came to academia about six years ago and part of what I wanted to do in making this career change was to refresh and revitalize my computer science side.

I realized that computer science had changed completely. It used to be about algorithms and making codes run fast, but now it’s about data and artificial intelligence. There are the interpretable methods like random forest algorithms, where we can tell how the machine is making its decisions. And then there are the black box methods, like convolutional neural networks.

Once in a while we can find some information about their inner workings, but most of the time we have to accept their answers and kind of probe around the edges to figure out the space in which we can use them and how reliable and accurate they are.

EG: What made you feel like you had to mount a defense of these black box algorithms?

EH: When I started talking with my colleagues, I found that the black box nature of many of these algorithms was a real problem for them. I could understand that because we’re scientists, we always want to know why and how.

It got me thinking as a bit of a contrarian, “Are black boxes all bad? Must we reject them?” Surely not, because human thought processes are fairly black box. We often rely on human thought processes that the thinker can’t necessarily explain.

It’s looking like we’re going to be stuck with these methods for a while, because they’re really helpful. They do amazing things. And so there’s a very pragmatic realization that these are the best methods we’ve got to do some really important problems, and we’re not right now seeing alternatives that are interpretable. We’re going to have to use them, so we better figure out how.

EG: In what situations do you think we should be using black box algorithms?

EH: I came up with three rules. The simplest rule is: when the cost of a bad decision is small and the value of a good decision is high, it’s worth it. The example I gave in the paper is targeted advertising. If you send an ad no one wants it doesn’t cost a lot. If you’re the receiver it doesn’t cost a lot to get rid of it.

There are cases where the cost is high, and that’s then we choose the black box if it’s the best option to do the job. Things get a little trickier here because we have to ask “what are the costs of bad decisions, and do we really have them fully characterized?” We also have to be very careful knowing that our systems may have biases, they may have limitations in where you can apply them, they may be breakable.

But at the same time, there are certainly domains where we’re going to test these systems so extensively that we know their performance in virtually every situation. And if their performance is better than the other methods, we need to do it. Self driving vehicles are a significant example—it’s almost certain they’re going to have to use black box methods, and that they’re going to end up being better drivers than humans.

The third rule is the more fun one for me as a scientist, and that’s the case where the black box really enlightens us as to a new way to look at something. We have trained a black box to recognize the fracture energy of breaking a piece of metal from a picture of the broken surface. It did a really good job, and humans can’t do this and we don’t know why.

What the computer seems to be seeing is noise. There’s a signal in that noise, and finding it is very difficult, but if we do we may find something significant to the fracture process, and that would be an awesome scientific discovery.

EG: Do you think there’s been too much emphasis on interpretability?

EH: I think the interpretability problem is a fundamental, fascinating computer science grand challenge and there are significant issues where we need to have an interpretable model. But how I would frame it is not that there’s too much emphasis on interpretability, but rather that there’s too much dismissiveness of uninterpretable models.

I think that some of the current social and political issues surrounding some very bad black box outcomes have convinced people that all machine learning and AI should be interpretable because that will somehow solve those problems.

Asking humans to explain their rationale has not eliminated bias, or stereotyping, or bad decision-making in humans. Relying too much on interpreted ability perhaps puts the responsibility in the wrong place for getting better results. I can make a better black box without knowing exactly in what way the first one was bad.

EG: Looking further into the future, do you think there will be situations where humans will have to rely on black box algorithms to solve problems we can’t get our heads around?

EH: I do think so, and it’s not as much of a stretch as we think it is. For example, humans don’t design the circuit map of computer chips anymore. We haven’t for years. It’s not a black box algorithm that designs those circuit boards, but we’ve long since given up trying to understand a particular computer chip’s design.

With the billions of circuits in every computer chip, the human mind can’t encompass it, either in scope or just the pure time that it would take to trace every circuit. There are going to be cases where we want a system so complex that only the patience that computers have and their ability to work in very high-dimensional spaces is going to be able to do it.

So we can continue to argue about interpretability, but we need to acknowledge that we’re going to need to use black boxes. And this is our opportunity to do our due diligence to understand how to use them responsibly, ethically, and with benefits rather than harm. And that’s going to be a social conversation as well as as a scientific one.

*Responses have been edited for length and style

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