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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.
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.”
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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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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Picture a robot. In all likelihood, you just pictured a sleek metallic or chrome-white humanoid. Yet the vast majority of robots in the world around us are nothing like this; instead, they’re specialized for specific tasks. Our cultural conception of what robots are dates back to the coining of the term robots in the Czech play, Rossum’s Universal Robots, which originally envisioned them as essentially synthetic humans.
The vision of a humanoid robot is tantalizing. There are constant efforts to create something that looks like the robots of science fiction. Recently, an old competitor in this field returned with a new model: Toyota has released what they call the T-HR3. As humanoid robots go, it appears to be pretty dexterous and have a decent grip, with a number of degrees of freedom making the movements pleasantly human.
This humanoid robot operates mostly via a remote-controlled system that allows the user to control the robot’s limbs by exerting different amounts of pressure on a framework. A VR headset completes the picture, allowing the user to control the robot’s body and teleoperate the machine. There’s no word on a price tag, but one imagines a machine with a control system this complicated won’t exactly be on your Christmas list, unless you’re a billionaire.
Toyota is no stranger to robotics. They released a series of “Partner Robots” that had a bizarre affinity for instrument-playing but weren’t often seen doing much else. Given that they didn’t seem to have much capability beyond the automaton that Leonardo da Vinci made hundreds of years ago, they promptly vanished. If, as the name suggests, the T-HR3 is a sequel to these robots, which came out shortly after ASIMO back in 2003, it’s substantially better.
Slightly less humanoid (and perhaps the more useful for it), Toyota’s HSR-2 is a robot base on wheels with a simple mechanical arm. It brings to mind earlier machines produced by dream-factory startup Willow Garage like the PR-2. The idea of an affordable robot that could simply move around on wheels and pick up and fetch objects, and didn’t harbor too-lofty ambitions to do anything else, was quite successful.
So much so that when Robocup, the international robotics competition, looked for a platform for their robot-butler competition @Home, they chose HSR-2 for its ability to handle objects. HSR-2 has been deployed in trial runs to care for the elderly and injured, but has yet to be widely adopted for these purposes five years after its initial release. It’s telling that arguably the most successful multi-purpose humanoid robot isn’t really humanoid at all—and it’s curious that Toyota now seems to want to return to a more humanoid model a decade after they gave up on the project.
What’s unclear, as is often the case with humanoid robots, is what, precisely, the T-HR3 is actually for. The teleoperation gets around the complex problem of control by simply having the machine controlled remotely by a human. That human then handles all the sensory perception, decision-making, planning, and manipulation; essentially, the hardest problems in robotics.
There may not be a great deal of autonomy for the T-HR3, but by sacrificing autonomy, you drastically cut down the uses of the robot. Since it can’t act alone, you need a convincing scenario where you need a teleoperated humanoid robot that’s less precise and vastly more expensive than just getting a person to do the same job. Perhaps someday more autonomy will be developed for the robot, and the master maneuvering system that allows humans to control it will only be used in emergencies to control the robot if it gets stuck.
Toyota’s press release says it is “a platform with capabilities that can safely assist humans in a variety of settings, such as the home, medical facilities, construction sites, disaster-stricken areas and even outer space.” In reality, it’s difficult to see such a robot being affordable or even that useful in the home or in medical facilities (unless it’s substantially stronger than humans). Equally, it certainly doesn’t seem robust enough to be deployed in disaster zones or outer space. These tasks have been mooted for robots for a very long time and few have proved up to the challenge.
Toyota’s third generation humanoid robot, the T-HR3. Image Credit: Toyota
Instead, the robot seems designed to work alongside humans. Its design, standing 1.5 meters tall, weighing 75 kilograms, and possessing 32 degrees of freedom in its body, suggests it is built to closely mimic a person, rather than a robot like ATLAS which is robust enough that you can imagine it being useful in a war zone. In this case, it might be closer to the model of the collaborative robots or co-bots developed by Rethink Robotics, whose tons of safety features, including force-sensitive feedback for the user, reduce the risk of terrible PR surrounding killer robots.
Instead the emphasis is on graceful precision engineering: in the promo video, the robot can be seen balancing on one leg before showing off a few poised, yoga-like poses. This perhaps suggests that an application in elderly care, which Toyota has ventured into before and which was the stated aim of their simple HSR-2, might be more likely than deployment to a disaster zone.
The reason humanoid robots remain so elusive and so tempting is probably because of a simple cognitive mistake. We make two bad assumptions. First, we assume that if you build a humanoid robot, give its joints enough flexibility, throw in a little AI and perhaps some pre-programmed behaviors, then presto, it will be able to do everything humans can. When you see a robot that moves well and looks humanoid, it seems like the hardest part is done; surely this robot could do anything. The reality is never so simple.
We also make the reverse assumption: we assume that when we are finally replaced, it will be by perfect replicas of our own bodies and brains that can fulfill all the functions we used to fulfill. Perhaps, in reality, the future of robots and AI is more like its present: piecemeal, with specialized algorithms and specialized machines gradually learning to outperform humans at every conceivable task without ever looking convincingly human.
It may well be that the T-HR3 is angling towards this concept of machine learning as a platform for future research. Rather than trying to program an omni-capable robot out of the box, it will gradually learn from its human controllers. In this way, you could see the platform being used to explore the limits of what humans can teach robots to do simply by having them mimic sequences of our bodies’ motion, in the same way the exploitation of neural networks is testing the limits of training algorithms on data. No one machine will be able to perform everything a human can, but collectively, they will vastly outperform us at anything you’d want one to do.
So when you see a new android like Toyota’s, feel free to marvel at its technical abilities and indulge in the speculation about whether it’s a PR gimmick or a revolutionary step forward along the road to human replacement. Just remember that, human-level bots or not, we’re already strolling down that road.
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Can A.I. Be Taught to Explain Itself?Cliff Kuang | New York Times“Kosinski’s results suggested something stranger: that artificial intelligences often excel by developing whole new ways of seeing, or even thinking, that are inscrutable to us. It’s a more profound version of what’s often called the ‘black box’ problem—the inability to discern exactly what machines are doing when they’re teaching themselves novel skills—and it has become a central concern in artificial-intelligence research.”
Semi-Synthetic Life Form Now Fully Armed and OperationalAntonio Regalado | MIT Technology Review “By this year, the team had devised a more stable bacterium. But it wasn’t enough to endow the germ with a partly alien code—it needed to use that code to make a partly alien protein. That’s what Romesberg’s team, reporting today in the journal Nature, says it has done.”
4 Strange New Ways to ComputeSamuel K. Moore | IEEE Spectrum “With Moore’s Law slowing, engineers have been taking a cold hard look at what will keep computing going when it’s gone…What follows includes a taste of both the strange and the potentially impactful.”
Google X and the Science of Radical CreativityDerek Thompson | The Atlantic “But what X is attempting is nonetheless audacious. It is investing in both invention and innovation. Its founders hope to demystify and routinize the entire process of making a technological breakthrough—to nurture each moonshot, from question to idea to discovery to product—and, in so doing, to write an operator’s manual for radical creativity.”
PRIVACY AND SECURITY
Uber Paid Hackers to Delete Stolen Data on 57 Million PeopleEric Newcomer | Bloomberg “Hackers stole the personal data of 57 million customers and drivers from Uber Technologies Inc., a massive breach that the company concealed for more than a year. This week, the ride-hailing firm ousted its chief security officer and one of his deputies for their roles in keeping the hack under wraps, which included a $100,000 payment to the attackers.”
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Con artistry is one of the world’s oldest and most innovative professions, and it may soon have a new target. Research suggests artificial intelligence may be uniquely susceptible to tricksters, and as its influence in the modern world grows, attacks against it are likely to become more common.
The root of the problem lies in the fact that artificial intelligence algorithms learn about the world in very different ways than people do, and so slight tweaks to the data fed into these algorithms can throw them off completely while remaining imperceptible to humans.
Much of the research into this area has been conducted on image recognition systems, in particular those relying on deep learning neural networks. These systems are trained by showing them thousands of examples of images of a particular object until they can extract common features that allow them to accurately spot the object in new images.
But the features they extract are not necessarily the same high-level features a human would be looking for, like the word STOP on a sign or a tail on a dog. These systems analyze images at the individual pixel level to detect patterns shared between examples. These patterns can be obscure combinations of pixel values, in small pockets or spread across the image, that would be impossible to discern for a human, but highly accurate at predicting a particular object.
“An attacker can trick the object recognition algorithm into seeing something that isn’t there, without these alterations being obvious to a human.”
What this means is that by identifying these patterns and overlaying them over a different image, an attacker can trick the object recognition algorithm into seeing something that isn’t there, without these alterations being obvious to a human. This kind of manipulation is known as an “adversarial attack.”
Early attempts to trick image recognition systems this way required access to the algorithm’s inner workings to decipher these patterns. But in 2016 researchers demonstrated a “black box” attack that enabled them to trick such a system without knowing its inner workings.
By feeding the system doctored images and seeing how it classified them, they were able to work out what it was focusing on and therefore generate images they knew would fool it. Importantly, the doctored images were not obviously different to human eyes.
These approaches were tested by feeding doctored image data directly into the algorithm, but more recently, similar approaches have been applied in the real world. Last year it was shown that printouts of doctored images that were then photographed on a smartphone successfully tricked an image classification system.
Another group showed that wearing specially designed, psychedelically-colored spectacles could trick a facial recognition system into thinking people were celebrities. In August scientists showed that adding stickers to stop signs in particular configurations could cause a neural net designed to spot them to misclassify the signs.
These last two examples highlight some of the potential nefarious applications for this technology. Getting a self-driving car to miss a stop sign could cause an accident, either for insurance fraud or to do someone harm. If facial recognition becomes increasingly popular for biometric security applications, being able to pose as someone else could be very useful to a con artist.
Unsurprisingly, there are already efforts to counteract the threat of adversarial attacks. In particular, it has been shown that deep neural networks can be trained to detect adversarial images. One study from the Bosch Center for AI demonstrated such a detector, an adversarial attack that fools the detector, and a training regime for the detector that nullifies the attack, hinting at the kind of arms race we are likely to see in the future.
While image recognition systems provide an easy-to-visualize demonstration, they’re not the only machine learning systems at risk. The techniques used to perturb pixel data can be applied to other kinds of data too.
“Bypassing cybersecurity defenses is one of the more worrying and probable near-term applications for this approach.”
Chinese researchers showed that adding specific words to a sentence or misspelling a word can completely throw off machine learning systems designed to analyze what a passage of text is about. Another group demonstrated that garbled sounds played over speakers could make a smartphone running the Google Now voice command system visit a particular web address, which could be used to download malware.
This last example points toward one of the more worrying and probable near-term applications for this approach: bypassing cybersecurity defenses. The industry is increasingly using machine learning and data analytics to identify malware and detect intrusions, but these systems are also highly susceptible to trickery.
At this summer’s DEF CON hacking convention, a security firm demonstrated they could bypass anti-malware AI using a similar approach to the earlier black box attack on the image classifier, but super-powered with an AI of their own.
Their system fed malicious code to the antivirus software and then noted the score it was given. It then used genetic algorithms to iteratively tweak the code until it was able to bypass the defenses while maintaining its function.
All the approaches noted so far are focused on tricking pre-trained machine learning systems, but another approach of major concern to the cybersecurity industry is that of “data poisoning.” This is the idea that introducing false data into a machine learning system’s training set will cause it to start misclassifying things.
This could be particularly challenging for things like anti-malware systems that are constantly being updated to take into account new viruses. A related approach bombards systems with data designed to generate false positives so the defenders recalibrate their systems in a way that then allows the attackers to sneak in.
How likely it is that these approaches will be used in the wild will depend on the potential reward and the sophistication of the attackers. Most of the techniques described above require high levels of domain expertise, but it’s becoming ever easier to access training materials and tools for machine learning.
Simpler versions of machine learning have been at the heart of email spam filters for years, and spammers have developed a host of innovative workarounds to circumvent them. As machine learning and AI increasingly embed themselves in our lives, the rewards for learning how to trick them will likely outweigh the costs.
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