Cautionary Note Regarding Forward-Looking Information This news release contains forward-looking information based on current expectations. Statements about the Company's plans and intentions, other potential transactions, development and completion of Wazabi, the benefits of Wazabi, revenues from Wazabi, acquisition of customers, product development, events, courses of action, and the potential of the Company's technology and operations, among others, are all forward-looking information. Forward-looking statements consist of statements that are not purely historical, including any statements regarding beliefs, plans, expectations or intentions regarding the future. Such information can generally be identified by the use of forwarding looking wording such as "may", "expect", "estimate", "anticipate", "intend", "believe" and "continue" or the negative thereof or similar variations. The reader is cautioned that assumptions used in the preparation of any forward-looking information may prove to be incorrect.

- Batch normalization (batch norm) is often used in an attempt to stabilize and accelerate training in deep neural networks. In many cases it indeed decreases the number of parameter updates required to achieve low training error. However, it also reduces robustness to small adversarial input perturbations and noise by double-digit percentages, as we show on five standard data-sets. Furthermore, substituting weight decay for batch norm is sufficient to nullify the relationship between adversarial vulnerability and the input dimension. Our work is consistent with a mean-field analysis that found that batch norm causes exploding gradients.

Alison Dunn and her students are helping aspiring entrepreneurs access the services that put them on the path to success. Through her project Creating Chatbots to Improve Audience Engagement, Journalism professor Alison had her students write, develop and deploy a chatbot for SURGE as part of the Advanced Social Journalism course. Mohawk's SURGE offers free one-on-one mentoring to Mohawk students and alumni who are entrepreneurs or want to become entrepreneurs. While the Centre has limited resources (just two full-time staff), it's got a huge audience that includes Mohawk students, staff, faculty and community members. Those factors result in long waits for students to access one of the Centre's experts.

In the next three years, as many as 120 million workers in the world's 12 largest economies may need to be retrained or reskilled as a result of AI and intelligent automation, according to a new IBM (NYSE: IBM) Institute for Business Value (IBV) study. In addition, only 41 percent of CEOs surveyed say that they have the people, skills and resources required to execute their business strategies. The study, which includes input from more than 5,670 global executives in 48 countries, points to compounding challenges that require a fundamental shift in how companies meet and manage changing workforce needs throughout all levels of the enterprise. According to the global research, the time it takes to close a skills gap through training has increased by more than 10 times in just four years. In 2014, it took three days on average to close a capability gap through training in the enterprise; in 2018, it took 36 days.

Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. Today, Artificial Intelligence is a very popular subject that is widely discussed in the technology and business circles. Many experts and industry analysts argue that AI or machine learning is the future – but if we look around, we are convinced that it's not the future – it is the present. With the advancement in technology, we are already connected to AI in one way or the other – whether it is Siri, Watson or Alexa. Yes, the technology is in its initial phase and more and more companies are investing resources in machine learning, indicating a robust growth in AI products and apps in the near future.

IBM (NYSE: IBM) today announced enhancements to Cloud Pak for Data that leverage the DataOps methodology to help clients get their data'business-ready' for AI. As corporate data continues to grow in both volume and complexity – often of mixed structures and types and from sources strewn across the enterprise – the time required to collect and organize these vast datasets into usable data for AI can stress resources and actually stall AI projects. Improving data preparation, management and automation are the pillars of the burgeoning and collaborative DataOps (data operations) principle, which outlines methods for automating and streamlining data flows across an enterprise. DataOps is also at the core of the "Organize" rung of IBM's AI Ladder strategy, from which clients can transform datasets into data that's tuned and prepared for AI. "The path to consistent success with AI projects begins with business-ready data and the methodology for delivering that is DataOps," said Rob Thomas, General Manager, IBM Data and AI.

IBM (NYSE: IBM) today announced enhancements to Cloud Pak for Data that leverage the DataOps methodology to help clients get their data'business-ready' for AI. As corporate data continues to grow in both volume and complexity – often of mixed structures and types and from sources strewn across the enterprise – the time required to collect and organize these vast datasets into usable data for AI can stress resources and actually stall AI projects. Improving data preparation, management and automation are the pillars of the burgeoning and collaborative DataOps (data operations) principle, which outlines methods for automating and streamlining data flows across an enterprise. DataOps is also at the core of the "Organize" rung of IBM's AI Ladder strategy, from which clients can transform datasets into data that's tuned and prepared for AI. "The path to consistent success with AI projects begins with business-ready data and the methodology for delivering that is DataOps," said Rob Thomas, General Manager, IBM Data and AI.

The QUEST project is designing new AI tools that will help science journalists contextualise their stories and tailor them to different types of news audiences. Artificial intelligence – or AI for short – is now ubiquitous. When I write'ubiquitous', I don't mean that AI software is present in most if not all aspects of our lives. Even if this presence might happen more soon than we think. No, what I mean that is AI is ubiquitous in today's ether – our conversations, our news and our politics.

According to Josh Sutton, chief executive at Agorai: "Artificial intelligence (AI) solutions are producing insights in seconds that used to take teams of people days or even weeks to produce. Early adopters are seeing financial benefits already." The main metrics for success are a reduction in insight time, followed by decreases in labour demands and overall expense. Mr Sutton adds: "McKinsey recently produced a report which states that over the next decade, early adopters of AI will dramatically outperform followers and laggards." "We are now at a point in time that is reminiscent of the mid-1990s, when the early winners of the internet were those who identified opportunities and experimented to address business problems," says Chris Duffey, strategic development manager at Adobe and author of Superhuman Innovation.

Hulu might not have the answer to The Great Question, but it's cooking up something tHGttG fans may appreciate... if it does things right. The streaming platform is developing an adaptation of Douglas Adams' beloved classic The Hitchhiker's Guide to the Galaxy, according to Deadline. Apparently, the project will be headed by Carlton Cuse, one of the showrunners behind Lost, Bates Motel and Locke & Key, as well as Jason Fuchs, whose writing credits include Gal Gadot's Wonder Woman. Cuse and Fuchs will write and executive produce the project under ABC Signature, the streaming division of ABC Studios, and Cuse's Genre Arts. Fuchs is reportedly writing the pilot script for what Deadline says is "a modern updating of the classic story," which started as a radio series in 1978.