Two years ago this weekend, GPT-3 was introduced to the world. You may not have heard of GPT-3, but there's a good chance you've read its work, used a website that runs its code, or even conversed with it through a chatbot or a character in a game. GPT-3 is an AI model -- a type of artificial intelligence -- and its applications have quietly trickled into our everyday lives over the past couple of years. In recent months, that trickle has picked up force: more and more applications are using AI like GPT-3, and these AI programs are producing greater amounts of data, from words, to images, to code. A lot of the time, this happens in the background; we don't see what the AI has done, or we can't tell if it's any good.

Class incremental learning or continual learning is referred to where we want our system to learn a new set of classes without forgetting any prior knowledge of old classes which makes it a challenging problem. It's what we call a general-purpose AI system and it's close to how we human learns. We use our prior knowledge to learn a new task quickly without forgetting any prior knowledge. Learning on a novel set of classes results in catastrophic forgetting- an abrupt degradation of performance on the original set of classes when the training objective is adapted to a newly added set of classes. Let's start with the model architecture and understand how the proposed method is applied to incremental learning tasks- The idea is based on the assumption that natural images lie on a low-dimensional manifold so we can utilize the easily available unlabeled data from the same domain to approximate the target distribution.

When it was first commercialized, the steam engine cost much more than other power sources available – until it didn't. The engine, developed to pump water from flooded mines, allowed for deeper and less costly digging of coal. Then came faster transportation with cheaper shipping of more products and – as accessibility increased with efficiency – more people. The full promise of a breakthrough isn't in what it does initially. It's in what it enables, eventually.

Artificial Intelligence (AI) systems have been involved in numerous scandals in recent years. For instance, take the COMPAS recidivism algorithm. The algorithm evaluated the likelihood that defendants will commit another crime in the future. It was widely used in the US criminal justice system to inform decisions about who can be set free at all stages of the process. In 2016, ProPublica exposed that COMPAS's predictions were biased: its mistakes favored white over black defendants. Black defendants were twice as likely to be labeled as high risk to reoffend but not actually reoffend.

Long COVID refers to the condition where people experience long-term effects from their infection with the SARS CoV-2 virus that is responsible for the COVID-19 disease (Coronavirus disease 2019) pandemic according to the U.S. Centers for Disease Control and Prevention (CDC). A new study published in The Lancet Digital Health applies artificial intelligence (AI) machine learning to identify patients with long COVID-19 using data from electronic health records with high accuracy. "Patients identified by our models as potentially having long COVID can be interpreted as patients warranting care at a specialty clinic for long COVID, which is an essential proxy for long COVID diagnosis as its definition continues to evolve," the researchers concluded. "We also achieve the urgent goal of identifying potential long COVID in patients for clinical trials." Globally there have been over 510 million confirmed cases of COVID-19 and more than 6.2 million deaths according to April 2022 statistics from Johns Hopkins University.

SEOUL, May 23, 2022 – Hyundai Motor Group (the Group) announced on April 29 that it recently carried out a project with Microsoft Korea to prove digital twin technology is effective at predicting an electric vehicle's battery lifespan and optimizing its battery management and performance. Using Microsoft's cloud service Azure, the Group created digital twins of actual electric vehicles (EVs) with the aim to improve the accuracy of battery lifespan prediction and customize battery management systems for each EV model. Based on the project's success, the Group will implement digital twin technology as a way to improve battery performance going forward. Through this collaboration, the Group created digital twins of EVs in a virtual space based on various driving data collected from actual EVs in the real world, and used the virtual EVs to predict the battery lifespan of each vehicle. This high-level, data-integrated analysis model uses artificial intelligence (AI), machine learning and physical models to comprehensively analyze information, such as charging and discharging cycles as well as parking and driving environments.

Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems. In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools. The focus is on practical skills - all the activities will be done in Python 3, a modern programming language used throughout astronomy.

With technological development, the time is getting closer when artificial intelligence can respond to stimuli like humans. Cat, was the name given to this system by its creators, who belong to Deepmind, an artificial intelligence developer company that was acquired in 2014 by Alphabet, whose main subsidiary is Google. It is a technology with artificial intelligence that is capable of carrying out up to 604 different activities. Among its activities are subtitling images describing their content, play different video games like Atari or organize boxes and/or elements in blocks by means of a robotic arm. This artificial intelligence uses language model technology that are the same systems used in the development of voice assistants such as Alexa or Google, which carry out the directed order by means of a spoken command by the user.

Artificial intelligence research group OpenAI (co-founded by Elon Musk, among others) has created DALL-E 2, a text-to-image generation program. The system takes a description written by a user and produces an image. The demo video is worth a watch (2 min). Here's a DALL-E 2 generated image from the description "Shiba Inu dog wearing a beret and black turtleneck." Bonus: DALL-E comes from "Salvador Dalí" combined with "WALL-E".

LSTM methodology, while introduced in the late 90's, has only recently become a viable and powerful forecasting technique.In this article, we are going to use LSTM RNN on a Rossman Pharmaceutical time series dataset to predict sales on a real-world business problem taken from Kaggle. Problem Statement Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.