You receive many labs and quizzes, and have the ability to ask questions and interact directly with the instructor. Hands-on labs include working tools including Python, Scikit-Learn, Keras, and Tensorflow. This course is led by a seasoned technology industry practitioner and executive with many years of hands-on, in-the-trenches data analysis and visualization work. It has been designed, produced and delivered by Starweaver.

When it comes to present-day automation and repetitive work, we have traditionally thought of actual physical machines, or an entire line of robotic arms and tools that process items from one end towards another. Today, however, we are now getting used to yet another, more virtual type of automation. This is none other than the concept of robotic process automation (RPA): the automation of processes based directly on software and artificial intelligence (AI). But how exactly can we define RPA? We take a look into its brief development history and a few real-world examples, to see how it works, as well as witness its impact on organizational productivity as a whole.

RBC's AI private cloud platform is the first-of-its-kind in Canada to deliver intelligent software applications and boost operational efficiency Royal Bank of Canada (RBC) and its AI research institute Borealis AI have partnered with Red Hat and NVIDIA to develop a new AI computing platform designed to transform the customer banking experience and help keep pace with rapid technology changes and evolving customer expectations. "Modern AI cannot exist without access to high performance computing. This collaboration means that we can conduct research at scale, and deploy machine learning applications in production with improved efficiency and speed to market." As AI models become more efficient and accurate, so do the computational complexities associated with them. RBC and Borealis AI set out to build an in-house AI infrastructure that would allow transformative intelligent applications to be brought to market faster and deliver an enhanced experience for clients.

Financial firms are working to identify potential fraud by analyzing how customers hold their phones, how fast they type and other information about mobile interactions--and the strategy is yielding results. Using artificial-intelligence tools to crunch behavioral data is often a more secure way to verify customers than traditional means such as passcodes, experts say. The Federal Bureau of Investigation warned companies last month that cybercriminals can circumvent typical multifactor-authentication techniques. One way is by calling a telecommunications company, posing as a customer and getting a service agent to switch that person's phone number to the criminal's device. The fraudster can then have the individual's bank send a one-time passcode to the phone and gain access to the target's bank account.

Fledgling gal-bots are the latest hires in the virtual assistant landscape. Meet Mia and Marge: two virtual assistants in the banking world – each brought into existence by women, both of whom carry deep institutional knowledge, subject matter expertise and long-standing credibility. UBank's Lee Hatton (Mia) and The Royal Bank of Scotland's (RBS) MaryAnn Fleming (Marge) are among 40 women who have been recognized as 2019's women leaders in A.I. by IBM. These leaders have succeeded in garnering acceptance of A.I. in the workplace, elevating their customers' experience and their companies' brands. It seems mortgage consumer complaints consistently surface around the loan application process according to UBank CEO, Lee Hatton.

Before Siri and Alexa, there was Watson. Appearing as a contestant on "Jeopardy!" made IBM's Watson a household name. But since its debut -- and win -- in 2011, the computer has morphed into something else entirely: An artificial intelligence tool for business. The company opened up Watson in the cloud wars, making the technology available on competitors' clouds last month. Behind the Watson branding are career technologists making the tool work for business customers.

Companies today have access to more data about their customers and products than ever before. In fact, most companies only use 1% of their data. But with the help of predictive analytics and AI, companies can dig deeper into their data to provide personalized customer experiences. Predictive analytics use science to predict what will happen in the future--everything from what customers will want to how the market will perform and the biggest trends. Brands can use this information to target the right customers and provide personalized service and recommendations.

LAS VEGAS--It's become a common analogy for the use of predictive analysis in business technology: Wayne Gretzky became the best hockey player of his generation not because he skated to where the puck was, but because he skated to where the puck was going. Similarly, financial institutions are hoping to get ahead of the growing and seemingly insurmountable problem of payment card fraud not just by looking at who cyber-attackers are going after currently but who they are likely to defraud in the near future. At the Black Hat USA conference here last week, a pair of researchers -- one from Royal Bank of Canada and the other from a service provider that focuses on dark web intelligence -- presented on their joint effort to use machine learning, predictive analytics and transactional data together to get a handle on which cardholders might be the next victims of cyber-crime. With the vast stores of payment card, transactional, personal, demographic and historical fraud data to work from, it would seem that card-issuing banks already have a lot of information with which to work to help them determine the direction of fraudulent activity. The problem with having so much data is it is hard to find the right information at the right time.

Silicon Valley computer chip giant NVIDIA Corp. on Wednesday became the latest foreign tech giant to establish an artificial intelligence lab in Canada, hiring University of Toronto computer science assistant professor Sanja Fidler to lead the effort. The company, which has about one-quarter of its 50 downtown Toronto employees focused on AI, plans to at least double the size of its operation, with most of the new hires tasked "to work specifically on pie-in-the-sky kinds of AI research," said Rev Lebaredian, an NVIDIA vice president. Ms. Fidler, a Slovenian-born academic, will devote about half her time to NVIDIA and the balance to supervising graduate students at U of T, where she has gained a reputation as one of the bright lights of the computer science department, winning several research and faculty awards, including a nod as professor of the year in 2014-15 from the faculty's student union. She also gained notoriety along with fellow U of T researchers for using AI to help people determine if their outfits were stylish and also to generate Christmas songs based on computer analysis of holiday images. Ms. Fidler was not available for an interview. NVIDIA, best known as a graphics processing powerhouse in the computer gaming industry, was one of the earliest Silicon Valley companies to shift its business to serve the burgeoning AI industry, providing a platform and tools to serve the market for self-driving cars, autonomous robots and super-computing applications.

In my recent series on artificial intelligence for smarter investing, one of the key themes is the importance of collaboration between computer programmers and financial subject matter experts. Most efforts to use AI in finance fail because programmers don't understand the financial data while the financial experts don't understand technology. Not only has this bank made concrete efforts to foster collaboration between programmers and financial experts, its CEO has experience in both fields. These efforts are already improving profitability and efficiency, which when combined with a cheap valuation make Royal Bank of Canada (RY) this week's Long Idea. Technological advancements – especially in the field of artificial intelligence – are going to be the primary driver of competitiveness and profitability among financial institutions in the coming years. As I wrote about in my article "Big Banks Will Win the Fintech Revolution", virtually all the major financial institutions have publicly recognized the importance of investing in technological innovation.