Tag Archives: Artificial intelligence
A Radical New Technique Lets AI Learn With Practically No Data
Karen Hao | MIT Technology Review
“Shown photos of a horse and a rhino, and told a unicorn is something in between, [children] can recognize the mythical creature in a picture book the first time they see it. …Now a new paper from the University of Waterloo in Ontario suggests that AI models should also be able to do this—a process the researchers call ‘less than one’-shot, or LO-shot, learning.”
Artificial General Intelligence: Are We Close, and Does It Even Make Sense to Try?
Will Douglas Heaven | MIT Technology Review
“A machine that could think like a person has been the guiding vision of AI research since the earliest days—and remains its most divisive idea. …So why is AGI controversial? Why does it matter? And is it a reckless, misleading dream—or the ultimate goal?”
The Race for a Super-Antibody Against the Coronavirus
Apoorva Mandavilli | The New York Times
“Dozens of companies and academic groups are racing to develop antibody therapies. …But some scientists are betting on a dark horse: Prometheus, a ragtag group of scientists who are months behind in the competition—and yet may ultimately deliver the most powerful antibody.”
How to Build a Spacecraft to Save the World
Daniel Oberhaus | Wired
“The goal of the Double Asteroid Redirection Test, or DART, is to slam the [spacecraft] into a small asteroid orbiting a larger asteroid 7 million miles from Earth. …It should be able to change the asteroid’s orbit just enough to be detectable from Earth, demonstrating that this kind of strike could nudge an oncoming threat out of Earth’s way. Beyond that, everything is just an educated guess, which is exactly why NASA needs to punch an asteroid with a robot.”
Inside Gravity’s Daring Mission to Make Jetpacks a Reality
Oliver Franklin-Wallis | Wired
“The first time someone flies a jetpack, a curious thing happens: just as their body leaves the ground, their legs start to flail. …It’s as if the vestibular system can’t quite believe what’s happening. This isn’t natural. Then suddenly, thrust exceeds weight, and—they’re aloft. …It’s that moment, lift-off, that has given jetpacks an enduring appeal for over a century.”
FUTURE OF FOOD
Inside Singapore’s Huge Bet on Vertical Farming
Megan Tatum | MIT Technology Review
“…to cram all [of Singapore’s] gleaming towers and nearly 6 million people into a land mass half the size of Los Angeles, it has sacrificed many things, including food production. Farms make up no more than 1% of its total land (in the United States it’s 40%), forcing the small city-state to shell out around $10 billion each year importing 90% of its food. Here was an example of technology that could change all that.”
The Effort to Build the Mathematical Library of the Future
Kevin Hartnett | Quanta
“Digitizing mathematics is a longtime dream. The expected benefits range from the mundane—computers grading students’ homework—to the transcendent: using artificial intelligence to discover new mathematics and find new solutions to old problems.”
Image credit: Kevin Mueller / Unsplash Continue reading
Using a brain-inspired approach, scientists from Nanyang Technological University, Singapore (NTU Singapore) have developed a way for robots to have the artificial intelligence (AI) to recognize pain and to self-repair when damaged. Continue reading
The upcoming US presidential election seems set to be something of a mess—to put it lightly. Covid-19 will likely deter millions from voting in person, and mail-in voting isn’t shaping up to be much more promising. This all comes at a time when political tensions are running higher than they have in decades, issues that shouldn’t be political (like mask-wearing) have become highly politicized, and Americans are dramatically divided along party lines.
So the last thing we need right now is yet another wrench in the spokes of democracy, in the form of disinformation; we all saw how that played out in 2016, and it wasn’t pretty. For the record, disinformation purposely misleads people, while misinformation is simply inaccurate, but without malicious intent. While there’s not a ton tech can do to make people feel safe at crowded polling stations or up the Postal Service’s budget, tech can help with disinformation, and Microsoft is trying to do so.
On Tuesday the company released two new tools designed to combat disinformation, described in a blog post by VP of Customer Security and Trust Tom Burt and Chief Scientific Officer Eric Horvitz.
The first is Microsoft Video Authenticator, which is made to detect deepfakes. In case you’re not familiar with this wicked byproduct of AI progress, “deepfakes” refers to audio or visual files made using artificial intelligence that can manipulate peoples’ voices or likenesses to make it look like they said things they didn’t. Editing a video to string together words and form a sentence someone didn’t say doesn’t count as a deepfake; though there’s manipulation involved, you don’t need a neural network and you’re not generating any original content or footage.
The Authenticator analyzes videos or images and tells users the percentage chance that they’ve been artificially manipulated. For videos, the tool can even analyze individual frames in real time.
Deepfake videos are made by feeding hundreds of hours of video of someone into a neural network, “teaching” the network the minutiae of the person’s voice, pronunciation, mannerisms, gestures, etc. It’s like when you do an imitation of your annoying coworker from accounting, complete with mimicking the way he makes every sentence sound like a question and his eyes widen when he talks about complex spreadsheets. You’ve spent hours—no, months—in his presence and have his personality quirks down pat. An AI algorithm that produces deepfakes needs to learn those same quirks, and more, about whoever the creator’s target is.
Given enough real information and examples, the algorithm can then generate its own fake footage, with deepfake creators using computer graphics and manually tweaking the output to make it as realistic as possible.
The scariest part? To make a deepfake, you don’t need a fancy computer or even a ton of knowledge about software. There are open-source programs people can access for free online, and as far as finding video footage of famous people—well, we’ve got YouTube to thank for how easy that is.
Microsoft’s Video Authenticator can detect the blending boundary of a deepfake and subtle fading or greyscale elements that the human eye may not be able to see.
In the blog post, Burt and Horvitz point out that as time goes by, deepfakes are only going to get better and become harder to detect; after all, they’re generated by neural networks that are continuously learning from and improving themselves.
Microsoft’s counter-tactic is to come in from the opposite angle, that is, being able to confirm beyond doubt that a video, image, or piece of news is real (I mean, can McDonald’s fries cure baldness? Did a seal slap a kayaker in the face with an octopus? Never has it been so imperative that the world know the truth).
A tool built into Microsoft Azure, the company’s cloud computing service, lets content producers add digital hashes and certificates to their content, and a reader (which can be used as a browser extension) checks the certificates and matches the hashes to indicate the content is authentic.
Finally, Microsoft also launched an interactive “Spot the Deepfake” quiz it developed in collaboration with the University of Washington’s Center for an Informed Public, deepfake detection company Sensity, and USA Today. The quiz is intended to help people “learn about synthetic media, develop critical media literacy skills, and gain awareness of the impact of synthetic media on democracy.”
The impact Microsoft’s new tools will have remains to be seen—but hey, we’re glad they’re trying. And they’re not alone; Facebook, Twitter, and YouTube have all taken steps to ban and remove deepfakes from their sites. The AI Foundation’s Reality Defender uses synthetic media detection algorithms to identify fake content. There’s even a coalition of big tech companies teaming up to try to fight election interference.
One thing is for sure: between a global pandemic, widespread protests and riots, mass unemployment, a hobbled economy, and the disinformation that’s remained rife through it all, we’re going to need all the help we can get to make it through not just the election, but the rest of the conga-line-of-catastrophes year that is 2020.
Image Credit: Darius Bashar on Unsplash Continue reading
“I’m sorry, Dave. I’m afraid I can’t do that.” HAL’s cold, if polite, refusal to open the pod bay doors in 2001: A Space Odyssey has become a defining warning about putting too much trust in artificial intelligence, particularly if you work in space.
In the movies, when a machine decides to be the boss (or humans let it) things go wrong. Yet despite myriad dystopian warnings, control by machines is fast becoming our reality.
Algorithms—sets of instructions to solve a problem or complete a task—now drive everything from browser search results to better medical care.
They are helping design buildings. They are speeding up trading on financial markets, making and losing fortunes in micro-seconds. They are calculating the most efficient routes for delivery drivers.
In the workplace, self-learning algorithmic computer systems are being introduced by companies to assist in areas such as hiring, setting tasks, measuring productivity, evaluating performance, and even terminating employment: “I’m sorry, Dave. I’m afraid you are being made redundant.”
Giving self‐learning algorithms the responsibility to make and execute decisions affecting workers is called “algorithmic management.” It carries a host of risks in depersonalizing management systems and entrenching pre-existing biases.
At an even deeper level, perhaps, algorithmic management entrenches a power imbalance between management and worker. Algorithms are closely guarded secrets. Their decision-making processes are hidden. It’s a black-box: perhaps you have some understanding of the data that went in, and you see the result that comes out, but you have no idea of what goes on in between.
Algorithms at Work
Here are a few examples of algorithms already at work.
At Amazon’s fulfillment center in south-east Melbourne, they set the pace for “pickers,” who have timers on their scanners showing how long they have to find the next item. As soon as they scan that item, the timer resets for the next. All at a “not quite walking, not quite running” speed.
Or how about AI determining your success in a job interview? More than 700 companies have trialed such technology. US developer HireVue says its software speeds up the hiring process by 90 percent by having applicants answer identical questions and then scoring them according to language, tone, and facial expressions.
Granted, human assessments during job interviews are notoriously flawed. Algorithms,however, can also be biased. The classic example is the COMPAS software used by US judges, probation, and parole officers to rate a person’s risk of re-offending. In 2016 a ProPublica investigation showed the algorithm was heavily discriminatory, incorrectly classifying black subjects as higher risk 45 percent of the time, compared with 23 percent for white subjects.
How Gig Workers Cope
Algorithms do what their code tells them to do. The problem is this code is rarely available. This makes them difficult to scrutinize, or even understand.
Nowhere is this more evident than in the gig economy. Uber, Lyft, Deliveroo, and other platforms could not exist without algorithms allocating, monitoring, evaluating, and rewarding work.
Over the past year Uber Eats’ bicycle couriers and drivers, for instance, have blamed unexplained changes to the algorithm for slashing their jobs, and incomes.
Rider’s can’t be 100 percent sure it was all down to the algorithm. But that’s part of the problem. The fact those who depend on the algorithm don’t know one way or the other has a powerful influence on them.
This is a key result from our interviews with 58 food-delivery couriers. Most knew their jobs were allocated by an algorithm (via an app). They knew the app collected data. What they didn’t know was how data was used to award them work.
In response, they developed a range of strategies (or guessed how) to “win” more jobs, such as accepting gigs as quickly as possible and waiting in “magic” locations. Ironically, these attempts to please the algorithm often meant losing the very flexibility that was one of the attractions of gig work.
The information asymmetry created by algorithmic management has two profound effects. First, it threatens to entrench systemic biases, the type of discrimination hidden within the COMPAS algorithm for years. Second, it compounds the power imbalance between management and worker.
Our data also confirmed others’ findings that it is almost impossible to complain about the decisions of the algorithm. Workers often do not know the exact basis of those decisions, and there’s no one to complain to anyway. When Uber Eats bicycle couriers asked for reasons about their plummeting income, for example, responses from the company advised them “we have no manual control over how many deliveries you receive.”
When algorithmic management operates as a “black box” one of the consequences is that it is can become an indirect control mechanism. Thus far under-appreciated by Australian regulators, this control mechanism has enabled platforms to mobilize a reliable and scalable workforce while avoiding employer responsibilities.
“The absence of concrete evidence about how the algorithms operate”, the Victorian government’s inquiry into the “on-demand” workforce notes in its report, “makes it hard for a driver or rider to complain if they feel disadvantaged by one.”
The report, published in June, also found it is “hard to confirm if concern over algorithm transparency is real.”
But it is precisely the fact it is hard to confirm that’s the problem. How can we start to even identify, let alone resolve, issues like algorithmic management?
Fair conduct standards to ensure transparency and accountability are a start. One example is the Fair Work initiative, led by the Oxford Internet Institute. The initiative is bringing together researchers with platforms, workers, unions, and regulators to develop global principles for work in the platform economy. This includes “fair management,” which focuses on how transparent the results and outcomes of algorithms are for workers.
Understandings about impact of algorithms on all forms of work is still in its infancy. It demands greater scrutiny and research. Without human oversight based on agreed principles we risk inviting HAL into our workplaces.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Image Credit: PickPik Continue reading