#Generative Adversarial Networks

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Helena Sarin — Sunday Math School for Adults (animated ERC-721 token certified by Verisart, 2024)

[olivergisttv]

How to Use AI to Detect Deepfake Videos

Deepfake videos, where artificial intelligence (AI) is used to manipulate videos to make people appear as if they are saying or doing things they never did, have become increasingly sophisticated. As these videos pose risks in various areas such as misinformation, fraud, and personal privacy, detecting deepfakes has become critical. Here’s how you can use AI to identify and protect yourself from…

[skilldux]

The two main components of a neural network architecture known as a generative adversarial network are a generator and a discriminator. The discriminator compares the artificial data such as text or images with the real data and attempts to discern differences between the two. The generator's objective is to produce data that is so realistic that the discriminator is unable to distinguish it from genuine data, producing outputs that are incredibly lifelike.

[krissym72]

Guide On How To Master The Latest AI-Generated Art Techniques And Trends

Delve into the world of cutting-edge artistry with this comprehensive guide on mastering the latest AI-generated art techniques and trends. From exploring the wonders of neural networks to harnessing the power of algorithms, this imperative resource will equip you with the knowledge and skills needed to stay ahead in the ever-evolving realm of digital art. Discover the potential pitfalls and…

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[•] fase mineral _

28.6.23

[jcmarchi]

AI multi-speaker lip-sync has arrived

New Post has been published on https://thedigitalinsider.com/ai-multi-speaker-lip-sync-has-arrived/

AI multi-speaker lip-sync has arrived

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Rask AI, an AI-powered video and audio localisation tool, has announced the launch of its new Multi-Speaker Lip-Sync feature. With AI-powered lip-sync, 750,000 users can translate their content into 130+ languages to sound as fluent as a native speaker.  

For a long time, there has been a lack of synchronisation between lip movements and voices in dubbed content. Experts believe this is one of the reasons why dubbing is relatively unpopular in English-speaking countries. In fact, lip movements make localised content more realistic and therefore more appealing to audiences.

There is a study by Yukari Hirata, a professor known for her work in linguistics, which says that watching lip movements (rather than gestures) helps to perceive difficult phonemic contrasts in the second language. Lip reading is also one of the ways we learn to speak in general.   

Today, with Rask’s new feature, it’s possible to take localised content to a new level, making dubbed videos more natural.

The AI automatically restructures the lower face based on references. It takes into account how the speaker looks and what they are saying to make the end result more realistic. 

How it works:

Upload a video with one or more people in the frame.

Translate the video into another language.

Press the ‘Lip Sync Check’ button and the algorithm will evaluate the video for lip sync compatibility.

If the video passes the check, press ‘Lip Sync’ and wait for the result.

Download the video.

According to Maria Chmir, founder and CEO of Rask AI, the new feature will help content creators expand their audience. The AI visually adjusts lip movements to make a character appear to speak the language as fluently as a native speaker. 

The technology is based on generative adversarial network (GAN) learning, which consists of a generator and a discriminator. Both the generator and the discriminator compete with each other to stay one step ahead of the other. The generator clearly generates content (lip movements), while the discriminator is responsible for quality control.     

The beta release is available to all Rask subscription customers.

(Editor’s note: This article is sponsored by Rask AI)

Tags: ai, artificial intelligence, GAN, Generative Adversarial Network, lip sync, rask, rask ai

[skilldux]

The financial, healthcare, and entertainment sectors are just a few of the businesses that GANs have an impact on. Their capacity to produce synthetic data enables businesses to build AI systems that are more effective, and uses like deep fake generation and picture restoration are creating new opportunities in media and industry. Understanding GANs has become essential for anyone seeking a career in artificial intelligence and machine learning due to their widespread significance.

[krissym72]

Exploring How AI Art Works: Unveiling the Creative Potential of Artificial Intelligence

Welcome to the fascinating world of AI art! If you’ve ever marveled at a mesmerizing digital painting or been captivated by an otherworldly image created seemingly out of thin air, chances are you’ve encountered the wonders of AI art. In recent years, the intersection of artificial intelligence and artistic expression has sparked a revolution in the way we perceive and create art. Picture this:…

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why did they shove ai research into fontaine

[thedevmaster-tdm]

STOP Using Fake Human Faces in AI

[skilldux]

GANs AI Courses:

The creation of generative adversarial networks, or GANs, has been one of the most fascinating advances in artificial intelligence and machine learning in recent years. GANs, which were first presented by Ian Goodfellow in 2014, have completely changed the way we generate data. They can now produce artificial music and video as well as incredibly realistic images. There are several tutorials and courses on generative adversarial networks (GANs) available if you want to delve into this game-changing technology.

Generative Adversarial Networks (GANs): What Are They?

The two main components of a neural network architecture known as a generative adversarial network are a generator and a discriminator. The discriminator compares the artificial data such as text or images with the real data and attempts to discern differences between the two. The generator's objective is to produce data that is so realistic that the discriminator is unable to distinguish it from genuine data, producing outputs that are incredibly lifelike.

There are numerous uses for GANs, including:

Creation of images and videos

Text-to-picture conversion

Style conversion

creation of music

Augmenting data for machine learning

Why Take a GANs Course?

The financial, healthcare, and entertainment sectors are just a few of the businesses that GANs have an impact on. Their capacity to produce synthetic data enables businesses to build AI systems that are more effective, and uses like deep fake generation and picture restoration are creating new opportunities in media and industry. Understanding GANs has become essential for anyone seeking a career in artificial intelligence and machine learning due to their widespread significance.

The Greatest Courses on Generative Adversarial Networks

The following are some of the best Generative Adversarial Networks courses available if you're ready to start studying about GANs:

1. Deep Knowledge.GAN Specialization in AI

This course provides an extensive introduction to GANs and is hosted on SkillDux. It covers topics such as deep convolutional GANs (DCGANs), conditional GANs, and progressive expanding GANs. For individuals who want both academic understanding and real-world experience, this course is perfect because it includes practical tasks that allow you to design your own GANs from scratch.

2. Generative Adversarial Networks (GANs) on Skilldux:

 From Novice to Expert For those who wish to begin from the beginning, this course is ideal. It explores the several kinds of GANs and discusses their operation, construction, and training. The course is organized neatly and offers tutorials for practical application, code examples, and concise explanations.

3. Fast.ai: GANs Available to All:

Fast.ai is renowned for providing AI and machine learning instruction in an approachable manner. Regardless of programming background, this course on GANs aims to make GANs accessible to anyone by offering an easy-to-follow introduction. It's a wonderful location for novices to start, with clear explanations and useful examples.

4.  Generative Adversarial Networks:

If one prefers a more scholarly approach, SkillDux has free online courses on GANs. This course is a great resource for anyone interested in learning more about the theoretical features of GANs, even though it does require prior knowledge of neural networks and deep learning.

The Greatest Tutorials for Generative Adversarial Networks

Online tutorials on generative adversarial networks abound in addition to formal courses. These lectures are great for anyone who wants to quickly learn about GANs because they frequently offer brief, practical introductions to the subject.

TensorFlow GAN Tutorial:

TensorFlow offers an approachable guide for creating GANs from the ground up. It takes you through the fundamentals of GAN architecture and demonstrates how to use Keras and TensorFlow to put them into practice.

PyTorch DCGAN Tutorial:

 This tutorial explains how to build a deep convolutional GAN for PyTorch fans. For individuals who are already acquainted with PyTorch and want to learn how to use the library to create GANs, it is an excellent resource.

Towards Data Science GAN Guide:

There are a number of GAN lessons available on Medium's Towards Data Science platform, ranging from fundamental implementations to more complex subjects like style-based GANs and Cycle GANs.

In summary

There are numerous Generative Adversarial Networks courses and tutorials available to assist you in getting started, regardless of your level of experience with machine learning. Since GANs are a game-changing technology that is redefining numerous industries, this is the ideal moment to master this innovative method. You can develop the knowledge and abilities necessary to produce potent generative models and advance AI by investigating these courses and tutorials.

[krissym72]

A Beginner's Guide to Creating Your Own AI Image Generator

In recent years, the intersection of artificial intelligence (AI) and art has sparked a revolution in creative expression. AI art generation, powered by sophisticated algorithms and neural networks, has enabled artists and enthusiasts alike to explore new realms of creativity and produce mesmerizing artworks that push the boundaries of traditional art forms. The importance of creating your own…

[river-taxbird]

There is no such thing as AI.

How to help the non technical and less online people in your life navigate the latest techbro grift.

I've seen other people say stuff to this effect but it's worth reiterating. Today in class, my professor was talking about a news article where a celebrity's likeness was used in an ai image without their permission. Then she mentioned a guest lecture about how AI is going to help finance professionals. Then I pointed out, those two things aren't really related.

The term AI is being used to obfuscate details about multiple semi-related technologies.

Traditionally in sci-fi, AI means artificial general intelligence like Data from star trek, or the terminator. This, I shouldn't need to say, doesn't exist. Techbros use the term AI to trick investors into funding their projects. It's largely a grift.

What is the term AI being used to obfuscate?

If you want to help the less online and less tech literate people in your life navigate the hype around AI, the best way to do it is to encourage them to change their language around AI topics.

By calling these technologies what they really are, and encouraging the people around us to know the real names, we can help lift the veil, kill the hype, and keep people safe from scams. Here are some starting points, which I am just pulling from Wikipedia. I'd highly encourage you to do your own research.

Machine learning (ML): is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines "discover" their "own" algorithms, without needing to be explicitly told what to do by any human-developed algorithms. (This is the basis of most technologically people call AI)

Language model: (LM or LLM) is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on. (This would be your ChatGPT.)

Generative adversarial network (GAN): is a class of machine learning framework and a prominent framework for approaching generative AI. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. (This is the source of some AI images and deepfakes.)

Diffusion Models: Models that generate the probability distribution of a given dataset. In image generation, a neural network is trained to denoise images with added gaussian noise by learning to remove the noise. After the training is complete, it can then be used for image generation by starting with a random noise image and denoise that. (This is the more common technology behind AI images, including Dall-E and Stable Diffusion. I added this one to the post after as it was brought to my attention it is now more common than GANs.)

I know these terms are more technical, but they are also more accurate, and they can easily be explained in a way non-technical people can understand. The grifters are using language to give this technology its power, so we can use language to take it's power away and let people see it for what it really is.

[mariacallous]

Federal employees are seeking a temporary restraining order as part of a class action lawsuit accusing a group of Elon Musk’s associates of allegedly operating an illegally connected server from the fifth floor of the US Office of Personnel Management’s (OPM) headquarters in Washington, DC.

An attorney representing two federal workers—Jane Does 1 and 2—filed a motion this morning arguing that the server’s continued operation not only violates federal law but is potentially exposing vast quantities of government staffers’ personal information to hostile foreign adversaries through unencrypted email.

A copy of the motion, filed in the DC District Court by National Security Counselors, a Washington-area public-interest law firm, was obtained by WIRED exclusively in advance. WIRED previously reported that Musk had installed several lackeys in OPM’s top offices, including individuals with ties to xAI, Neuralink, and other companies he owns.

The initial lawsuit, filed on January 27, cites reports that Musk’s associates illegally connected a server to a government network for the purposes of harvesting information, including the names and email accounts of federal employees. The server was installed on the agency’s premises, the complaint alleges, without OPM—the government’s human resources department—conducting a mandatory privacy impact assessment required under federal law.

Under the 2002 E-Government Act, agencies are required to perform privacy assessments prior to making “substantial changes to existing information technology” when handling information “in identifiable form.” Notably, prior to the installation of the server, OPM did not have the technical capability to email the entire federal workforce from a single email account.

“[A]t some point after 20 January 2025, OPM allowed unknown individuals to simply bypass its existing systems and security protocols,” Tuesday’s motion claims, “for the stated purpose of being able to communicate directly with those individuals without involving other agencies. In short, the sole purpose of these new systems was expediency.”

OPM did not immediately respond to a request for comment.

If the motion is granted, OPM would be forced to disconnect the server until the assessment is done. As a consequence, the Trump administration’s plans to drastically reduce the size of the federal workforce would likely face delays. The email account linked to the server—HR@opm.gov—is currently being used to gather information from federal workers accepting buyouts under the admin’s “deferred resignation program,” which is set to expire on February 6.

“Under the law, a temporary restraining order is an extraordinary remedy,” notes National Security Counselors’ executive director, Kel McClanahan. “But this is an extraordinary situation.”

Before issuing a restraining order, courts apply what’s known as the “balance of equities” doctrine, weighing the burdens and costs on both parties. In this case, however, McClanahan argues that the injunction would inflict “no hardship” on the government whatsoever. February 6 is an “arbitrary deadline,” he says, and the administration could simply continue to implement the resignation program “through preexisting channels.”

“We can't wait for the normal course of litigation when all that information is just sitting there in some system nobody knows about with who knows what protections,” McClanahan says. “In a normal case, we might be able to at least count on the inspector general to do something, but Trump fired her, so all bets are off.”

The motion further questions whether OPM violated the Administrative Procedure Act, which prohibits federal agencies from taking actions “not in accordance with the law.” Under the APA, courts may “compel agency action”—such as a private assessment—when it is “unlawfully withheld.”

Employees at various agencies were reportedly notified last month to be on the lookout for messages originating from the HR@opm.gov account. McClanahan’s complaint points to a January 23 email from acting Homeland Security secretary Benjamine Huffman instructing DHS employees that the HR@opm.gov account “can be considered trusted.” In the following days, emails were blasted out twice across the executive branch instructing federal workers to reply “Yes” in both cases.

The same account was later used to transmit the “Fork in the Road” missive promoting the Trump administration’s legally dubious “deferred resignation program,” which claims to offer federal workers the opportunity to quit but continue receiving paychecks through September. Workers who wished to participate in the program were instructed to reply to the email with “Resign.”

As WIRED has reported, even the new HR chief of DOGE, Musk’s task force, was unable to answer basic questions about the offer.

The legal authority underlying the program is unclear, and federal employee union leaders are warning workers not to blindly assume they will actually get paid. In a floor speech last week, Senator Tim Kaine advised workers not to be fooled: “There’s no budget line item to pay people who are not showing up for work.” Patty Murray, ranking Democrat on the Senate Appropriations Committee, similarly warned Monday: “There is no funding allocated to agencies to pay staff for this offer.”

McClanahan’s lawsuit highlights the government’s response to the OPM hack of 2015, which compromised personnel records on more than 22 million people, including some who’d undergone background checks to obtain security clearances. A congressional report authored by House Republicans following the breach pinned the incident on a “breakdown in communications” between OPM’s chief information officer and its inspector general: “The future effectiveness of the agency’s information technology and security efforts,” it says, “will depend on a strong relationship between these two entities moving forward.”

OPM’s inspector general, Krista Boyd, was fired by President Donald Trump in the midst of the “Friday night purge” on January 24—one day after the first HR@opm.gov email was sent.

“We are witnessing an unprecedented exfiltration and seizure of the most sensitive kinds of information by unelected, unvetted people with no experience, responsibility, or right to it,” says Sean Vitka, policy director at the Demand Progress Education Fund, which is supporting the action. “Millions of Americans and the collective interests of the United States desperately need emergency intervention from the courts. The constitutional crisis is already here.”