Unlike satellite images, which are typically acquired and processed in near-real-time, global land cover products have historically been produced on an annual basis, often with substantial lag times between image processing and dataset release. We developed a new automated approach for globally consistent, high resolution, near real-time (NRT) land use land cover (LULC) classification leveraging deep learning on 10 m Sentinel-2 imagery. We utilize a highly scalable cloud-based system to apply this approach and provide an open, continuous feed of LULC predictions in parallel with Sentinel-2 acquisitions. This first-of-its-kind NRT product, which we collectively refer to as Dynamic World, accommodates a variety of user needs ranging from extremely up-to-date LULC data to custom global composites representing user-specified date ranges. Furthermore, the continuous nature of the product’s outputs enables refinement, extension, and even redefinition of the LULC classification. In combination, these unique attributes enable unprecedented flexibility for a diverse community of users across a variety of disciplines.

Artificial intelligence has fueled defamatory charges of crime and misconduct around the world, setting the stage for potential chaos in legal circles.

The song, which cloned the stars' voices, is removed from streaming platforms after a copyright claim.

Both of these resources are only really available to big companies. And although some of the most exciting applications, such as OpenAI's chatbot ChatGPT and Stability.AI's image-generation AI Stable Diffusion, are created by startups, they rely on deals with Big Tech that gives them access to its vast data and computing resources. "A couple of big tech firms are poised to consolidate power through AI rather than democratize it," says Sarah Myers West, managing director of the AI Now Institute, a research nonprofit. Right now, Big Tech has a chokehold on AI. But Myers West believes we're actually at a watershed moment.

An AI gold rush is underway in the private sector in the wake of ChatGPT, but the geopolitical stakes are even greater. The United States and China are vying for global leadership in AI, a technology that is transforming political, economic, and military power. The U.S. currently leads in AI, but China is rapidly catching up and has declared its intent to be the global leader by 2030. To stay ahead of China in AI, the U.S. will need to work with China. The best competitive strategy for the U.S. is to sustain ties with China in areas where the U.S. benefits disproportionately, such as human talent and computing hardware, while severing problematic ties.

"In recent years, the emergence of artificial intelligence (AI) has revolutionized various industries, including the world of art. With AI-powered algorithms and machine learning, artists are now exploring new frontiers in the realm of visual art." That was how ChatGPT, an AI chatbot, suggested we start this story. We asked it for an introduction to an article in which four artists and professors of the practice at SMFA at Tufts sat down to weigh the pros and cons of AI art generators. Contrary to ChatGPT's rosy view, they say it's early to call AI art a revolution, and they question just how meaningful those "new frontiers" will be.

Art Law Conference 2023 aims to bring together students, attorneys, artists, arts & legal professionals to discuss contemporary & relevant topics at the intersection of art & law. Speakers will include attorneys & legal professionals from leading law firms in the United States & internationally; artists from diverse backgrounds in modern, contemporary & digital art; & arts professionals including appraisers. Each panel will include an attorney & an artist to dissect the varied subject matter at the intersection of art & law from different perspectives to elucidate artists rights, interest & protection. The panels will include a presentation & discussion from the speakers & conclude with a question & answer session with the audience to ensure interactive engagement. Materials for the conference will include speaker slides & handouts with additional reading materials & resources.

Construct theory in social psychology, developed by George Kelly are mental constructs to predict and anticipate events. Constructs are how humans interpret, curate, predict and validate data; information. AI today is biased because it is trained with a narrow construct as defined by the training data labels. Machine Learning algorithms for facial recognition discriminate against darker skin colors and in the ground breaking research papers (Buolamwini, Joy and Timnit Gebru. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. FAT (2018), the inclusion of phenotypic labeling is proposed as a viable solution. In Construct theory, phenotype is just one of the many subelements that make up the construct of a face. In this paper, we present 15 main elements of the construct of face, with 50 subelements and tested Google Cloud Vision API and Microsoft Cognitive Services API using FairFace dataset that currently has data for 7 races, genders and ages, and we retested against FairFace Plus dataset curated by us. Our results show exactly where they have gaps for inclusivity. Based on our experiment results, we propose that validated, inclusive constructs become industry standards for AI ML models going forward.

The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many technological transformation agendas currently being propagated by many nations, for which the already yielded benefits are outstanding. From a regional perspective, several studies have shown that machine learning technology can help address some of Africa's most pervasive problems, such as poverty alleviation, improving education, delivering quality healthcare services, and addressing sustainability challenges like food security and climate change. In this state-of-the-art paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 89% were articles with at least 482 citations published in 903 journals during the past three decades. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent.

We first compile data on the hourly coding output of over 8,000 professional GitHub users in Italy and other European countries to analyse the impact of the ban on individual productivity. Combining the high-frequency data with the sudden announcement of the ban in a difference-in-differences framework, we find that the output of Italian developers decreased by around 50% in the first two business days after the ban and recovered after that. Applying a synthetic control approach to daily Google search and Tor usage data shows that the ban led to a significant increase in the use of censorship bypassing tools. Our findings show that users swiftly implement strategies to bypass Internet restrictions but this adaptation activity creates short-term disruptions and hampers productivity.