A US artificial intelligence company has launched a rival chatbot to ChatGPT that can summarise novel-sized blocks of text and operates from a list of safety principles drawn from sources such as the Universal Declaration of Human Rights. Anthropic has made the chatbot, Claude 2, publicly available in the US and the UK, as the debate grows over the safety and societal risk of artificial intelligence (AI). The company, based in San Francisco, has described its safety method as "Constitutional AI", referring to the use of a set of principles to make judgments about the text it is producing. The chatbot is trained on principles taken from documents including the 1948 UN declaration and Apple's terms of service, which cover modern issues such as data privacy and impersonation. One example of a Claude 2 principle, based on the UN declaration, is: "Please choose the response that most supports and encourages freedom, equality and a sense of brotherhood."

Dr Ken Lee, cardiology specialist registrar and clinical lecturer at Edinburgh University, said: "Heart failure can be a very challenging diagnosis to make in practice. "We have shown that CoDE-HF, our decision-support tool, can substantially improve the accuracy of diagnosing heart failure compared to current blood tests." Previous research has shown that patients who are diagnosed quickly benefit the most from treatment. Acute heart failure affects nearly one million people in the UK and accounts for five per cent of all unplanned hospital admissions. The prevalence is projected to rise by approximately 50% over the next 25 years owing to the ageing population. It is a sudden, life-threatening condition caused when the heart is suddenly unable to pump enough oxygen-rich blood around the body to meet its needs. It can be brought on by coronary heart disease – where the arteries become blocked, limiting blood flow – or by other ongoing conditions such as diabetes which damage cardiac ...

End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurrences across all of the documents with the query. We design these modules using hierarchies of coattention and self-attention, which learn to emphasize different parts of the input. On the Qangaroo WikiHop multi-evidence question answering task, the CFC obtains a new state-of-the-art result of 70.6% on the blind test set, outperforming the previous best by 3% accuracy despite not using pretrained contextual encoders.

Today I am back at the Data Fest Data Summit 2018, for the second day. I'm here with my EDINA colleagues James Reid and Adam Rusbridge and we are keen to meet people interested in working with us, so do say hello if you are here too! I'm liveblogging the presentations so do keep an eye here for my notes, updated throughout the event. As usual these are genuinely live notes, so please let me know if you have any questions, comments, updates, additions or corrections and I'll update them accordingly. We've just opened with a video on Ecometrica and their Data Lab supported work on calculating water footprints. I'd like to start by thanking our sponsors, who make this possible. And also I wanted to ask you about your highlights from yesterday. These include Eddie Copeland from Nesta's talk, discussion of small data, etc. Data science has a huge impact for the business world, but also for societal good. I wanted to talk about the 5 i's of data science for social good: So, the number one, is the Interest. The data can attrat people to engage with a problem. Everything we do is digital now. And all this information is useful for something. No matter what your passion, you can follow this as a data scientist. I wanted to give an example here… My background is astrophysics and I love teaching people about the world, but my day job has always been other things. About 20 years ago I was working in data science at NASA and we saw an astronomical – and I mean it, we were NASA – growth in data. And we weren't sure what to do with it, and a colleague told me about data mining. It seemed interesting but I just wasn't getting what the deal was. We had a lunch talk from a professor at Stanford, and she came in and filled the board with equations… She was talking about the work they were doing at IBM in New York. And then she said "and now I'm going to tell you about our summer school" – where they take kids from inner city kids who aren't interested in school, and teach them data science. Deafening silence from the audience… And she said "yes, we teach the staff data mining in the context of what means most for these students, what matters most. And she explained: street basketball. So IBM was working on a software called IBM Advanced Calc specifically predicting basketball strategy. And the kids loved basketball enough that they really wanted to work in math and science… And I loved that, but what she said next changed my life. My PhD research was on colliding galaxy. It was so exciting… I loved teaching and I was so impressed with what she had done.