At first glance, Renée DiResta thought the LinkedIn message seemed normal enough.
The sender, Keenan Ramsey, mentioned that they both belonged to a LinkedIn group for entrepreneurs. She punctuated her greeting with a grinning emoji before pivoting to a pitch for software.
“Quick question — have you ever considered or looked into a unified approach to message, video, and phone on any device, anywhere?”
DiResta wasn’t interested and would have ignored the message entirely, but then she looked closer at Ramsey’s profile picture. Little things seemed off in what should have been a typical corporate headshot. Ramsey was wearing only one earring. Bits of her hair disappeared and then reappeared. Her eyes were aligned right in the middle of the image. Read More
Tag Archives: Image Recognition
Yesterday Marked the Death of Art as an Industry
Starting yesterday, AI is now definitively better than human artists in almost every sense of the word. Here’s why human art & design is about to crumble.
OnMarch 6th, 2022, OpenAI released DALL-E 2: their “new AI system that can create realistic images and art from a description in natural language”.
I don’t say this lightly: this new AI system is not just on-par with human artists. It is definitively better than humans in almost every sense of the word. Read More
OpenAI’s new DALL.E model turns your words into pieces of art
OpenAI takes the wraps off DALL·E 2, its second-generation text-to-image generator.
OpenAI, the AI research startup, has announced(opens in new tab) DALL·E 2, an update to its text-to-image generator that looks like a serious step forward.
In essence, DALL·E 2 can create art from a natural language input, such as: “a painting of a fox sitting in a field at sunrise in the style of Claude Monet”. OpenAI says the goal is to create “original, realistic images and art” that can “combine concepts, attributes, and styles”. Read More
ZooBuilder: 2D and 3D Pose Estimation for Quadrupeds
NVIDIA Research Turns 2D Photos Into 3D Scenes in the Blink of an AI
Instant NeRF is a neural rendering model that learns a high-resolution 3D scene in seconds — and can render images of that scene in a few milliseconds.
When the first instant photo was taken 75 years ago with a Polaroid camera, it was groundbreaking to rapidly capture the 3D world in a realistic 2D image. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds.
Known as inverse rendering, the process uses AI to approximate how light behaves in the real world, enabling researchers to reconstruct a 3D scene from a handful of 2D images taken at different angles. The NVIDIA Research team has developed an approach that accomplishes this task almost instantly — making it one of the first models of its kind to combine ultra-fast neural network training and rapid rendering. Read More
Impressive photo restoration by AI !
This new and completely free AI model can fix most of your old pictures in a split second!
Do you also have old pictures of yourself or close ones that didn’t age well or that you, or your parents, took before we could produce high-quality images? I do, and I felt like those memories were damaged forever. Boy, was I wrong!
This new and completely free AI model can fix most of your old pictures in a split second. It works well even with very low or high-quality inputs, which is typically quite the challenge.
This week’s paper called Towards Real-World Blind Face Restoration with Generative Facial Prior tackles the photo restoration task with outstanding results. What’s even cooler is that you can try it yourself and in your preferred way. They have open-sourced their code, created a demo and online applications for you to try right now. If the results you’ve seen above aren’t convincing enough, just watch the video and let me know what you think in the comments, I know it will blow your mind! Read More
PeopleLens: Using AI to support social interaction between children who are blind and their peers
CSLIM Brings AI-Generated Art to ‘The World,’ or Vice Versa
Welcome to the world of tomorrow, where artificial intelligence can create East Asian landscape paintings that rival 11th century masters’ — with ownership that can be verified on the blockchain. Such is the vision of South Korean generative artist CSLIM, whose upcoming NFT collection will feature 5,000 artworks that the engineer and developer attests were created by AI that has studied the data of 80,000 classical East Asian landscape paintings from the sixth through 13th centuries via machine learning. Exquisitely combining cultural tradition and cutting-edge technology, “The World” will drop on Feb. 21 — only at Crypto.com/NFT. Read More
I asked an AI to paint 10 famous sci-fi book titles in one minute. Here are the results.
Fifty years ago, top scientists believed AI would never be able to beat humans at chess.
We all know how that turned out.
The newest goalpost is art. As an AI artist, people routinely tell me how my art isn’t real, or how it lacks humanity because it’s machine generated.
True, there’s some credence to these claims. But I would venture most people are simply uncomfortable with the notion that AI is starting to produce art faster & better than humans.
After my last post on AI art blew up, it naturally attracted a fair amount of similar criticism. Much was in reference to a supposed lack of diversity in the samples (apparently all of the landscapes looked the same).
To respond to this criticism, I decided to run an A100 GPU for one minute. Read More
People Trust Deepfake Faces Generated by AI More Than Real Ones, Study Finds
The proliferation of deepfake technology is raising concerns that AI could start to warp our sense of shared reality. New research suggests AI-synthesized faces don’t simply dupe us into thinking they’re real people, we actually trust them more than our fellow humans.
In 2018, Nvidia wowed the world with an AI that could churn out ultra-realistic photos of people that don’t exist. Its researchers relied on a type of algorithm known as a generative adversarial network (GAN), which pits two neural networks against each other, one trying to spot fakes and the other trying to generate more convincing ones. Given enough time, GANS can generate remarkably good counterfeits.
Since then, capabilities have improved considerably, with some worrying implications: enabling scammers to trick people, making it possible to splice people into porn movies without their consent, and undermining trust in online media. While it’s possible to use AI itself to spot deepfakes, tech companies’ failures to effectively moderate much less complicated material suggests this won’t be a silver bullet. Read More
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