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#432563 This Week’s Awesome Stories From ...

Pedro Domingos on the Arms Race in Artificial Intelligence
Christoph Scheuermann and Bernhard Zand | Spiegel Online
“AI lowers the cost of knowledge by orders of magnitude. One good, effective machine learning system can do the work of a million people, whether it’s for commercial purposes or for cyberespionage. Imagine a country that produces a thousand times more knowledge than another. This is the challenge we are facing.”

Gene Therapy Could Free Some People From a Lifetime of Blood Transfusions
Emily Mullin | MIT Technology Review
“A one-time, experimental treatment for an inherited blood disorder has shown dramatic results in a small study. …[Lead author Alexis Thompson] says the effect on patients has been remarkable. ‘They have been tied to this ongoing medical therapy that is burdensome and expensive for their whole lives,’ she says. ‘Gene therapy has allowed people to have aspirations and really pursue them.’ ”

The Revolutionary Giant Ocean Cleanup Machine Is About to Set Sail
Adele Peters | Fast Company
“By the end of 2018, the nonprofit says it will bring back its first harvest of ocean plastic from the North Pacific Gyre, along with concrete proof that the design works. The organization expects to bring 5,000 kilograms of plastic ashore per month with its first system. With a full fleet of systems deployed, it believes that it can collect half of the plastic trash in the Great Pacific Garbage Patch—around 40,000 metric tons—within five years.”

Autonomous Boats Will Be on the Market Sooner Than Self-Driving Cars
Tracey Lindeman | Motherboard
“Some unmanned watercraft…may be at sea commercially before 2020. That’s partly because automating all ships could generate a ridiculous amount of revenue. According to the United Nations, 90 percent of the world’s trade is carried by sea and 10.3 billion tons of products were shipped in 2016.”

Style Is an Algorithm
Kyle Chayka | Racked
“Confronting the Echo Look’s opaque statements on my fashion sense, I realize that all of these algorithmic experiences are matters of taste: the question of what we like and why we like it, and what it means that taste is increasingly dictated by black-box robots like the camera on my shelf.”

How Apple Will Use AR to Reinvent the Human-Computer Interface
Tim Bajarin | Fast Company
“It’s in Apple’s DNA to continually deliver the ‘next’ major advancement to the personal computing experience. Its innovation in man-machine interfaces started with the Mac and then extended to the iPod, the iPhone, the iPad, and most recently, the Apple Watch. Now, get ready for the next chapter, as Apple tackles augmented reality, in a way that could fundamentally transform the human-computer interface.”

Advanced Microscope Shows Cells at Work in Incredible Detail
Steve Dent | Engadget
“For the first time, scientists have peered into living cells and created videos showing how they function with unprecedented 3D detail. Using a special microscope and new lighting techniques, a team from Harvard and the Howard Hughes Medical Institute captured zebrafish immune cell interactions with unheard-of 3D detail and resolution.”

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#432249 New Malicious AI Report Outlines Biggest ...

Everyone’s talking about deep fakes: audio-visual imitations of people, generated by increasingly powerful neural networks, that will soon be indistinguishable from the real thing. Politicians are regularly laid low by scandals that arise from audio-visual recordings. Try watching the footage that could be created of Barack Obama from his speeches, and the Lyrebird impersonations. You could easily, today or in the very near future, create a forgery that might be indistinguishable from the real thing. What would that do to politics?

Once the internet is flooded with plausible-seeming tapes and recordings of this sort, how are we going to decide what’s real and what isn’t? Democracy, and our ability to counteract threats, is already threatened by a lack of agreement on the facts. Once you can’t believe the evidence of your senses anymore, we’re in serious trouble. Ultimately, you can dream up all kinds of utterly terrifying possibilities for these deep fakes, from fake news to blackmail.

How to solve the problem? Some have suggested that media websites like Facebook or Twitter should carry software that probes every video to see if it’s a deep fake or not and labels the fakes. But this will prove computationally intensive. Plus, imagine a case where we have such a system, and a fake is “verified as real” by news media algorithms that have been fooled by clever hackers.

The other alternative is even more dystopian: you can prove something isn’t true simply by always having an alibi. Lawfare describes a “solution” where those concerned about deep fakes have all of their movements and interactions recorded. So to avoid being blackmailed or having your reputation ruined, you just consent to some company engaging in 24/7 surveillance of everything you say or do and having total power over that information. What could possibly go wrong?

The point is, in the same way that you don’t need human-level, general AI or humanoid robotics to create systems that can cause disruption in the world of work, you also don’t need a general intelligence to threaten security and wreak havoc on society. Andrew Ng, AI researcher, says that worrying about the risks from superintelligent AI is like “worrying about overpopulation on Mars.” There are clearly risks that arise even from the simple algorithms we have today.

The looming issue of deep fakes is just one of the threats considered by the new malicious AI report, which has co-authors from the Future of Humanity Institute and the Centre for the Study of Existential Risk (among other organizations.) They limit their focus to the technologies of the next five years.

Some of the concerns the report explores are enhancements to familiar threats.

Automated hacking can get better, smarter, and algorithms can adapt to changing security protocols. “Phishing emails,” where people are scammed by impersonating someone they trust or an official organization, could be generated en masse and made more realistic by scraping data from social media. Standard phishing works by sending such a great volume of emails that even a very low success rate can be profitable. Spear phishing aims at specific targets by impersonating family members, but can be labor intensive. If AI algorithms enable every phishing scam to become sharper in this way, more people are going to get gouged.

Then there are novel threats that come from our own increasing use of and dependence on artificial intelligence to make decisions.

These algorithms may be smart in some ways, but as any human knows, computers are utterly lacking in common sense; they can be fooled. A rather scary application is adversarial examples. Machine learning algorithms are often used for image recognition. But it’s possible, if you know a little about how the algorithm is structured, to construct the perfect level of noise to add to an image, and fool the machine. Two images can be almost completely indistinguishable to the human eye. But by adding some cleverly-calculated noise, the hackers can fool the algorithm into thinking an image of a panda is really an image of a gibbon (in the OpenAI example). Research conducted by OpenAI demonstrates that you can fool algorithms even by printing out examples on stickers.

Now imagine that instead of tricking a computer into thinking that a panda is actually a gibbon, you fool it into thinking that a stop sign isn’t there, or that the back of someone’s car is really a nice open stretch of road. In the adversarial example case, the images are almost indistinguishable to humans. By the time anyone notices the road sign has been “hacked,” it could already be too late.

As the OpenAI foundation freely admits, worrying about whether we’d be able to tame a superintelligent AI is a hard problem. It looks all the more difficult when you realize some of our best algorithms can be fooled by stickers; even “modern simple algorithms can behave in ways we do not intend.”

There are ways around this approach.

Adversarial training can generate lots of adversarial examples and explicitly train the algorithm not to be fooled by them—but it’s costly in terms of time and computation, and puts you in an arms race with hackers. Many strategies for defending against adversarial examples haven’t proved adaptive enough; correcting against vulnerabilities one at a time is too slow. Moreover, it demonstrates a point that can be lost in the AI hype: algorithms can be fooled in ways we didn’t anticipate. If we don’t learn about these vulnerabilities until the algorithms are everywhere, serious disruption can occur. And no matter how careful you are, some vulnerabilities are likely to remain to be exploited, even if it takes years to find them.

Just look at the Meltdown and Spectre vulnerabilities, which weren’t widely known about for more than 20 years but could enable hackers to steal personal information. Ultimately, the more blind faith we put into algorithms and computers—without understanding the opaque inner mechanics of how they work—the more vulnerable we will be to these forms of attack. And, as China dreams of using AI to predict crimes and enhance the police force, the potential for unjust arrests can only increase.

This is before you get into the truly nightmarish territory of “killer robots”—not the Terminator, but instead autonomous or consumer drones which could potentially be weaponized by bad actors and used to conduct attacks remotely. Some reports have indicated that terrorist organizations are already trying to do this.

As with any form of technology, new powers for humanity come with new risks. And, as with any form of technology, closing Pandora’s box will prove very difficult.

Somewhere between the excessively hyped prospects of AI that will do everything for us and AI that will destroy the world lies reality: a complex, ever-changing set of risks and rewards. The writers of the malicious AI report note that one of their key motivations is ensuring that the benefits of new technology can be delivered to people as quickly, but as safely, as possible. In the rush to exploit the potential for algorithms and create 21st-century infrastructure, we must ensure we’re not building in new dangers.

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#431987 OptoForce Industrial Robot Sensors

OptoForce Sensors Providing Industrial Robots with

a “Sense of Touch” to Advance Manufacturing Automation

Global efforts to expand the capabilities of industrial robots are on the rise, as the demand from manufacturing companies to strengthen their operations and improve performance grows.

Hungary-based OptoForce, with a North American office in Charlotte, North Carolina, is one company that continues to support organizations with new robotic capabilities, as evidenced by its several new applications released in 2017.

The company, a leading robotics technology provider of multi-axis force and torque sensors, delivers 6 degrees of freedom force and torque measurement for industrial automation, and provides sensors for most of the currently-used industrial robots.

It recently developed and brought to market three new applications for KUKA industrial robots.

The new applications are hand guiding, presence detection, and center pointing and will be utilized by both end users and systems integrators. Each application is summarized below and what they provide for KUKA robots, along with video demonstrations to show how they operate.

Photo By: www.optoforce.com

Hand Guiding: With OptoForce’s Hand Guiding application, KUKA robots can easily and smoothly move in an assigned direction and selected route. This video shows specifically how to program the robot for hand guiding.

Presence Detection: This application allows KUKA robots to detect the presence of a specific object and to find the object even if it has moved. Visit here to learn more about presence detection.
Center Pointing: With this application, the OptoForce sensor helps the KUKA robot find the center point of an object by providing the robot with a sense of touch. This solution also works with glossy metal objects where a vision system would not be able to define its position. This video shows in detail how the center pointing application works.

The company’s CEO explained how these applications help KUKA robots and industrial automation.

Photo By: www.optoforce.com
“OptoForce’s new applications for KUKA robots pave the way for substantial improvements in industrial automation for both end users and systems integrators,” said Ákos Dömötör, CEO of OptoForce. “Our 6-axis force/torque sensors are combined with highly functional hardware and a comprehensive software package, which include the pre-programmed industrial applications. Essentially, we’re adding a ‘sense of touch’ to KUKA robot arms, enabling these robots to have abilities similar to a human hand, and opening up numerous new capabilities in industrial automation.”

Along with these new applications recently released for KUKA robots, OptoForce sensors are also being used by various companies on numerous industrial robots and manufacturing automation projects around the world. Examples of other uses include: path recording, polishing plastic and metal, box insertion, placing pins in holes, stacking/destacking, palletizing, and metal part sanding.

Specifically, some of the projects current underway by companies include: a plastic parting line removal; an obstacle detection for a major car manufacturing company; and a center point insertion application for a car part supplier, where the task of the robot is to insert a mirror, completely centered, onto a side mirror housing.

For more information, visit www.optoforce.com.

This post was provided by: OptoForce

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#431872 AI Uses Titan Supercomputer to Create ...

You don’t have to dig too deeply into the archive of dystopian science fiction to uncover the horror that intelligent machines might unleash. The Matrix and The Terminator are probably the most well-known examples of self-replicating, intelligent machines attempting to enslave or destroy humanity in the process of building a brave new digital world.
The prospect of artificially intelligent machines creating other artificially intelligent machines took a big step forward in 2017. However, we’re far from the runaway technological singularity futurists are predicting by mid-century or earlier, let alone murderous cyborgs or AI avatar assassins.
The first big boost this year came from Google. The tech giant announced it was developing automated machine learning (AutoML), writing algorithms that can do some of the heavy lifting by identifying the right neural networks for a specific job. Now researchers at the Department of Energy’s Oak Ridge National Laboratory (ORNL), using the most powerful supercomputer in the US, have developed an AI system that can generate neural networks as good if not better than any developed by a human in less than a day.
It can take months for the brainiest, best-paid data scientists to develop deep learning software, which sends data through a complex web of mathematical algorithms. The system is modeled after the human brain and known as an artificial neural network. Even Google’s AutoML took weeks to design a superior image recognition system, one of the more standard operations for AI systems today.
Computing Power
Of course, Google Brain project engineers only had access to 800 graphic processing units (GPUs), a type of computer hardware that works especially well for deep learning. Nvidia, which pioneered the development of GPUs, is considered the gold standard in today’s AI hardware architecture. Titan, the supercomputer at ORNL, boasts more than 18,000 GPUs.
The ORNL research team’s algorithm, called MENNDL for Multinode Evolutionary Neural Networks for Deep Learning, isn’t designed to create AI systems that cull cute cat photos from the internet. Instead, MENNDL is a tool for testing and training thousands of potential neural networks to work on unique science problems.
That requires a different approach from the Google and Facebook AI platforms of the world, notes Steven Young, a postdoctoral research associate at ORNL who is on the team that designed MENNDL.
“We’ve discovered that those [neural networks] are very often not the optimal network for a lot of our problems, because our data, while it can be thought of as images, is different,” he explains to Singularity Hub. “These images, and the problems, have very different characteristics from object detection.”
AI for Science
One application of the technology involved a particle physics experiment at the Fermi National Accelerator Laboratory. Fermilab researchers are interested in understanding neutrinos, high-energy subatomic particles that rarely interact with normal matter but could be a key to understanding the early formation of the universe. One Fermilab experiment involves taking a sort of “snapshot” of neutrino interactions.
The team wanted the help of an AI system that could analyze and classify Fermilab’s detector data. MENNDL evaluated 500,000 neural networks in 24 hours. Its final solution proved superior to custom models developed by human scientists.
In another case involving a collaboration with St. Jude Children’s Research Hospital in Memphis, MENNDL improved the error rate of a human-designed algorithm for identifying mitochondria inside 3D electron microscopy images of brain tissue by 30 percent.
“We are able to do better than humans in a fraction of the time at designing networks for these sort of very different datasets that we’re interested in,” Young says.
What makes MENNDL particularly adept is its ability to define the best or most optimal hyperparameters—the key variables—to tackle a particular dataset.
“You don’t always need a big, huge deep network. Sometimes you just need a small network with the right hyperparameters,” Young says.
A Virtual Data Scientist
That’s not dissimilar to the approach of a company called H20.ai, a startup out of Silicon Valley that uses open source machine learning platforms to “democratize” AI. It applies machine learning to create business solutions for Fortune 500 companies, including some of the world’s biggest banks and healthcare companies.
“Our software is more [about] pattern detection, let’s say anti-money laundering or fraud detection or which customer is most likely to churn,” Dr. Arno Candel, chief technology officer at H2O.ai, tells Singularity Hub. “And that kind of insight-generating software is what we call AI here.”
The company’s latest product, Driverless AI, promises to deliver the data scientist equivalent of a chessmaster to its customers (the company claims several such grandmasters in its employ and advisory board). In other words, the system can analyze a raw dataset and, like MENNDL, automatically identify what features should be included in the computer model to make the most of the data based on the best “chess moves” of its grandmasters.
“So we’re using those algorithms, but we’re giving them the human insights from those data scientists, and we automate their thinking,” he explains. “So we created a virtual data scientist that is relentless at trying these ideas.”
Inside the Black Box
Not unlike how the human brain reaches a conclusion, it’s not always possible to understand how a machine, despite being designed by humans, reaches its own solutions. The lack of transparency is often referred to as the AI “black box.” Experts like Young say we can learn something about the evolutionary process of machine learning by generating millions of neural networks and seeing what works well and what doesn’t.
“You’re never going to be able to completely explain what happened, but maybe we can better explain it than we currently can today,” Young says.
Transparency is built into the “thought process” of each particular model generated by Driverless AI, according to Candel.
The computer even explains itself to the user in plain English at each decision point. There is also real-time feedback that allows users to prioritize features, or parameters, to see how the changes improve the accuracy of the model. For example, the system may include data from people in the same zip code as it creates a model to describe customer turnover.
“That’s one of the advantages of our automatic feature engineering: it’s basically mimicking human thinking,” Candel says. “It’s not just neural nets that magically come up with some kind of number, but we’re trying to make it statistically significant.”
Moving Forward
Much digital ink has been spilled over the dearth of skilled data scientists, so automating certain design aspects for developing artificial neural networks makes sense. Experts agree that automation alone won’t solve that particular problem. However, it will free computer scientists to tackle more difficult issues, such as parsing the inherent biases that exist within the data used by machine learning today.
“I think the world has an opportunity to focus more on the meaning of things and not on the laborious tasks of just fitting a model and finding the best features to make that model,” Candel notes. “By automating, we are pushing the burden back for the data scientists to actually do something more meaningful, which is think about the problem and see how you can address it differently to make an even bigger impact.”
The team at ORNL expects it can also make bigger impacts beginning next year when the lab’s next supercomputer, Summit, comes online. While Summit will boast only 4,600 nodes, it will sport the latest and greatest GPU technology from Nvidia and CPUs from IBM. That means it will deliver more than five times the computational performance of Titan, the world’s fifth-most powerful supercomputer today.
“We’ll be able to look at much larger problems on Summit than we were able to with Titan and hopefully get to a solution much faster,” Young says.
It’s all in a day’s work.
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#431827 How Technology Disrupts and Drives The ...

Photo credit Technology sets us apart and puts us at the forefront, and one industry that is definitely embracing automation and robotics readily is the motor industry. Technology has touched every aspect of motoring – from updates to the manufacturing process that produce much safer and more reliable vehicles to black box diagnostics being used …
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