What do we do with archival images in the time of AI image creation? As I have been writing this article, Dall-E and similar projects have become extremely popular. I had a chance to access Midjourney, a platform similar to Dall-E.

I've been meaning to post for quite some time about a subject that has become very central to me and my work. Maths has been a constant in my life since early childhood. Coming from a family where maths was highly valued it was natural for me to gravitate towards mathematics at university. Recently, I have been reading about the history of mathematics. I was surprised to discover that the first recorded zero appeared in Mesopotamia around 3 B.C. and that the first use of negative numbers appeared in China around 200 B.C.

The amount of scientific research in AI has been growing exponentially over the last few years, making it challenging for scientists and practitioners to keep track of the progress. Reports suggest that the number of ML papers doubles every 23 months. One of the reasons behind it is that AI is being leveraged in diverse disciplines like mathematics, statistics, physics, medicine, and biochemistry. This poses a unique challenge of organising different ideas and understanding new scientific connections. To this end, a group of researchers led by Mario Krenn and others from the Max Planck Institute for the Science of Light (MPL), Erlangen, Germany, the University of California, the University of Toronto, etc., jointly released a study on high-quality link prediction in an exponentially growing knowledge network.

We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices. This lets us identify several changes to both the sampling and training processes, as well as preconditioning of the score networks. Together, our improvements yield new state-of-the-art FID of 1.79 for CIFAR-10 in a class-conditional setting and 1.97 in an unconditional setting, with much faster sampling (35 network evaluations per image) than prior designs. To further demonstrate their modular nature, we show that our design changes dramatically improve both the efficiency and quality obtainable with pre-trained score networks from previous work, including improving the FID of a previously trained ImageNet-64 model from 2.07 to near-SOTA 1.55, and after re-training with our proposed improvements to a new SOTA of 1.36.

All my articles on GPT-3 as of October 2022. My favorite language model and how to use it for multiple purposes in online applications with pure JavaScript and minimal PHP..

Over the last decade, Artificial intelligence (AI) has become embedded in every aspect of our society and lives. From chatbots and virtual assistants like Siri and Alexa to automated industrial machinery and self-driving cars, it's hard to ignore its impact. Today, the technology most commonly used to achieve AI is machine learning – advanced software algorithms designed to carry out one specific task, such as answering questions, translating languages or navigating a journey – and become increasingly good at it as they are exposed to more and more data. Worldwide, spending by governments and business on AI technology will top $500 billion in 2023, according to IDC research. But how will it be used, and what impact will it have? Here, I outline what I believe will be the most important trends around the use of AI in business and society over the next 12 months.

In Sept. 2020, The Guardian published an opinion piece written by a program. The artificial intelligence, called GPT-3, is a large language model developed by OpenAI, and it posed a bold question in the headline of its machine-generated text: "A robot wrote this entire article. Are you scared yet, human?" Indeed, it is a scary time to be a professional writer. Click here to view original web page at thenextweb.com

Probabilistic Circuits (PCs) are a unified framework for tractable probabilistic models that support efficient computation of various probabilistic queries (e.g., marginal probabilities). One key challenge is to scale PCs to model large and high-dimensional real-world datasets: we observe that as the number of parameters in PCs increases, their performance immediately plateaus. This phenomenon suggests that the existing optimizers fail to exploit the full expressive power of large PCs. We propose to overcome such bottleneck by latent variable distillation: we leverage the less tractable but more expressive deep generative models to provide extra supervision over the latent variables of PCs. Specifically, we extract information from Transformer-based generative models to assign values to latent variables of PCs, providing guidance to PC optimizers. Experiments on both image and language modeling benchmarks (e.g., ImageNet and WikiText-2) show that latent variable distillation substantially boosts the performance of large PCs compared to their counterparts without latent variable distillation. In particular, on the image modeling benchmarks, PCs achieve competitive performance against some of the widely-used deep generative models, including variational autoencoders and flow-based models, opening up new avenues for tractable generative modeling.

We tested it and got impressed! We took the latest RealPython episode for 1h 10 minutes. It took us 56 minutes with a basic CPU to convert the audio file into almost perfect text transcription with the smallest Whisper model. Next, we show in steps using Whisper in practice with just a few lines of Python code. This tutorial explains with single code a way to use the Whisper model both on your local machine and in a cloud environment.

Computer programs can now create never-before-seen images in seconds. Feed one of these programs some words, and it will usually spit out a picture that actually matches the description, no matter how bizarre. They often feature hands with extra fingers or digits that bend and curve unnaturally. Image generators have issues with text, coming up with nonsensical signs or making up their own alphabet. But these image-generating programs -- which look like toys today -- could be the start of a big wave in technology. "In the last three months, the words'generative AI' went from, 'no one even discussed this' to the buzzword du jour," said David Beisel, a venture capitalist at NextView Ventures.