AI ART
Is 🔵 GPT 3 for Free ?
The AI Tools You Need to Know About
My number one goal in life is to see more AI artists. Art, society, and AI are tightly intertwined, and AI artists have a lot to say about art and education. And artists of all mediums can benefit and learn a lot from machine learning art.
So whether you’re exploring machine learning or artificial intelligence, or you’re an artist (of all mediums), or you’re just interested in how art is changing, or you want to experiment with making your own art, read on. My specialty is datasculpting, and this article is focused on my own work. If you’re more interested in general AI art, check out the excellent http://art.mlearning.ai newsletter.
What is AI Art? Artificial intelligence (AI) has been around for a long time, but the term “artificial intelligence” was coined in 1956 by John McCarthy. It refers to machines that can do things that are not easy for humans, like playing chess or beating you at Jeopardy! Artificial intelligence research has advanced rapidly over the last few decades, and today we have computers that can recognize objects in images just as well as humans. We also have computers that can learn from experience and improve their performance with feedback. And we have systems that use machine learning algorithms to create art. Machine learning algorithms are used in many different fields of science and engineering, including computer vision (seeing), speech recognition (understanding), natural language processing (understanding human language), data mining (finding patterns in data). These techniques are now being applied to art-making: creating new kinds of art using artificial neural networks, generative adversarial networks, and deep reinforcement learning agents… I could go on forever!
The goal here is not to provide an exhaustive list of all possible approaches; instead, I want to focus on my work with : 1. Generative adversarial networks (GANs). 2. Generative Pre-trained Transformer 3 (GPT-3)
Why Generative Adversarial Networks? GANs are a family of algorithms that use two neural networks to generate new images. The first network is called the generator, and it takes an input image as input and outputs another image as output. The second network is called the discriminator, and it takes the output from the generator as input and tries to classify whether or not it’s real or fake. If you can fool both networks at once, then you have created art! GANs were invented by Ian Goodfellow in 2014. They’re used in many different fields: machine learning (for example, for speech recognition), computer vision (to make realistic images from photos), text generation (to make natural texts from data like tweets), music synthesis (to create new songs out of existing ones)… I could go on forever here too!
Generative Adversarial Networks vs. Generative Models vs. Generative Art The terms “generative adversarial networks,” “generative models,” and “generative art” all refer to similar things: generatively generated imagery.
In generative art, one of the networks only has access to a small subset of its parameters during training: just the values that control how much it updates itself each time it generates an image. So the generator tries to fool the discriminator into thinking that a picture is actually by making random changes in its parameters. These changes can be minimal — for example, they might change the color of pixels slightly or add some noise around edges — but they can also be bigger: adding new features like fur or feathers!
The discriminator network tries to figure out whether these changes make sense by looking at how similar the resulting image looks concerning what was input as input. If there are no apparent differences between what was input and what came out as output, then this would mean that all of those random parameter changes were actually useful; if not, then they weren’t
Generative Pre-trained Transformer 3 (GPT-3) OpenAI’s Generative Pre-trained Transformer 3 (GPT-3) is a 175 billion machine learning parameter language model that uses deep learning to produce human-like texts. GPT-3, introduced in May 2020, is part of the trend in natural language processing (NLP) systems that use pre-trained representations. These representations combine information from large datasets of text with neural network models that, in many cases, are task-specific.
🔵 I wanted to share with you handy links ( GAN , GPT 3 *) FREE AI tools. GPT-3 access without the wait.
Everything that I produce is open source, and you can follow the process on my Substack feed. ( Codex - translates natural language to code)
If you’re wondering whether you should invest your time learning an artificial intelligence algorithm, then I firmly believe that the answer is yes. Understanding how these algorithms work can give you a leg up on beating them at their own game. And with the AI tools at your disposal, beating them will be much more accessible than ever before.
- Check out my instagram with new material every week
- If you enjoyed this, follow me on Medium for more
- Want to collaborate? Let’s connect on LinkedIn
- https://linktr.ee/datasculptor
- 3D Machine Learning generated model on sketchfab
- 3 months free (during which you can spend the 18$ credit)
- 🔵 GET FULL MEDIUM ACCESS
