Friend said this to me: the other day Watch what the event
The creator of the A.I.-generated porn, who posted it on platforms like PornHub and OnlyFans, told Vice that he used StyleGAN2, an open-source algorithm built by Nvidia. If you’ve seen highly realistic fake faces online, like ThisPersonDoesNotExist.com, they’ve likely been generated by StyleGAN2.
But this technology didn’t show up overnight. There’s a clear path from some of the earliest modern image-generating algorithms to this phenomenon of A.I.-generated porn. Here’s what it looks like.
These technologies are still in their infancy, and they’ll continue to become more accurate and convincing. Some of the applications of these technologies are genuinely entertaining — check out the Avengers singing the “Sweet Child O’ Mine” scene from Step Brothers — but ultimately, these algorithms are also now much easier for anyone to use for malicious ends. And without any recourse, deepfake’s harms might outweigh their slight entertainment value.
These algorithms are able to be adapted to different domains. By training the algorithms on pornographic images rather than just faces, the system was able to adapt to generating something it might not have been ever intended for.
On The 12th Date of Christmas is hilarious because the male lead is one of my HMU favorites. He seems to hate his job. Not his pretend job, which in this film is some tech bro app company. Apps are the only technology currency that HMU understands, by the way. Well, apps and blogging. Anyway, this actor’s face is just always fed up with the whole pretense of the genre. And he has a beard. In HMU, that basically makes him Walter White. The woman is silly but whatever. We are here for Walter White. Watch.
GANS have also been ported to specifically make deepfakes, through open-source projects like DeepFaceLab and Wav2Lip. The ease of using these services can’t be overstated: The Wav2Lip project’s website shows how a single line of code can be used to automatically make the subject of a video lip-sync to any audio file.
We have seen how to generate street view house numbers with DCGAN implementation on the SVHN dataset. The images generated can be further improved by tuning the hyperparameters. One could also opt for deeper layers than the one here. Doing so would however result in an increased number of parameters which again would take a lot of time to train. Now open your Jupyter Notebook and implement the same. In the next article, I shall walk you through the image-to-image translation with CycleGANs. Cheers!
Storybook is a development environment (sandbox) for UI components. It allows developers to look through an extensive catalogue of components in which they can test and analyze it’s different states. It is built for Vue, React, Angular, and many more frameworks. This is particularly useful for testing components and their edge cases. You can easily test components without the need of any business logic.
Earlier this week Vice reported the latest example of one of these harms: Coders were using images of sexual abuse to train algorithms to make porn. The article details how nonconsensual images were compiled by an anonymous PhD student into a dataset and combined with off-the-shelf algorithms to generate custom videos.
If you can have negative interest rates and pour out money, and incur more and more debt relative to productive capacity, you’d think the world would have discovered it in the first couple of thousand years rather than just coming on it now. We will see.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision…arxiv.org
There are now multiple open-source, freely available methods for creating synthetic faces built on GAN architecture. And as cloud services like Amazon’s AWS and Google Cloud have become easier to access, so has the ability to train these algorithms. The most well-known in the A.I. research world is StyleGAN, made by Nvidia. It was released in December 2018, and while able to produce incredibly high-quality images of fake faces, the images also contained strange blobs and digital artifacts. Less than a year later, the Nvidia team released StyleGAN2, which fixed the algorithm’s architecture to prevent those blobs and artifacts from forming, as well as improving the fidelity of the images.
This description from Warren about all the free money we’ve all been getting access to because of a health crisis may explain why Warren has sold a lot of his US bank stocks recently.
Like nuclear power or rocket propulsion, artificial intelligence is considered a “dual-use” technology, which means that its capacity for harm is equal to its potential for good.
Less than 10 years ago, some of the most basic artificial intelligence algorithms, like image recognition, required the sort of computing power typically found in data centers. Today, those tools are available on your smartphone, and are far more powerful and precise.
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The influential finance blog Zero Hedge wrote recently that Warren “appears to now be quietly betting against the United States,” because “the famously anti-gold investor has abandoned banks — the backbone of America’s credit-driven economy — in favor of a gold miner.”
Goodfellow’s initial research on GANs performed well on industry benchmarks, but many of the images he created still looked like hellish blobs that only represented ideas in abstract and inhuman ways. By 2016, other researchers had started experimenting with the technique and found ways to make lifelike images, albeit at small resolutions. One of the standout papers of the time showed how researchers could generate realistic images of bedrooms, as well as rudimentary attempts at generating faces. This research again showed that GANs were able to adapt based on the kind of data they were trained on. The idea worked as well for faces as it did for bedrooms, meaning the networks were actually able to identify patterns in a variety of different types of images.
Can you keep doing what we’re doing now? The world has been able to do it for now a dozen years or so [since 2008]. We may be facing a period where we’re testing that hypothesis that you can continue it with a lot more force than we’ve tested it before.
On The 12th Date of Christmas is hilarious because the male lead is one of my HMU favorites. He seems to hate his job. Not his pretend job, which in this film is some tech bro app company. Apps are the only technology currency that HMU understands, by the way. Well, apps and blogging. Anyway, this actor’s face is just always fed up with the whole pretense of the genre. And he has a beard. In HMU, that basically makes him Walter White. The woman is silly but whatever. We are here for Walter White. Watch.
Like nuclear power or rocket propulsion, artificial intelligence is considered a “dual-use” technology, which means that its capacity for harm is equal to its potential for good.
We used BCEwithLogitsLoss(), which combines a sigmoid activation function (we want the discriminator to output a value 0–1 indicating whether an image is real or fake) and binary cross-entropy loss.We have seen how to generate street view house numbers with DCGAN implementation on the SVHN dataset. The images generated can be further improved by tuning the hyperparameters. One could also opt for deeper layers than the one here. Doing so would however result in an increased number of parameters which again would take a lot of time to train. Now open your Jupyter Notebook and implement the same. In the next article, I shall walk you through the image-to-image translation with CycleGANs. Cheers!Image generation algorithms leapt forward in capability in 2014, with the creation of generative adversarial networks, or GANs. The idea, which A.I. researcher Ian Goodfellow originally thought up during an argument at a bar, was to pit algorithms against each other to generate the best outcome. To generate an image, you would have a “generator” and a “discriminator.” The generator would make images, and the discriminator would try to determine if it was real or fake, based on real images it had been trained on. Only the most realistic images would be accepted by the discriminator, ensuring that the final result was the cream of the A.I.-generated crop.
