She covers topics like artificial intelligence, augmented and virtual reality, blockchain, and big data, to name a few.Cas is also co-owner of an esports organization and spends much of her time teaching gamers how to make a living doing what they love while bringing positivity to the gaming community.

First it was the mobile revolution that disrupted the banking system, now artificial intelligence (AI) is looking to make its mark. Finn.ai, a Vancouver, BC-based fintech company, is looking to digitally transform financial institutions and make banking easier for consumers by offering an AI-powered personal banking and financial management assistant. With Finn.ai's smart virtual assistant, banks can provide a personalized wealth manager for every single client at zero marginal cost, and through any conversational channel, such as text, Facebook Messenger and other Android or iOS apps. "Our AI-powered day-to-day banking concierge will help banks evolve from a utility to a value-added service," explains Ruby Lin, the company's growth and partnerships lead. "What banks can traditionally do through a web/mobile app, Finn.ai can do through a conversational interface: pay bills, transfer money to friends, check balances, find ATMs, find product information and customer support through a mix of AI and human interaction."

Currently, much of machine learning is opaque, a genuine black box. Machines may be given an extraordinarily complex task and come up with what appears to be a reasonable solution, but they are largely incapable of explaining how or why they came up with that solution. That's why some of the smartest AI researchers in the industry are now hot on the trail of finding new ways to make machines understandable for humans. Much of that focus is on an emerging field known as Explainable AI (XAI), which in very simple terms is the ability of machines to explain their rationale, characterize the strengths and weaknesses of their decision-making process, and, most importantly, convey a sense of how they will behave in the future. This field of XAI is going to be hugely important, with a number of important social, legal and ethical implications.

Stop words typically remove such things as "a, an, the, it". Often this can be beneficial when we are classifying based on topics, which are well described by nouns and adjectives. However some text classification tasks are more abstract. Consider classifying fiction and non-fiction articles on the same topic, what would the difference between these two writing styles be? They would probably use the same nouns but what about the frequency of "the" vs "an" or "he" vs "they"?

Using artificial intelligence (AI), Google engineers are now producing entirely new sounds humans have never heard before. According to Wired, using the mathematical characteristics of notes that emerge from the combination various instruments, AI can create countless sounds no human has ever heard before. Google Magenta, a small group of AI researchers building systems that can create their own art, has recently been working a project called NSynth or Neural Synthesizer. Its team members, Jessie Engel and Cinjon Resnick, are collaborating with members of Google Brain, the tech giant's core AI lab where researchers explore neural networks. NSynth's goal is to give musicians a completely new range of tools they can use to make music, possibly taking the musical industry to a whole new level.

It wasn't enough that technologies became so smart they made some of us feel less "clever" in our everyday life. Now they came to invade our working environment and our productivity with their intelligence as well. Artificial Intelligence is the buzz in pretty much every industry. But of course what interests us more is AI's contribution to sales and marketing and their results. Currently the B2B sales world knows they need to incorporate AI into their sales process.

Automation is happening, and it will bring substantial benefits to businesses and economies worldwide, but it won't arrive overnight. A new McKinsey Global Institute report finds realizing automation's full potential requires people and technology to work hand in hand. Recent developments in robotics, artificial intelligence, and machine learning have put us on the cusp of a new automation age. Robots and computers can not only perform a range of routine physical work activities better and more cheaply than humans, but they are also increasingly capable of accomplishing activities that include cognitive capabilities once considered too difficult to automate successfully, such as making tacit judgments, sensing emotion, or even driving. Automation will change the daily work activities of everyone, from miners and landscapers to commercial bankers, fashion designers, welders, and CEOs. But how quickly will these automation technologies become a reality in the workplace?

Thank you all for the huge response to this emerging course! We are delighted to have over 2300 students in over 102 different countries and for the overwhelmingly positive and thoughtful reviews. It's such a privilege to share this important topic with everyday people in a clear and understandable way. In this introductory course, the "Backyard Data Scientist" will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the "techno sphere around us", why it's important now, and how it will dramatically change our world today and for days to come. We'll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science: To make sense of the Machine part of Machine Learning, we'll explore the Machine Learning process: Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete.

Artificial Intelligence has effectively convinced its necessity to the entire world by performing excellently in various industries. Almost all the industries including manufacturing, healthcare, construction, online retail, etc. are adapting to the reality of IoT to leverage its advantages. Machine learning technology is constantly evolving and the current trends in the field promise that every enterprise will be data driven and will have the capacity of using machine learning in the cloud to incorporate artificial intelligence apps. Companies will be successful in analyzing large complex data and providing meticulous insights without spending a huge amount on installing and maintaining machine learning systems. The three newest machine learning trends that will make this possible are Data Flywheels, The Algorithm Economy, and Cloud Hosted Intelligence. In the coming years, every application built will be an intelligent app by incorporating open source algorithms and machine learning codes.

Looking around in 2017, it's hard to find an industry that hasn't been affected by the rise of artificial intelligence (AI). From manufacturing to health care to finance, AI is everywhere -- and according to experts, its presence is only going to increase from here on out. The auto industry is also at the forefront of AI integration. AI can help streamline transportation and reduce costs. Let's look at some of the ways the industry is utilizing AI.