This paper contains a feasibility study of deep neural networks for the classification of Euro banknotes with respect to requirements of central banks on the ATM and high speed sorting industry. Instead of concentrating on the accuracy for a large number of classes as in the famous ImageNet Challenge we focus thus on conditions with few classes and the requirement of rejection of images belonging clearly to neither of the trained classes (i.e. classification in a so-called 0-class). These special requirements are part of frameworks defined by central banks as the European Central Bank and are met by current ATMs and high speed sorting machines. We also consider training and classification time on state of the art GPU hardware. The study concentrates on the banknote recognition whereas banknote class dependent authenticity and fitness checks are a topic of its own which is not considered in this work.
Did your voice give it away? US start-up Canary Speech is developing deep-learning algorithms to detect if people have neurological conditions like Parkinson's or Alzheimer's disease just by listening to the sound of their voice. And it's found a controversial source of audio data to train its algorithms on: phone calls to a health insurer. The health insurer – which Canary Speech would not name but says is "a very large American healthcare and insurance provider" – has provided the company with hundreds of millions of phone calls that have been collected over the past 15 years and are labelled with information about the speaker's medical history and demographic background. Using this data, the company says its algorithms could pick up on vocal cues that distinguish someone with a particular condition from someone without that condition.
An HPE keynote session at this year's HPC on Wall Street event was geared to helping financial firms dip their toe into emergent technologies such as AI and deep learning. Today's financial services companies must continually strive to gain a competitive edge in a highly data-intensive industry. With the emergence of big data, firms are struggling to manage the onslaught of complex data from many sources, stay on top of evolving regulations, and boost data security. High performance computing (HPC) technologies are not only helping financial firms ease the pains associated with explosive data growth, but they are also becoming absolutely essential to survival. Emergent technologies and new HPC innovations to hit the financial sector were the focus of the 14th annual HPC on Wall Street event held earlier this week in New York City.
Artificial intelligence based on medical claims data outperforms traditional models in stratifying patient risk. ABSTRACT Objectives: Current models for patient risk prediction rely on practitioner expertise and domain knowledge. This study presents a deep learning model--a type of machine learning that does not require human inputs--to analyze complex clinical and financial data for population risk stratification. Methods: "Skip-Gram," an unsupervised deep learning approach that uses neural networks for prediction modeling, used data from 2014 and 2015 to predict the risk of hospitalization in 2016. The area under the curve (AUC) of the deep learning model was compared with that of both the Clinical Classifications Software and the commercial DxCG Intelligence predictive risk models, each with and without demographic and utilization features.
Co-founder and CEO Nigel Toon laughs at that interview opener -- perhaps because he sold his previous company to the chipmaker back in 2011. "I'm sure Nvidia will be successful as well," he ventures. "They're already being very successful in this market… And being a viable competitor and standing alongside them, I think that would be a worthy aim for ourselves." Toon also flags what he couches an "interesting absence" in the competitive landscape vis-a-vis other major players "that you'd expect to be there" -- e.g. A recent report by analyst Gartner suggests AI technologies will be in almost every software product by 2020.