Cécile Wendling also led a roundtable on governance tools for responsible AI during the conference organized by Impact AI on AXA's Java site on January 25, 2019. Artificial intelligence will impact insurance in several ways. First of all, it can help change the way insurance companies interact with customers and improve the customer experience. Take the example of damage occurring overnight during a major disaster. At a time when a traditional call center may be closed or busy, we can now imagine customers contacting a chat bot or voice bot to get instructions on the first steps to take in case of damage.

I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anandkumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.

Back in the 1980s, Artificial Intelligence (AI) just had to look good in the movies and be able to power fancy talking cars, zappy spaceships and various forms of fantastical cyborgs who would one day roam the planet and possibly destroy the human race. Fast-forward to 2019 and we find ourselves deep in the AI renaissance (or perhaps first'real' birth of AI, rather than any form of rebirth) as we now have the processing power, memory capacity, cloud network breadth and sophisticated algorithmic intelligence to actually apply AI to our lives. But there's a problem -- we (the humans) who build the AI brains need to be able to construct them with a pure enough form of digital DNA such that they stay clean of any form of discriminatory bias. Major cloud networks have already been criticized for employing software that discriminates against women; a well-known search engine has been accused of featuring ethnic bias in results when looking for'unprofessional hairstyles'; an equally well-known social network has been criticized for showing certain job ads only to men; and the list goes on. The question the tech industry must now face is: how to we rid AI of bias in all its forms and ensure fair play for all in the age of computer-driven decision making? One set of answers comes from Women Leading in AI (WLinAI), a network of leaders working in tech, science, politics, business and think tanks – the group is demanding that the UK government takes back control of technology.

Recommender System research suffers currently from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap we propose to generate more massive user/item interaction data sets by expanding pre-existing public data sets. User/item incidence matrices record interactions between users and items on a given platform as a large sparse matrix whose rows correspond to users and whose columns correspond to items. Our technique expands such matrices to larger numbers of rows (users), columns (items) and non zero values (interactions) while preserving key higher order statistical properties. We adapt the Kronecker Graph Theory to user/item incidence matrices and show that the corresponding fractal expansions preserve the fat-tailed distributions of user engagements, item popularity and singular value spectra of user/item interaction matrices. Preserving such properties is key to building large realistic synthetic data sets which in turn can be employed reliably to benchmark Recommender Systems and the systems employed to train them. We provide algorithms to produce such expansions and apply them to the MovieLens 20 million data set comprising 20 million ratings of 27K movies by 138K users. The resulting expanded data set has 10 billion ratings, 864K items and 2 million users in its smaller version and can be scaled up or down. A larger version features 655 billion ratings, 7 million items and 17 million users.

In this paper, we propose a robust sequential learning strategy for training large-scale Recommender Systems (RS) over implicit feedback mainly in the form of clicks. Our approach relies on the minimization of a pairwise ranking loss over blocks of consecutive items constituted by a sequence of non-clicked items followed by a clicked one for each user. Parameter updates are discarded if for a given user the number of sequential blocks is below or above some given thresholds estimated over the distribution of the number of blocks in the training set. This is to prevent from an abnormal number of clicks over some targeted items, mainly due to bots; or very few user interactions. Both scenarios affect the decision of RS and imply a shift over the distribution of items that are shown to the users. We provide a theoretical analysis showing that in the case where the ranking loss is convex, the deviation between the loss with respect to the sequence of weights found by the proposed algorithm and its minimum is bounded. Furthermore, experimental results on five large-scale collections demonstrate the efficiency of the proposed algorithm with respect to the state-of-the-art approaches, both regarding different ranking measures and computation time.

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Samsung Electronics will introduce its new products and solutions to its business partners around the world at Samsung Forum 2019. During the two-month event, strategic products including the 2019 QLED TV lineup as well as customized products for regional markets will be showcased. Based on'New Bixby,' Samsung's intelligence platform, Connected Solution will also be exhibited where global business partners can interact with various Samsung products. Starting with the European Forum, Samsung will invite media and partners from Europe, Southwest Asia and Latin America to Porto of Portugal from February 12th to 22nd. From March 7th to 11th, Samsung will host the Middle East and CIS (Commonwealth of Independent States) Forum in Antalya of Turkey.

A group of computer scientists once backed by Elon Musk has caused some alarm by developing an advanced artificial intelligence (AI) they say is too dangerous to release to the public. OpenAI, a research non-profit based in San Francisco, says its "chameleon-like" language prediction system, called GPT–2, will only ever see a limited release in a scaled-down version, due to "concerns about malicious applications of the technology". That's because the computer model, which generates original paragraphs of text based on what it is given to'read', is a little too good at its job. The system devises "synthetic text samples of unprecedented quality" that the researchers say are so advanced and convincing, the AI could be used to create fake news, impersonate people, and abuse or trick people on social media. "GPT–2 is trained with a simple objective: predict the next word, given all of the previous words within some text," the OpenAI team explains on its blog.

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Artificial Intelligence is the next key game changer for business, but Irish companies are not as prepared for AI as global business, a new survey has revealed. Almost half of Irish chief executives believe AI will have a bigger impact on the world than the internet revolution, yet half of them are planning AI initiatives in their companies, compared to 77% of chief executives globally. This was one of the key findings of PwC's 2019 Irish CEO survey. "If you look at the global survey, a lot of that awareness or that belief that AI will significantly change how we do business is coming from the US," Feargal O'Rourke, Managing Partner with PwC Ireland, explained. "They are probably just getting an earlier line of sight of the impact of AI, but I think Irish CEOs are certainly waking up to it. Almost two thirds of CEOs believe that it will significantly change how they do business in the next five years, but yet only half of them have plans in place to address this, so there is a bit of a gap there," Mr O'Rourke said.