Depending on how paranoid you are, this research from Stanford and Google will be either terrifying or fascinating. A machine learning agent intended to transform aerial images into street maps and back was found to be cheating by hiding information it would need later in “a nearly imperceptible, high-frequency signal.” Clever girl!
This occurrence reveals a problem with computers that has existed since they were invented: they do exactly what you tell them to do.
The intention of the researchers was, as you might guess, to accelerate and improve the process of turning satellite imagery into Google’s famously accurate maps. To that end the team was working with what’s called a CycleGAN — a neural network that learns to transform images of type X and Y into one another, as efficiently yet accurately as possible, through a great deal of experimentation.
In some early results, the agent was doing well — suspiciously well. What tipped the team off was that, when the agent reconstructed aerial photographs from its street maps, there were lots of details that didn’t seem to be on the latter at all. For instance, skylights on a roof that were eliminated in the process of creating the street map would magically reappear when they asked the agent to do the reverse process:
Although it is very difficult to peer into the inner workings of a neural network’s processes, the team could easily audit the data it was generating. And with a little experimentation, they found that the CycleGAN had indeed pulled a fast one.
The intention was for the agent to be able to interpret the features of either type of map and match them to the correct features of the other. But what the agent was actually being graded on (among other things) was how close an aerial map was to the original, and the clarity of the street map.
So it didn’t learn how to make one from the other. It learned how to subtly encode the features of one into the noise patterns of the other. The details of the aerial map are secretly written into the actual visual data of the street map: thousands of tiny changes in color that the human eye wouldn’t notice, but that the computer can easily detect.
In fact, the computer is so good at slipping these details into the street maps that it had learned to encode any aerial map into any street map! It doesn’t even have to pay attention to the “real” street map — all the data needed for reconstructing the aerial photo can be superimposed harmlessly on a completely different street map, as the researchers confirmed:
The colorful maps in (c) are a visualization of the slight differences the computer systematically introduced. You can see that they form the general shape of the aerial map, but you’d never notice it unless it was carefully highlighted and exaggerated like this.
This practice of encoding data into images isn’t new; it’s an established science called steganography, and it’s used all the time to, say, watermark images or add metadata like camera settings. But a computer creating its own steganographic method to evade having to actually learn to perform the task at hand is rather new. (Well, the research came out last year, so it isn’t new new, but it’s pretty novel.)
One could easily take this as a step in the “the machines are getting smarter” narrative, but the truth is it’s almost the opposite. The machine, not smart enough to do the actual difficult job of converting these sophisticated image types to each other, found a way to cheat that humans are bad at detecting. This could be avoided with more stringent evaluation of the agent’s results, and no doubt the researchers went on to do that.
As always, computers do exactly what they are asked, so you have to be very specific in what you ask them. In this case the computer’s solution was an interesting one that shed light on a possible weakness of this type of neural network — that the computer, if not explicitly prevented from doing so, will essentially find a way to transmit details to itself in the interest of solving a given problem quickly and easily.
This is really just a lesson in the oldest adage in computing: PEBKAC. “Problem exists between keyboard and computer.” Or as HAL put it: “It can only be attributable to human error.”
The paper, “CycleGAN, a Master of Steganography,” was presented at the Neural Information Processing Systems conference in 2017. Thanks to Fiora Esoterica and Reddit for bringing this old but interesting paper to my attention.
Samsung Display will mass-produce the first 90Hz OLED laptop screens
Samsung Display has announced that it will mass-produce the world’s first 90Hz OLED laptops starting in Q1 of 2021. Initially, the company will produce “very large quantities” of 14-inch 90Hz OLED displays aimed at laptops and notebooks. According to Samsung Displaying CEO Joo Sun Choi, the screens will start production in March.
A 90Hz refresh rate is a significant improvement compared to most laptops and notebooks that currently offer a 60Hz refresh rate. Samsung Display says it’s collaborating closely with global computer manufacturers to usher in a new level of excellence for refresh rates. Samsung believes the adoption of the new OLED panels will happen quickly despite the fact that the panel requires the use of a “high-spec graphics card.”
The faster refresh rate updates static images 90 times per second, making the movement on-screen look more lifelike. Faster updates for changes in motion on screen provide smoother visuals appearing more seamless to the viewer. OLED screens can transition between scenes more quickly than LCD screens with the same refresh rate.
Samsung Display says its 90Hz OLED refresh rate is ten times that of the fastest screen response time on the market today. The company notes its 90Hz OLED offers speed on par with 120Hz LCD screens. The high refresh rate of OLED screens makes them particularly well-suited to gaming and watching movies.
In testing, Samsung Display tested blur length using the same motion picture of a car driving fast and found image drag for a 90Hz OLED and a 120Hz LCD measured 0.9mm and 1mm, respectively. Samsung says that its OLED screens smear very little and offer practically the same rate as faster LCDs. It’s unclear what sort of pricing the new OLED will enter the market at.
iPad Mini 6 leak sounds almost too good to be true
While Apple continues to push the iPads as the next generation of computers, that rhetoric really only applies to its iPad Pro line. The lines between its various iPads, however, have started to blur, especially with the addition of Apple Pencil support all the way down to the bottom. The iPad Mini, however, still remains Apple’s simplest and most affordable entry point, making these details about the alleged iPad Mini 6 sound more like a wishlist.
Right off the bat, the thought that Apple would even dare mar any of its screens with a punch-hole cutout sounds almost insane, given the company’s attention to design and details. Then again, it did introduce a rather large “bucket” notch and never backed out from that design on the iPhones. It’s exactly because of that stubbornness that it feels almost unlikely that the iPad Mini 6 would sport such a hole.
Pigtou, collaborating with @xleaks, still has more to share, though. The iPad Mini 6, the site claims, will have Apple’s first-ever in-display fingerprint sensor, something the company has been rumored to be working on for years. Given the criticism of the technology and praise for Apple’s Face ID, that again sounds like a step backward.
The rest of the iPad Mini 6 will remain the same, though. The camera will still be small and probably negligible affair while the edges still bear the soft curves of previous iPad Minis. In other words, it won’t be adopting the new iPad and iPad Pro appearances anytime soon.
If these do come to pass, the iPad Mini 6 will have a very competitive screen that will be larger than its predecessors without actually increasing the size of the device. This, however, doesn’t sound like the Apple we know but, to be fair, the company has managed to shock everyone from time to time, for better or, sometimes, for worse.
Google Calendar now works offline on desktop Chrome
Google has dozens of apps and services, some of them a bit esoteric while many are popular and useful for both end users and businesses. One thing that is common among most of these is the need for an Internet connection. Google, after all, is primarily an Internet company. That Internet connection, however, might not always be available or stable and, fortunately, Google is making one of its key apps available offline, at least for some set of users.
A centuries-old tool, the humble calendar has gotten upgrades over the years, especially recently in the age of the Internet and apps. Many modern calendars today offer functionality our ancestors could only dream of but some, like Google Calendar, require an Internet connection to even use. No problem if you’re always at home or at the office or have a stable Internet connection with you all the time.
That will not always be the case, though, and you might start having a panic attack when you suddenly get disconnected and have to check whether you’re clear for the next few weeks. Fortunately, that will no longer be the case with this new offline functionality for Google Calendar, provided you meet some of its requirements.
For one, this only works if you’re using Google Calendar on Google Chrome for the desktop, and Google is unsurprisingly silent if it will work on any other desktop web browser. And while you can actually peer into any time into the future, you can only look four weeks into the past, practically just the previous month.
The biggest requirement is that this feature is only available for Google Workspace customers and subscribers, so those with personal accounts will still have to hope for that stable Internet connection when they need to view their events and appointments. Admins can enable or disable this feature as needed while end users can also disable it even if their admin switched it to on.
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