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) …
AI and RPA are only beginning to transform how business is done in the insurance industry. We can expect to see burgeoning usage in operations, customer service, risk assessment, and mitigation and regulatory compliance. Insurance companies are only beginning to harness the potential of artificial intelligence (AI) and robotic process automation (RPA). AI refers to computer systems that can mimic human capabilities by learning and solving problems. RPA is an emerging form of business process automation technology based on using software robots or AI "workers."
No matter whether you were a straight-A student at university or more a student of beer pong, it's extremely unlikely that your positive memories of college took place in an examination hall. Beyond being generally miserable, exams exacerbate anxiety and other mental health issues, and do a poor job of assessing skills like critical thinking and creativity. Time-pressured tests are used as the key filter for several prestigious professions and universities and, some argue, for no apparent good reason. Given this sad state of affairs, it should be positive to see supervised exams and tests fall slowly out of vogue. Headmasters and professors have urged that more flexible, less time-pressured assessments like essays and written assignments should replace exams.
A team of material scientists at Haverford College has shown how human bias in data can impact the results of machine-learning algorithms used to predict new reagents for use in making desired products. In their paper published in the journal Nature, the group describes testing a machine-learning algorithm with different types of datasets and what they found. One of the more well-known applications of machine-learning algorithms is in facial recognition. But there are possible problems with such algorithms. One such problem occurs when a facial algorithm intended to look for an individual among many faces has been trained using people of just one race.
WIRE)--Baker Hughes, a GE company (NYSE:BHGE) and C3.ai today announced the launch of BHC3 Reliability, the first artificial intelligence (AI) software application developed by the BakerHughesC3.ai Unveiled at BHGE's annual digital conference, UNIFY2019, the now generally available application uses deep learning predictive models, natural language processing, and machine vision to continuously aggregate data from plant-wide sensor networks, enterprise systems, maintenance notes, and piping and instrumentation schematics. Using historical and real-time data from entire systems, the BHC3 Reliability machine learning models identify anomalous conditions that lead to equipment failure and process upsets. Application alerts enable proactive action by operators to reduce downtime and lost revenue. Applicable to operations across all sectors of the energy value chain, BHC3 Reliability's system-of-systems approach scales to any number of assets and processes across offshore and onshore platforms, compressor stations, refineries, and petrochemical plants, reducing downtime and increasing productivity.
A medical AI expert shares views from his experiences at the seminar. More than 30 local government representatives and experts in academic, medical, and industrial fields were invited to explore the pressing issues, pain points, and future development of artificial intelligence (AI) application in medicine in Nanning, Guangxi Zhuang autonomous region. Held by the Chinese Health Information and Big Data Association (CHIBDA) and the Big Data Development Bureau of Guangxi Zhuang Autonomous Region, the seminar aimed to promote the AI application in medical treatment. Participants conducted a discussion on the challenges encountered in the innovative cooperation of medical AI in its use, production, learning, and research, exploring the cooperation models between AI enterprises and hospitals from various perspectives. Combined with the local conditions in Guangxi, they also provided valuable experience and advice for the development of medical AI.
The report on the Global Deep Learning Software Market offers complete data on the Deep Learning Software market. Components, for example, main players, analysis, size, situation of the business, SWOT analysis, and best patterns in the market are included in the report. In addition to this, the report sports numbers, tables, and charts that offer a clear viewpoint of the Deep Learning Software market. The top Players/Vendors Artelnics, Bright Computing, BAIR, Intel, Cognex, IBM, Keras, Microsoft, VLFeat, NIVIDA, PaddlePaddle, Torch, SignalBox, Wolfram of the global Deep Learning Software market are further covered in the report. The latest data has been presented in the study on the revenue numbers, product details, and sales of the major firms.
The World Economic Forum (WEF) recently released a report detailing the ten "world-changing technologies that are poised to rattle the status quo." Let's see for ourselves what these technologies have to offer. Some developments in the bioplastics industry allow lignin, a component of wood, to be broken down into its simpler components using engineered solvents. With this possible, plastics can then be made from it. Lignin is found in wood waste and agricultural byproducts which otherwise doesn't have any other function.
Time to explain ergodicity, ruin and (again) rationality. Recall from the previous chapter that to do science (and other nice things) requires survival t not the other way around? Consider the following thought experiment. First case, one hundred persons go to a Casino, to gamble a certain set amount each and have complimentary gin and tonic –as shown in the cartoon in Figure x. Some may lose, some may win, and we can infer at the end of the day what the "edge" is, that is, calculate the returns simply by counting the money left with the people who return. We can thus figure out if the casino is properly pricing the odds.
Artificial intelligence and machine learning may be ideal for picking up the day-to-day tasks of running enterprises, but still fall flat when it comes to innovation or reacting to unforeseen or one-off events. While enterprise-grade AI is still a ways off, it's incumbent on business and IT leaders to start piloting and exploring the advantages AI potentially offers. That's the word coming out of a recent report from the MIT Task Force on the Work of the Future, which looked at AI as part of a broad range of changes sweeping the employment scene and workplace. "We are a long way from AI systems that can read the news, re-plan supply chains in response to anticipated events like Brexit or trade disputes, and adapt production tasks to new sources of parts and materials," state the report's authors, David Autor of the National Bureau of Economic Research, along with David Mindell and Elisabeth Reynolds, both with MIT. For starters, data – the fuel that propels AI decision-making – is not ready for the leap.