France


EU tech regulator backs UK plans for digital tax, despite Trump threats

The Guardian

The European Union's leading tech regulator has thrown her weight behind the British government's plans to press ahead with a digital tax despite threats from Donald Trump. Margrethe Vestager, the EU competition commissioner recently promoted to take charge of Europe's digital policy as well, said she was a "strong supporter" of national digital taxes in order to advance the chances of an international agreement. She said the EU would revive plans for a digital tax within a year if international efforts to find a solution failed. "I think it is very important that we keep up the momentum. Because of this very fundamental injustice that most people and businesses pay their taxes and they are competing with businesses who create value but do not pay taxes," she said in an interview with the Guardian and other European newspapers.


ATTOL: Autonomous Taxiing, Take-Off and Landing test flight

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Sign in to report inappropriate content. Airbus has successfully performed the first fully automatic vision-based take-off using an Airbus Family test aircraft at Toulouse-Blagnac airport. The test crew comprising of two pilots, two flight test engineers and a test flight engineer took off initially at around 10h15 on 18 December and conducted a total of 8 take-offs over a period of four and a half hours.


Feature-based time series analysis

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I used this example in my talk at useR!2019 in Toulouse, and it is also the basis of a vignette in the package, and a recent blog post by Mitchell O'Hara-Wild. The data set contains domestic tourist visitor nights in Australia, disaggregated by State, Region and Purpose. An example of a feature would be the autocorrelation function at lag 1 -- it is a numerical summary capturing some aspect of the time series. Autocorrelations at other lags are also features, as are the autocorrelations of the first differenced series, or the seasonally differenced series, etc. Values close to 1 indicate a highly seasonal time series, while values close to 0 indicate a time series with little seasonality.


University diploma Artificial intelligence and intellectual property - CEIPI - University of Strasbourg

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The impact of the development of computer science on the knowledge of law is phenomenal and fundamental. Yet, few lawyers have the expertise to understand the impact of new algorithmic methods in their practice. The objectives of the training are twofold: the first is to transfer knowledge and skills in this high-tech sector, while the second is to provide technical training to lawyers. The university degree "Artificial Intelligence and Intellectual Property" has, on the one hand, a goal to remedy this lack in the field of intellectual property rights. Indeed, if there are many training courses on the digital and the law, none sufficiently understates the new issues of artificial intelligence in the field of intellectual property rights, in order to understand and control the issues of protection of these new types of creation, their usefulness to the implementation of rights, as well as their technical and economic environment.


Is ReRAM Ready to Leave the R&D Phase?

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TORONTO – Resistive RAM is the epitome of an "emerging" memory in that, for the most part, it's still in the research and development phase. "I'd say the most progress is being in R&D," said Jim Handy, principal analyst with Objective Analysis, and co-author of the report Emerging Memories Ramp Up. There are many small companies working to commercialize it, such as Crossbar and Weebit Nano, and there's also a lot of research work being done at LETI, a technology research institute of CEA Tech based in France. The DRAM manufacturers are also dabbling in it, he said, but no one is rushing to bring out a part, although every foundry that offers MRAM also has some sort of ReRAM capability. Essentially, the major memory makers are hedging their bets.


LEAK: Commission considers facial recognition ban in AI 'white paper'

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The European Commission is considering measures to impose a temporary ban on facial recognition technologies used by both public and private actors, according to a draft white paper on Artificial Intelligence obtained by EURACTIV. If implemented, the plans could throw current AI projects off course in some EU countries, including Germany's wish to roll out automatic facial recognition at 134 railway stations and 14 airports. France also has plans to establish a legal framework permitting video surveillance systems to be embedded with facial recognition technologies. The Commission paper, which gives an insight into proposals for a European approach to Artificial Intelligence, stipulates that a future regulatory framework could "include a time–limited ban on the use of facial recognition technology in public spaces." The document adds that the "use of facial recognition technology by private or public actors in public spaces would be prohibited for a definite period (e.g. More generally, the draft White Paper, the completed version of which the Commission should publish towards the end of February, features five regulatory options for Artificial Intelligence across the bloc. A Voluntary Labelling framework could consist of a legal instrument whereby developers could "chose to comply, on a voluntary basis, with requirements for ethical and trustworthy artificial intelligence." Should compliance in this area be guaranteed, a'label' of ethical or trustworthy artificial intelligence would be granted, with binding conditions. Option two focuses on a specific area of public concern – the use of artificial intelligence by public authorities – as well as the employment of facial recognition technologies generally. In the former area, the paper states that the EU could adopt an approach akin to the stance taken by Canada in its Directive on Automated Decision Making, which sets out minimum standards for government departments that wish to use an Automated Decision System. As for facial recognition, the Commission document highlights provisions from the EU's General Data Protection Regulation, which give citizens "the right not to be subject of a decision based solely on automated processing, including profiling." In the third area which the Commission is currently priming for regulation, legally binding instruments would apply only "to high–risk applications of artificial intelligence.


Zinier Raises $90 Million to Transform Field Service Workforces

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Zinier, the leader in intelligent field service automation, announced that it has raised $90 million in Series C funding to transform field service workforces with AI-driven automation. New investor ICONIQ Capital led the round with new participation from Tiger Global Management, and return investors Accel, Founders Fund, Nokia-backed NGP Capital, France-based Newfund Capital and Qualcomm Ventures LLC. The funding will support global customer adoption and expansion of Zinier's AI-driven field service automation platform, ISAC. In the past year, more than $100 million was invested in Zinier to transform the way field service work gets done. From the electricity that lights up homes to wireless connectivity that enables communication around the world, it's expected that these services work on demand.


Sidetrade and Solocal SignAI Partnership Deal

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Sidetrade, the Artificial Intelligent platform dedicated to accelerating company revenue, and Solocal, the French digital marketing leader, have signed a four-year partnership agreement in the field of Artificial Intelligence (AI). Sidetrade's machine learning algorithms will allow Solocalto digitalize and automate financial relations with their 399,000 clients,and optimize debt collection.Using AIto boost operating efficiency Launched in 2017, Solocal's transformation project named Solocal 2020 represents a new ambition: become a champion of digital in France. "To achieve this transformation, our priority was to invest in operational projects with high added value, based on structured, optimized and automated processes, and a client-driven approach", explains Eric Boustouller, CEO of Solocal. "With this partnership, our Finance Department shows their determination to make full use of today's latest technologies, especially in the field of Artificial Intelligence. Given how essential it is to effectively manage financial relations with clients, we must boost the operational efficiency of our teams, who have immense volumes of data to manage. It is in this context that we have chosen Sidetrade to support us with this transformation."


Sidetrade and Solocal SignAI Partnership Deal

#artificialintelligence

Sidetrade, the Artificial Intelligent platform dedicated to accelerating company revenue, and Solocal, the French digital marketing leader, have signed a four-year partnership agreement in the field of Artificial Intelligence (AI). Sidetrade's machine learning algorithms will allow Solocalto digitalize and automate financial relations with their 399,000 clients,and optimize debt collection.Using AIto boost operating efficiency Launched in 2017, Solocal's transformation project named Solocal 2020 represents a new ambition: become a champion of digital in France. "To achieve this transformation, our priority was to invest in operational projects with high added value, based on structured, optimized and automated processes, and a client-driven approach", explains Eric Boustouller, CEO of Solocal. "With this partnership, our Finance Department shows their determination to make full use of today's latest technologies, especially in the field of Artificial Intelligence. Given how essential it is to effectively manage financial relations with clients, we must boost the operational efficiency of our teams, who have immense volumes of data to manage. It is in this context that we have chosen Sidetrade to support us with this transformation."


Machine Learning Shapes Microwaves for a Computer's Eye - Advanced Science News

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Engineers from Duke University and the Institut de Physique de Nice in France have developed a new method to identify objects using microwaves that improves accuracy while reducing the associated omputing time and power requirements. The system could provide a boost to object identification and speed in fields where both are critical, such as autonomous vehicles, security screening and motion sensing. It also jointly determines optimal hardware settings that reveal the most important data while simultaneously discovering what the most important data actually is. In a proof-of-principle study, the setup correctly identified a set of 3D numbers using tens of measurements instead of the hundreds or thousands typically required. The results appear in the journal Advanced Science and are a collaboration between David Smith, the James Duke Distinguished Professor of Electrical and Computer Engineering at Duke, and Roarke Horstmeyer, assistant professor of biomedical engineering at Duke. "Object identification schemes typically take measurements and go to all this trouble to make an image for people to look at and appreciate," said Horstmeyer. "But that's inefficient because the computer doesn't need to'look' at an image at all." "This approach circumvents that step and allows the program to capture details that an image-forming process might miss while ignoring other details of the scene that it doesn't need," added Aaron Diebold, a research assistant in Smith's lab.