Synthetic Media & Generative AI News and Discussions

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Yuli Ban
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And remember my friend, future events such as these will affect you in the future
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Yuli Ban
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Facebook Researchers Say They Can Detect Deepfakes And Where They Came From
Facebook researchers say they've developed artificial intelligence that can identify so-called "deepfakes" and track their origin by using reverse engineering.

Deepfakes are altered photos, videos, and still images that use artificial intelligence to appear like the real thing. They've become increasingly realistic in recent years, making it harder to detect the real from the fake with just the naked eye.

The technological advances for deepfake productions have concerned experts that warn these fake images can be used by malicious actors to spread misinformation.

Examples of deepfake videos that used the likeness of Tom Cruise, Former President Barack Obama, and House Speaker Nancy Pelosi went viral and have shown the development of the technology over time.
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Yuli Ban
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It's the next generation of GauGAN!
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What.
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Yuli Ban
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OKAY!
SO. REMEMBER ON THE ORIGINAL FORUM the inciting post I made for this thread's first version about how AI would one day be able to generate video games?
We're in the early days of just that.
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Yuli Ban
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And remember my friend, future events such as these will affect you in the future
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Voice AI is scary good now. Video game actors hate it.
Fans of the game were stoked to have a new, albeit unofficial addition to The Witcher 3 to play. Voice actors and some other observers, however, were less than thrilled. You see, A Night to Remember not only features new content, it features new voice lines. Specifically, the modder used AI trained on voice actor Doug Cockle’s speech to generate brand new voice lines for Geralt, the character he portrays.
“If this is true, this is just heartbreaking,” tweeted Jay Britton, a voice actor with credits in Divinity: Original Sin 2 and Pathfinder. “Yes, AI might be able to replace things but should it? We literally get to decide. Replacing actors with AI is not only a legal minefield but an utterly soulless choice.”
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FitVid: Overfitting in Pixel-Level Video Prediction
An agent that is capable of predicting what happens next can perform a variety of tasks through planning with no additional training. Furthermore, such an agent can internally represent the complex dynamics of the real-world and therefore can acquire a representation useful for a variety of visual perception tasks. This makes predicting the future frames of a video, conditioned on the observed past and potentially future actions, an interesting task which remains exceptionally challenging despite many recent advances. Existing video prediction models have shown promising results on simple narrow benchmarks but they generate low quality predictions on real-life datasets with more complicated dynamics or broader domain.

There is a growing body of evidence that underfitting on the training data is one of the primary causes for the low quality predictions. In this paper, we argue that the inefficient use of parameters in the current video models is the main reason for underfitting. Therefore, we introduce a new architecture, named FitVid, which is capable of severe overfitting on the common benchmarks while having similar parameter count as the current state-of-the-art models. We analyze the consequences of overfitting, illustrating how it can produce unexpected outcomes such as generating high quality output by repeating the training data, and how it can be mitigated using existing image augmentation techniques. As a result, FitVid outperforms the current state-of-the-art models across four different video prediction benchmarks on four different metrics.
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And remember my friend, future events such as these will affect you in the future
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