The draft text, presented today by the rapporteur, says that the public debate should shift towards a focus on the enormous potential of AI, which offers humankind the unique chance to improve almost every area of our lives. AI could help combat climate change, pandemics and global hunger, and enhance quality of life through personalised medicine. According to the draft document, AI can substantially increase productivity, innovation, growth and job creation. The EU should not regulate AI as a technology; instead, the type, intensity and timing of regulatory intervention should solely depend on the type of risk associated with a particular use of an AI system. The text warns that the EU is currently falling behind in the global tech race that will determine the future political and economic global power balance.

Artificial Intelligence (AI) is already part of our lives and is extensively entering the space sector to offer value-added Earth Observation (EO) products and services. Copernicus data and other georeferenced data sources are often highly heterogeneous, distributed and semantically fragmented. Large volumes of satellite data (images and associated metadata) are frequently coming to the Earth from Sentinel constellation, offering a basis for creating value-added products that go beyond the space sector. The analysis and data fusion of all streams of data need to take advantage of the existing DIAS and HPC infrastructures, as well as the Galileo-enabled mobile devices when required by the involved end users to deliver fully automated processes in decision support systems. CALLISTO project integrates Copernicus data, already indexed in DIAS platforms such as ONDA-DIAS, utilizing High Performance Computing infrastructures for enhanced scalability when needed.

NASA has revealed a stunning mosaic of fake space images used to train its astronomical algorithms – but has hidden within it is a single image of a genuine cosmic marvel. The mosaic below consists of 225 images, 224 of which are fake, created by artificial intelligence (AI). But one shows a real interstellar phenomenon, taken by NASA's Hubble Space Telescope. NASA uses computer algorithms to crunch digital images of the real night sky taken by robotic telescopes, in order to find stars and galaxies and measure their properties. This mosaic consists of 225 images, 224 of which are fake. One is real - but can you spot which one?

The government is undertaking research to explore how AI R&D is successfully commercialised and brought to market. The Department for Digital, Culture, Media and Sport (DCMS), along with the Office for Artificial Intelligence and Digital Standards and Internet Governance (DSIG), are leading the research project. Research consultants Oxford Insights and Cambridge Econometrics have been commissioned with exploring the ways'technology transfer' happens for AI, and are seeking to conduct interviews with those with knowledge of the industry. Who is being invited to take part? Oxford Insights and Cambridge Econometrics would like to speak individuals with experience and knowledge of the AI development ecosystem, Innovate UK and other funding programmes, Standards Developing Organisations (SDOs), AI patents, AI R&D in the public and private sectors, AI funding and Venture Capital, and AI policy.

Cybersecurity best practices are greatly aided by using Artificial Intelligence (AI) and Machine Learning (ML) technology, as shown by this sector's growth. According to one study, the market for artificial intelligence in cybersecurity is expected to reach $46.3 billion by 2027. AI drastically improves a business's cybersecurity posture by applying the technology to help identify, isolate, or remediate potential cyber threats from penetrating a business's network. Read more: AI vs Machine Learning: What Are Their Differences & Impacts? The technology gets better over time: As AI/ML learns a business network's behavior and recognizes patterns on the network over time, it becomes more difficult for hackers to penetrate a business's network.

Collibra, a data governance and intelligence platform that helps businesses unlock insights from disparate data sources, has raised $250 million in a series G round of funding at a $5.25 billion valuation. Founded out of Belgium in 2008, Collibra develops various products that constitute part of what Collibra calls its data intelligence cloud, allowing both technical and business users to collaborate and combine data silos to find hidden meaning in their wealth of information. This includes data catalog, which is for discovering and classifying data; data privacy, which serves centralized tooling to address regulatory requirements; data lineage, which maps relationships between applications and systems; data quality, and data governance. "Collibra is a new approach to the [data] complexity problem, offering a single system of data engagement that supports data modernization, digital transformation, compliance and privacy," Collibra CEO Felix Van de Maele told VentureBeat. "Collibra makes sure everyone in an organization is working with the same set of information -- we offer the only platform that can unite an entire organization by delivering accurate data for every use, for every user and across every source."

Singapore has launched a national artificial intelligence (AI) programme in finance to build deep AI capabilities in its financial sector and strengthen customer service, risk management and business competitiveness. Announced by Singapore's deputy prime minister, Heng Swee Keat, at the Singapore FinTech Festival, the programme is a joint initiative by the Monetary Authority of Singapore (MAS) and the National AI Office at the Smart Nation and Digital Government Office (SNDGO). Through the programme, which is part of Singapore's broader national AI strategy, financial institutions will be able to enhance their ability to research, develop and deploy AI solutions to increase productivity and create new jobs, among other goals. MAS and SNDGO will provide funding, contribute government data and bring together experts to drive AI adoption in the financial sector. One of the key initiatives to be developed under the programme is an AI technical platform called Nova! to generate insights on financial risk.

Any community in which membership is optional may eventually break apart, or fork. For example, forks may occur in political parties, business partnerships, social groups, cryptocurrencies, and federated governing bodies. Forking is typically the product of informal social processes or the organized action of an aggrieved minority, and it is not always amicable. Forks usually come at a cost, and can be seen as consequences of collective decisions that destabilize the community. Here, we provide a social choice setting in which agents can report preferences not only over a set of alternatives, but also over the possible forks that may occur in the face of disagreement. We study this social choice setting, concentrating on stability issues and concerns of strategic agent behavior.

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.

This study proposes a novel method to assess damages in the built environment using a deep learning workflow to quantify it. Thanks to an automated crawler, aerial images from before and after a natural disaster of 50 epicenters worldwide were obtained from Google Earth, generating a 10,000 aerial image database with a spatial resolution of 2 m per pixel. The study utilizes the algorithm Seg-Net to perform semantic segmentation of the built environment from the satellite images in both instances (prior and post-natural disasters). For image segmentation, Seg-Net is one of the most popular and general CNN architectures. The Seg-Net algorithm used reached an accuracy of 92% in the segmentation. After the segmentation, we compared the disparity between both cases represented as a percentage of change. Such coefficient of change represents the damage numerically an urban environment had to quantify the overall damage in the built environment. Such an index can give the government an estimate of the number of affected households and perhaps the extent of housing damage.