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Category Archives: General

How Selfish Are You? It Matters for MIT’s New Self-Driving Algorithm

· · Posted in Automation, General, Science and Society, Topics ·

How Selfish Are You? It Matters for MIT’s New Self-Driving Algorithm

Our personalities impact almost everything we do, from the career path we choose to the way we interact with others to how we spend our free time.

But what about the way we drive—could personality be used to predict whether a driver will cut someone off, speed, or, say, zoom through a yellow light instead of braking?

There must be something to the idea that those of us who are more mild-mannered are likely to drive a little differently than the more assertive among us. At least, that’s what a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is betting on.

“Working with and around humans means figuring out their intentions to better understand their behavior,” said graduate student Wilko Schwarting, lead author on the paper published this week in Proceedings of the National Academy of Sciences. “People’s tendencies to be collaborative or competitive often spills over into how they behave as drivers. In this paper we sought to understand if this was something we could actually quantify.”

The team is building a model that classifies drivers according to how selfish or selfless they are, then uses that classification to help predict how drivers will behave on the road. Ideally, the system will help improve safety for self-driving cars by integrating a degree of ‘humanity’ into how their software perceives its surroundings; right now, human drivers and their cars are just another object, not much different than a tree or a sign.

But unlike trees and signs, humans have behavioral patterns and motivations. For greater success on roads that are still dominated by us mercurial humans, the CSAIL team believes, driverless cars should take our personalities into account.

How Selfish Are You?

About how important is your own well-being to you vs. the well-being of other people? It’s a hard question to answer without specifying who the other people are; your answer would likely differ if we’re talking about your friends, loved ones, strangers, or people you actively dislike.

In social psychology, social value orientation (SVO) refers to people’s preferences for allocating resources between themselves and others. The two broad categories people can fall into are pro-social (people who are more cooperative, and expect cooperation from others) and pro-self (pretty self-explanatory: “Me first!”).

Based on drivers’ behavior in two different road scenarios—merging and making a left turn—the CSAIL team’s model classified drivers as pro-social or egoistic. Slowing down to let someone merge into your lane in front of you would earn you a pro-social classification, while cutting someone off or not slowing down to allow a left turn would make you egoistic.

On the Road

The system then uses these classifications to model and predict drivers’ behavior. The team demonstrated that using their model, errors in predicting the behavior of other cars were reduced by 25 percent.

In a left-turn simulation, for example, their car would wait when an approaching car had an egoistic driver, but go ahead and make the turn when the other driver was prosocial. Similarly, if a self-driving car is trying to merge into the left lane and it’s identified the drivers in that lane as egoistic, it will assume they won’t slow down to let it in, and will wait to merge behind them. If, on the other hand, the self-driving car knows that the human drivers in the left lane are prosocial, it will attempt to merge between them since they’re likely to let it in.

So how does this all translate to better safety?

It’s essentially a starting point for imbuing driverless cars with some of the abilities and instincts that are innate to humans. If you’re driving down the highway and you see a car swerving outside its lane, you’ll probably distance yourself from that car because you know it’s more likely to cause an accident. Our senses take in information we can immediately interpret and act on, and this includes predictions about what might happen based on observations of what just happened. Our observations can clue us in to a driver’s personality (the swerver must be careless) or simply to the circumstances of a given moment (the swerver was texting).

But right now, self-driving cars assume all human drivers behave the same way, and they have no mechanism for incorporating observations about behavioral differences between drivers into their decisions.

“Creating more human-like behavior in autonomous vehicles (AVs) is fundamental for the safety of passengers and surrounding vehicles, since behaving in a predictable manner enables humans to understand and appropriately respond to the AV’s actions,” said Schwarting.

Though it may feel a bit unsettling to think of an algorithm lumping you into a category and driving accordingly around you, maybe it’s less unsettling than thinking of self-driving cars as pre-programmed, oblivious robots unable to adapt to different driving styles.

The team’s next step is to apply their model to pedestrians, bikes, and other agents frequently found in driving environments. They also plan to look into other robotic systems acting among people, like household robots, and integrating social value orientation into their algorithms.

Image Credit: Image by Free-Photos from Pixabay

Source: Singularity Hub:  https://singularityhub.com/2019/11/19/how-selfish-are-you-it-matters-for-mits-new-self-driving-algorithm/

NASA Announces Ninth Consecutive Clean Financial Audit Opinion

· · Posted in General, Space ·

[rNASA Announces Ninth Consecutive Clean Financial Audit Opinion The NASA Office of the Chief Financial Officer (OCFO) has led the way for an unmodified audit opinion on the agency’s fiscal year 2019 (FY 2019) financial statements. This represents NASA’s ninth consecutive “clean” opinion from an independent accounting firm – the highest opinion possible. Source: NASA Breaking news http://www.nasa.gov/press-release/nasa-announces-ninth-consecutive-clean-financial-audit-opinion

NASA Highlights Science on 19th SpaceX Resupply Mission to Space Station

· · Posted in General, Space ·

[rNASA Highlights Science on 19th SpaceX Resupply Mission to Space Station NASA will host a media teleconference at 1 p.m. EST Wednesday, Nov. 20, to discuss select science investigations launching on the next SpaceX commercial resupply flight to the International Space Station. Source: NASA Breaking news http://www.nasa.gov/press-release/nasa-highlights-science-on-19th-spacex-resupply-mission-to-space-station

New Companies Join Growing Ranks of NASA Partners for Artemis Program

· · Posted in General, Space ·

[rNew Companies Join Growing Ranks of NASA Partners for Artemis Program NASA has added five American companies to the pool of vendors that will be eligible to bid on proposals to provide deliveries to the surface of the Moon through the agency’s Commercial Lunar Payload Services (CLPS) initiative. Source: NASA Breaking news http://www.nasa.gov/press-release/new-companies-join-growing-ranks-of-nasa-partners-for-artemis-program

We’re Making Progress in Explainable AI, but Major Pitfalls Remain

· · Posted in Artificial Intelligence, General, Science and Society, Topics ·

We’re Making Progress in Explainable AI, but Major Pitfalls Remain

Machine learning algorithms are starting to exceed human performance in many narrow and specific domains, such as image recognition and certain types of medical diagnoses. They’re also rapidly improving in more complex domains such as generating eerily human-like text. We increasingly rely on machine learning algorithms to make decisions on a wide range of topics, from what we collectively spend billions of hours watching to who gets the job.

But machine learning algorithms cannot explain the decisions they make.
How can we justify putting these systems in charge of decisions that affect people’s lives if we don’t understand how they’re arriving at those decisions?

This desire to get more than raw numbers from machine learning algorithms has led to a renewed focus on explainable AI: algorithms that can make a decision or take an action, and tell you the reasons behind it.

What Makes You Say That?

In some circumstances, you can see a road to explainable AI already. Take OpenAI’s GTP-2 model, or IBM’s Project Debater. Both of these generate text based on a large corpus of training data, and try to make it as relevant as possible to the prompt that’s given. If these models were also able to provide a quick run-down of the top few sources in that corpus of training data they were drawing information from, it may be easier to understand where the “argument” (or poetic essay about unicorns) was coming from.

This is similar to the approach Google is now looking at for its image classifiers. Many algorithms are more sensitive to textures and the relationship between adjacent pixels in an image, rather than recognizing objects by their outlines as humans do. This leads to strange results: some algorithms can happily identify a totally scrambled image of a polar bear, but not a polar bear silhouette.

Previous attempts to make image classifiers explainable relied on significance mapping. In this method, the algorithm would highlight the areas of the image that contributed the most statistical weight to making the decision. This is usually determined by changing groups of pixels in the image and seeing which contribute to the biggest change in the algorithm’s impression of what the image is. For example, if the algorithm is trying to recognize a stop sign, changing the background is unlikely to be as important as changing the sign.

Google’s new approach changes the way that its algorithm recognizes objects, by examining them at several different resolutions and searching for matches to different “sub-objects” within the main object. You or I might recognize an ambulance from its flashing lights, its tires, and its logo; we might zoom in on the basketball held by an NBA player to deduce their occupation, and so on. By linking the overall categorization of an image to these “concepts,” the algorithm can explain its decision: I categorized this as a cat because of its tail and whiskers.

Even in this experiment, though, the “psychology” of the algorithm in decision-making is counter-intuitive. For example, in the basketball case, the most important factor in making the decision was actually the player’s jerseys rather than the basketball.

Can You Explain What You Don’t Understand?

While it may seem trivial, the conflict here is a fundamental one in approaches to artificial intelligence. Namely, how far can you get with mere statistical associations between huge sets of data, and how much do you need to introduce abstract concepts for real intelligence to arise?

At one end of the spectrum, Good Old-Fashioned AI or GOFAI dreamed up machines that would be entirely based on symbolic logic. The machine would be hard-coded with the concept of a dog, a flower, cars, and so forth, alongside all of the symbolic “rules” which we internalize, allowing us to distinguish between dogs, flowers, and cars. (You can imagine a similar approach to a conversational AI would teach it words and strict grammatical structures from the top down, rather than “learning” languages from statistical associations between letters and words in training data, as GPT-2 broadly does.)

Such a system would be able to explain itself, because it would deal in high-level, human-understandable concepts. The equation is closer to: “ball” + “stitches” + “white” = “baseball”, rather than a set of millions of numbers linking various pathways together. There are elements of GOFAI in Google’s new approach to explaining its image recognition: the new algorithm can recognize objects based on the sub-objects they contain. To do this, it requires at least a rudimentary understanding of what those sub-objects look like, and the rules that link objects to sub-objects, such as “cats have whiskers.”

The issue, of course, is the—maybe impossible—labor-intensive task of defining all these symbolic concepts and every conceivable rule that could possibly link them together by hand. The difficulty of creating systems like this, which could handle the “combinatorial explosion” present in reality, helped to lead to the first AI winter.

Meanwhile, neural networks rely on training themselves on vast sets of data. Without the “labeling” of supervised learning, this process might bear no relation to any concepts a human could understand (and therefore be utterly inexplicable).

Somewhere between these two, hope explainable AI enthusiasts, is a happy medium that can crunch colossal amounts of data, giving us all of the benefits that recent, neural-network AI has bestowed, while showing its working in terms that humans can understand.

Image Credit: Image by Seanbatty from Pixabay

Source: Singularity Hub:  https://singularityhub.com/2019/11/18/the-progress-were-making-in-explainable-ai-and-the-pitfalls-that-remain/

To Reinvent Healthcare, Stop Treating Patients and Start Building Communities

· · Posted in Events, Exponential Medicine, Future of Health, General, Science and Society ·

To Reinvent Healthcare, Stop Treating Patients and Start Building Communities

It’s not news that healthcare costs in the US are sky-high. What might be news to some, though, is that year over year, we’re spending more money to treat illness and disease, but getting less out of it.

According to Asif Dhar MD, Chief Informatics Officer at Deloitte, spending on diabetes alone went up by $75 billion in the five years from 2012-2017. Addiction—from alcoholism to prescription drugs to illegal drugs—costs Americans $740 a year in healthcare, crime, and lost productivity. The suicide rate in the US has gone up 33 percent in less than 2 decades; we’re 3 times more likely to kill ourselves than we are to be killed.

“Of the top ten reasons people die globally, five are inherently related to behaviors: what we eat, how we sleep, the medications we take,” Dhar said in a talk at Singularity University’s Exponential Medicine summit last week. He emphasized that spending more money isn’t the way to better health; rather, we should shift some of our focus away from treating sick individuals and towards creating healthy communities.

“80 percent of the problem has to do with the way we live, how we engage in healthy behaviors, and what our communities are like,” Dhar said. “We can move from healthcare to health by expanding the aperture of what we’re reinventing.”

Living Badly

Average life expectancy in the US is declining for the first time since 1918, and if you look at the top causes—drug overdoses, suicide, and liver disease—it can be pretty easily argued that we’re dying younger not because we can’t cure disease, but because we’re not living well.

Asif Dhar exponential medicine
Asif Dhar at Exponential Medicine

The “emperor of all maladies” (an apt moniker bestowed on cancer by physician and author Siddharta Mukherjee), is a good example. 42 percent of all cancers are preventable, stemming from factors like an unhealthy diet, smoking, and a lack of exercise. “That effectively means that 659,000 cancer cases never needed to happen,” Dhar said. “Is that a question of care, or is it a question of how we live?”

To be sure, our healthcare system is flawed. Medical error rates are high, care is absurdly expensive, and there are curable diseases that haven’t been cured, Dhar pointed out. But by the time people get to a doctor or hospital, they’re already sick; the damage has already been done. So why aren’t we trying harder to dig down to the roots where that damage starts? How do we figure out where those roots are? And how big of a difference could it make if we focused on nourishing roots rather than bandaging or reviving dying trees?

A Community Focus

Dhar and his colleagues think communities are at the root of our health, and that if we focus on building healthier communities, the payoffs in quality of life, years of life, and dollars saved will all be significant. A team at Deloitte came up with five consistent themes for how communities can reduce costs while improving the health of their people.

1. Empower proactive health and well-being management

2. Foster a sense of community and well-being

3. Engage digital technologies with behavioral science

4. Meaningfully use data to improve outcomes

5. Enable new innovative models for the way these societies work

For a concrete example of what it looks like to apply these steps, Dhar pointed to Oklahoma City. After it was designated America’s fattest city in 2007, the city’s mayor decided to fight back by implementing a program called “This City Is Going on a Diet.” A website was created to track progress, with around 47,000 people signing up to report their weight loss. The government poured money into building sidewalks, bike trails, parks, and wellness centers, encouraging people to ditch their cars and use more active modes of transportation. Within five years, the city’s residents collectively lost a million pounds.

“The one thing we know that gets people to walk is to create places that are enticing so they want to walk there,” said Dick Jackson, a UCLA medical professor and former director of the Centers for Disease Control’s National Center for Environmental Health. “Telling people to exercise, to go to the gym, doesn’t seem to work. You have to build it into daily life.”

Geisinger Health System (serving Pennsylvania and New Jersey) used a community focus in a slightly different way than Oklahoma City. After realizing they were spending more on treating type 2 diabetes patients but outcomes weren’t improving, the health system decided to take another route and zeroed in on patients’ diets. “They started thinking about food as medicine,” Dhar said, “asking if they could start prescribing food. They did, and the results were astounding.”

Geisinger created a “Fresh Food Farmacy,” sourcing food from local food banks and providing patients with two meals’ worth of food per day, five days per week, for as long as they need it. Among pilot participants, there was a reduction of 80 percent in treatment cost and an average 2.1 percent reduction in patients’ HbA1C levels.

“When it comes to the social determinants of health, we know there are many more causes impacting the health of a population than access to quality medical care,” said David Feinberg, Geisinger’s former CEO. “We want to transform healthcare at its core by focusing on preventative care, behavioral health, and economic growth.”

Nourishing the Roots

Creating health-focused communities and empowering people to take their wellness into their own hands will have huge implications for the American healthcare system. Dhar emphasized that while people don’t always make optimal health choices for themselves, they’re far more likely to engage in healthy behaviors when they’re in an environment that enables, encourages, and models healthy choices.

Examining the root causes of poor health in a community then transforming those factors from the ground up will require larger up-front investments, but will save cities, health systems, and individuals millions of dollars over time—and give everyone a higher quality of life in the process.

“What was thought of as completely impossible is completely doable today,” Dhar concluded.

Image Credit: Image by S. Hermann & F. Richter from Pixabay

Source: Singularity Hub:  https://singularityhub.com/2019/11/17/to-reinvent-healthcare-stop-treating-patients-and-start-building-communities/

This Week’s Awesome Tech Stories From Around the Web (Through November 16)

· · Posted in Curation, General, Science and Society, Topics ·

This Week’s Awesome Tech Stories From Around the Web (Through November 16)

ARTIFICIAL INTELLIGENCE

Computers Evolve a New Path Toward Human Intelligence
Matthew Hutson | Quanta
“In a recent paper, Clune argues that open-ended discovery is likely the fastest path toward artificial general intelligence—machines with nearly all the capabilities of humans. …Such open-ended exploration might lead to human-level intelligence via paths we never would have anticipated—or a variety of alien intelligences that could teach us a lot about intelligence in general.”

COMPUTING

The Computing Power to Train AI Is Now Rising Seven Times Faster Than Ever Before
Karen Hao | MIT Technology Review
“In June, an analysis from researchers at the University of Massachusetts, Amherst, showed how these increasing computational costs directly translate into carbon emissions. In their paper, they also noted how the trend exacerbates the privatization of AI research because it undermines the ability for academic labs to compete with much more resource-rich private ones.”

GENETICS

CRISPR’s Unwanted Anniversary
Jennifer Doudna | Science
“The ‘CRISPR babies’ saga should motivate active discussion and debate about human germline editing. With a new such study under consideration in Russia, appropriate regulation is urgently needed. …Ensuring responsible use of genome editing will enable CRISPR technology to improve the well-being of millions of people and fulfill its revolutionary potential.”

BIOTECH

DNA Could Be One of a Million Possible Genetic Molecules
Ryan F. Mandelbaum | Gizmodo
“Considering these other possible genetic molecules could help scientists better understand the origin of RNA and DNA and why they look the way they do on Earth. It might also help synthetic biologists hoping to use DNA-like molecules as biological storage drives …now that the list is out there, expect scientists to begin digging in and exploring whether any of these possible molecules have use in the real world.”

SPACE

Meet OffWorld, the Startup That Wants to Mine the Moon With a Swarm of Robots
Luke Dormehl | Digital Trends
“The company envisions a future in which millions of smart robots work together using swarm intelligence ‘on and offworld’ to build the infrastructure of tomorrow. Long term, they even imagine the possibility of using the robots to mine for materials which could be used to build new chips ‘with zero reliance on terrestrial supply’i.”

SCIENCE

This Mesmerizing 3D Map Visualizes Millions of Scientific Studies
Evan Nicole Brown | Fast Company
“i‘We track here how papers cite and are cited across disciplines, using data on tens of millions of scientific articles… We pay particular attention to articles that appeared in Nature. In our view, this snapshot, for all its idiosyncrasies, reveals how scientific work is ever more becoming a mixture of disciplines.’i”

Image Credit: Fabrizio Chiagano / Unsplash

Source: Singularity Hub:  https://singularityhub.com/2019/11/16/this-weeks-awesome-tech-stories-from-around-the-web-through-november-16/

NASA to Announce Additional Commercial Moon Delivery Providers

· · Posted in General, Space ·

[rNASA to Announce Additional Commercial Moon Delivery Providers NASA will host a media teleconference at 4:30 p.m. EST Monday, Nov. 18, to announce additional American companies joining the competitive pool for delivery services to the surface of the Moon through the agency’s Commercial Lunar Payload Services (CLPS) project. Source: NASA Breaking news http://www.nasa.gov/press-release/nasa-to-announce-additional-commercial-moon-delivery-providers

Within 10 Years, We’ll Travel by Hyperloop, Rockets, and Avatars

· · Posted in Experts, Future, General, Science and Society ·

Within 10 Years, We’ll Travel by Hyperloop, Rockets, and Avatars

What’s faster than autonomous vehicles and flying cars?

Try Hyperloop, rocket travel, and robotic avatars. Hyperloop is currently working towards 670 mph (1080 kph) passenger pods, capable of zipping us from Los Angeles to downtown Las Vegas in under 30 minutes. Rocket Travel (think SpaceX’s Starship) promises to deliver you almost anywhere on the planet in under an hour. Think New York to Shanghai in 39 minutes.

But wait, it gets even better…

As 5G connectivity, hyper-realistic virtual reality, and next-gen robotics continue their exponential progress, the emergence of “robotic avatars” will all but nullify the concept of distance, replacing human travel with immediate remote telepresence.

Let’s dive in.

Hyperloop One: LA to SF in 35 Minutes

Did you know that Hyperloop was the brainchild of Elon Musk? Just one in a series of transportation innovations from a man determined to leave his mark on the industry.

In 2013, in an attempt to shorten the long commute between Los Angeles and San Francisco, the California state legislature proposed a $68 billion budget allocation for what appeared to be the slowest and most expensive bullet train in history.

Musk was outraged. The cost was too high, the train too sluggish. Teaming up with a group of engineers from Tesla and SpaceX, he published a 58-page concept paper for “The Hyperloop,” a high-speed transportation network that used magnetic levitation to propel passenger pods down vacuum tubes at speeds of up to 670 mph. If successful, it would zip you across California in 35 minutes—just enough time to watch your favorite sitcom.

In January 2013, venture capitalist Shervin Pishevar, with Musk’s blessing, started Hyperloop One with myself, Jim Messina (former White House Deputy Chief of Staff for President Obama), and tech entrepreneurs Joe Lonsdale and David Sacks as founding board members. A couple of years after that, the Virgin Group invested in this idea, Richard Branson was elected chairman, and Virgin Hyperloop One was born.

“The Hyperloop exists,” says Josh Giegel, co-founder and chief technology officer of Hyperloop One, “because of the rapid acceleration of power electronics, computational modeling, material sciences, and 3D printing.”

Thanks to these convergences, there are now ten major Hyperloop One projects—in various stages of development—spread across the globe. Chicago to DC in 35 minutes. Pune to Mumbai in 25 minutes. According to Giegel, “Hyperloop is targeting certification in 2023. By 2025, the company plans to have multiple projects under construction and running initial passenger testing.”

So think about this timetable: Autonomous car rollouts by 2020. Hyperloop certification and aerial ridesharing by 2023. By 2025—going on vacation might have a totally different meaning. Going to work most definitely will.

But what’s faster than Hyperloop?

Rocket Travel

As if autonomous vehicles, flying cars, and Hyperloop weren’t enough, in September of 2017, speaking at the International Astronautical Congress in Adelaide, Australia, Musk promised that for the price of an economy airline ticket, his rockets will fly you “anywhere on Earth in under an hour.”

Musk wants to use SpaceX’s megarocket, Starship, which was designed to take humans to Mars, for terrestrial passenger delivery. The Starship travels at 17,500 mph. It’s an order of magnitude faster than the supersonic jet Concorde.

Think about what this actually means: New York to Shanghai in 39 minutes. London to Dubai in 29 minutes. Hong Kong to Singapore in 22 minutes.

So how real is the Starship?

“We could probably demonstrate this [technology] in three years,” Musk explained, “but it’s going to take a while to get the safety right. It’s a high bar. Aviation is incredibly safe. You’re safer on an airplane than you are at home.”

That demonstration is proceeding as planned. In September 2017, Musk announced his intentions to retire his current rocket fleet, both the Falcon 9 and Falcon Heavy, and replace them with the Starships in the 2020s.

Less than a year later, LA mayor Eric Garcetti tweeted that SpaceX was planning to break ground on an 18-acre rocket production facility near the port of Los Angeles. And April of this year marked an even bigger milestone: the very first test flights of the rocket.

Thus, sometime in the next decade or so, “off to Europe for lunch” may become a standard part of our lexicon.

Avatars

Wait, wait, there’s one more thing.

While the technologies we’ve discussed will decimate the traditional transportation industry, there’s something on the horizon that will disrupt travel itself. What if, to get from A to B, you didn’t have to move your body? What if you could quote Captain Kirk and just say “Beam me up, Scotty”?

Well, shy of the Star Trek transporter, there’s the world of avatars.

An avatar is a second self, typically in one of two forms. The digital version has been around for a couple of decades. It emerged from the video game industry and was popularized by virtual world sites like Second Life and books-turned-blockbusters like Ready Player One.

A VR headset teleports your eyes and ears to another location, while a set of haptic sensors shifts your sense of touch. Suddenly, you’re inside an avatar inside a virtual world. As you move in the real world, your avatar moves in the virtual.

Use this technology to give a lecture and you can do it from the comfort of your living room, skipping the trip to the airport, the cross-country flight, and the ride to the conference center.

Robots are the second form of avatars. Imagine a humanoid robot that you can occupy at will. Maybe, in a city far from home, you’ve rented the bot by the minute—via a different kind of ridesharing company—or maybe you have spare robot avatars located around the country.

Either way, put on VR goggles and a haptic suit, and you can teleport your senses into that robot. This allows you to walk around, shake hands, and take action—all without leaving your home.

And like the rest of the tech we’ve been talking about, even this future isn’t far away.

In 2018, entrepreneur Dr. Harry Kloor recommended to All Nippon Airways (ANA), Japan’s largest airline, the design of an Avatar XPRIZE. ANA then funded this vision to the tune of $10 million to speed the development of robotic avatars. Why? Because ANA knows this is one of the technologies likely to disrupt their own airline industry, and they want to be ready.

ANA recently announced its “newme” robot that humans can use to virtually explore new places. The colorful robots have Roomba-like wheeled bases and cameras mounted around eye-level, which capture surroundings viewable through VR headsets.

If the robot was stationed in your parents’ home, you could cruise around the rooms and chat with your family at any time of day. After revealing the technology at Tokyo’s Combined Exhibition of Advanced Technologies in October, ANA plans to deploy 1,000 newme robots by 2020.

With virtual avatars like newme, geography, distance, and cost will no longer limit our travel choices. From attractions like the Eiffel Tower or the pyramids of Egypt to unreachable destinations like the moon or deep sea, we will be able to transcend our own physical limits, explore the world and outer space, and access nearly any experience imaginable.

Final Thoughts

Individual car ownership has enjoyed over a century of ascendancy and dominance.

The first real threat it faced—today’s ride-sharing model—only showed up in the last decade. But that ridesharing model won’t even get ten years to dominate. Already, it’s on the brink of autonomous car displacement, which is on the brink of flying car disruption, which is on the brink of Hyperloop and rockets-to-anywhere decimation. Plus, avatars.

The most important part: All of this change will happen over the next ten years. Welcome to a future of human presence where the only constant is rapid change.

Note: This article—an excerpt from my next book The Future Is Faster Than You Think, co-authored with Steven Kotler, to be released January 28th, 2020—originally appeared on my tech blog at diamandis.com. Read the original article here.

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Image Credit: Virgin Hyperloop One

Source: Singularity Hub:  https://singularityhub.com/2019/11/15/within-10-years-well-travel-by-hyperloop-rockets-and-avatars/

This Boat Was 3D Printed—And Bigger, Wilder Projects Will Soon Follow

· · Posted in 3D Printing, General, Science and Society, Topics ·

This Boat Was 3D Printed—And Bigger, Wilder Projects Will Soon Follow

There’s a new technological answer to the iconic line “you’re going to need a bigger boat” from Jaws: 3D printing one.

Last month, the University of Maine revealed 3Dirigo, a 25-foot, 5,000-pound boat that set new records for the world’s largest solid 3D-printed object and largest 3D-printed vessel. Incidentally, it was created by the new titleholder of the largest prototype polymer 3D printer belt. If there isn’t a belt to go with the title, the printer in question can surely print one in XXXL-size.

3Dirigo was printed in just three days. Dr. Habib Dagher, Executive Director of the University of Maine’s Advanced Structures & Composites Center, told CNBC that a typical development and manufacturing process for a craft of similar size “could take months or even a year.”

The speed of production and sheer size of the finished product illustrate how many 3D printing processes are climbing out of the Trough of Disappointment in Gartner’s (in)famous Hype Cycle. Increasing development speed and new use cases point towards further advances in the coming few years.

Industries All Aboard for 3D Printers

While 3Dirigo itself is impressive, it will not necessarily hold on to all its records for long. The University of Maine’s new Big Area Additive Manufacturing Machine (BAAM) 3D printer is designed to print objects up to 100 feet long by 22 feet wide by 10 feet high, with a top printing speed of 500 pounds per hour. BAAM is a one-of-a-kind printer, and it will be used in connection with a variety of research and production initiatives, including rapid prototyping of civilian, defense, and infrastructure applications.

Mass-produced 3D printers are also growing, with many now capable of printing objects bigger than human beings.

The increase in printing sizes is likely a contributing factor to companies and organizations busily integrating 3D printers of all sizes in their setups. A report from Deloitte estimates that large public companies will spend more than $2.7 billion on 3D printing—including enterprise 3D printers, materials, and services—in 2019. In 2020, the figure will climb to more than $3 billion. 80 percent of enterprises taking part in a Sculpteo questionnaire said that 3D printing is enabling them to innovate faster. 51 percent of the interviewed enterprises said they’re already actively using 3D printing in production.

Hitting New Speeds

Early on in the lifetime of 3D printers, speed and precision were like opposing forces; you couldn’t have both. Since then, much has changed, and continues to do so. Australian 3D printer manufacturer Aurora’s RMP-1 provides a good example. It can print up to 350kg of material (metal) a day, which is a 2,000 percent increase in speed over its previous model.

Printing speeds could be about to get into warp territory thanks to several ongoing projects.

A group at the University of Michigan is using light and photoreactive resin to 3D print objects at speeds up to 100 times faster than traditional methods. A little further east, MIT professors Jamison Go and John Hart are working on 3D printing hardware called FastFFF (Fast Fused Filament Fabrication). The hardware can print at ten times the speed most 3D printers work at today and can use biodegradable cellulose instead of plastics.

Incidentally, cellulose is just one of an ever-growing multitude of materials 3D printers can use today.

Widening Use Cases

Speed and material flexibility are core to creating new use cases and leading to advances across industries.

One recent project uses a building block approach to creating whole organs. Much-famed Beyond Meat is soon going to have competition from Israeli startup Redefine Meat, who is printing a mix of fat, water, and plant fibers into something that will have the shape, texture, and taste of meat.

Another area where we are seeing rapid advances is 3D printing construction elements, and in some cases whole buildings. Like with boats, the process can be at least as fast as constructing a building by traditional methods. At last year’s South By Southwest festival, the start-up ICON showcased just that in the form of a 650-square-foot 3D printed house that cost under $10,000 and took under 24 hours to complete.

Is the Future Walking 3D Printers?

Although 3D printing’s advances are impressive, they’re unlikely to become the only way we produce things. Even with their new abilities, 3D printers will likely struggle to compete with traditional productions methods for jobs like printing out 100,000 identical spoons or metal-stamping a million identical parts. However, 3D printing is making it easier to create innovative products faster, tailor and customize products, and lower time-to-market.

As is often the case, it’s in combination with other exponential technologies that the printers could reach their full potential. For example, if we were to combine 3D printers with robots and let them loose. Imagine, as Danish GXN Innovation, The Danish AM Hub, and Map Architects did, mobile 3D printers capable of autonomous crawling, swimming, or flying, producing necessary parts on spec wherever they go.

If this nutty-sounding vision comes to pass, we’ll be seeing stranger things than boats churning out of 3D printers. But then again, if you’d heard just 10 years ago that a 25-foot boat was going to be made by a printer, you’d never have believed it.

Image Credit: University of Maine

Source: Singularity Hub:  https://singularityhub.com/2019/11/14/this-boat-was-3d-printed-and-bigger-wilder-projects-will-soon-follow/