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Research laboratory Midjourney has paused free trials of its image-generation software after users cranked out realistic deepfakes including of former US president Donald Trump getting arrested and Pope Francis in a puffer jacket. Midjourney responded to a request Thursday for a trial with a message saying it could not be provided and to try again another day. The imagery created using the artificial intelligence platform, particularly those of Trump and the pontiff which went viral, have put a spotlight on the San Francisco-based lab. "Due to a combination of extraordinary demand and trial abuse we are temporarily disabling free trials until we have our next improvements to the system deployed," Midjourney founder David Holz said in a post this week on the company's Discord channel. The service generates realistic looking images based on written prompts made by users.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. OpenAI's revolutionary chatbot ChatGPT has been all over the news in recent months, triggering technology giants such as Google and Baidu to accelerate their AI roadmaps. ChatGPT is built on OpenAI's GPT language model and provides a variety of functions, such as engaging in conversations, answering questions, generating written text, debugging code, conducting sentiment analysis, translating languages and much more. Looking at the technologies of this moment in time, nothing seems to be as pivotal to the future of humanity as generative AI. The idea of scaling the creation of intelligence through machines will touch on everything that happens around us, and the momentum in the generative AI space created by ChatGPT's sudden ascent is inspiring.

Google recently published a scientific paper showing how an artificial intelligence model is able to predict a number of systemic biomarkers from a simple photo of the eye. How were such results arrived at? We discuss this in this article. Diagnosis of disease often requires examinations with expensive instruments and then interpretation by a medical professional who is trained. This is not always possible.

As a data scientist or machine learning engineer, you spend much of your time improving a model's performance by creating new features, comparing different types of models, trying out new model architectures, and much more. In the end, it's the score on the test set that counts, so that is what you focus on when deciding on a model. However, as important as the model performance may be, there are other, secondary criteria you shouldn't forget about. What do you get from a model with almost perfect scores, if your MLOps department can't host it? How does the user feel, if the prediction is accurate, but it takes ages to get it?

Welcome to Hyperparameter Optimization for Machine Learning. In this course, you will learn multiple techniques to select the best hyperparameters and improve the performance of your machine learning models. If you are regularly training machine learning models as a hobby or for your organization and want to improve the performance of your models, if you are keen to jump up in the leader board of a data science competition, or you simply want to learn more about how to tune hyperparameters of machine learning models, this course will show you how. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about hyperparameter tuning. Throughout this comprehensive course, we cover almost every available approach to optimize hyperparameters, discussing their rationale, their advantages and shortcomings, the considerations to have when using the technique and their implementation in Python.

Emily M. Bender, a Professor in the Department of Linguistics at the University of Washington and the co-author of the first paper the letter cites, tweeted that this open letter is "dripping with #Aihype" and that the letter misuses her research. The letter says, "AI systems with human-competitive intelligence can pose profound risks to society and humanity, as shown by extensive research," but Bender counters that her research specifically points to current large language models and their use within oppressive systems--which is much more concrete and pressing than hypothetical future AI.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. With 2022 well behind us, taking stock in how machine learning (ML) has evolved -- as a discipline, technology and industry -- is critical. With AI and ML spend expected to continue to grow, companies are seeking ways to optimize rising investments and ensure value, especially in the face of a challenging macroeconomic environment. With that in mind, how will organizations invest more efficiently while maximizing ML's impact? How will big tech's austerity pivot influence how ML is practiced, deployed, and executed moving forward?

Etienne Bernard, is the Co-Founder & CEO of NuMind a software company founded in June 2022 specializing in developing machine learning tools. Etienne is an expert in AI & machine learning. After a PhD (ENS) & postdoc (MIT) in statistical physics, Etienne joined Wolfram Research where he became the head of machine learning for 7 years. During this time, Etienne led the development of automatic learning tools, a user-friendly deep learning framework, and various machine learning applications. What initially attracted you to machine learning?

On Thursday, the Center for AI and Digital Policy (CAIDP), an advocacy nonprofit, filed a complaint with the Federal Trade Commission(Opens in a new tab) (FTC) targeting OpenAI. The complaint argues that the company's latest large language model, GPT-4, which can be used to power ChatGPT, is in violation of FTC rules against deception and unfairness. This comes on the heels of an open letter signed by major figures in AI, including Elon Musk, which called for a six-month pause on the training of systems more powerful than GPT-4. The complaint asks the Commission "to initiate an investigation into OpenAI and find that the commercial release of GPT-4 violates Section 5 of the FTC Act." This section, the complaint explains, provides guidance about AI and outlines the "emerging norms for the governance of AI that the United States government has formally endorsed."