Twitter announced that it has acquired London-based Fabula AI. The financial terms of transactions are not disclosed. The announcement stated that Twitter has established a research group lead by Sandeep Pandey. The research groups look into areas like natural language processing, reinforcement learning, ML ethics, recommendation systems, and graph deep learning. In one of the posts titled "Fake News revealed through artificial intelligence", it was revealed that Fabula AI team, Michael Bronstein, professor and researcher at the USI Institute of Computational Science (ICS), fellow ICS researchers Federico Monti and Dr Davide Eynard, developed a new method based on algorithms and artificial intelligence that could prove to be the most effective solution to the spreading of fake news through the Internet.
Twitter has just announced it has picked up London-based Fabula AI. The deep learning startup has been developing technology to try to identify online disinformation by looking at patterns in how fake stuff vs genuine news spreads online -- making it an obvious fit for the rumor-riled social network. Social media giants remain under increasing political pressure to get a handle on online disinformation to ensure that manipulative messages don't, for example, get a free pass to fiddle with democratic processes. Twitter says the acquisition of Fabula will help it build out its internal machine learning capabilities -- writing that the UK startup's "world-class team of machine learning researchers" will feed an internal research group it's building out, led by Sandeep Pandey, its head of ML/AI engineering. This research group will focus on "a few key strategic areas such as natural language processing, reinforcement learning, ML ethics, recommendation systems, and graph deep learning" -- now with Fabula co-founder and chief scientist, Michael Bronstein, as a leading light within it.
Do neutrinos, the elementary particles, have something in common with fake news on social media? The peculiar and positive answer comes from a group of researchers at USI Institute of Computational Science, and it shows how both their behaviour can be represented using the same data structure. Such structure is based on a non-Euclidean geometry and can be studied through a new class of algorithms: the Graph Convolutional Neural Networks (GCNN). Such algorithms are highly complex mathematical models, and the research work carried out by Federico Monti, member of Prof. Michael Bronstein group, earned him the award for the best scientific contribution assigned by ICMLA, the most important international conference in the field. Monti, in collaboration with other colleagues from New York University, Berkeley and Imperial College, had the opportunity to collaborate with the Lawrence Berkley National Laboratory on data acquired by the IceCube Neutrino Observatory at the South Pole.
We are excited to announce that, to help us get there, we have acquired Fabula AI (Fabula), a London-based start-up, with a world-class team of machine learning researchers who employ graph deep learning to detect network manipulation. Graph deep learning is a novel method for applying powerful ML techniques to network-structured data. The result is the ability to analyze very large and complex datasets describing relations and interactions, and to extract signals in ways that traditional ML techniques are not capable of doing. Twitter has been criticized for the amount of fake news and misinformation that easily spreads on its platform. Though the company has taken steps to combat such misinformation in recent years, fake news is still a major problem for the social network.
UK startup Fabula AI reckons it's devised a way for artificial intelligence to help user generated content platforms get on top of the disinformation crisis that keeps rocking the world of social media with antisocial scandals. Even Facebook's Mark Zuckerberg has sounded a cautious note about AI technology's capability to meet the complex, contextual, messy and inherently human challenge of correctly understanding every missive a social media user might send, well-intentioned or its nasty flip-side. "It will take many years to fully develop these systems," the Facebook founder wrote two years ago, in an open letter discussing the scale of the challenge of moderating content on platforms thick with billions of users. "This is technically difficult as it requires building AI that can read and understand news." But what if AI doesn't need to read and understand news in order to detect whether it's true or false? Step forward Fabula, which has patented what it dubs a "new class" of machine learning algorithms to detect "fake news" -- in the emergent field of "Geometric Deep Learning"; where the datasets to be studied are so large and complex that traditional machine learning techniques struggle to find purchase on this'non-Euclidean' space.