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#433785 DeepMind’s Eerie Reimagination of the ...
If a recent project using Google’s DeepMind were a recipe, you would take a pair of AI systems, images of animals, and a whole lot of computing power. Mix it all together, and you’d get a series of imagined animals dreamed up by one of the AIs. A look through the research paper about the project—or this open Google Folder of images it produced—will likely lead you to agree that the results are a mix of impressive and downright eerie.
But the eerie factor doesn’t mean the project shouldn’t be considered a success and a step forward for future uses of AI.
From GAN To BigGAN
The team behind the project consists of Andrew Brock, a PhD student at Edinburgh Center for Robotics, and DeepMind intern and researcher Jeff Donahue and Karen Simonyan.
They used a so-called Generative Adversarial Network (GAN) to generate the images. In a GAN, two AI systems collaborate in a game-like manner. One AI produces images of an object or creature. The human equivalent would be drawing pictures of, for example, a dog—without necessarily knowing what a dog exactly looks like. Those images are then shown to the second AI, which has already been fed images of dogs. The second AI then tells the first one how far off its efforts were. The first one uses this information to improve its images. The two go back and forth in an iterative process, and the goal is for the first AI to become so good at creating images of dogs that the second can’t tell the difference between its creations and actual pictures of dogs.
The team was able to draw on Google’s vast vaults of computational power to create images of a quality and life-like nature that were beyond almost anything seen before. In part, this was achieved by feeding the GAN with more images than is usually the case. According to IFLScience, the standard is to feed about 64 images per subject into the GAN. In this case, the research team fed about 2,000 images per subject into the system, leading to it being nicknamed BigGAN.
Their results showed that feeding the system with more images and using masses of raw computer power markedly increased the GAN’s precision and ability to create life-like renditions of the subjects it was trained to reproduce.
“The main thing these models need is not algorithmic improvements, but computational ones. […] When you increase model capacity and you increase the number of images you show at every step, you get this twofold combined effect,” Andrew Brock told Fast Company.
The Power Drain
The team used 512 of Google’s AI-focused Tensor Processing Units (TPU) to generate 512-pixel images. Each experiment took between 24 and 48 hours to run.
That kind of computing power needs a lot of electricity. As artist and Innovator-In-Residence at the Library of Congress Jer Thorp tongue-in-cheek put it on Twitter: “The good news is that AI can now give you a more believable image of a plate of spaghetti. The bad news is that it used roughly enough energy to power Cleveland for the afternoon.”
Thorp added that a back-of-the-envelope calculation showed that the computations to produce the images would require about 27,000 square feet of solar panels to have adequate power.
BigGAN’s images have been hailed by researchers, with Oriol Vinyals, research scientist at DeepMind, rhetorically asking if these were the ‘Best GAN samples yet?’
However, they are still not perfect. The number of legs on a given creature is one example of where the BigGAN seemed to struggle. The system was good at recognizing that something like a spider has a lot of legs, but seemed unable to settle on how many ‘a lot’ was supposed to be. The same applied to dogs, especially if the images were supposed to show said dogs in motion.
Those eerie images are contrasted by other renditions that show such lifelike qualities that a human mind has a hard time identifying them as fake. Spaniels with lolling tongues, ocean scenery, and butterflies were all rendered with what looks like perfection. The same goes for an image of a hamburger that was good enough to make me stop writing because I suddenly needed lunch.
The Future Use Cases
GAN networks were first introduced in 2014, and given their relative youth, researchers and companies are still busy trying out possible use cases.
One possible use is image correction—making pixillated images clearer. Not only does this help your future holiday snaps, but it could be applied in industries such as space exploration. A team from the University of Michigan and the Max Planck Institute have developed a method for GAN networks to create images from text descriptions. At Berkeley, a research group has used GAN to create an interface that lets users change the shape, size, and design of objects, including a handbag.
For anyone who has seen a film like Wag the Dog or read 1984, the possibilities are also starkly alarming. GANs could, in other words, make fake news look more real than ever before.
For now, it seems that while not all GANs require the computational and electrical power of the BigGAN, there is still some way to reach these potential use cases. However, if there’s one lesson from Moore’s Law and exponential technology, it is that today’s technical roadblock quickly becomes tomorrow’s minor issue as technology progresses.
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#431603 What We Can Learn From the Second Life ...
For every new piece of technology that gets developed, you can usually find people saying it will never be useful. The president of the Michigan Savings Bank in 1903, for example, said, “The horse is here to stay but the automobile is only a novelty—a fad.” It’s equally easy to find people raving about whichever new technology is at the peak of the Gartner Hype Cycle, which tracks the buzz around these newest developments and attempts to temper predictions. When technologies emerge, there are all kinds of uncertainties, from the actual capacity of the technology to its use cases in real life to the price tag.
Eventually the dust settles, and some technologies get widely adopted, to the extent that they can become “invisible”; people take them for granted. Others fall by the wayside as gimmicky fads or impractical ideas. Picking which horses to back is the difference between Silicon Valley millions and Betamax pub-quiz-question obscurity. For a while, it seemed that Google had—for once—backed the wrong horse.
Google Glass emerged from Google X, the ubiquitous tech giant’s much-hyped moonshot factory, where highly secretive researchers work on the sci-fi technologies of the future. Self-driving cars and artificial intelligence are the more mundane end for an organization that apparently once looked into jetpacks and teleportation.
The original smart glasses, Google began selling Google Glass in 2013 for $1,500 as prototypes for their acolytes, around 8,000 early adopters. Users could control the glasses with a touchpad, or, activated by tilting the head back, with voice commands. Audio relay—as with several wearable products—is via bone conduction, which transmits sound by vibrating the skull bones of the user. This was going to usher in the age of augmented reality, the next best thing to having a chip implanted directly into your brain.
On the surface, it seemed to be a reasonable proposition. People had dreamed about augmented reality for a long time—an onboard, JARVIS-style computer giving you extra information and instant access to communications without even having to touch a button. After smartphone ubiquity, it looked like a natural step forward.
Instead, there was a backlash. People may be willing to give their data up to corporations, but they’re less pleased with the idea that someone might be filming them in public. The worst aspect of smartphones is trying to talk to people who are distractedly scrolling through their phones. There’s a famous analogy in Revolutionary Road about an old couple’s loveless marriage: the husband tunes out his wife’s conversation by turning his hearing aid down to zero. To many, Google Glass seemed to provide us with a whole new way to ignore each other in favor of our Twitter feeds.
Then there’s the fact that, regardless of whether it’s because we’re not used to them, or if it’s a more permanent feature, people wearing AR tech often look very silly. Put all this together with a lack of early functionality, the high price (do you really feel comfortable wearing a $1,500 computer?), and a killer pun for the users—Glassholes—and the final recipe wasn’t great for Google.
Google Glass was quietly dropped from sale in 2015 with the ominous slogan posted on Google’s website “Thanks for exploring with us.” Reminding the Glass users that they had always been referred to as “explorers”—beta-testing a product, in many ways—it perhaps signaled less enthusiasm for wearables than the original, Google Glass skydive might have suggested.
In reality, Google went back to the drawing board. Not with the technology per se, although it has improved in the intervening years, but with the uses behind the technology.
Under what circumstances would you actually need a Google Glass? When would it genuinely be preferable to a smartphone that can do many of the same things and more? Beyond simply being a fashion item, which Google Glass decidedly was not, even the most tech-evangelical of us need a convincing reason to splash $1,500 on a wearable computer that’s less socially acceptable and less easy to use than the machine you’re probably reading this on right now.
Enter the Google Glass Enterprise Edition.
Piloted in factories during the years that Google Glass was dormant, and now roaring back to life and commercially available, the Google Glass relaunch got under way in earnest in July of 2017. The difference here was the specific audience: workers in factories who need hands-free computing because they need to use their hands at the same time.
In this niche application, wearable computers can become invaluable. A new employee can be trained with pre-programmed material that explains how to perform actions in real time, while instructions can be relayed straight into a worker’s eyeline without them needing to check a phone or switch to email.
Medical devices have long been a dream application for Google Glass. You can imagine a situation where people receive real-time information during surgery, or are augmented by artificial intelligence that provides additional diagnostic information or questions in response to a patient’s symptoms. The quest to develop a healthcare AI, which can provide recommendations in response to natural language queries, is on. The famously untidy doctor’s handwriting—and the associated death toll—could be avoided if the glasses could take dictation straight into a patient’s medical records. All of this is far more useful than allowing people to check Facebook hands-free while they’re riding the subway.
Google’s “Lens” application indicates another use for Google Glass that hadn’t quite matured when the original was launched: the Lens processes images and provides information about them. You can look at text and have it translated in real time, or look at a building or sign and receive additional information. Image processing, either through neural networks hooked up to a cloud database or some other means, is the frontier that enables driverless cars and similar technology to exist. Hook this up to a voice-activated assistant relaying information to the user, and you have your killer application: real-time annotation of the world around you. It’s this functionality that just wasn’t ready yet when Google launched Glass.
Amazon’s recent announcement that they want to integrate Alexa into a range of smart glasses indicates that the tech giants aren’t ready to give up on wearables yet. Perhaps, in time, people will become used to voice activation and interaction with their machines, at which point smart glasses with bone conduction will genuinely be more convenient than a smartphone.
But in many ways, the real lesson from the initial failure—and promising second life—of Google Glass is a simple question that developers of any smart technology, from the Internet of Things through to wearable computers, must answer. “What can this do that my smartphone can’t?” Find your answer, as the Enterprise Edition did, as Lens might, and you find your product.
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#430855 Why Education Is the Hardest Sector of ...
We’ve all heard the warning cries: automation will disrupt entire industries and put millions of people out of jobs. In fact, up to 45 percent of existing jobs can be automated using current technology.
However, this may not necessarily apply to the education sector. After a detailed analysis of more than 2,000-plus work activities for more than 800 occupations, a report by McKinsey & Co states that of all the sectors examined, “…the technical feasibility of automation is lowest in education.”
There is no doubt that technological trends will have a powerful impact on global education, both by improving the overall learning experience and by increasing global access to education. Massive open online courses (MOOCs), chatbot tutors, and AI-powered lesson plans are just a few examples of the digital transformation in global education. But will robots and artificial intelligence ever fully replace teachers?
The Most Difficult Sector to Automate
While various tasks revolving around education—like administrative tasks or facilities maintenance—are open to automation, teaching itself is not.
Effective education involves more than just transfer of information from a teacher to a student. Good teaching requires complex social interactions and adaptation to the individual student’s learning needs. An effective teacher is not just responsive to each student’s strengths and weaknesses, but is also empathetic towards the student’s state of mind. It’s about maximizing human potential.
Furthermore, students don’t just rely on effective teachers to teach them the course material, but also as a source of life guidance and career mentorship. Deep and meaningful human interaction is crucial and is something that is very difficult, if not impossible, to automate.
Automating teaching is an example of a task that would require artificial general intelligence (as opposed to narrow or specific intelligence). In other words, this is the kind of task that would require an AI that understands natural human language, can be empathetic towards emotions, plan, strategize and make impactful decisions under unpredictable circumstances.
This would be the kind of machine that can do anything a human can do, and it doesn’t exist—at least, not yet.
We’re Getting There
Let’s not forget how quickly AI is evolving. Just because it’s difficult to fully automate teaching, it doesn’t mean the world’s leading AI experts aren’t trying.
Meet Jill Watson, the teaching assistant from Georgia Institute of Technology. Watson isn’t your average TA. She’s an IBM-powered artificial intelligence that is being implemented in universities around the world. Watson is able to answer students’ questions with 97 percent certainty.
Technologies like this also have applications in grading and providing feedback. Some AI algorithms are being trained and refined to perform automatic essay scoring. One project has achieved a 0.945 correlation with human graders.
All of this will have a remarkable impact on online education as we know it and dramatically increase online student retention rates.
Any student with a smartphone can access a wealth of information and free courses from universities around the world. MOOCs have allowed valuable courses to become available to millions of students. But at the moment, not all participants can receive customized feedback for their work. Currently, this is limited by manpower, but in the future that may not be the case.
What chatbots like Jill Watson allow is the opportunity for hundreds of thousands, if not millions, of students to have their work reviewed and all their questions answered at a minimal cost.
AI algorithms also have a significant role to play in personalization of education. Every student is unique and has a different set of strengths and weaknesses. Data analysis can be used to improve individual student results, assess each student’s strengths and weaknesses, and create mass-customized programs. Algorithms can analyze student data and consequently make flexible programs that adapt to the learner based on real-time feedback. According to the McKinsey Global Institute, all of this data in education could unlock between $900 billion and $1.2 trillion in global economic value.
Beyond Automated Teaching
It’s important to recognize that technological automation alone won’t fix the many issues in our global education system today. Dominated by outdated curricula, standardized tests, and an emphasis on short-term knowledge, many experts are calling for a transformation of how we teach.
It is not enough to simply automate the process. We can have a completely digital learning experience that continues to focus on outdated skills and fails to prepare students for the future. In other words, we must not only be innovative with our automation capabilities, but also with educational content, strategy, and policies.
Are we equipping students with the most important survival skills? Are we inspiring young minds to create a better future? Are we meeting the unique learning needs of each and every student? There’s no point automating and digitizing a system that is already flawed. We need to ensure the system that is being digitized is itself being transformed for the better.
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