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Evolving AI Art
Evolving AI Art
The video discusses the process of evolving images using AI, starting with selecting an image, giving a prompt, and generating variations through an evolving process. The purpose of this process is exploration, to find beautiful and unimagined artwork or cute cats utilizing an inconceivably enormous and unsearchable image space. Input for text-to-image models allows users to enter a simple prompt and receive a vast array of possible images that satisfy that prompt, also allowing for the creation of entirely new images and organizing and cataloging existing ones in latent space. The Pick Breeder method is an efficient and natural way of mutating, selecting, and reproducing genes that perform the best to create images, allowing people to follow evolutionary threads and discover unexpected beauty through branching paths with powerful AI tools.
The AI that creates any picture you want, explained
The text-to-image revolution, explained
This video discusses how machine learning algorithms can be used to generate images based on text descriptions, and how this technology can be used to create artwork. The video interviews James Gurney, an American illustrator, who discusses the implications of this technology on copyright law and the art world.
Guide to MidJourney AI Art - How to get started FREE!
Guide to MidJourney AI Art - How to get started FREE!
In this video, the speaker introduces MidJourney, a tool that generates AI art based on prompts, and provides step-by-step instructions on how to get started with it. They demonstrate how to use commands to change the style and quality of the generated images, using examples such as "3D render" or "dripping ink sketch." Additionally, they explain the community section of the MidJourney website, where users can find inspiration and copy prompts to try out themselves. The speaker also shares their journey with AI art and provides additional resources and codes for those interested in learning more.
Additionally, they discuss the community section of the MidJourney website, where users can find inspiration and copy prompts to try out themselves. The narrator also provides tips on how to use MidJourney responsibly, such as adding a disclaimer when sharing generated art online.
MidJourney -Getting Started [New & Updated] A quick tutorial to get you started in AI art generation
MidJourney -Getting Started [New & Updated] A quick tutorial to get you started in AI art generation
The video tutorial provides a comprehensive overview of how to use MidJourney's AI art generation platform, which can only be accessed through Discord. The speaker explains the different subscription modes available, how to create prompts using artists and various conditions, how to use switches to remove unwanted elements from AI-generated images, and how to upscale and adjust aspect ratios of images. They also provide tips on how to generate unique AI art using prompts with visual appeal and by using the variation button before upscaling. Overall, MidJourney is presented as a tool for artistic exploration and departure rather than a means of creating finished works of art.
ChatGPT, Explained: What to Know About OpenAI's Chatbot | Tech News Briefing Podcast | Wall Street Journal
ChatGPT, Explained: What to Know About OpenAI's Chatbot | Tech News Briefing Podcast | WSJ
Chatbots are now available to the public and can be used to ask questions and get responses. There are concerns about how these tools could be used, but experts say that people should use them to enhance their work, not replace their roles.
CS 156 Lecture 01 - The Learning Problem
Caltech's Machine Learning Course - CS 156. Lecture 01 - The Learning ProblemThe first lecture of Yaser Abu-Mostafa's machine learning course introduces the learning problem, which is the process of finding patterns in data to make predictions without human intervention. He explains the need for mathematical formalization to abstract practical learning problems and introduces the first algorithm for machine learning in the course, the perceptron model, which uses a weight vector to classify data points into binary categories. The lecture also covers different types of learning, including supervised, unsupervised, and reinforcement learning, and presents a supervised learning problem to the audience to address the issue of determining a target function for learning.The professor covers various topics related to machine learning. He emphasizes the need to avoid bias when selecting data sets, as well as the importance of collecting a sufficient amount of data. The professor also discusses the role of the hypothesis set in machine learning and the impact of the choice of error function on the optimization technique. He also touches on the criteria for including machine learning methods in the course and his focus on providing practical knowledge rather than pure theory.
Lecture 2. Is Learning Feasible?
Caltech's Machine Learning Course - CS 156. Lecture 02 - Is Learning Feasible?
The lecture discusses the feasibility of learning, specifically the use of machine learning in determining patterns from given data. The lecturer introduces the concept of nu and mu in probability and how it relates to the learning problem. The addition of probability is explored, enabling the feasibility of learning without compromising the target function, meaning no assumptions need to be made about the function that will be learned. The concept of overfitting and how it relates to model sophistication is discussed, with a larger number of hypotheses leading to poorer generalization. Ultimately, the lecture concludes with a request to review the slide on the implication of nu equals mu.
Lecture 3 -The Linear Model I
Caltech's Machine Learning Course - CS 156. Lecture 03 -The Linear Model I
This lecture covers the topics of linear models in machine learning, input representation, the perceptron algorithm, the pocket algorithm, and linear regression, including its use in classification. The professor emphasizes the importance of using real data to try out different ideas and introduces the concept of features to simplify the learning algorithm's life. The lecture also discusses the computational aspects of the pseudo-inverse in linear regression, and the problems that can arise when using linear regression for classification on non-separable data. Finally, the concept of using nonlinear transformations to make data more linear is presented, with an example demonstrating how to achieve separable data using the transformation x1² and x2² from the origin.
Also the professor covers various topics related to the linear model in machine learning. He discusses nonlinear transformations and guidelines on selecting them, in-sample and out-of-sample errors in binary classification, using linear regression for correlation analysis, and deriving meaningful features from input. The professor also emphasizes the importance of understanding the distinction between E_in and E_out and how they impact model performance. Lastly, he touches on the relationship between linear regression and maximum likelihood estimation, the use of nonlinear transformations, and the role of theory in understanding machine learning concepts.
Lecture 4 - Error and Noise
Caltech's Machine Learning Course - CS 156. Lecture 04 - Error and Noise
In Lecture 04 of the machine learning course, Professor Abu-Mostafa discusses the importance of error and noise in real-life machine learning problems. He explains the concept of nonlinear transformation using the feature space Z, which is essential in preserving linearity in learning. The lecture also covers the components of the supervised learning diagram, emphasizing the importance of error measures in quantifying the performance of the hypothesis. Noisy targets are introduced as a typical component of real-world learning problems, which must be considered when minimizing the in-sample error. The lecture ends with a discussion on the theory of learning and its relevance in evaluating the in-sample error, out-of-sample error, and model complexity.
The professor explains how changes in the probability distribution can affect the learning algorithm and how error measures can differ for different applications. He also discusses the algorithm for linear regression, the use of squared error versus absolute value for error measures in optimization, and the tradeoff between complexity and performance in machine learning models. The professor clarifies the difference between the input space and feature extraction and notes that the theory for how to simultaneously improve generalization and minimize error will be covered in the coming lectures.
Lecture 5 - Training Versus Testing
Caltech's Machine Learning Course - CS 156. Lecture 05 - Training Versus Testing
In Lecture 5 of his course on Learning From Data, Professor Abu-Mostafa discusses the concepts of error and noise in machine learning, the difference between training and testing, and the growth function, which measures the maximum number of dichotomies that can be produced by a hypothesis set for a given number of points. He also introduces the break point, which corresponds to the complexity of a hypothesis set and guarantees a polynomial growth rate in N if it exists, and discusses various examples of hypothesis sets such as positive rays, intervals, and convex sets. The lecture emphasizes the importance of understanding these concepts and their mathematical frameworks in order to fully comprehend the complexity of hypothesis sets and their potential for feasible learning.
The professor covered various topics related to training versus testing. He addressed questions from the audience about non-binary target and hypotheses functions and the tradeoff of shattering points. The professor explained the importance of finding a growth function and why it is preferred over using 2 to the power of N to measure the probability of generalization being high. Additionally, he discussed the relationship between the break point and the learning situation, noting that the existence of the break point means that learning is feasible, while the value of the break point tells us the resources needed to achieve a certain performance. Finally, the professor explained the alternatives to Hoeffding and why he is sticking to it to ensure people become familiar with it.