Recent work has demonstrated that language models can be trained to identify the author of much shorter literary passages than has been thought feasible for traditional stylometry. We replicate these results for authorship and extend them to a new dataset measuring novel genre. We find that LLMs are able to distinguish authorship and genre, but they do so in different ways. Some models seem to rely more on memorization, while others benefit more from training to learn author/genre characteristics. We then use three methods to probe one high-performing LLM for features that define style. These include direct syntactic ablations to input text as well as two methods that look at model internals. We find that authorial style is easier to define than genre-level style and is more impacted by minor syntactic decisions and contextual word usage. However, some traits like pronoun usage and word order prove significant for defining both kinds of literary style.

Offering the potential of enhanced resolution with accelerated scan times for magnetic resonance imaging (MRI), SmartSpeed (Philips), an emerging artificial intelligence (AI)-enabled software, has garnered FDA 510(k) clearance. In comparison to other MRI modalities, Philips said the addition of SmartSpeed to the company's Compressed SENSE MR acceleration engine offers a threefold reduction in MRI scanning time and increases image resolution up to 65 percent. "Philips' AI-based SmartSpeed reconstruction is the new benchmark among acceleration techniques for us. It improves on the company's existing Compressed SENSE (MR acceleration engine) in all aspects and allows a reduction in scan times with excellent image quality and diagnostic confidence," noted Grischa Bratke, MD, who is affiliated with the Department of Radiology at the University Hospital of Cologne in Germany. Philips noted that application of the AI reconstruction algorithm with SmartSpeed at the front end of the MR signal facilitates a high signal-to-noise ratio that enhances image quality and enables small lesion detection.

It's been a banner year for the smart home, with new players and new products flooding the market. It's never been easier or less expensive to convert an ordinary home into a technological wonderland. The biggest developments have come with the widespread adoption of voice control, with Amazon's Echo family way out in front in terms of new products and third-party support. But the Noon Lighting System, which shipped late in the year, is easily the most exciting smart home product we've seen to date. We reviewed a lot of great products this year--and we encountered a few dogs as well--but this list represents our picks for the best of the best.

Machine Learning is the buzzword of the moment. In recent years, news stories raving about its possibilities have soared, Google searches for the term have quadrupled, and companies across the globe are scrambling to figure out how to capitalize on the excitement by bringing it into their product mix. While that can be a great thing, claims made by some businesses about what machine learning can do are wildly exaggerated. That makes it crucial to cut through the noise and get to grips with its potential, limitations, and what you can realistically achieve with your resources – so that any investment makes solid business sense. The pair joined forces to deliver an in-depth webinar on machine learning and business intelligence, which you can view in full here.

A man walks into a dermatologist's practice… He's deeply concerned; he has an illness that the doctor doesn't recognise. After some research online and a discussion with her colleagues at the practice, it turns out that it's a rare complaint. The doctor then starts looking at possible medications, comparing drug side effects that might react adversely with the patient's current prescriptions. But what if the doctor had a powerful resource at her disposal: a repository of medical information and insights? What if she also had access to smart, accurate clinical decision support and the kind of intelligent predictive analytics that could cut down the time spent looking for answers and help her get straight to diagnosing and treating the patient's problem…what if they had the help of Artificial Intelligence?

Machine Learning is the buzzword of the moment. In recent years, news stories raving about its possibilities have soared, Google searches for the term have quadrupled, and companies across the globe have been scrambling to figure out how to capitalize on the excitement by bringing it into their product mix. While that can be a great thing, claims made by some businesses about what Machine Learning can do are wildly exaggerated. That makes it crucial to cut through the noise and get to grips with its potential, limitations, and what you can realistically achieve with your resources so that any investment makes solid business sense -- so say Philip Lima, CEO of Mashey, and Boaz Farkash, Head of Product Management at Sisense. The pair joined forces to deliver an in-depth webinar on Machine Learning and business intelligence, which you can view in full here.

There's no doubt that there are currently three big trends in business models: Internet of Things (IoT), Big Data and artificial intelligence (AI). From the still-fragmented internet of things to rapid fluctuations in computing paradigms to how AI is reshaping how we live, everybody's talking about these trends but what's really going on? Here's the lowdown on what you need to understand about them, and the future that lies in the year ahead from a consumer perspective. Big data, as defined in Wikipedia, is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Since the data sets are so huge, the challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy.

One of the biggest problems with connected health wearables is that they produce too much data. And most people agree that the solution to that problem is to get artificial intelligence on the job to analyse, digest and make sense of all that data so that it becomes useful and possibly even life-saving. The role of AI in the future of healthcare was the talk of this week's two day AI Summit in London. In the talks and on the show floor, tech companies were outlining some very bold visions for what's to come. Take Jeroen Tas, chief innovation and strategy officer for Philips, who was pitching what he referred to as "the next frontier in personalised health" or "anatomical intelligence".

There's no doubt that there are currently three big trends in business models: Internet of Things (IoT), Big Data and artificial intelligence (AI). From the still-fragmented internet of things to rapid fluctuations in computing paradigms to how AI is reshaping how we live, everybody's talking about these trends but what's really going on? Here's the lowdown on what you need to understand about them, and the future that lies in the year ahead from a consumer perspective. Big data, as defined in Wikipedia, is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Since the data sets are so huge, the challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy.

There's no doubt that there are currently three big trends in business models: Internet of Things (IoT), Big Data and artificial intelligence (AI). From the still-fragmented internet of things to rapid fluctuations in computing paradigms to how AI is reshaping how we live, everybody's talking about these trends but what's really going on? Here's the lowdown on what you need to understand about them, and the future that lies in the year ahead from a consumer perspective. Big data, as defined in Wikipedia, is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Since the data sets are so huge, the challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy.