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This clever AI hid data from its creators to cheat at its appointed task – TechCrunch

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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:

The original map, left; the street map generated from the original, center; and the aerial map generated only from the street map. Note the presence of dots on both aerial maps not represented on the street map.

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 map at right was encoded into the maps at left with no significant visual changes.

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.



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ARK: Ultimate Survivor Edition Review For Nintendo Switch: Fight For Your Fun

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Whether you’re playing the standard version of “ARK” or this new all-in-one Switch port, the fundamental game is the same: Your character wakes up in a semi-random spot on your chosen map, then you get to work crafting survival implements and putting together a shelter. Eventually you branch out into bigger and better stuff, and even start to tame dinosaurs to act as mounts, protectors, or specialized material gatherers.

Gather materials and supplies, craft tools and gear, level-up to learn more crafting recipes, gather more materials, craft better stuff, and so on. All while balancing your character’s need for food and water, navigating extreme temperatures, and trying not to get eaten by prehistoric animals. Comparing it to “Minecraft” might seem disingenuous, but the game runs on similar principles.

Some things are a bit more complicated in “ARK,” however, even without the need for terrain manipulation found in “Minecraft.” There are a lot of status effects to consider (get too warm, too cold, poisoned, knocked out, broken bones), and you have to craft everything — including the parts needed to build yourself a home. It’s a satisfying enough feedback loop of steady progression, but it also feels a bit hamstrung by its history.

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The Apple Watch Ultra’s Oceanic+ App Just Landed

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Apple broke the good news in a blog post, talking about the new Oceanic+ app as well as its creation process. Made through a collaboration with Huish Outdoors, the app is meant to turn the Apple Watch Ultra into a proper diving computer fit to serve even serious divers. Prior to the launch of Oceanic+, the watch came equipped with basic software called “Depth.” This allowed divers to check current depth, the temperature of the water, maximum depth reached, and how long they’ve been underwater. The new release expands those options considerably.

Apart from the above, Oceanic+ unlocks a lot of useful trackers. You’ll be able to track no-decompression time, how long it will take you to reach the surface, the gas mix currently in use for scuba divers, haptic feedback, a dive planner, and how fast you’re ascending when it’s time to swim back up to the surface. More importantly, the app comes with color-coded warnings. Moreover, if you use Oceanic+ on the iPhone, it will also provide some extra information about your dive.

The app is available for the Apple Watch Ultra as long as you’re running watchOS 9.1 or later. It also needs to be paired with at least an iPhone 8 (or later) running iOS 16.1 and above. You can access a lot of its features for free, but if you want the premium version, it will cost you $9.99 a month, or $79.99 a year.

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Miles Teller’s Vintage Ford Bronco Truck Is Incredible

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Miles Teller’s vintage Bronco looks to be in immaculate condition, with every detail looked after (via Daily Mail). The car looks fantastic and is likely a dream to handle out on the open road.

Teller’s Bronco appears to incorporate time-accurate details from the lights to auxiliary additions like the side mirrors and windshield wipers. Similarly, the windows and windshield are period-accurate for the 1960s models and don’t make use of rounded edges or faces. Ford states that the windshield was adjustable for a brilliant day of driving — Laying the windshield down flat against the hood and locking it into place allows for completely free flowing of air through the vehicle. It’s unclear whether Teller’s Bronco retains this capability, but the car looks stunning, nonetheless.

His Bronco is painted in a light blue shade and the tone simply pops in the sunlight. It’s clear that his vehicle is cleaned and polished regularly, including the undercarriage that makes the project of mobility possible. Daily Mail reports that a vintage Bronco (excluding specialty vehicles, like Big Oly) is typically valued between $8,000 and as much as $40,000. This makes owning a piece of history and adding improvements to match Teller’s aesthetic something that many car owners can actually accomplish if they wish.

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