After looking at standard ECG tests, Artificial Intelligence (AI) can help identify patients most likely to die of any medical cause within a year, claim researchers. To reach this conclusion, researchers from Geisinger Health System in Pennsylvania analysed the results of 1.77 million ECGs and other records from almost 400,000 patients. The team used this data to compare machine learning-based models that either directly analysed the raw ECG signals or relied on aggregated human-derived measures (standard ECG features typically recorded by a cardiologist) and commonly diagnosed disease patterns. The neural network model that directly analysed the ECG signals was found to be superior for predicting one-year risk of death. Surprisingly, the neural network was able to accurately predict risk of death even in patients deemed by a physician to have a normal ECG.

Pizza lunch will be provided at 12:15pm, with the talk following at 12:30pm. All students, alumni, staff and faculty are welcome to register for this free event using the registration link below. "Communication has been weaponized, used to provoke, mislead and influence the public in numerous insidious ways. Disinformation was just the first stage of an evolving trend of using information to subvert democracy, confuse rival states, define the narrative and control public opinion. Using large, unregulated, open environments that tech companies once promised would "empower" ordinary people, disinformation has spread rapidly across the globe.

Human brains are extremely energy-efficient. When a person thinks in a concentrated manner, his or her brain consumes a mere 21 watts of electricity. But AI doing the same degree of intensive thinking requires over 10,000 times more electricity. If that is the case, the international competitiveness of businesses will depend on factors concerning the supply and cost of electricity in their home country. How, then, does Japan stand with regard to power supply and cost?

My previous post on summarising 57 research papers turned out to be quite useful for people working in this field, so it is about time for a sequel. Below you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). They cover a wide range of different topics, authors and venues. These are not meant to be reviews showing my subjective opinion, but instead I aim to provide a blunt and concise overview of the core contribution of each publication. Given how many papers are published in our area every year, it is getting more and more difficult to keep track of all of them. The goal of this post is to save some time for both new and experienced readers in the field and allow them to get a quick overview of 74 research papers in about 30 minutes reading time. I set out to post 60 summaries (up from 50 compared to last time). At the end, I also include the summaries for my own published papers since the last iteration (papers 61-74). A transformer architecture that is trained as a language model on a large corpus, then fine-tuned for individual text classification and similarity tasks. Multiple sentences are combined together into a single sequence using delimiters in order to work with the same model.

Microsoft rose to dominance during the '80s and '90s thanks to the success of its Windows operating system running on Intel's processors, a cosy relationship nicknamed "Wintel". Now Microsoft hopes that another another hardware–software combo will help it recapture that success--and catch rivals Amazon and Google in the race to provide cutting-edge artificial intelligence through the cloud. Microsoft hopes to extend the popularity of its Azure cloud platform with a new kind of computer chip designed for the age of AI. Starting today, Microsoft is providing Azure customers with access to chips made by the British startup Graphcore. Graphcore, founded in Bristol, UK, in 2016, has attracted considerable attention among AI researchers--and several hundred million dollars in investment--on the promise that its chips will accelerate the computations required to make AI work.

Sign in to report inappropriate content. An incredible human story of the Ugandan developer, Nazirini Siraji, and the app created by her small team in Mbale, Uganda, with learning from their local Google Developer Group community. Harnessing the power of TensorFlow and Machine Learning, this free app helps farmers identify and treat Fall Armyworm, reducing the massive crop devastation currently impacting Uganda and Africa. Find your local GDG chapter https://goo.gle/33ER1Kd

It's the classic trope of buddy cop movies: you introduce two characters with little in common aside from the job that they do. Maybe one's a maverick and the other is a stickler for doing things by the book. At first they don't get along. Perhaps one is new to the precinct and the other fears that they're being phased out as a result. But, wouldn't you know it, they turn out to be a great team.

It's the classic trope of buddy cop movies: you introduce two characters with little in common aside from the job that they do. Maybe one's a maverick and the other is a stickler for doing things by the book. At first they don't get along. Perhaps one is new to the precinct and the other fears that they're being phased out as a result. But, wouldn't you know it, they turn out to be a great team.

Do you have 3 / 4 years of coding experience? Do you want to upskill in Machine Learning, and network with some of Dublin's most talented women in the tech space? Workday is running a women in tech workshop for developers to come together for a hands-on introduction to Machine Learning, free of charge. Workday's Laura Uzcategui will be leading an'Introduction to Machine Learning' workshop, working through an example live on the evening for you to follow along with. So bring along your laptop and get ready to enter the world of Machine Learning!