In the wake of digital revolution, the implementation of automation has been rocketing very high in recent times in all the industries and domains. Consequently, the demand for best Robotic Process Automation (RPA) solutions have plummeted compared to traditional automation with the companies implementing RPA to compete and stay afloat in the business game. With RPA receiving a lot of attention recently, it has taken a leap forward to transform the dynamics of business operations for its abilities to reduce the turnaround time, enhance accuracy and productivity, and better regulatory compliance. RPA is a technological solution for automating manual business procedures to allow organizations to stay competitive and flourish in the technological landscape. By combining automation with artificial intelligence, organizations can restore numerous rule-based, repetitive, and human operations.

Software Freedom Conservancy, a not-for-profit organization that provides support and legal services for open source software projects, has called on the open source community to ditch GitHub after quitting the code-hosting and collaboration platform itself. The move comes a week after Microsoft-owned GitHub launched the commercial version of Copilot, an AI-powered pair-programmer that collaborates with software developers by suggesting lines or functions as they type. It's a little like Gmail's Smart Compose feature, which strives to expedite your email writing by suggesting the next piece of text in your message using contextual cues. Software Freedom Conservancy is financially backed by a number of big-name companies, such as Google, Red Hat, and Mozilla, and its members span more than 40 projects, including Git (which GitHub relies heavily on), Selenium, and Godot. While the Software Freedom Conservancy's beef with GitHub predates Copilot by some margin, it seems that GitHub's latest launch is the final straw.

A photographer has created portraits of people who do not exist but were instead made with the artificial intelligence (AI) program Dall-E 2. Mathieu Stern, a French photographer, used the nascent software that is not yet easily available to the public to create photorealistic portraits of fictitious people that he documented in a YouTube video. Stern, who recently made a series of wild camera designs on the program, started by instructing Dall-E to create an image of "a young beautiful woman wearing a yellow kimono, in a tropical greenhouse." "At first the lack of information about the camera, the lens, and the general look of the image, led to rather unimpressive results," Stern explains on YouTube. "So to help Dall-E, some details must be added to the general description, like the lens, the camera, the film, and adding some words like bokeh." Stern says the best results came after adding the word "Graflex."

Artificial intelligence has completely upended the business world. Whether you’re a fledgling startup or a billion-dollar global conglomerate, the way you do business today is radically different than it was just five years ago. In the modern paradigm, one of your company’s greatest assets is the data generated...

The U.K. is planning to tweak an existing law to allow text and data mining "for any purpose," in a move that's designed to boost artificial intelligence (AI) development across the country. The announcement constitutes part of a broader strategy to "level up" AI and transform the U.K. into what it calls a "global AI superpower" -- and part of this will involve reassessing existing intellectual property (IP) laws. Following a two-month consultation period where stakeholders from across the industrial spectrum were asked for input, including rightsholders, academics, lawyers, trade organisations and businesses, the U.K.'s Intellectual Property Office (IPO) today published its response and confirmed what will (and won't) be changing moving forward. Text and data mining (TDM) is pivotal to the development of new AI applications, allowing researchers and businesses to copy and harness disparate datasets to train their algorithms. However, gaining access to enough relevant data has inherent challenges -- the data is often owned by third-parties that may only want to make data available under a commercial license, if they make it available at all.

In this paper, a shallow Ritz-type neural network for solving elliptic equations with delta function singular sources on an interface is developed. There are three novel features in the present work; namely, (i) the delta function singularity is naturally removed, (ii) level set function is introduced as a feature input, (iii) it is completely shallow, comprising only one hidden layer. We first introduce the energy functional of the problem and then transform the contribution of singular sources to a regular surface integral along the interface. In such a way, the delta function singularity can be naturally removed without introducing a discrete one that is commonly used in traditional regularization methods, such as the well-known immersed boundary method. The original problem is then reformulated as a minimization problem. We propose a shallow Ritz-type neural network with one hidden layer to approximate the global minimizer of the energy functional. As a result, the network is trained by minimizing the loss function that is a discrete version of the energy. In addition, we include the level set function of the interface as a feature input of the network and find that it significantly improves the training efficiency and accuracy. We perform a series of numerical tests to show the accuracy of the present method and its capability for problems in irregular domains and higher dimensions.

Electronic health records (EHRs) offer great promises for advancing precision medicine and, at the same time, present significant analytical challenges. Particularly, it is often the case that patient-level data in EHRs cannot be shared across institutions (data sources) due to government regulations and/or institutional policies. As a result, there are growing interests about distributed learning over multiple EHRs databases without sharing patient-level data. To tackle such challenges, we propose a novel communication efficient method that aggregates the local optimal estimates, by turning the problem into a missing data problem. In addition, we propose incorporating posterior samples of remote sites, which can provide partial information on the missing quantities and improve efficiency of parameter estimates while having the differential privacy property and thus reducing the risk of information leaking. The proposed approach, without sharing the raw patient level data, allows for proper statistical inference and can accommodate sparse regressions. We provide theoretical investigation for the asymptotic properties of the proposed method for statistical inference as well as differential privacy, and evaluate its performance in simulations and real data analyses in comparison with several recently developed methods.

You might not notice it, but you've likely adopted artificial intelligence into your daily life. It can be as simple as personalizing your news feeds, searching for products on shopping sites or voice-to-text conversion on smartphones. It can also be applied to more sophisticated tasks like predicting court outcomes in cases involving employment law or used for robotic welding applications. The transformative power of AI is also an economic growth driver, which is why the Canadian government has given the green light to advancing the country's AI strategy. According to a recent announcement from Minister of Innovation, Science and Industry François-Philippe Champagne, more than $443 million in Budget 2021 is designated for the second phase of the pan-Canadian Artificial Intelligence Strategy.

This paper presents MultiSenGE that is a new large scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. MultiSenGE contains 8,157 patches of 256 256 pixels for the Sentinel-2 L2A, Sentinel-1 GRD images in VV-VH polarization and a Regional large scale Land Use/Land Cover (LULC) topographic reference database. With MultiSenGE, we contribute to the recents developments towards shared data use and machine learning methods in the field of environmental science. The purpose of this dataset is to propose relevant and easy-access dataset to explore deep learning methods. We use MultiSenGE to evaluate the performance for urban areas using well-known deep learning techniques. These results serve as a baseline for future research on remote sensing applications using the multi-temporal and multimodal aspects of MultiSenGE. With all patches georeferenced at a 10 meters spatial resolution covering the whole Grand-Est Region, MultiSenGE provides an opportunity for environmental benchmark dataset will help to advance data-driven techniques for land use/land cover remote sensing applications.

Artificial intelligence "acts" unethically in ways that are different from humans, even if the harms that both AI and humans can cause are similar. For example, even if both humans and AI can invade people's privacy, discriminate, or cause physical harm, artificial intelligence does not act with intention to cause such harm. Rather, the harm results from how artificial intelligence collects and processes data. Currently, artificial intelligence cannot achieve consciousness, though one Google engineer disagrees. Today, the type of artificial intelligence that companies are creating and incorporating into their operations and decision systems is artificial narrow intelligence, which refers to a computer's ability to perform a single task or limited tasks extremely well.