Now, a group of NLP researchers and enthusiasts, including graduates from Tsinghua University, Peking University, and Zhejiang University, have introduced ChineseGLUE, a benchmark designed to encourage the development and assessment of Chinese language models.
Now a group of researchers from the Seattle-based Allen Institute for Artificial Intelligence (AI2) have shown how trigger words and phrases can “inflict targeted errors” on natural language processing (NLP) model outputs, prompting them to generate racist and hostile content.
Since Google Research introduced its Bidirectional Transformer (BERT) in 2018 the model has gained unprecedented popularity among researchers. Now, a group of researchers from the National Cheng Kung University Tainan in Taiwan are challenging BERT’s efficacy.
Although natural language processing (NLP) has been around for decades, the recent and rapid rise of deep learning algorithms together with the increasing availability of massive amounts of text data are creating new and appealing opportunities for the tech across many industry sectors, including in the investment world.
If we ask one of today’s AI-powered voice assistants like Alexa and Siri to tell a joke, it might very well come up with something that puts a smile on our face. If however we then asked “Why do you think that joke is funny?” the bot would be stuck for a response. AI researchers want to change that.
Natural language processing has made significant progress in the past year, but few frameworks focus directly on NLP or sequence modeling. Google Brain recently released Lingvo, a deep learning framework based on TensorFlow. Synced invited Ni Lao, Chief Science Officer at Mosaix, to share his thoughts on Lingvo.
The Conference on Computer Vision and Pattern Recognition (CVPR) is one of the world’s top computer vision (CV) conferences. CVPR 2019 runs June 15 through June 21 in Long Beach, California, and the list of accepted papers for the prestigious gathering has now been released.
Papers With Code is a unique and useful resource that presents trending ML research along with the code to implement it. The site was created by Atlas ML CEO Robert Stojnic, aka “rstoj” on Reddit’s machine learning board. The latest version of Papers With Code has added 950+ unique machine learning tasks, 500+ State-of-the-Art result leaderboards and 8500+ papers with code.
The amount of news information a person can routinely access these days would have been unimaginable a hundred years ago. But we still have just 24 hours in a day, and only a single pair of eyes to read, and so the question arises: how to get as much valuable news as possible in a limited time?