Deforestation is gaining an increasingly importance due to its strong influence on the sorrounding environment, especially in developing countries where population has a disadvantaged economic condition and agriculture is the main source of income. In Ivory Coast, for instance, where the cocoa production is the most remunerative activity, it is not rare to assist to the replacement of portion of ancient forests with new cocoa plantations. In order to monitor this type of deleterious activities, satellites can be employed to recognize the disappearance of the forest to prevent it from expand its area of interest. In this study, Forest-Non-Forest map (FNF) has been used as ground truth for models based on Sentinel images input. State-of-the-art models U-Net, Attention U-Net, Segnet and FCN32 are compared over different years combining Sentinel-1, Sentinel-2 and cloud probability to create forest/non-forest segmentation. Although Ivory Coast lacks of forest coverage datasets and is partially covered by Sentinel images, it is demonstrated the feasibility to create models classifying forest and non-forests pixels over the area using open datasets to predict where deforestation could have occurred. Although a significant portion of the deforestation research is carried out on visible bands, SAR acquisitions are employed to overcome the limits of RGB images over areas often covered by clouds. Finally, the most promising model is employed to estimate the hectares of forest has been cut between 2019 and 2020.

Noting that nearly 40 percent of pancreatic cancer tumors smaller than 2 cm are missed on computed tomography (CT) assessment, the authors of a new study suggest that an emerging deep learning tool could have an impact in improving detection. In the study, conducted in Taiwan and published earlier today in Radiology, researchers examined the effectiveness of a deep learning tool for detecting malignant pancreatic tumors on contrast-enhanced CT in a nationwide validation test set consisting of 669 patients with pancreatic cancer and 804 participants in the control group.1 The deep learning tool was trained with contrast-enhanced CT scans from 546 patients with pancreatic cancer and 733 healthy control patients, according to the study. The researchers found that the deep learning tool had an 89.7 sensitivity rate and a 92.8 percent specificity rate for detecting pancreatic cancer in the nationwide validation test set. In local test set data drawn from 109 patients with pancreatic cancer at a tertiary referral center and 147 control participants, the study authors noted no significant differences in sensitivity between assessment by attending radiologists (96.1 percent) and the deep learning tool (90.2 percent).1 "This study developed an end-to-end, deep learning-based, computer-aided detection (CAD) tool that could accurately and robustly detect pancreatic cancers on contrast-enhanced CT scans. The CAD tool may be a useful supplement for radiologists to enhance detection of (prostate cancer)."

Artificial intelligence (AI) has started focusing on animal welfare including poultry farms in recent times. Farmers can leverage AI for its voice technology with a deep learning tool known as a bird-brained bot to gain information regarding baby chicks and chickens on their farms. AI can help to detect squawking chickens and get them out of distress by enhancing their health or physical conditions. The bird-brained bot is developed for the well-being of squawking chickens by listening to them carefully. The deep learning tool with the integrated voice technology can help to determine their issues and happiness with their squawking patterns. Instagram's New AI is the Common Creep Tech that Happily Invades Your Privacy But Why are Other Nations Worried?

Machine learning PhD students are in a unique position: they often need to run large-scale experiments to conduct state-of-the-art research but they don't have the support of the platform teams that industrial ML engineers can rely on. As former PhD students ourselves, we recount our hands-on experience with these challenges and explain how open-source tools like Determined would have made grad school a lot less painful. When we started graduate school as PhD students at Carnegie Mellon University (CMU), we thought the challenge laid in having novel ideas, testing hypotheses, and presenting research. Instead, the most difficult part was building out the tooling and infrastructure needed to run deep learning experiments. While industry labs like Google Brain and FAIR have teams of engineers to provide this kind of support, independent researchers and graduate students are left to manage on their own.

"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. Femoral component subsidence following total hip arthroplasty (THA) is a worrisome radiographic finding. This study developed and evaluated a deep learning tool to automatically quantify femoral component subsidence between two serial anteroposterior (AP) hip radiographs.

A new artificial intelligence (AI) system is about to be used to predict ice loss in the Arctic, a study reveals. The deep learning tool, called IceNet was created by scientists at the British Antarctic Survey (BAS) and has been trained with the past four decades of satellite data from the region. It's almost 95 per cent accurate in predicting whether sea ice will be present two months ahead – better than the leading physics-based model previously used by BAS – but it's been trained to predict as far as six months ahead. Sea ice in both the north and south poles naturally expands in the winter and shrinks in the summer. But sea ice is very hard to predict because it has'very complex interactions' with the atmosphere above and the ocean below.

Evgeniy is a specialist in software development, technological entrepreneurship and emerging technologies. In recent years, companies' growing focus on big data has led to increased digitalization demands. The avalanche of data has forced businesses to reconsider software modernization approaches. With that in mind, let's look at how enterprises use AI in intelligent analysis, hyperautomation and cybersecurity in the world of big data. Data orientation is the future of business, and the survival of companies depends on efficiently processing external and internal information.

With more than 74 percent of Gen Z spending their free time online – averaging around 10 hours per day – it's safe to say their online and offline worlds are becoming entwined. With increased social media usage now the norm and all of us living our lives online a little bit more, we must look for ways to mitigate risks, protect our safety and filter out communications that are causing concern. Step forward, Artificial Intelligence (AI) – advanced machine learning technology that plays an important role in modern life and is fundamental in how today's social networks function. With just one click AI tools such as chatbots, algorithms and auto-suggestions impact what you see on your screen and how often you see it, creating a customised feed that has completely changed the way we interact on these platforms. By analysing our behaviours, deep learning tools can determine habits, likes and dislike and only display material they anticipate you will enjoy.

Machine learning PhD students are in a unique position: they often need to run large-scale experiments to conduct state-of-the-art research but they don't have the support of the platform teams that industrial ML engineers can rely on. As former PhD students ourselves, we recount our hands-on experience with these challenges and explain how open-source tools like Determined would have made grad school a lot less painful. When we started graduate school as PhD students at Carnegie Mellon University (CMU), we thought the challenge laid in having novel ideas, testing hypotheses, and presenting research. Instead, the most difficult part was building out the tooling and infrastructure needed to run deep learning experiments. While industry labs like Google Brain and FAIR have teams of engineers to provide this kind of support, independent researchers and graduate students are left to manage on their own.

Unlike any other time, the past decade has seen unprecedented development in the field of Artificial Intelligence (AI). There are a lot of talks on machine learning doing things humans currently do in our workplace. Deep learning is leading in some of the fronts of machine learning making practical changes. Deep learning is an artificial intelligence function that imitates the working of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural network (ANN).