A.1 Compute Usage The seven billion parameter language model we used as part of Frozen used model parallelism with To generate a 2-way question with n inner-shots, the following process is followed: 1. Sample two classes c "this is a dax" or "this is a blicket" accordingly 5. Select one of c Assign the truncated caption "this is a" to In 1. five distinct classes are sampled All images are stored at 224 224 resolution. To generate Real-Name miniImagenet, the same process is followed, except that in steps 4. and 6., "this is a dax"), the (first) class "this is a fruit bat"). For the evaluations in this paper, we again only take images from the validation set. In this work, we only consider 2-way Fast-VQA. To generate Guided-VQA, the same process is followed, except that in step 3. the (first) class name The Open-Ended miniImageNet, Real-Name miniImageneNet, Fast-VQA and Guided-VQA evaluations are available at https://fh295.github.io/frozen.html.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. I am a proud late adopter of new technology. I had a StarTAC well into the 21st century, fearing the limitless access to digital information and services that smartphones would bring and the way they would rob us of our time and attention and humanity. Though this realization offers little solace as I stare into my phone hundreds of hours a day.) I traveled with my books of CDs and my Discman well into the era when Transportation Security Administration agents would look at them with curiosity and suspicion.

A.1 Compute Usage The seven billion parameter language model we used as part of Frozen used model parallelism with the strategy from [39] to partition one instance of the model over four accelerators. Each instance had a batch size of 8. To reach a batch size of 128 in this configuration, we additionally employed data parallelism with 16 synchronous replicas. The whole system was trained on a 4x8 TPUv3 [15] topology for about 12 hours, which is when validation set performance for Conceptual Captions led us to do early stopping. A.2 Frozen Architecture Details The pretrained transformer language model we used has a GPT-like architecture [30]. It consists of a series of identical residual layers, each comprised of a self-attention operation followed by a positionwise MLP.

Among the many challenges that physicians face, one of the most cumbersome is clinical documentation. In a study published by the Journal of Graduate Medical Education, it was found that nearly 92% of physicians surveyed reported that "documentation obligations are excessive," and 73% reported that clinical documentation often has a negative impact on patient care. The goal behind detailed clinical documentation is to ultimately ensure great record keeping: in an ideal world, a comprehensive patient chart enables any treating provider to see a patient's entire medical and treatment history. Furthermore, the healthcare system has been built in such a way that documentation plays a critical administrative role. Healthcare organizations use patient charts to code and bill for services provided.

To doctors, pajama time means homework. In fact, it's a common phrase describing the nighttime ritual of finishing up clinical notes about the patients they saw earlier that day. As demands for notes and data to chronicle patient interactions from hospital administration and insurance industry payers have increased, the amount of time physicians spend on the computer has squeezed their already tight schedules. A 2017 study published in Annals of Family Medicine found that primary care physicians spend nearly six hours a day interacting with their electronic health records systems during and after clinic hours. Amid pandemic burnout, the stress is enough for doctors to hand over the work of writing clinical notes to an AI-based tool, even if it could create patient data privacy risks and medical errors.

For the past few decades, robots have been confined to the factory floor. Robotic arms, concealed in big industrial buildings, weld cars, inspected items on conveyor belts and build complicated things. This is all well hidden behind closed doors. And for good reason -- industrial robots are bulky, limited, and sometimes dangerous. As robot tech and AI have advanced, robots have exploded into pedestrian spaces.

Microsoft said on Monday that had agreed to buy Nuance Communications Inc. for about $56 a share, or almost $20 billion including debt. At first glance, it may seem like a strange candidate for what would become Microsoft's second-largest acquisition after its $26 billion deal for LinkedIn Corp. For much of the last decade, Nuance's sales have stagnated as the early pioneer of speech-recognition products wasn't able to innovate fast enough. Given the impressive technology and potential inside its latest AI solution for health care, a purchase of Nuance makes sense. The game-changing product is the Nuance Dragon Ambient eXperience, or DAX, which was released in February 2020.

Despite the rapid growth in attention on eXplainable AI (XAI) of late, explanations in the literature provide little insight into the actual functioning of Neural Networks (NNs), significantly limiting their transparency. We propose a methodology for explaining NNs, providing transparency about their inner workings, by utilising computational argumentation (a form of symbolic AI offering reasoning abstractions for a variety of settings where opinions matter) as the scaffolding underpinning Deep Argumentative eXplanations (DAXs). We define three DAX instantiations (for various neural architectures and tasks) and evaluate them empirically in terms of stability, computational cost, and importance of depth. We also conduct human experiments with DAXs for text classification models, indicating that they are comprehensible to humans and align with their judgement, while also being competitive, in terms of user acceptance, with existing approaches to XAI that also have an argumentative spirit.

IBM has published a number of notebooks, datasets and more resources from IBM Research on their Data Asset eXchange (DAX) for developers to use free of cost. DAX is an online hub with curated, free data sets that AI developers and data scientists can use under open data licenses. The resources on DAX take the form of open-source code, security and compliance information, specialist learning tools and even expert support. Among the recent additions are three Watson Studio projects. IBM Watson Studio is an enterprise-focused AI developer tool which helps data scientists and researchers to build models and prepare data at scale across any cloud.

IBM has launched a repository of datasets for training which data scientists can pick and mix to train their deep learning and machine learning models. The IBM Data Asset eXchange (DAX) is designed to complement the Model Asset eXchange it launched earlier this year, which offers researchers and developers models to deploy or train with their own data. In a blog announcing the data exchange, a quartet of IBM luminaries, wrote "Developers adopting ML models need open data that they can use confidently under clearly defined open data licenses." The data sets in question will be covered by the Linux Foundation's Community Data License Agreement (CDLA) open data licensing framework to enable data sharing and collaboration – "where possible". DAX will also provide "unique access to various IBM and IBM Research datasets."