AI and the Future of Work: AI is the fourth industrial revolution and 84% of companies know that they need to invest in AI to have a competitive advantage. But how do we actually use AI in our day to day processes while delivering a personalized human touch to each buyer? Learn how to integrate Intelligent Virtual Assistants into your organization to drive greater workforce productivity and job satisfaction. Build higher performing teams by automating the repetitive, routine tasks and freeing up people to do what they do best - build trusted relationships, perform the higher-value tasks that require more complex decision making and emotional intelligence to drive forward progress. A Marketer's Guide to Selfcare Using Intelligent Virtual Assistants Let's be real.

Artificial Intelligence (AI) has been the talk of the town for a while now. I remember the fiasco from chatbots that replied instantly. We have all texted Siri saying'I love you' at least once in our lives. It hasn't been long since then, but Artificial Intelligence, in a very short span, has embedded itself in our lives. The technology first emerged in the mid-1950s. Some feared it would replace the human workforce in the future, but for now, we are safe.

This year, self-driving cars started getting pretty good. Deep fakes video started getting pretty convincing. Our virtual assistants got to the point where they could understand us well enough to do some simple things, like tell us the weather or get driving directions home. When it comes to artificial intelligence, we have reached an inflection point. The technology is good enough to use. Next year promises to be a breakout year for AI, as it starts to permeate all aspects of our lives. Here are predictions for 2020 from some of the world's top AI experts. Jen Snell is VP of product marketing at Verint, where she leads a product strategy team focused on intelligent self-service, conversational AI, automation, and analytics. She is a frequent speaker and a leading contributor on topics shaping the development and design of interactive technologies. Follow her on Twitter @JenniferLSnell and on LinkedIn.

How can banks and financial institutions deliver great experiences in the fragmented digital world? Successful financial services firms have reputations and brands that convey trust. Many have embraced digital channels for reaching and engaging clients and prospects and, ultimately, for building relationships with them. But in today's world, true success focuses on the experience--the sum total of offerings, interactions, and transactions. An integrated, programmatic, and highly automated approach offers the best way to accomplish this goal. So: How do you do it?

Silas: High Performance, Explainable and V erifiable Machine Learning Hadrien Bride, Zh e H ou Griffith University, Nathan, Brisbane, Australia Jie Dong Dependable Intelligence Pty Ltd, Brisbane, Australia Jin Song Dong National University of Singapore, Singapore Ali Mirjalili Griffith University, Nathan, Brisbane, AustraliaAbstract This paper introduces a new classification tool named Silas, which is built to provide a more transparent and dependable data analytics service. A focus of Silas is on providing a formal foundation of decision trees in order to support logical analysis and verification of learned prediction models. This paper describes the distinct features of Silas: The Model Audit module formally verifies the prediction model against user specifications, the Enforcement Learning module trains prediction models that are guaranteed correct, the Model Insight and Prediction Insight modules reason about the prediction model and explain the decision-making of predictions. We also discuss implementation details ranging from programming paradigm to memory management that help achieve high-performance computation.1. Introduction Machine learning has enjoyed great success in many research areas and industries, including entertainment [1], self-driving cars [2], banking [3], medical diagnosis [4], shopping [5], and among many others. However, the wide adoption of machine learn-Preprint submitted to Elsevier October 4, 2019 arXiv:1910.01382v1 The ramifications of the black-box approach are multifold. First, it may lead to unexpected results that are only observable after the deployment of the algorithm. For instance, Amazon's Alexa offered porn to a child [6], a self-driving car had a deadly accident [7], etc. Some of these accidents result in lawsuits or even lost lives, the cost of which is immeasurable. Second, it prevents the adoption in some applications and industries where an explanation is mandatory or certain specifications must be satisfied. For example, in some countries, it is required by law to give a reason why a loan application is rejected. In recent years, eXplainable AI (XAI) has been gaining attention, and there is a surge of interest in studying how prediction models work and how to provide formal guarantees for the models. A common theme in this space is to use statistical methods to analyse prediction models.

Consider this: you buy a nice shiny new pair of headphones. When you unbox the headphones there's a manual that says'download our app to set up'. So you get the app, you create an account, you connect your headphones. You very much enjoy the sound quality. Even though the purpose of this app is to'set up' your headphones, it also provides a gateway to your behaviour.

Oracle has added AI voice commands to its Digital Assistant, offering users an alternative way to interact with its various business apps. The company on Tuesday also announced that the bot will integrate with Microsoft's work stream collaboration platform, Teams. Oracle's Digital Assistant was launched last year as an interface to its portfolio of enterprise software applications, which includes sales, marketing and human resource tools. The virtual assistant uses AI techniques such as machine learning and natural language processing and understanding to interpret user intent; it's designed to automate processes such as expense approvals and meeting re-schedules. The Digital Assistant was initially targeted at text interactions and embedded in various communication and collaboration tools such as Slack, WeChat and Facebook Messenger, as well as user websites and as a standalone mobile app.

The phrase "artificial intelligence" in pop culture often conjures up dystopian images such as the sentient computer Hal 9000 from the 1968 film "2001: A Space Odyssey" that killed people for its self preservation; or the cyborg assassin with a metal endoskeleton in director James Cameron's "The Terminator." In recent years, our fascination with the potential of AI has taken a more starry-eyed turn, as shown in the 2013 sci-fi drama "Her," where the main character falls in love with a virtual assistant. In reality, artificial intelligence (AI) technology is quickly permeating every aspect of our lives. From Amazon's voice-activated Alexa to writing technology that helps managers craft job postings, AI is in our hearts, homes and workplaces. And it's only going to become a bigger part of our lives: Experts call the rise of AI the driving force behind the fourth industrial revolution. On a recent afternoon at the NVIDIA robotics research lab in Seattle's University District, researchers use a simulated kitchen to test robots' ability to perform simple tasks such as grabbing objects.

Artificial intelligence (AI) and machine learning (ML) have already changed the way we interact with each other and devices, and are poised to continually enhance markets, industries, and businesses globally. With ML and AI being used more rapidly, early adopters are experiencing significant competitive advantages. Arthur Samuel, an AI pioneer and the inventor of the first computerized checkers game, had a startling epiphany in 1959 that ultimately made today's algorithms possible. Up until then, most computer scientists assumed humans had to teach computers everything. Arthur Samuel realized computers could learn autonomously and coined the term "machine learning" (ML).

The rapid improvement in speech recognition technology, which over the past decade has given rise to virtual speech assistants such as Apple's Siri, Amazon's Alexa, Google Voice, Microsoft Cortana and others, is spilling into new areas that are beginning to have a significant impact on the workplace. These consumer speech portals have already raised extensive new privacy concerns. "Computers have a much greater ability to organize, access and evaluate human communications than do people," said Marc Rotenberg, the president and executive director of the Electronic Privacy Information Center in Washington. In 2015, the group filed a complaint with the Federal Trade Commission against Samsung, arguing that the capture and storage of conversations by their smart TVs was a new threat to privacy. Speech transcription potentially pushes traditional privacy concerns into new arenas both at home and work, he said.