Tag Archives: humanoid
Have Scientists Discovered the Cure for Potholes?Angela Chen | The Verge"Self-healing asphalt has been tested on 12 different roads in the Netherlands, and one of these has been functioning and open to the public since 2010. All are still in perfect condition, but Schlangen notes that even normal asphalt roads are fine for about seven to 10 years and that it’s in upcoming years that we’ll really start to see the difference. He estimates that the overall cost of the material would be 25 percent more expensive than normal asphalt, but it could double the life of the road."
The Little Robot That Taught the Big Robot a Thing or TwoMatt Simon | WIRED"New research out today from the MIT Computer Science and Artificial Intelligence Laboratory takes a big step toward making such seamless transfers of knowledge a reality. It all begins with a little robot named Optimus and its friend, the famous 6-foot-tall humanoid Atlas."
A Cheap, Simple Way to Make Anything a Touch PadRachel Metz | MIT Technology Review"Researchers at Carnegie Mellon University say they’ve come up with a way to make many kinds of devices responsive to touch just by spraying them with conductive paint, adding electrodes, and computing where you press on them…Called Electrick, it can be used with materials like plastic, Jell-O, and silicone, and it could make touch tracking a lot cheaper, too, since it relies on already available paint and parts, Zhang says."
A New 3D Printing Technology Uses Electricity to Create Stronger Objects for ManufacturingBrian Heater | TechCrunch"FuseBox’s thrust is simultaneously dead simple and entirely complex, but at the most elementary level, it utilizes heat and electricity to increase the temperature of the material before and after each level is deposited. This serves to strengthen the body of the printed product where it’s traditionally weakest during the FDM (fused deposition modeling) print – the same layer-by-layer technology employed by MakerBot and the majority of desktop 3D printers."
What Is America's Secret Space Shuttle For?Marina Koren | The Atlantic"The news that the military had a space shuttle quietly orbiting Earth for more than 700 days came as a surprise to some. Why didn’t we know about this thing, the reaction seemed to go. The reaction illustrated the distinct line between the country’s civilian and military activities in space, and how much the general public knows about each."
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Press Release by: Robotart.org
Robots Have Learned to Paint in Second Year of Robotic Art Contest
Seattle, Wash – April 19, 2017 – It was just announced that Google has developed AI that can sketch images. It should therefore come as no surprise that dozens of robots from around the world are now also painting with a brush, and many of them are quite skilled.
The Robot Art 2017 competition (http://robotart.org) returns for a second year with over 39 painting robots, more than twice the amount participants it had in its inaugural year. In addition to more robots, there is more artwork. More than 200 paintings have been submitted. With regards to the quality of the artwork, the event’s sponsor and organizer, Andrew Conru, sums it up best,
“The quality of the paintings for many of the teams have reached levels that are comparable with human artists. Many of this year’s entries are expressive, layered, and complex.”
The creativity of the teams and robots was evident not only in the artwork they produced, but also in how they went about making the art. Of the 39 painting robots, no two teams took the exact same approach. The Manibus Team captured the movements of a ballerina and painted it to canvas. HEARTalion built a robot that paints based on emotional interactions with humans. share your inner unicorn used brainwaves to control a mark making mobile robot. Other teams built custom robots that capitalized on their innate lack of precision to make abstract work such as Anguis, a soft snake robot that slithers around its canvas. Other robots were built to collaborate with their artistic creators such as Sander Idzerda’s and Christian H. Seidler’s entries.
Robot Painter. Photo Credit: Robotart.orgTwo returning entries that were notable for their skilled approach to representational paintings in last year’s contest, have gone abstract. e-David submitted multiple abstract self-portraits, not of a human, but of the robot itself. Each of its works was a collaboration between an artist and the machine where most of the decisions were actually made by e-David as it continually watched and optimized its own progress on the canvas with multiple external cameras. CloudPainter also submitted multiple abstract portraits. It’s subjects were taken from photoshoots performed by the robot itself. For several of CloudPainter’s paintings, the only artistic decision made by an artist was to schedule the photoshoot. The robot then used artificial intelligence and deep learning to make all other “artistic” decisions including taking the photos, making an original abstract composition from its favorite, and then executing each brushstroke until it had calculated it had done the best it could to render its original abstract composition.
Robot Painter. Photo Credit: Robotart.orgThe Robot Art 2017 competition will be running between now and May 15th when more than $100,000 in awards will be given to the top painting robots. Winners will be determined based on a combination of public voting, professional judges consisting of working artists, critics, and technologists, and by how well the team met the spirit of the competition – that is to create something beautiful using a physical brush and robotics. The public can see the artwork vote on their favorite robotic paintings at https://robotart.org/artworks/.
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Twenty years ago, IBM computer Deep Blue beat the world's greatest chess player in a first for machines. How far has artificial intelligence come since then? Continue reading
Will artificial intelligence “destroy humanity?” Probably not.
But I am concerned that AI and robotics will massively impact the future of work.
McKinsey & Co. predicts that 45 percent of jobs today will be automated out of existence in only 20 years.
This weighs on me.
While the magnitude of the coming change doesn’t bother me, it's the speed of the change I’m worried about.
(Note: We’ve seen such change before. America went from a society of 84 percent farmers in 1810 to only 2 percent farmers today).
This is a post about one mechanism to buffer the impact of rapid technological unemployment.
In this post, I’ll make the case for universal basic income (UBI) and unpack some of the common misconceptions of giving money away for free.
Today, there are 700 million people around the world living in extreme poverty (defined by the World Bank as $1.25/day (in 2005 prices)).
According to the Brookings Institute, just $80 billion would lift all of them out of extreme poverty.
We spend twice this amount in global aid every year. If only we could give the funds directly to the people who need it most.
In a recent Abundance 360 webinar, I interviewed Michael Faye, the co-founder of GiveDirectly, who presented some compelling data about the disruption of philanthropy through peer-to-peer aid.
Let’s dive in.
What is GiveDirectly?
GiveDirectly is the largest UBI experiment to date.
Over the next 12 years, GiveDirectly is running a controlled trial across 4 villages in Kenya, with more than 26,000 participants.
In addition to a control group, one village will receive a regular basic income for 12 years, another for 2 years, and yet another will receive a single lump sum equivalent to 2 years' worth of income.
Within each village, everyone (man, woman and child) receives the same equal payment of roughly 75 cents per day regardless of their current wealth.
Incredibly, since launching the experiment in 2012, GiveDirectly has distributed more than $100 million in total donations for people in extreme poverty.
The data they are accumulating on the efficacy of UBI is incredible.
Here are the top three takeaways from our conversation.
1. Philanthropy is ripe for disruption
Most of today’s billion-dollar non-profits and NGOs are incredibly inefficient and bureaucratic.
Michael estimates only about “15–20 percent of donations” actually get to recipients, adding that in many cases “the current system is so complex that many of the agencies themselves don’t know the actual number.”
Many programs and donations are in-kind items, such as foods, which are often resold at a discount because the recipients simply don't want them.
By giving cash instead of goods, combined with its mobile-enabled technology stack, GiveDirectly flips that ratio.
For every dollar, 90 cents end up in the hand of the recipient.
2. Directly giving cash has counter-intuitive positive byproducts
As a society, we underestimate the ability of the poor to make decisions in their best interest.
We want to prescribe who gets what, how much, and under what conditions.
For example, Michael asks, “If you ask a child whether they’d prefer to give a poor person a cow, or give them money?” They typically respond that it's better to give a cow. It feels better.
We are also hesitant to give cash for fear that it will lead to increased substance abuse, or lead to laziness.
However, well-documented studies consistently show that cash transfers tend to:
Cause a decline in the purchase of alcohol or tobacco.
Lead to an increase in the hours worked.
For example, in Sri Lanka, a study of one-time transfers found that men’s annual income had increased by 64–96 percent of the grant amount after five years.
In Uganda, 4 years after a small one-time donation, recipients were earning 41 percent more than those who had not received the donation.
3. Cash transfers lead to better health and social outcomes
Looking at over 160 studies across 30 countries and 56 cash transfer programs, the Overseas Development Institute recently performed a meta analysis, finding positive results across areas such as education, health and nutrition, savings and investment, and employment.
Specific to health, studies have found:
Large increases in children’s height and weight in South Africa
Reductions in HIV infections and psychological distress in Malawi
Reductions in low birth weight in Uruguay
Reductions in child labor as well as increases in childhood schooling
Decreases in domestic violence
My closing thoughts
Technological unemployment is coming fast, and it has the potential to lead to significant social unrest.
We need to be proposing and running experiments to validate solutions that work across geographies and cultures at scale.
UBI is one idea. I salute the passionate entrepreneurs who are launching experiments to uncover their solutions.
What will you do to make an impact?
We have the raw materials to create a world of abundance. Let’s get to work.
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To create a new drug, researchers have to test tens of thousands of compounds to determine how they interact. And that’s the easy part; after a substance is found to be effective against a disease, it has to perform well in three different phases of clinical trials and be approved by regulatory bodies.
It’s estimated that, on average, one new drug coming to market can take 1,000 people, 12-15 years, and up to $1.6 billion.
There has to be a better way—and now it seems there is.
Last week, researchers published a paper detailing an artificial intelligence system made to help discover new drugs, and significantly shorten the amount of time and money it takes to do so.
The system is called AtomNet, and it comes from San Francisco-based startup AtomWise. The technology aims to streamline the initial phase of drug discovery, which involves analyzing how different molecules interact with one another—specifically, scientists need to determine which molecules will bind together and how strongly. They use trial and error and process of elimination to analyze tens of thousands of compounds, both natural and synthetic.
AtomNet takes the legwork out of this process, using deep learning to predict how molecules will behave and how likely they are to bind together. The software teaches itself about molecular interaction by identifying patterns, similar to how AI learns to recognize images.
Remember the 3D models of atoms you made in high school, where you used pipe cleaners and foam balls to represent the connections between protons, neutrons and electrons? AtomNet uses similar digital 3D models of molecules, incorporating data about their structure to predict their bioactivity.
As AtomWise COO Alexander Levy put it, “You can take an interaction between a drug and huge biological system and you can decompose that to smaller and smaller interactive groups. If you study enough historical examples of molecules…you can then make predictions that are extremely accurate yet also extremely fast.”
“Fast” may even be an understatement; AtomNet can reportedly screen one million compounds in a day, a volume that would take months via traditional methods.
AtomNet can’t actually invent a new drug, or even say for sure whether a combination of two molecules will yield an effective drug. What it can do is predict how likely a compound is to work against a certain illness. Researchers then use those predictions to narrow thousands of options down to dozens (or less), focusing their testing where there’s more likely to be positive results.
The software has already proven itself by helping create new drugs for two diseases, Ebola and multiple sclerosis. The MS drug has been licensed to a British pharmaceutical company, and the Ebola drug is being submitted to a peer-reviewed journal for additional analysis.
While AtomNet is a promising technology that will make discovering new drugs faster and easier, it’s worth noting that the future of medicine is also moving towards a proactive rather than reactive approach; rather than solely inventing drugs to cure sick people, focus will shift to carefully monitoring our health and taking necessary steps to keep us from getting sick in the first place.
Last year, the Chan Zuckerberg Initiative donated $3 billion in a pledge to “cure all diseases.” It’s an ambitious and somewhat quixotic goal, but admirable nonetheless. In another example of the movement towards proactive healthcare, the XPRIZE foundation recently awarded $2.5 million for a device meant to facilitate home-based diagnostics and personal health monitoring. Proactive healthcare technology is likely to keep advancing and growing in popularity.
That doesn’t mean reactive healthcare shouldn’t advance alongside it; fifty or one hundred years from now, people will still be getting sick and will still need medicine to help cure them. AtomNet is the first software of its kind, and it may soon see others following in its footsteps in the effort to apply AI to large-scale challenges.
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