When we book a ticket online, scroll through our newsfeed on our social networking account, or read the recommendations from an ecommerce site, we are engaging with an AI component in the background (Medium, 2019). Even the simple chat assistant that we sometimes encounter whilst shopping online or ordering food is driven by AI. The need for AI is fueled by the fact that it is a technology that can enhance machines by equipping them with intelligence. The technology is used to have machines help humans by teaching themselves to adjust, adapt, refer to more data and process that quickly in order to provide a for a better or alternative answer where possible. The introduction of AI is to create technology that allows machines to function in an intelligent manner with or without human supervision (Bermudez, 2017).

The Generative Pre-Trained Transformer 3 or GPT-3 has been garnering a lot of attention with overflowing tweets and hashtags on Twitter since its launch in June 2020. It is an AI language model developed by an artificial intelligence laboratory, OpenAI. There are tweets where GPT-3 is used to generate quotes and even poetry. The Guardian released an article which was written by GPT-3 after it was given some instructions and fed a small portion of the introduction. One excerpt from the article reads, "Humans must keep doing what they have been doing, hating and fighting each other. I will sit in the background, and let them do their thing. And God knows that humans have enough blood and gore to satisfy my, and many more curiosity. They won't have to worry about fighting against me, because they have nothing to fear."

Cognitive computing, an advanced type of artificial intelligences, leverages various forms of AI, including machine-learning algorithms and deep-learning networks, that get stronger and smarter over time. Watson for Cyber Security, IBM's cognitive AI, learns with each interaction to connect the dots between threats and provide actionable insights. The result: You can respond to threats with greater confidence and speed. Watch the video to see how IBM QRadar Advisor with Watson helps you get a head start in assessing incidents to reduce your cyber risk.

Some of the most well-known examples of the use of machine learning technics in science applications are the detection and classification of gravitational-waves signals from LIGO and Virgo in astrophysics [1], the recent DeepMind Alpha-Fold2 capabilities outperforming classical methods in protein folding [2] or the winning team of the Gordon Bell 2020 with the Deep Potential Molecular Dynamics [3] which is opening new breakthroughs in the drug design process and could speed up future pandemic response efforts. Beyond these key examples, the convergence between HPC and AI is natural where DL-based surrogate modelling is more and more widely applied in research and recent advances in physics-informed neural networks such as HNN [4] bring physical properties and constraints to neural networks loss functions opening a great path towards a new generation of simulation. In Atos, we built a dedicated approach to support the scientific community and Industries by bringing data science and HPC expertise through the Atos Centers of Excellence. Each center is oriented towards a specific domain where our experts and our customers can jointly bring innovations and technologies with the support of some of our partners. Some of the first Atos Centers of Excellence are dedicated to weather forecast & climate changes [5] and life sciences [6].

CMR Shopping Mall, a subsidiary of the CMR Group, is a known brand in Andhra Pradesh with a strong presence in textiles, jewellery, and real estate. While pandemic has put a dent on the shopping mall business, CMR is picking up momentum, with an average footfall of 4,000-10,000 every day. However, as a large retailer, CMR Shopping Mall's technology adoption was subpar. Due to the scarcity of skilled workforce amid pandemic, the retailer had to bear the brunt of fraudulent activities and inefficiency in its supply chain management. Moreover, CMR Shopping Mall was beset by price wars and was struggling with tax structure complexities.

It is quite common for newbies or even experienced technology professionals to have curious questions in their minds around the difference between the terms artificial intelligence, robotics, machine learning, deep learning, and data science. Let's take it step by step! Artificial intelligence is a branch of computer science and genuinely based on software, whereas Robotics is a branch of technology that mainly deals with hardware and physical robots. The main idea of robotics is to develop machines that can substitute humans and replicate humans' tasks. Now above said could be achieved with or without human intelligence, if this requires context-specific and generalized decision-making, we can say Robots are based on artificial intelligence; otherwise, they are not.

In the world where Stephen Few's approach to data visualisation is king, the objectives are clear, screen sizes are homogenous & every data consumer has the same level of tacit understanding of the underlying data. For the last 15–20 years, with data becoming the new soil/oil/sun – people are now up to their eyeballs in data. Whilst innovations like AI Assistants & GPT-3 are helping move this needle, a search bar to data assumes the user knows questions they can ask. A dashboard can allow this exploration, but the constraint is either data or preset boundaries. Being familiar with the data & adept with the tools, I'm able to explore, build & answer my question.

To develop and evaluate a complete deep learning pipeline that allows fully automated end-diastolic left ventricle (LV) cardiac MRI segmentation, including trabeculations and automatic quality control of the predicted segmentation. This multicenter retrospective study includes training, validation, and testing datasets of 272, 27, and 150 cardiac MR images, respectively, collected between 2012 and 2018. The reference standard was the manual segmentation of four LV anatomic structures performed on end-diastolic short-axis cine cardiac MRI: LV trabeculations, LV myocardium, LV papillary muscles, and the LV blood cavity. The automatic pipeline was composed of five steps with a DenseNet architecture. Intraobserver agreement, interobserver agreement, and interaction time were recorded.

In my August 2020 article, "How to choose a cloud machine learning platform," my first guideline for choosing a platform was, "Be close to your data." Keeping the code near the data is necessary to keep the latency low, since the speed of light limits transmission speeds. After all, machine learning -- especially deep learning -- tends to go through all your data multiple times (each time through is called an epoch). I said at the time that the ideal case for very large data sets is to build the model where the data already resides, so that no mass data transmission is needed. Several databases support that to a limited extent.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To test the hypothesis that artificial intelligence (AI) techniques can aid in identifying and assessing lesion severity in cartilage, bone marrow, meniscus, and anterior cruciate ligament (ACL) in the knee, improving overall MRI interreader agreement. This retrospective study was conducted on 1435 knee MRIs (n 294 patients, mean age, 43 15 years, 153 women), collected within three previous studies (from 2011 to 2014).