A recent article published in the Guardian caught the attention of internet users worldwide. Unlike ordinary works of journalism that go viral, however, this particular piece was not written by a human. In a style that is evocative and attention-grabbing, The Guardian aptly titled it: "A robot wrote this entire article. Are you scared yet, human?" The "robot" in question is GPT-3, or "Generative Pre-Trained Transformative 3", OpenAI's third iteration of an autoregressive language model that uses deep learning to produce human-like text.

Improving radiologist efficiency and preventing burnout is a primary goal for healthcare providers. A nationwide study published in Mayo Clinic Proceedings in 2015 showed radiologist burnout percentage at a concerning 61% [1]. In additon, the report concludes that "burnout and satisfaction with work-life balance in US physicians worsened from 2011 to 2014. More than half of US physicians are now experiencing professional burnout."[2] As technologists, we're looking for ways to put new and innovative solutions in the hands of physicians to make them more efficient, reduce burnout, and improve care quality.

To develop a deep learning model that segments intracranial structures on head CT scans. In this retrospective study, a primary dataset containing 62 normal noncontrast head CT scans from 62 patients (mean age, 73 years; age range, 27–95 years) acquired between August and December 2018 was used for model development. Eleven intracranial structures were manually annotated on the axial oblique series. The dataset was split into 40 scans for training, 10 for validation, and 12 for testing. After initial training, eight model configurations were evaluated on the validation dataset and the highest performing model was evaluated on the test dataset.

RADAR satellites can collect massive amounts of remote sensing data that can detect ground movements--surface defomations--at volcanoes in near real time. These ground movements could signal impending volcanic activity and unrest; however, clouds and other atmospheric and instrumental disturbances can introduce significant errors in those ground movement measurements. Now, Penn State researchers have used artificial intelligence (AI) to clear up that noise, drastically facilitating and improving near real-time observation of volcanic movements and the detection of volcanic activity and unrest. "The shape of volcanoes is constantly changing and much of that change is due to underground magma movements in the magma plumbing system made of magma reservoirs and conduits," said Christelle Wauthier, associate professor of geosciences and Institute for Data and Computational Sciences (ICDS) faculty fellow. "Much of this movement is subtle and cannot be picked up by the naked eye."

Artificial Intelligence is the new buzzword that no one can go without. The reasons are numerous; AI has given as self-driven cars, fancy robots that have close to human intelligence, and many many more. Experts predict that AI will significantly improve the lives of humans in years to come. Even now, we are enjoying some of the benefits of this awesome technology. Artificial Intelligence is the act of giving machines the ability to perform human-level tasks without explicit programming.

OpenAI is an AI research and deployment company whose mission is to ensure that artificial general intelligence benefits all of humanity. In July OpenAI released the GPT-3, a new language model trained with 175 billion parameters, 10x more than any previous non-sparse language model, capable of programing, designing and even talking about politics or economy. Here there is a Twitter thread with some of the most curious cases. Even if there was a huge hype, the CEO of OpenAI and former president of Y Combinator, Sam Altman literally said "The GPT-3 hype is way too much. It is impressive but it still has serious weaknesses and sometimes makes very silly mistakes".

Since the beginning of the photography, Image colorization may have been reserved for those with artistic talent in the past, but now thanks to Artificial Intelligence, is it possible to colorize black and white images and video with outstanding quality. One interesting example is the paper Fully Automatic Video Colorization with Self-Regularization and Diversity ( you can read it here), which refers to one experiment by the Hong Kong University of Science and Technology, which presents a fully automatic method for colorizing black and white films without any human guidance or references. Typical image colorization methods require some sort of labeled reference. A key innovation of this paper is a novel framework consisting of a colorization network with self-learning techniques. The researchers used the ranked diversity loss function proposed in a CVPR paper to differentiate different solution modes.

Using Deep Learning DC-GAN to add featured effect on anything. After my final project submission and earning a Certificate of Accomplishment in the course I just want to share with you what I did. May be this could help someone well understand and use DCGAN. For this project I chose to create an application to wear eyeglasses or hats to people without glasses or hats, using DCGAN (Deep Convolutional Generative Adversarial Networks) and hat or/and eyeglass vectors through the VGG model network we used during the course. DC-GAN uses AutoEncoder (AE) and GAN (Generative Adversarial Networks) to generate a featured output according to the input you fit in it.

Here you can find a list of the AI-related seminars that are scheduled to take place between now and the end of November 2020. We've also listed recent past seminars that are available for you to watch. All events detailed here are free and open for anyone to attend virtually. This list includes forthcoming seminars scheduled to take place between 15 October and 30 November. Doing more with less: deep Learning for physics at the Large Hadron Collider Speaker: Maurizio Pierini (CERN) Organised by: University of Oxford To receive the Zoom room link, send an empty email to: request.zoom.ox.ml.and.physics

Are GANs the next step in Deep Learning? Well, the subset of Machine Learning was once described by Yoshua Bengio as the most interesting idea in the last 10 years of ML, with the technique of using two neural networks against each other to generate new, synthetic instances of data that can pass for real data, opening many doors in the world of AI. That said, we wanted to explore some of the applications of GANs currently being used through the below 5 must-watch presentations from DeepMind, NASA, MIT, Insitro and Université de Montréal. In this presentation, Francesco introduces a new deep generative model for the genetic analysis of medical imaging, combining both convolutional neural networks and structured linear mixed models to extract latent imaging features in the context of genetic association studies. The linked presentation includes an application of the method to brain MRI images from the Alzheimer's Disease Neuroimaging Initiative dataset, where we reveal novel and known risk genes for neurological and psychiatric disorders. Genetic association studies and the process of evaluation during study is covered before looking at both the phenotypes and genetic variants of participants.