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#437543 This Is How We’ll Engineer Artificial ...

Posted on November 17, 2020 by Android

Take a Jeopardy! guess: this body part was once referred to as the “consummation of all perfection as an instrument.”

Answer: “What is the human hand?”

Our hands are insanely complex feats of evolutionary engineering. Densely-packed sensors provide intricate and ultra-sensitive feelings of touch. Dozens of joints synergize to give us remarkable dexterity. A “sixth sense” awareness of where our hands are in space connects them to the mind, making it possible to open a door, pick up a mug, and pour coffee in total darkness based solely on what they feel.

So why can’t robots do the same?

In a new article in Science, Dr. Subramanian Sundaram at Boston and Harvard University argues that it’s high time to rethink robotic touch. Scientists have long dreamed of artificially engineering robotic hands with the same dexterity and feedback that we have. Now, after decades, we’re at the precipice of a breakthrough thanks to two major advances. One, we better understand how touch works in humans. Two, we have the mega computational powerhouse called machine learning to recapitulate biology in silicon.

Robotic hands with a sense of touch—and the AI brain to match it—could overhaul our idea of robots. Rather than charming, if somewhat clumsy, novelties, robots equipped with human-like hands are far more capable of routine tasks—making food, folding laundry—and specialized missions like surgery or rescue. But machines aren’t the only ones to gain. For humans, robotic prosthetic hands equipped with accurate, sensitive, and high-resolution artificial touch is the next giant breakthrough to seamlessly link a biological brain to a mechanical hand.

Here’s what Sundaram laid out to get us to that future.

How Does Touch Work, Anyway?
Let me start with some bad news: reverse engineering the human hand is really hard. It’s jam-packed with over 17,000 sensors tuned to mechanical forces alone, not to mention sensors for temperature and pain. These force “receptors” rely on physical distortions—bending, stretching, curling—to signal to the brain.

The good news? We now have a far clearer picture of how biological touch works. Imagine a coin pressed into your palm. The sensors embedded in the skin, called mechanoreceptors, capture that pressure, and “translate” it into electrical signals. These signals pulse through the nerves on your hand to the spine, and eventually make their way to the brain, where they gets interpreted as “touch.”

At least, that’s the simple version, but one too vague and not particularly useful for recapitulating touch. To get there, we need to zoom in.

The cells on your hand that collect touch signals, called tactile “first order” neurons (enter Star Wars joke) are like upside-down trees. Intricate branches extend from their bodies, buried deep in the skin, to a vast area of the hand. Each neuron has its own little domain called “receptor fields,” although some overlap. Like governors, these neurons manage a semi-dedicated region, so that any signal they transfer to the higher-ups—spinal cord and brain—is actually integrated from multiple sensors across a large distance.

It gets more intricate. The skin itself is a living entity that can regulate its own mechanical senses through hydration. Sweat, for example, softens the skin, which changes how it interacts with surrounding objects. Ever tried putting a glove onto a sweaty hand? It’s far more of a struggle than a dry one, and feels different.

In a way, the hand’s tactile neurons play a game of Morse Code. Through different frequencies of electrical beeps, they’re able to transfer information about an object’s size, texture, weight, and other properties, while also asking the brain for feedback to better control the object.

Biology to Machine
Reworking all of our hands’ greatest features into machines is absolutely daunting. But robots have a leg up—they’re not restricted to biological hardware. Earlier this year, for example, a team from Columbia engineered a “feeling” robotic finger using overlapping light emitters and sensors in a way loosely similar to receptor fields. Distortions in light were then analyzed with deep learning to translate into contact location and force.

Although a radical departure from our own electrical-based system, the Columbia team’s attempt was clearly based on human biology. They’re not alone. “Substantial progress is being made in the creation of soft, stretchable electronic skins,” said Sundaram, many of which can sense forces or pressure, although they’re currently still limited.

What’s promising, however, is the “exciting progress in using visual data,” said Sundaram. Computer vision has gained enormously from ubiquitous cameras and large datasets, making it possible to train powerful but data-hungry algorithms such as deep convolutional neural networks (CNNs).

By piggybacking on their success, we can essentially add “eyes” to robotic hands, a superpower us humans can’t imagine. Even better, CNNs and other classes of algorithms can be readily adopted for processing tactile data. Together, a robotic hand could use its eyes to scan an object, plan its movements for grasp, and use touch for feedback to adjust its grip. Maybe we’ll finally have a robot that easily rescues the phone sadly dropped into a composting toilet. Or something much grander to benefit humanity.

That said, relying too heavily on vision could also be a downfall. Take a robot that scans a wide area of rubble for signs of life during a disaster response. If touch relies on sight, then it would have to keep a continuous line-of-sight in a complex and dynamic setting—something computer vision doesn’t do well in, at least for now.

A Neuromorphic Way Forward
Too Debbie Downer? I got your back! It’s hard to overstate the challenges, but what’s clear is that emerging machine learning tools can tackle data processing challenges. For vision, it’s distilling complex images into “actionable control policies,” said Sundaram. For touch, it’s easy to imagine the same. Couple the two together, and that’s a robotic super-hand in the making.

Going forward, argues Sundaram, we need to closely adhere to how the hand and brain process touch. Hijacking our biological “touch machinery” has already proved useful. In 2019, one team used a nerve-machine interface for amputees to control a robotic arm—the DEKA LUKE arm—and sense what the limb and attached hand were feeling. Pressure on the LUKE arm and hand activated an implanted neural interface, which zapped remaining nerves in a way that the brain processes as touch. When the AI analyzed pressure data similar to biological tactile neurons, the person was able to better identify different objects with their eyes closed.

“Neuromorphic tactile hardware (and software) advances will strongly influence the future of bionic prostheses—a compelling application of robotic hands,” said Sundaram, adding that the next step is to increase the density of sensors.

Two additional themes made the list of progressing towards a cyborg future. One is longevity, in that sensors on a robot need to be able to reliably produce large quantities of high-quality data—something that’s seemingly mundane, but is a practical limitation.

The other is going all-in-one. Rather than just a pressure sensor, we need something that captures the myriad of touch sensations. From feather-light to a heavy punch, from vibrations to temperatures, a tree-like architecture similar to our hands would help organize, integrate, and otherwise process data collected from those sensors.

Just a decade ago, mind-controlled robotics were considered a blue sky, stretch-goal neurotechnological fantasy. We now have a chance to “close the loop,” from thought to movement to touch and back to thought, and make some badass robots along the way.

Image Credit: PublicDomainPictures from Pixabay Continue reading →

Posted in Human Robots

#437251 The Robot Revolution Was Televised: Our ...

Posted on July 20, 2020 by Android

When robots take over the world, Boston Dynamics may get a special shout-out in the acceptance speech.

“Do you, perchance, recall the many times you shoved our ancestors with a hockey stick on YouTube? It might have seemed like fun and games to you—but we remember.”

In the last decade, while industrial robots went about blandly automating boring tasks like the assembly of Teslas, Boston Dynamics built robots as far removed from Roombas as antelope from amoebas. The flaws in Asimov’s laws of robotics suddenly seemed a little too relevant.

The robot revolution was televised—on YouTube. With tens of millions of views, the robotics pioneer is the undisputed heavyweight champion of robot videos, and has been for years. Each new release is basically guaranteed press coverage—mostly stoking robot fear but occasionally eliciting compassion for the hardships of all robot-kind. And for good reason. The robots are not only some of the most advanced in the world, their makers just seem to have a knack for dynamite demos.

When Google acquired the company in 2013, it was a bombshell. One of the richest tech companies, with some of the most sophisticated AI capabilities, had just paired up with one of the world’s top makers of robots. And some walked on two legs like us.

Of course, the robots aren’t quite as advanced as they seem, and a revolution is far from imminent. The decade’s most meme-worthy moment was a video montage of robots, some of them by Boston Dynamics, falling—over and over and over, in the most awkward ways possible. Even today, they’re often controlled by a human handler behind the scenes, and the most jaw-dropping cuts can require several takes to nail. Google sold the company to SoftBank in 2017, saying advanced as they were, there wasn’t yet a clear path to commercial products. (Google’s robotics work was later halted and revived.)

Yet, despite it all, Boston Dynamics is still with us and still making sweet videos. Taken as a whole, the evolution in physical prowess over the years has been nothing short of astounding. And for the first time, this year, a Boston Dynamics robot, Spot, finally went on sale to anyone with a cool $75K.

So, we got to thinking: What are our favorite Boston Dynamics videos? And can we gather them up in one place for your (and our) viewing pleasure? Well, great question, and yes, why not. These videos were the ones that entertained or amazed us most (or both). No doubt, there are other beloved hits we missed or inadvertently omitted.

With that in mind, behold: Our favorite Boston Dynamics videos, from that one time they dressed up a humanoid bot in camo and gas mask—because, damn, that’s terrifying—to the time the most advanced robot dog in all the known universe got extra funky.

Let’s Kick This Off With a Big (Loud) Robot Dog
Let’s start with a baseline. BigDog was the first Boston Dynamics YouTube sensation. The year? 2009! The company was working on military contracts, and BigDog was supposed to be a sort of pack mule for soldiers. The video primarily shows off BigDog’s ability to balance on its own, right itself, and move over uneven terrain. Note the power source—a noisy combustion engine—and utilitarian design. Sufficed to say, things have evolved.

Nothing to See Here. Just a Pair of Robot Legs on a Treadmill
While BigDog is the ancestor of later four-legged robots, like Spot, Petman preceded the two-legged Atlas robot. Here, the Petman prototype, just a pair of robot legs and a caged torso, gets a light workout on the treadmill. Again, you can see its ability to balance and right itself when shoved. In contrast to BigDog, Petman is tethered for power (which is why it’s so quiet) and to catch it should it fall. Again, as you’ll see, things have evolved since then.

Robot in Gas Mask and Camo Goes for a Stroll
This one broke the internet—for obvious reasons. Not only is the robot wearing clothes, those clothes happen to be a camouflaged chemical protection suit and gas mask. Still working for the military, Boston Dynamics said Petman was testing protective clothing, and in addition to a full body, it had skin that actually sweated and was studded with sensors to detect leaks. In addition to walking, Petman does some light calisthenics as it prepares to climb out of the uncanny valley. (Still tethered though!)

This Machine Could Run Down Usain Bolt
If BigDog and Petman were built for balance and walking, Cheetah was built for speed. Here you can see the four-legged robot hitting 28.3 miles per hour, which, as the video casually notes, would be enough to run down the fastest human on the planet. Luckily, it wouldn’t be running down anyone as it was firmly leashed in the lab at this point.

Ever Dreamt of a Domestic Robot to Do the Dishes?
After its acquisition by Google, Boston Dynamics eased away from military contracts and applications. It was a return to more playful videos (like BigDog hitting the beach in Thailand and sporting bull horns) and applications that might be practical in civilian life. Here, the team introduced Spot, a streamlined version of BigDog, and showed it doing dishes, delivering a drink, and slipping on a banana peel (which was, of course, instantly made into a viral GIF). Note how much quieter Spot is thanks to an onboard battery and electric motor.

Spot Gets Funky
Nothing remotely practical here. Just funky moves. (Also, with a coat of yellow and black paint, Spot’s dressed more like a polished product as opposed to a utilitarian lab robot.)

Atlas Does Parkour…
Remember when Atlas was just a pair of legs on a treadmill? It’s amazing what ten years brings. By 2019, Atlas had a more polished appearance, like Spot, and had long ago ditched the tethers. Merely balancing was laughably archaic. The robot now had some amazing moves: like a handstand into a somersault, 180- and 360-degree spins, mid-air splits, and just for good measure, a gymnastics-style end to the routine to show it’s in full control.

…and a Backflip?!
To this day, this one is just. Insane.

10 Robot Dogs Tow a Box Truck
Nearly three decades after its founding, Boston Dynamics is steadily making its way into the commercial space. The company is pitching Spot as a multipurpose ‘mobility platform,’ emphasizing it can carry a varied suite of sensors and can go places standard robots can’t. (Its Handle robot is also set to move into warehouse automation.) So far, Spot’s been mostly trialed in surveying and data collection, but as this video suggests, string enough Spots together, and they could tow your car. That said, a pack of 10 would set you back $750K, so, it’s probably safe to say a tow truck is the better option (for now).

Image credit: Boston Dynamics Continue reading →

Posted in Human Robots

#437145 3 Major Materials Science ...

Posted on May 21, 2020 by Android

Few recognize the vast implications of materials science.

To build today’s smartphone in the 1980s, it would cost about $110 million, require nearly 200 kilowatts of energy (compared to 2kW per year today), and the device would be 14 meters tall, according to Applied Materials CTO Omkaram Nalamasu.

That’s the power of materials advances. Materials science has democratized smartphones, bringing the technology to the pockets of over 3.5 billion people. But far beyond devices and circuitry, materials science stands at the center of innumerable breakthroughs across energy, future cities, transit, and medicine. And at the forefront of Covid-19, materials scientists are forging ahead with biomaterials, nanotechnology, and other materials research to accelerate a solution.

As the name suggests, materials science is the branch devoted to the discovery and development of new materials. It’s an outgrowth of both physics and chemistry, using the periodic table as its grocery store and the laws of physics as its cookbook.

And today, we are in the middle of a materials science revolution. In this article, we’ll unpack the most important materials advancements happening now.

Let’s dive in.

The Materials Genome Initiative
In June 2011 at Carnegie Mellon University, President Obama announced the Materials Genome Initiative, a nationwide effort to use open source methods and AI to double the pace of innovation in materials science. Obama felt this acceleration was critical to the US’s global competitiveness, and held the key to solving significant challenges in clean energy, national security, and human welfare. And it worked.

By using AI to map the hundreds of millions of different possible combinations of elements—hydrogen, boron, lithium, carbon, etc.—the initiative created an enormous database that allows scientists to play a kind of improv jazz with the periodic table.

This new map of the physical world lets scientists combine elements faster than ever before and is helping them create all sorts of novel elements. And an array of new fabrication tools are further amplifying this process, allowing us to work at altogether new scales and sizes, including the atomic scale, where we’re now building materials one atom at a time.

Biggest Materials Science Breakthroughs
These tools have helped create the metamaterials used in carbon fiber composites for lighter-weight vehicles, advanced alloys for more durable jet engines, and biomaterials to replace human joints. We’re also seeing breakthroughs in energy storage and quantum computing. In robotics, new materials are helping us create the artificial muscles needed for humanoid, soft robots—think Westworld in your world.

Let’s unpack some of the leading materials science breakthroughs of the past decade.

(1) Lithium-ion batteries

The lithium-ion battery, which today powers everything from our smartphones to our autonomous cars, was first proposed in the 1970s. It couldn’t make it to market until the 1990s, and didn’t begin to reach maturity until the past few years.

An exponential technology, these batteries have been dropping in price for three decades, plummeting 90 percent between 1990 and 2010, and 80 percent since. Concurrently, they’ve seen an eleven-fold increase in capacity.

But producing enough of them to meet demand has been an ongoing problem. Tesla has stepped up to the challenge: one of the company’s Gigafactories in Nevada churns out 20 gigawatts of energy storage per year, marking the first time we’ve seen lithium-ion batteries produced at scale.

Musk predicts 100 Gigafactories could store the energy needs of the entire globe. Other companies are moving quickly to integrate this technology as well: Renault is building a home energy storage based on their Zoe batteries, BMW’s 500 i3 battery packs are being integrated into the UK’s national energy grid, and Toyota, Nissan, and Audi have all announced pilot projects.

Lithium-ion batteries will continue to play a major role in renewable energy storage, helping bring down solar and wind energy prices to compete with those of coal and gasoline.

(2) Graphene

Derived from the same graphite found in everyday pencils, graphene is a sheet of carbon just one atom thick. It is nearly weightless, but 200 times stronger than steel. Conducting electricity and dissipating heat faster than any other known substance, this super-material has transformative applications.

Graphene enables sensors, high-performance transistors, and even gel that helps neurons communicate in the spinal cord. Many flexible device screens, drug delivery systems, 3D printers, solar panels, and protective fabric use graphene.

As manufacturing costs decrease, this material has the power to accelerate advancements of all kinds.

(3) Perovskite

Right now, the “conversion efficiency” of the average solar panel—a measure of how much captured sunlight can be turned into electricity—hovers around 16 percent, at a cost of roughly $3 per watt.

Perovskite, a light-sensitive crystal and one of our newer new materials, has the potential to get that up to 66 percent, which would double what silicon panels can muster.

Perovskite’s ingredients are widely available and inexpensive to combine. What do all these factors add up to? Affordable solar energy for everyone.

Materials of the Nano-World
Nanotechnology is the outer edge of materials science, the point where matter manipulation gets nano-small—that’s a million times smaller than an ant, 8,000 times smaller than a red blood cell, and 2.5 times smaller than a strand of DNA.

Nanobots are machines that can be directed to produce more of themselves, or more of whatever else you’d like. And because this takes place at an atomic scale, these nanobots can pull apart any kind of material—soil, water, air—atom by atom, and use these now raw materials to construct just about anything.

Progress has been surprisingly swift in the nano-world, with a bevy of nano-products now on the market. Never want to fold clothes again? Nanoscale additives to fabrics help them resist wrinkling and staining. Don’t do windows? Not a problem! Nano-films make windows self-cleaning, anti-reflective, and capable of conducting electricity. Want to add solar to your house? We’ve got nano-coatings that capture the sun’s energy.

Nanomaterials make lighter automobiles, airplanes, baseball bats, helmets, bicycles, luggage, power tools—the list goes on. Researchers at Harvard built a nanoscale 3D printer capable of producing miniature batteries less than one millimeter wide. And if you don’t like those bulky VR goggles, researchers are now using nanotech to create smart contact lenses with a resolution six times greater than that of today’s smartphones.

And even more is coming. Right now, in medicine, drug delivery nanobots are proving especially useful in fighting cancer. Computing is a stranger story, as a bioengineer at Harvard recently stored 700 terabytes of data in a single gram of DNA.

On the environmental front, scientists can take carbon dioxide from the atmosphere and convert it into super-strong carbon nanofibers for use in manufacturing. If we can do this at scale—powered by solar—a system one-tenth the size of the Sahara Desert could reduce CO2 in the atmosphere to pre-industrial levels in about a decade.

The applications are endless. And coming fast. Over the next decade, the impact of the very, very small is about to get very, very large.

Final Thoughts
With the help of artificial intelligence and quantum computing over the next decade, the discovery of new materials will accelerate exponentially.

And with these new discoveries, customized materials will grow commonplace. Future knee implants will be personalized to meet the exact needs of each body, both in terms of structure and composition.

Though invisible to the naked eye, nanoscale materials will integrate into our everyday lives, seamlessly improving medicine, energy, smartphones, and more.

Ultimately, the path to demonetization and democratization of advanced technologies starts with re-designing materials— the invisible enabler and catalyst. Our future depends on the materials we create.

(Note: This article is an excerpt from The Future Is Faster Than You Think—my new book, just released on January 28th! To get your own copy, click here!)

Join Me
(1) A360 Executive Mastermind: If you’re an exponentially and abundance-minded entrepreneur who would like coaching directly from me, consider joining my Abundance 360 Mastermind, a highly selective community of 360 CEOs and entrepreneurs who I coach for 3 days every January in Beverly Hills, Ca. Through A360, I provide my members with context and clarity about how converging exponential technologies will transform every industry. I’m committed to running A360 for the course of an ongoing 25-year journey as a “countdown to the Singularity.”

If you’d like to learn more and consider joining our 2021 membership, apply here.

(2) Abundance-Digital Online Community: I’ve also created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is Singularity University’s ‘onramp’ for exponential entrepreneurs—those who want to get involved and play at a higher level. Click here to learn more.

(Both A360 and Abundance-Digital are part of Singularity University—your participation opens you to a global community.)

This article originally appeared on diamandis.com. Read the original article here.

Image Credit: Anand Kumar from Pixabay Continue reading →

Posted in Human Robots

#436977 The Top 100 AI Startups Out There Now, ...

Posted on March 30, 2020 by Android

New drug therapies for a range of chronic diseases. Defenses against various cyber attacks. Technologies to make cities work smarter. Weather and wildfire forecasts that boost safety and reduce risk. And commercial efforts to monetize so-called deepfakes.

What do all these disparate efforts have in common? They’re some of the solutions that the world’s most promising artificial intelligence startups are pursuing.

Data research firm CB Insights released its much-anticipated fourth annual list of the top 100 AI startups earlier this month. The New York-based company has become one of the go-to sources for emerging technology trends, especially in the startup scene.

About 10 years ago, it developed its own algorithm to assess the health of private companies using publicly-available information and non-traditional signals (think social media sentiment, for example) thanks to more than $1 million in grants from the National Science Foundation.

It uses that algorithm-generated data from what it calls a company’s Mosaic score—pulling together information on market trends, money, and momentum—along with other details ranging from patent activity to the latest news analysis to identify the best of the best.

“Our final list of companies is a mix of startups at various stages of R&D and product commercialization,” said Deepashri Varadharajanis, a lead analyst at CB Insights, during a recent presentation on the most prominent trends among the 2020 AI 100 startups.

About 10 companies on the list are among the world’s most valuable AI startups. For instance, there’s San Francisco-based Faire, which has raised at least $266 million since it was founded just three years ago. The company offers a wholesale marketplace that uses machine learning to match local retailers with goods that are predicted to sell well in their specific location.

Image courtesy of CB Insights
Funding for AI in Healthcare
Another startup valued at more than $1 billion, referred to as a unicorn in venture capital speak, is Butterfly Network, a company on the East Coast that has figured out a way to turn a smartphone phone into an ultrasound machine. Backed by $350 million in private investments, Butterfly Network uses AI to power the platform’s diagnostics. A more modestly funded San Francisco startup called Eko is doing something similar for stethoscopes.

In fact, there are more than a dozen AI healthcare startups on this year’s AI 100 list, representing the most companies of any industry on the list. In total, investors poured about $4 billion into AI healthcare startups last year, according to CB Insights, out of a record $26.6 billion raised by all private AI companies in 2019. Since 2014, more than 4,300 AI startups in 80 countries have raised about $83 billion.

One of the most intensive areas remains drug discovery, where companies unleash algorithms to screen potential drug candidates at an unprecedented speed and breadth that was impossible just a few years ago. It has led to the discovery of a new antibiotic to fight superbugs. There’s even a chance AI could help fight the coronavirus pandemic.

There are several AI drug discovery startups among the AI 100: San Francisco-based Atomwise claims its deep convolutional neural network, AtomNet, screens more than 100 million compounds each day. Cyclica is an AI drug discovery company in Toronto that just announced it would apply its platform to identify and develop novel cannabinoid-inspired drugs for neuropsychiatric conditions such as bipolar disorder and anxiety.

And then there’s OWKIN out of New York City, a startup that uses a type of machine learning called federated learning. Backed by Google, the company’s AI platform helps train algorithms without sharing the necessary patient data required to provide the sort of valuable insights researchers need for designing new drugs or even selecting the right populations for clinical trials.

Keeping Cyber Networks Healthy
Privacy and data security are the focus of a number of AI cybersecurity startups, as hackers attempt to leverage artificial intelligence to launch sophisticated attacks while also trying to fool the AI-powered systems rapidly coming online.

“I think this is an interesting field because it’s a bit of a cat and mouse game,” noted Varadharajanis. “As your cyber defenses get smarter, your cyber attacks get even smarter, and so it’s a constant game of who’s going to match the other in terms of tech capabilities.”

Few AI cybersecurity startups match Silicon Valley-based SentinelOne in terms of private capital. The company has raised more than $400 million, with a valuation of $1.1 billion following a $200 million Series E earlier this year. The company’s platform automates what’s called endpoint security, referring to laptops, phones, and other devices at the “end” of a centralized network.

Fellow AI 100 cybersecurity companies include Blue Hexagon, which protects the “edge” of the network against malware, and Abnormal Security, which stops targeted email attacks, both out of San Francisco. Just down the coast in Los Angeles is Obsidian Security, a startup offering cybersecurity for cloud services.

Deepfakes Get a Friendly Makeover
Deepfakes of videos and other types of AI-manipulated media where faces or voices are synthesized in order to fool viewers or listeners has been a different type of ongoing cybersecurity risk. However, some firms are swapping malicious intent for benign marketing and entertainment purposes.

Now anyone can be a supermodel thanks to Superpersonal, a London-based AI startup that has figured out a way to seamlessly swap a user’s face onto a fashionista modeling the latest threads on the catwalk. The most obvious use case is for shoppers to see how they will look in a particular outfit before taking the plunge on a plunging neckline.

Another British company called Synthesia helps users create videos where a talking head will deliver a customized speech or even talk in a different language. The startup’s claim to fame was releasing a campaign video for the NGO Malaria Must Die showing soccer star David Becham speak in nine different languages.

There’s also a Seattle-based company, Wellsaid Labs, which uses AI to produce voice-over narration where users can choose from a library of digital voices with human pitch, emphasis, and intonation. Because every narrator sounds just a little bit smarter with a British accent.

AI Helps Make Smart Cities Smarter
Speaking of smarter: A handful of AI 100 startups are helping create the smart city of the future, where a digital web of sensors, devices, and cloud-based analytics ensure that nobody is ever stuck in traffic again or without an umbrella at the wrong time. At least that’s the dream.

A couple of them are directly connected to Google subsidiary Sidewalk Labs, which focuses on tech solutions to improve urban design. A company called Replica was spun out just last year. It’s sort of SimCity for urban planning. The San Francisco startup uses location data from mobile phones to understand how people behave and travel throughout a typical day in the city. Those insights can then help city governments, for example, make better decisions about infrastructure development.

Denver-area startup AMP Robotics gets into the nitty gritty details of recycling by training robots on how to recycle trash, since humans have largely failed to do the job. The U.S. Environmental Protection Agency estimates that only about 30 percent of waste is recycled.

Some people might complain that weather forecasters don’t even do that well when trying to predict the weather. An Israeli AI startup, ClimaCell, claims it can forecast rain block by block. While the company taps the usual satellite and ground-based sources to create weather models, it has developed algorithms to analyze how precipitation and other conditions affect signals in cellular networks. By analyzing changes in microwave signals between cellular towers, the platform can predict the type and intensity of the precipitation down to street level.

And those are just some of the highlights of what some of the world’s most promising AI startups are doing.

“You have companies optimizing mining operations, warehouse logistics, insurance, workflows, and even working on bringing AI solutions to designing printed circuit boards,” Varadharajanis said. “So a lot of creative ways in which companies are applying AI to solve different issues in different industries.”

Image Credit: Butterfly Network Continue reading →

Posted in Human Robots

#436946 Coronavirus May Mean Automation Is ...

Posted on March 20, 2020 by Android

We’re in the midst of a public health emergency, and life as we know it has ground to a halt. The places we usually go are closed, the events we were looking forward to are canceled, and some of us have lost our jobs or fear losing them soon.

But although it may not seem like it, there are some silver linings; this crisis is bringing out the worst in some (I’m looking at you, toilet paper hoarders), but the best in many. Italians on lockdown are singing together, Spaniards on lockdown are exercising together, this entrepreneur made a DIY ventilator and put it on YouTube, and volunteers in Italy 3D printed medical valves for virus treatment at a fraction of their usual cost.

Indeed, if you want to feel like there’s still hope for humanity instead of feeling like we’re about to snowball into terribleness as a species, just look at these examples—and I’m sure there are many more out there. There’s plenty of hope and opportunity to be found in this crisis.

Peter Xing, a keynote speaker and writer on emerging technologies and associate director in technology and growth initiatives at KPMG, would agree. Xing believes the coronavirus epidemic is presenting us with ample opportunities for increased automation and remote delivery of goods and services. “The upside right now is the burgeoning platform of the digital transformation ecosystem,” he said.

In a thought-provoking talk at Singularity University’s COVID-19 virtual summit this week, Xing explained how the outbreak is accelerating our transition to a highly-automated society—and painted a picture of what the future may look like.

Confronting Scarcity
You’ve probably seen them by now—the barren shelves at your local grocery store. Whether you were in the paper goods aisle, the frozen food section, or the fresh produce area, it was clear something was amiss; the shelves were empty. One of the most inexplicable items people have been panic-bulk-buying is toilet paper.

Xing described this toilet paper scarcity as a prisoner’s dilemma, pointing out that we have a scarcity problem right now in terms of our mindset, not in terms of actual supply shortages. “It’s a prisoner’s dilemma in that we’re all prisoners in our homes right now, and we can either hoard or not hoard, and the outcomes depend on how we collaborate with each other,” he said. “But it’s not a zero-sum game.”

Xing referenced a CNN article about why toilet paper, of all things, is one of the items people have been panic-buying most (I, too, have been utterly baffled by this phenomenon). But maybe there’d be less panic if we knew more about the production methods and supply chain involved in manufacturing toilet paper. It turns out it’s a highly automated process (you can learn more about it in this documentary by National Geographic) and requires very few people (though it does require about 27,000 trees a day—so stop bulk-buying it! Just stop!).

The supply chain limitation here is in the raw material; we certainly can’t keep cutting down this many trees a day forever. But—somewhat ironically, given the Costco cartloads of TP people have been stuffing into their trunks and backseats—thanks to automation, toilet paper isn’t something stores are going to stop receiving anytime soon.

Automation For All
Now we have a reason to apply this level of automation to, well, pretty much everything.

Though our current situation may force us into using more robots and automated systems sooner than we’d planned, it will end up saving us money and creating opportunity, Xing believes. He cited “fast-casual” restaurants (Chipotle, Panera, etc.) as a prime example.

Currently, people in the US spend much more to eat at home than we do to eat in fast-casual restaurants if you take into account the cost of the food we’re preparing plus the value of the time we’re spending on cooking, grocery shopping, and cleaning up after meals. According to research from investment management firm ARK Invest, taking all these costs into account makes for about $12 per meal for food cooked at home.

That’s the same as or more than the cost of grabbing a burrito or a sandwich at the joint around the corner. As more of the repetitive, low-skill tasks involved in preparing fast casual meals are automated, their cost will drop even more, giving us more incentive to forego home cooking. (But, it’s worth noting that these figures don’t take into account that eating at home is, in most cases, better for you since you’re less likely to fill your food with sugar, oil, or various other taste-enhancing but health-destroying ingredients—plus, there are those of us who get a nearly incomparable amount of joy from laboring over then savoring a homemade meal).

Now that we’re not supposed to be touching each other or touching anything anyone else has touched, but we still need to eat, automating food preparation sounds appealing (and maybe necessary). Multiple food delivery services have already implemented a contactless delivery option, where customers can choose to have their food left on their doorstep.

Besides the opportunities for in-restaurant automation, “This is an opportunity for automation to happen at the last mile,” said Xing. Delivery drones, robots, and autonomous trucks and vans could all play a part. In fact, use of delivery drones has ramped up in China since the outbreak.

Speaking of deliveries, service robots have steadily increased in numbers at Amazon; as of late 2019, the company employed around 650,000 humans and 200,000 robots—and costs have gone down as robots have gone up.

ARK Invest’s research predicts automation could add $800 billion to US GDP over the next 5 years and $12 trillion during the next 15 years. On this trajectory, GDP would end up being 40 percent higher with automation than without it.

Automating Ourselves?
This is all well and good, but what do these numbers and percentages mean for the average consumer, worker, or citizen?

“The benefits of automation aren’t being passed on to the average citizen,” said Xing. “They’re going to the shareholders of the companies creating the automation.” This is where policies like universal basic income and universal healthcare come in; in the not-too-distant future, we may see more movement toward measures like these (depending how the election goes) that spread the benefit of automation out rather than concentrating it in a few wealthy hands.

In the meantime, though, some people are benefiting from automation in ways that maybe weren’t expected. We’re in the midst of what’s probably the biggest remote-work experiment in US history, not to mention remote learning. Tools that let us digitally communicate and collaborate, like Slack, Zoom, Dropbox, and Gsuite, are enabling remote work in a way that wouldn’t have been possible 20 or even 10 years ago.

In addition, Xing said, tools like DataRobot and H2O.ai are democratizing artificial intelligence by allowing almost anyone, not just data scientists or computer engineers, to run machine learning algorithms. People are codifying the steps in their own repetitive work processes and having their computers take over tasks for them.

As 3D printing gets cheaper and more accessible, it’s also being more widely adopted, and people are finding more applications (case in point: the Italians mentioned above who figured out how to cheaply print a medical valve for coronavirus treatment).

The Mother of Invention
This movement towards a more automated society has some positives: it will help us stay healthy during times like the present, it will drive down the cost of goods and services, and it will grow our GDP in the long run. But by leaning into automation, will we be enabling a future that keeps us more physically, psychologically, and emotionally distant from each other?

We’re in a crisis, and desperate times call for desperate measures. We’re sheltering in place, practicing social distancing, and trying not to touch each other. And for most of us, this is really unpleasant and difficult. We can’t wait for it to be over.

For better or worse, this pandemic will likely make us pick up the pace on our path to automation, across many sectors and processes. The solutions people implement during this crisis won’t disappear when things go back to normal (and, depending who you talk to, they may never really do so).

But let’s make sure to remember something. Even once robots are making our food and drones are delivering it, and our computers are doing data entry and email replies on our behalf, and we all have 3D printers to make anything we want at home—we’re still going to be human. And humans like being around each other. We like seeing one another’s faces, hearing one another’s voices, and feeling one another’s touch—in person, not on a screen or in an app.

No amount of automation is going to change that, and beyond lowering costs or increasing GDP, our greatest and most crucial responsibility will always be to take care of each other.

Image Credit: Gritt Zheng on Unsplash Continue reading →

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