We present ReasonBert, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts. Unlike existing pre-training methods that only harvest learning signals from local contexts of naturally occurring texts, we propose a generalized notion of distant supervision to automatically connect multiple pieces of text and tables to create pre-training examples that require long-range reasoning. Different types of reasoning are simulated, including intersecting multiple pieces of evidence, bridging from one piece of evidence to another, and detecting unanswerable cases. We conduct a comprehensive evaluation on a variety of extractive question answering datasets ranging from single-hop to multi-hop and from text-only to table-only to hybrid that require various reasoning capabilities and show that ReasonBert achieves remarkable improvement over an array of strong baselines. Few-shot experiments further demonstrate that our pre-training method substantially improves sample efficiency.

What if media were optimized to benefit people? This thought-provoking question is at the core of Harmony Labs' mission. A nonprofit organization headquartered in New York City, Harmony Labs strives to better understand the impact of media on society, and build communities and tools to reform and transform media systems. As Brian Wanieswki, Executive Director at Harmony Labs puts it: "The media systems that we have now, for better or worse, have become outrage machines and sorting machines that put people into groups of like minds. The business incentive structures of these systems are such that the more outrage there is, the more profit there is. Political events across the world in recent years have borne out what these media systems produce, and it's really pretty toxic, and pretty hard to get anything done within. There are all kinds of natural divisions between people, but these media systems tend to reinforce these divisions. So, the first question that we're asking is, What's the scope of this problem? And then, What can we do to solve it?"

The University of Adelaide's Australian Institute for Machine Learning is ranked among world leaders in the application of AI, computer vision and …

Deep learning, a subset of machine learning based on artificial neural networks, has enabled applications with performance levels approaching those of …

The ability of computers to understand and generate language took a huge leap forward in 2017 when researchers at Google developed new natural -anguage AI models called Transformers. Some of the experts who built and trained those seminal models have since branched out on their own by founding the Toronto-based startup Cohere, which today announced a new $40 million Series A funding round. The technology that undergirds Cohere's natural-language processing models was originally developed by the Toronto-based Google Brain team. Two of that team's members, Aidan Gomez and Nick Frosst (along with a third cofounder, Ivan Zhang), started Cohere two years ago to further develop and commercialize the models, which are delivered to customers through an API. Cohere is backed by neural network pioneer and Turing Award winner Geoffrey Hinton, who led the Toronto Google Brain team, as well as some other big names in the AI world like Stanford computer science professor Fei-Fei Li. "Very large language models are now giving computers a much better understanding of human communication," Hinton said in a statement to Fast Company.

Next up is adding artificial intelligence to your skillset with An Easy Introduction To AI And Deep Learning, which will cover self-driving cars, Siri …

A startup is using artificial intelligence to find new sources of … EVs and renewable energy, and machine learning can help narrow the search.

This paper provides the first formalisation and empirical demonstration of a particular safety concern in reinforcement learning (RL)-based news and social media recommendation algorithms. This safety concern is what we call "user tampering" -- a phenomenon whereby an RL-based recommender system may manipulate a media user's opinions, preferences and beliefs via its recommendations as part of a policy to increase long-term user engagement. We provide a simulation study of a media recommendation problem constrained to the recommendation of political content, and demonstrate that a Q-learning algorithm consistently learns to exploit its opportunities to 'polarise' simulated 'users' with its early recommendations in order to have more consistent success with later recommendations catering to that polarisation. Finally, we argue that given our findings, designing an RL-based recommender system which cannot learn to exploit user tampering requires making the metric for the recommender's success independent of observable signals of user engagement, and thus that a media recommendation system built solely with RL is necessarily either unsafe, or almost certainly commercially unviable.

Fake news is an infodemic, a disease worse than anything else we've ever seen. And it's been around for longer than Covid19. Sometimes, fake news has little impact. But when times are uncertain, and a global crisis is in effect, people look for information that alleviates their fear. Unfortunately, fear leaves people more susceptible to accepting misleading information as the real deal.

Earlier this year, Apple officially discontinued Music Memos, an iPhone app that allowed musicians to quickly record audio and develop new song ideas. Now, a new startup called Tape It is stepping in to fill the void with an app that improves audio recordings by offering a variety of features, including higher-quality sound, automatic instrument detection, support for markers, notes, and images, and more. The idea for Tape It comes from two friends and musicians, Thomas Walther and Jan Nash. Walther had previously spent three and a half years at Spotify, following its 2017 acquisition of the audio detection startup Sonalytic, which he had co-founded. Nash, meanwhile, is a classically trained opera singer, who also plays bass and is an engineer.