Clinical language models are important for many applications in healthcare, but their development depends on access to extensive clinical text for pretraining. However, obtaining clinical notes from electronic health records (EHRs) at scale is challenging due to patient privacy concerns. In this study, we rephrase existing clinical notes using LLMs to generate synthetic pretraining corpora, drawing inspiration from previous work on rephrasing web data. We examine four popular small-sized LLMs (<10B) to create synthetic clinical text to pretrain both decoder-based and encoder-based language models. The method yields better results in language modeling and downstream tasks than previous synthesis approaches without referencing real clinical text. We find that augmenting original clinical notes with synthetic corpora from different LLMs improves performances even at a small token budget, showing the potential of this method to support pretraining at the institutional level or be scaled to synthesize large-scale clinical corpora.

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Dr. Stephen Murphy had conducted countless hip replacement operations before, but this one was different. In this one, he and his team could see a 3D hologram overlaid on the patient -- a digital model of the patient's body that existed directly in his line of vision. The surgical team had a form of X-ray vision with augmented reality. "We had done a lot of testing on real human specimens, so we knew what it was going to look like, but to see it in a live patient for the first time was just unbelievable," Murphy said in an interview with Freethink. "It feels to the surgeon as if she has been transported inside of the patient."

Spend your working life identifying, analysing, quantifying and ascribing monetary value to risk, and you're likely to have a fairly strong aversion to it. Or more accurately, an aversion to undertaking new endeavours with inadequately understood consequences. The insurance industry is, on any number of levels, the very definition of risk-averse. And yet, insurance still has an appetite for innovation. If the insurtech sector is any indication, an interest in and requirement for new solutions has been recognised and is being addressed.

Mathematical modelling is ubiquitous in the financial industry and drives key decision processes. Any given model provides only a crude approximation to reality and the risk of using an inadequate model is hard to detect and quantify. By contrast, modern data science techniques are opening the door to more robust and data-driven model selection mechanisms. However, most machine learning models are "black-boxes" as individual parameters do not have meaningful interpretation. The aim of this paper is to combine the above approaches achieving the best of both worlds. Combining neural networks with risk models based on classical stochastic differential equations (SDEs), we find robust bounds for prices of derivatives and the corresponding hedging strategies while incorporating relevant market data. The resulting model called neural SDE is an instantiation of generative models and is closely linked with the theory of causal optimal transport. Neural SDEs allow consistent calibration under both the risk-neutral and the real-world measures. Thus the model can be used to simulate market scenarios needed for assessing risk profiles and hedging strategies. We develop and analyse novel algorithms needed for efficient use of neural SDEs. We validate our approach with numerical experiments using both local and stochastic volatility models.

Philippe Nieuwbourg, who ran the world's famous "Informatics Museum" at the time, graciously allowed me use one of his sets to tape it. The video was called "Business Intelligence 2020". And its goal was to recap some of the community's predictions for where the Data and Analytics space would be by 2020. At the time, some of the biggest market players hadn't gone public yet. In fact, the industry hadn't even embraced "The Cloud", "Big Data" or even "A.I."... How much the world has changed.

Philippe Nieuwbourg, who ran the world's famous "Informatics Museum" at the time, graciously allowed me use one of his sets to tape it. The video was called "Business Intelligence 2020". And its goal was to recap some of the community's predictions for where the Data and Analytics space would be by 2020. At the time, some of the biggest market players hadn't gone public yet. In fact, the industry hadn't even embraced "The Cloud", "Big Data" or even "A.I."... How much the world has changed.

The 1st International Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) was held in 2018 in conjunction with the 13th International Conference on Human-Robot Interaction, and brought together researchers from the fields of Human-Robot Interaction (HRI), Robotics, Artificial Intelligence, and Virtual, Augmented, and Mixed Reality in order to identify challenges in mixed reality interactions between humans and robots. This inaugural workshop featured a keynote talk from Blair MacIntyre (Mozilla, Georgia Tech), a panel discussion, and twenty-nine papers presented as lightning talks and/or posters. In this report, we briefly survey the papers presented at the workshop and outline some potential directions for the community.

Intelligence amplification (IA) (also referred to as cognitive augmentation and machine augmented intelligence) refers to the effective use of information technology in augmenting human intelligence. The idea was first proposed in the 1950s and 1960s. IA is sometimes contrasted with AI (artificial intelligence), that is, the project of building a human-like intelligence in the form of an autonomous technological system such as a computer or robot. AI has encountered many fundamental obstacles, practical as well as theoretical, which for IA seem moot, as it needs technology merely as an extra support for an autonomous intelligence that has already proven to function. Moreover, IA has a long history of success, since all forms of information technology, from the abacus to writing to the Internet, have been developed basically to extend the information processing capabilities of the human mind.

This week saw a lot of machines taking over for humans in both the pharmacy and warehousing industries and augmented reality finally go mainstream in retail. China continues to commit to innovating faster than any other country and This Week In China Tech is our way to keep you on top of the most important stories coming from the mainland. Here are this week's headlines. JD.com, one of China's largest e-commerce companies, announced the new Augmented Reality (AR) Innovation Alliance (article in Chinese) and brought three new AR products to market. The alliance brings together hundreds of corporations, including Intel, Wal-Mart, Vipshop, Lenovo, and Carslan.