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) …
HSBC is replacing more manual processes, with artificial intelligence (AI) being used to automate when ATMs need to be refilled. The technology, developed by HSBC's operations and technology teams, has been trialled in Hong Kong, where the bank has 1,200 ATMs. The iCash AI technology has reduced ATM refills, which are done by third parties, by 15% – saving $1m. To calculate how much money is needed and where, iCash uses live ATM data and predictive machine learning algorithms that factor in seasonality, holidays, public events, location and recent withdrawal trends. The bank said it was a challenge to predict how much cash each ATM might need.
This paper proposes to model chaos in the ATM cash withdrawal time series of a big Indian bank and forecast the withdrawals using deep learning methods. It also considers the importance of day-of-the-week and includes it as a dummy exogenous variable. We first modelled the chaos present in the withdrawal time series by reconstructing the state space of each series using the lag, and embedding dimension found using an auto-correlation function and Cao's method. This process converts the uni-variate time series into multi variate time series. The "day-of-the-week" is converted into seven features with the help of one-hot encoding. Then these seven features are augmented to the multivariate time series. For forecasting the future cash withdrawals, using algorithms namely ARIMA, random forest (RF), support vector regressor (SVR), multi-layer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN), long short term memory neural network and 1-dimensional convolutional neural network. We considered a daily cash withdrawals data set from an Indian commercial bank. After modelling chaos and adding exogenous features to the data set, we observed improvements in the forecasting for all models. Even though the random forest (RF) yielded better Symmetric Mean Absolute Percentage Error (SMAPE) value, deep learning algorithms, namely LSTM and 1D CNN, showed similar performance compared to RF, based on t-test.
As technologies become increasingly capable of taking on a wide variety of repeatable tasks, many workers may find themselves increasingly nervous about their place in the workforce. Anxieties about technology in the workplace are nothing new -- in fact, they go back centuries. The good news is that the fear of humans being replaced en masse by machines has never been borne out by reality. Rather, history has repeatedly shown that as machines transform whole industries, they also create new opportunities for human workers. Indeed, the US is one of the most developed economies, and therefore one of the most automated, but it also currently has record-low unemployment.
Silicon Valley Bank, which has helped fund more than 30,000 startups, yesterday released a report on "The Future of Robotics: An Inside View on Innovation in Robotics." It described trends in production, business models, and the adoption of robotics reflecting the increasing maturity of Industry 4.0. The report also addressed concerns about automation displacing jobs and public-policy reactions. Overall, the free Silicon Valley Bank (SVB) report (download PDF) was cautiously optimistic about the prospects for industrial automation. It cited rising U.S. productivity, maturing technologies and suppliers supporting a variety of applications, and a steady climb for robotics deployments, particularly in Asia.
As the world grows increasingly connected, growing concern regarding the influence of artificial intelligence (AI) has been bubbling to the surface, affecting perceptions by industries big and small along with the general populace. Spurred on by sensationalized media predictions of AI taking over human decision-making and silver-screen tales of robot revolutions, there is a fear of allowing AI or its cousin, the Internet of Things (IoT), into our lives. Here is AI's man behind the curtain. One of the biggest sticking points is the popular – yet mistaken – notion that AI will cost people their jobs. In truth, the situation is just the opposite.
Modelling deontic notions through preferences  has the advantage of linking deontic notions to the manifold research on preferences, in multiple disciplines, such as philosophy, mathematics, economics and politics. In recent years, preferences have also been addressed within AI [15,8,18] and applications can be found in multi-agent systems  and recommender systems . We shall model deontic notions through ceteris-paribus preferences, namely, conditional preferences for a state of affairs over another state of affairs, all the rest being equal. In particular, we shall focus on the ceteris-paribus preference for a proposition over its complement. The idea of ceteris-paribus preferences was originally introduced by the philosopher and logician Georg von Wright .
Banking is becoming more convenient thanks to the Internet, and the future of the banking industry is growing increasingly digital. Whether discussing the future of retail banking or the future of mobile banking, technology is playing a larger role in our everyday transactions. The Internet of Things (IoT) is part of this rapid evolution toward the bank of the future, and both consumers and financial institutions need to adapt to these retail and mobile banking trends. Below, we've detailed the past, present, and future of the banking industry as it relates to the IoT, and how these emerging technologies will transform the way we conduct our financial business. Retail banks have actually been using an early prototype of an IoT device for decades: the automated teller machine (ATM).
Artificial Intelligence (AI) has made major progress in recent years. But even milestones like AlphaGo or the narrow AI used by big tech only scratch the surface of the seismic changes yet to come. Modern AI holds the potential to upend entire profession while unleashing brand new industries in the process. Old assumptions will no longer hold, and new realities will dictate those who are swallowed by the tides of change from those able to anticipate and ride the AI wave headlong into a prosperous future. Here's how businesses and employees can both leverage AI in the 2020s.
In the age of artificial intelligence, predicting which jobs will fall to automation is as much about what machines can do as it is about what they can't. More than half of all jobs in America -- both blue and white-collar -- are resistant to automation, according to an acclaimed study published in 2013 by two Oxford University researchers. Co-author Carl Benedikt Frey, who directs Oxford's Technology and Employment program, broke down three areas where human intelligence still beats artificial intelligence: perception and manipulation, social intelligence; and creativity. Each type has what Frey calls a "bottleneck," which slows the pace at which certain workforces can be automated. The premise is simple: Technology won't replace human workers if it can't do the job.