If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
LONDON – A U.K. parliamentary committee has released a report that highlights industrial automation in Japan, calling on the government to promote automation in British industries. Japan "has a long history of automation and is home to major robotics manufacturers," the House of Commons Business, Energy and Industrial Strategy Committee wrote in the report published Wednesday, adding that the country is "the origin for half of robots sold globally." Prime Minister Shinzo Abe's administration "has made a conscious choice to support automation as part of its'Abenomics' reforms, recognizing the need for continued growth and development of automation to enable the country to'drastically improve productivity,' " the report states. Referring to Japan's 2015 New Robot Strategy, the report urged the U.K. government to develop its own automation and artificial intelligence strategy by the end of 2020. The report cited an 2018 report by the International Federation of Robotics that ranked the U.K. in 22nd place in terms of robot density -- or the number of industrial robots per 10,000 workers.
You have now successfully trained your model! That wasn't too hard, was it? You're not entirely there yet; You still need to evaluate your neural network. In this case, you can already try to get a glimpse of well your model performs by picking 10 random images and by comparing the predicted labels with the real labels. You can first print them out, but why not use matplotlib to plot the traffic signs themselves and make a visual comparison? However, only looking at random images don't give you many insights into how well your model actually performs.
Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre‐surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.
This repository is an implementation of "MR‐based synthetic CT generation using a deep convolutional neural network method." This toy dataset just includes 367 paired images. We randomly divide data into training, validation, and test. Use main.py to train a DCNN model. Use main.py to test the DCNN model.
Frederick Mutual Insurance Company has signed a commercial agreement with Betterview – a start-up that develops solutions for the property insurance market – to leverage the latter's AI-powered property risk management platform. Terms of the agreement were not disclosed. Through the agreement, Frederick Mutual will utilize Betterview's property analytics to enhance the lifecycle of its policies – from loss control and underwriting to claims and catastrophe response. Betterview's platform is used by personal lines and commercial lines carriers to identify and score roofing conditions, as well as other related property risks. The start-up makes this possible through the use of machine learning and computer vision to analyze aircraft and satellite imagery.
During the past two years, Sales Mastery, Inc. (SMI) has been studying and cataloging the various artificial intelligence (AI) applications aimed at increasing sales rep productivity. With categories like: Appointment Setting; Automatic CRM Record Creation/Updating; Buyer Intent Analysis; Forecast Management; Guided Selling; and Prospect Engagement, to name just a few. It seems that AI is everywhere. A year ago, McKinsey published a report offering 3 other flavors of AI, in increasing levels of capability/complexity: Assisted Intelligence; Augmented Intelligence; and Autonomous Intelligence. These ranged from basically serving up facts/insights to the technology being able to generate its own outputs with minimal/no human intervention once started.
The Mercury ViewPoint SmartVisor doesn't just magnify, with the touch of a button it reads out to you as well. ViewPoint SmartVisor is a breakthrough in technology for anyone suffering from restricted sight. It also works great for people with central vision loss e.g. ViewPoint SmartVisor sits comfortably on the head giving clear reproduced natural and enhanced images in the magnification of your choice. See everything clearly in full colour, enhanced full colour or with different coloured foregrounds and backgrounds.
In the war to hire rock star candidates, the global talent acquisition & staffing technology and services market is undergoing a sea change. However, true potential will be unlocked only when organisations focus on the power of machine intelligence wherein experts build a knowledge graph of the existing workforce and the positions required within an organisation. This data-driven knowledge graph will broaden the scope; hiring companies would no longer have to manually dig through thousands of candidate applications and can leverage intelligent recommendations through machine learning. While machine learning would automate various repetitive aspects of the recruitment process, it would also help identify top candidates from large talent pools on job boards.
Having a compact, yet robust, structurally-based identifier or representation system for molecular structures is a key enabling factor for efficient sharing and dissemination of results within the research community. Such systems also lay down the essential foundations for machine learning and other data-driven research. While substantial advances have been made for small molecules, the polymer community has struggled in coming up with an efficient representation system. For small molecules, the basic premise is that each distinct chemical species corresponds to a well-defined chemical structure. This does not hold for polymers.