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.
Growing usage of big data in healthcare industry and imbalance between health workforce and patients is expected to drive the growth of the AI in healthcare market The artificial intelligence (AI) in healthcare market was valued at USD 667.1 million in 2016 and is expected to reach USD 7,988.8 million by 2022, at a CAGR of 52.68% between 2017 and 2022. The growth of this market is driven by the growing usage of Big Data in healthcare industry, ability of AI to improve patient outcomes, imbalance between health workforce and patients, reducing the healthcare costs, growing importance on precision medicine, cross-industry partnerships, and significant increase in venture capital investments in AI in healthcare domain. However, reluctance among medical practitioners to adopt AI-based technologies and ambiguous regulatory guidelines for medical software are the major factors restraining the growth of the AI in healthcare market. Faster calculations and lesser power consumption are the factors driving the growth of the hardware market for AI in healthcare Hardware which includes GPUs, DSPs, FPGAs, and neuromorphic chips is expected to grow at the highest rate in the offering segment of AI in healthcare. The GPU, DSP, and FPGA are widely used to implement the deep learning algorithm.
The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI estimated the impact on productivity of human-machine collaboration, the number of jobs that could be automated in major U.S. cities, and the size of the future AI in retail and healthcare markets; and found AI optimism among the general population, algorithms outperforming (again) pathologists, and that our very limited understanding of how our brains learn may improve machine learning. Do you think securing your devices and personal data will become more or less complicated over the next 12 months? DeepMind has developed a machine learning model that can label most animals at Tanzania's Serengeti National Park at least as well as humans while shortening the process by up to 9 months (it normally takes up to a year for volunteers to return labeled photos) [Engadget] In a simulation, biological learning algorithms outperformed state-of-the-art optimal learning curves in supervised learning of feedforward networks, indicating "the potency of neurobiological mechanisms" and opening "opportunities for developing a superior class of deep learning algorithms" [Scientific Reports] The AI in retail market is estimated to reach $4.3 billion by 2024 [P&S Intelligence] [e.g., Nike acquires Celect, August 6, 2019] The AI in healthcare market is estimated to reach $12.2 billion by 2023 [Market Research Future] [e.g., BlueDot has raised $7 million in Series A funding, August 7, 2019] AI companies funded in the last 3 months: 417 for total funding of $8.7 billion Data is eating the world quote of the week: "Although it is fashionable to say that we are producing more data than ever, the reality is that we always produced data, we just didn't know how to capture it in useful ways"--Subbarao Kambhampati, Arizona State University AI is eating the world quote of the week: "We advocate for a new perspective for designing benchmarks for measuring progress in AI. Unlike past decades where the community constructed a static benchmark dataset to work on for the next decade or two, we propose that future benchmarks should dynamically evolve together with the evolving state-of-the-art"--Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi, Allen Institute for Artificial Intelligence and the University of Washington
These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad. With this, Synechron's Global Accelerator programs now includes over 50 Accelerators for: Blockchain, AI Automation, InsurTech, RegTech, and AI Data Science and a dedicated team of over 300 employees globally.
In spite of noteworthy headway in innovation in various fields, health management and authoritative frameworks leave a ton of opportunity to get better. At present, in the greater part of the healthcare enterprises, the health record of a patient is put away manually which makes it harder to keep up such colossal measure of data. It is so difficult to keep up this healthcare information precisely. As a matter of first importance, all these information changes constantly, doctors are always moving all through systems, they are continually adopting new insurance coverage, they're changing office areas and changing their affiliations with facilities and clinics and the patient is analyzed at different health associations. So, the information keeps on changing except if doctors are great about reaching their systems each time one of those information fields changes which will drop out of synchronizing rapidly.