If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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 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 Clevy.io, 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.
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.
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.
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.
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.
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, 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.
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.
How will humanity manage the growth of artificial intelligence systems? To answer that, French and Canadian officials are drafting a blueprint for an expert council that they hope could be a prototype for global cooperation on AI policy. The Global Partnership for AI (GPAI), advanced over the past year by French president Emmanuel Macron and Canadian prime minister Justin Trudeau, has started to take shape in a series of transatlantic negotiations in the past few months. While many details have yet to be resolved, negotiators hope for a general understanding by the end of this year, according to Malik Ghallab, director emeritus of a French state robotics lab in Toulouse, who is active in the planning process. The idea is to create a standing forum – involving government, industry and academia – to monitor and debate the policy implications of AI globally.
We focus on the scenario in which messages pro and/or against one or multiple candidates are spread through a social network in order to affect the votes of the receivers. Several results are known in the literature when the manipulator can make seeding by buying influencers. In this paper, instead, we assume the set of influencers and their messages to be given, and we ask whether a manipulator ( e.g., the platform) can alter the outcome of the election by adding or removing edges in the social network. We study a wide range of cases distinguishing for the number of candidates or for the kind of messages spread over the network. We provide a positive result, showing that, except for trivial cases, manipulation is not affordable, the optimization problem being hard even if the manipulator has an unlimited budget ( i.e., he can add or remove as many edges as desired). Furthermore, we prove that our hardness results still hold in a reoptimization variant, where the manipulator already knows an optimal solution to the problem and needs to compute a new solution once a local modification occurs ( e.g., in bandit scenarios where estimations related to random variables change over time). Introduction Nowadays, social network media are the most used, if not the unique, sources of information. This indisputable fact turned out to influence most of our daily actions, and also to have severe effects on the political life of our countries. Indeed, in many of the recent political elections around the world, there has been evidence of the impact that false or incomplete news spread through these media influenced the electoral outcome. For example, in the recent US presidential election, Allcott and Gentzkow (2017) and Guess, Nyhan, and Reifler (2018) show that, on average, 92% of Americans remembered pro-Trump false news, while 23% of them remembered the pro-Clinton fake news. As another example, Ferrara (2017) shows that automated accounts in Twitter spread a considerable amount of political news in order to alter the outcome of 2017 French elections. Furthermore, Alaphilippe et al. (2018) and Giglietto et al. (2018) show that the fake news spread over the major social mediaCopyright c null 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). In this scenario, a natural question is to understand at which extent the spread of (mis)information on social network media may alter the result of a political election.