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Medtronic Completes Acquisition of Medicrea


Acquisition Expands Medtronic's Artificial Intelligence and Data Capabilities, Becoming the First Company to Offer an Integrated Spine Solution Including AI-Driven Surgical Planning, Personalized Spinal Implants and Robotic Assisted Surgery DUBLIN, Nov. 16, 2020 /CNW/ -- Medtronic plc (NYSE:MDT), the global leader in medical technology, today announced that it has completed its friendly tender offer for France-based Medicrea International (Euronext Growth Paris: FR0004178572 – ALMED Medicrea; OTCQX Best Market – MRNTF), a pioneer in the transformation of spinal surgery through artificial intelligence (AI), predictive modeling and patient specific implants. On July 15, 2020, the parties announced a friendly voluntary all-cash tender offer at the price of €7.00 per Medicrea share. As a result of completion of the tender offer, Medtronic currently owns in excess of 90% of Medicrea's share capital and voting rights and will shortly request the implementation of a squeeze-out procedure under French law, which will result in Medicrea becoming a wholly-owned subsidiary of Medtronic. This is Medtronic's seventh acquisition completed in 2020 and furthers Medtronic's strategic expansion into AI, machine learning and predictive analytics. Medicrea's product portfolio consists of 30 510(k) cleared or CE Marked implant technologies, utilized in spinal surgeries for adult deformity, pediatric deformity and degenerative disease.

Differences Between Europe and the United States on AI/Digital Policy: Comment Response to Roundtable Discussion on AI - Pierre-Antoine Gourraud, 2020


For AI policy, there are significant differences between Europe and the United States. The General Data Protection Regulation, which applies not only to EU companies but also to all American companies with European customers, is more protective than health insurance portability and accountability act for individual health data. Its Article 22 stipulates that citizens cannot be submitted to medical decisions generated by an automated source. For the creation and implementation of national health databases, European companies have an advantage over the United States because of their small sizes, single-payer systems, and existing national cohorts. For instance, France is in the process of developing a national health data platform (Health Data Hub [HDH]), as part of the Healthcare Law of July 14, 2019.1 It has its origins in the report presented by Cedric Villani to the French government in March 2018.2

AI, Machine Learning Playing Important Role in Fighting COVID-19 - AI Trends


AI and machine learning are playing an important role in fighting the pandemic brought on by COVID-19, with technological innovation and ingenuity being applied to large volumes of data to quickly identify patterns and gain insights. Efforts are underway to speed up research and treatment, and better understand how COVID-19 spreads. Chatbots employing AI are speeding up communication around the pandemic. One example is from, a French startup that launched a chatbot to make it easier for people to find official government communications about COVID-19, according to an account from the World Economic Forum. The bot is getting realtime information from the French government and the World Health Organization, to help relay known symptoms and answer questions about government policies.

An Investigation of COVID-19 Spreading Factors with Explainable AI Techniques Artificial Intelligence

Since COVID-19 was first identified in December 2019, various public health interventions have been implemented across the world. As different measures are implemented at different countries at different times, we conduct an assessment of the relative effectiveness of the measures implemented in 18 countries and regions using data from 22/01/2020 to 02/04/2020. We compute the top one and two measures that are most effective for the countries and regions studied during the period. Two Explainable AI techniques, SHAP and ECPI, are used in our study; such that we construct (machine learning) models for predicting the instantaneous reproduction number ($R_t$) and use the models as surrogates to the real world and inputs that the greatest influence to our models are seen as measures that are most effective. Across-the-board, city lockdown and contact tracing are the two most effective measures. For ensuring $R_t<1$, public wearing face masks is also important. Mass testing alone is not the most effective measure although when paired with other measures, it can be effective. Warm temperature helps for reducing the transmission.

Laziness in humans could be used to tell us apart from bots

Daily Mail - Science & tech

Humans' unique laziness when it comes to interacting on social media could be the key to telling us apart from artificially intelligent'bots', a new study shows. US researchers have identified behavioural trends of humans on Twitter that are absent in social media bots – namely a decrease in tweet length over time. The team studied how the behaviour of humans and bots changed over the course of a session on Twitter relating to political events. While humans get lazier as sessions progress and can't be bothered typing out long tweets, bots maintain consistent levels of engagement over time. Such a behavioural difference could inform new machine learning algorithms for bot detection software.

Tensor Decompositions for temporal knowledge base completion Machine Learning

Most algorithms for representation learning and link prediction in relational data have been designed for static data. However, the data they are applied to usually evolves with time, such as friend graphs in social networks or user interactions with items in recommender systems. This is also the case for knowledge bases, which contain facts such as (US, has president, B. Obama, [2009-2017]) that are valid only at certain points in time. For the problem of link prediction under temporal constraints, i.e., answering queries such as (US, has president, ?, 2012), we propose a solution inspired by the canonical decomposition of tensors of order 4. We introduce new regularization schemes and present an extension of ComplEx (Trouillon et al., 2016) that achieves state-of-the-art performance. Additionally, we propose a new dataset for knowledge base completion constructed from Wikidata, larger than previous benchmarks by an order of magnitude, as a new reference for evaluating temporal and non-temporal link prediction methods.

France to Deploy AI-Focused Supercomputer: Jean Zay


HPE announced today that it won the contract to build a supercomputer that will drive France's AI and HPC efforts. The computer will be part of GENCI, the French national infrastructure for HPC resources and facilities. The system, named Jean Zay after the French politician and cultural figure, came at the behest of an action issued by President of France Emmanuel Macron in support of the national strategy to make France the European leader in artificial intelligence research. Financed by GENCI and based on the HPE SGI 8600 platform, Jean Zay is slated to deliver a peak performance of 14 petaflops. Under a unified Omni-Path Architecture network, the system encompasses 1,528 Intel next-generation Xeon nodes and 261 GPU nodes, each with four Nvidia Tesla V100 (32GB) GPUs, 1,044 in all.

France says it carried out first armed drone strike in Mali, killing seven Islamic extremists

The Japan Times

PARIS – France's defense ministry announced Monday it had carried out its first armed drone strike, killing seven Islamic extremists in central Mali over the weekend. France joins a tiny group of countries that use armed drones, including the United States. The drone deployment came nearly one month after two French helicopters collided in Mali, killing 13 soldiers in the deadliest military loss for France in nearly four decades. A defense ministry statement said the drone strike took place Saturday while French President Emmanuel Macron was visiting neighboring Cote d'Ivoire, where France has a military base. Macron already had announced that French forces had killed 33 extremists that day.

New Zealand Global Information Society Watch


Algorithms and social media: A need for regulations to control harmful content? On 15 March 2019, a white supremacist committed a terrorist attack on two mosques in Christchurch, murdering 51 people as they were peacefully worshipping, injuring many others and live streaming the attack on Facebook. The attack was the worst of its kind in New Zealand's history and prompted an emotional nationwide outpouring of solidarity with Muslim communities. Our prime minister, Jacinda Ardern, moved quickly, travelling immediately to the Muslim communities affected, framing the attack as one on all New Zealanders, vowing compassion, refusing to ever say the name of the attacker, issuing a pledge to ban semi-automatic weapons of the kind used in the attack, and steering her people through a difficult emotional time of grief, anger and shock. The global response led Ardern and French President Emmanuel Macron to issue the #Christchurch Call,[1] calling for, among other things, an examination of the use of algorithms by social media platforms to identify and interfere with terrorist extremist online content. This country report critically examines the events, including discussion of technical measures to find and moderate the objectionable content.

IGF Daily Brief 2 - 27 November 2019 Digital Watch


HIGHLIGHTS FROM DAY 1 WHERE IS IQ'WHALO? What will our generation be remembered for? This year marks the second IGF attended by UN Secretary-General António Guterres. His opening speech last year – together with French President Macron's speech – carried substantive reflections on the state of global digital policy, and an encouraging vision for the digital developments ahead of us. This year's opening speech couldn't be more different. Characterised by examples of how the Internet is being misused and exploited, Guterres gave a stark account of the profound issues which are affecting today's technology and tomorrow's developments. 'It is for me an enormous frustration to be that today, not only we are still building physical walls to separate people, but that there is also the tendency to create some virtual walls in the Internet also to separate people.' The three main divides – the digital divide, the social divide, and the political divide – are still profound.