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The technology-driven world in which we now live is one filled with promise – cars that drive themselves, algorithms that respond to customer service inquiries, automated business intelligence on tap. Yet, this brave new world is also filled with challenges. For even as AI and automation increase productivity and improve our lives, their widespread adoption means that many work activities humans currently perform will soon be displaced – if they haven’t been already. What this mean, however, is that there will be a shortage of jobs in the future. In fact, according to the World Economic Forum (WEF), AI will create more jobs than it destroys – by 2022, though 75 million jobs are expected to be displaced by automation, 133 million new ones will emerge. Nonetheless, if we don’t take the right steps to educate and (re)train the workforce, what this massive revolution of the working world mean is that there will be a serious shortage of talent with the necessary AI skills to fill the new jobs that are created. In the next three years, as many as 120 million workers in the world’s twelve largest economies (including 11.5 million in the US) will need to be retrained or reskilled as a result of AI and intelligent automation, according to a new IBM Institute for Business Value study. Additionally, almost two-thirds of today’s students will end up working in jobs that do not yet exist. Not only does this AI skills gap impact prospects for individuals, but it also has a systemic effect on the ability of companies, industries, governments, and communities to realize the full potential of the world’s digital transformation. The challenge we now face is how to empower the workforce and – most importantly – the youth of today for the jobs of tomorrow. The AI Talent Shortage The extent of the global AI skills shortage is laid bare in the Global AI Talent Report 2019, published on the blog of software entrepreneur and CEO of Element AI, Jean-Francois Gagne, and based on analysis of Ph D-qualified authors publishing academic papers at world-leading AI conferences. The report identifies that there are 22,400 top AI academics around the world as of 2018. In addition, via a complementary survey of Linked In profiles, the report also finds that there is a total of 36,524 people who qualify as self-reported AI specialists, according to the authors’ search criteria. Though these figures represent significant increases over previous studies (up 19% over last year in the case of qualified AI specialists, and up 66% in the case of self-reported AI expertise on Linked In), given just how prolific AI and machine learning technologies are becoming, the number of AI experts around the world today remains alarmingly small. It should be noted here that Gagne’s study sits amongst other, much broader reports, such as the oft-quoted “Global AI Talent White Paper” by Chinese tech giant Tencent, which indicated that there were 300,000 AI researchers and practitioners worldwide in 2017. The number far exceeds the findings in Gagne’s report primarily because it includes entire technical teams that work in AI, and not just the specially-trained AI experts – i.e., those who are well-versed enough in the technology and with the specialist AI skills required to lead teams taking AI from research to application. However, no matter which figure you prefer, the fact remains that there are mere thousands of engineers with AI skills worldwide – but are needed. As such, AI remains a “job seeker’s market,” Gagne’s report concludes, with there being no shortage of opportunities in companies developing technology for things like self-driving cars, smart home products, digital assistants, and much more besides. Recent data from reveals that AI job postings rose 29.1% over the last year – and yet, no doubt due to the AI skills gap, searches for AI-related jobs decreased by 14.5% over the same period. “For example,” say Indeed’s analytics team, “consider data scientists, whose job is to take raw data and apply programming, visualizations and statistical modeling to extract actionable insights for organizations. Given that data is ‘the new oil,’ data scientists are in high demand, and our research shows job postings jumped 31% from 2017 to 2018. During the same period, however, job searches only increased by about 14%.” The shortage of AI skills is seen as a major barrier to the pace of the technology’s adoption. In fact, a recent poll confirmed that 56% of senior AI professionals believed that a lack of additional, qualified AI workers was the single biggest hurdle to be overcome in terms of achieving the necessary level of AI implementation across business operations. As an example, Element AI last year estimated that there were fewer than 10,000 people around the world who have the necessary skills to create fully-functional machine learning systems. In short, though huge demand already exists for AI skills, the shortage of talent is slowing down hiring – and without new AI hires, organizations simply cannot press forward with their AI strategies. The AI Talent Pool The AI talent pool is indeed shallow – so where in the world are the few individuals who possess AI skills working today? Well, according to the Global AI Talent Report 2019, the countries that are most committed to training top AI experts are also leading employment – and the US is ahead of them all. Authors trained in China accounted for almost 11% overall, followed by the UK (6%), Germany (5%), and Canada, France and Japan (4% each). Among the authors publishing academic papers at world-leading AI conferences, more than 44% earned their Ph. (Image source: jfgagne.ai) A similar geographical distribution characterized the study’s employment data. American employers attract the lion’s share of top AI talent – 46% worked for a US-based employer. China took the second spot on the list, accounting for 11% of employment, followed by the UK at 7%. Overall, five countries – the US, China, the UK, Germany, and Canada – accounted for 72% of the authors. But where are these highly-trained AI specialists employed? 77% were found to work in academia, with less than one quarter (23%) working in industry – though not all work for an employer based in the same country where they received their training. In fact, overall, it was found that almost a third (27%) of top AI talent were working in a different country. The study’s authors note that though the global map of these movements is complex and the story behind each move inevitably unique and personal, the data nonetheless allows for some observations about the flow of AI skills across national borders. First, certain countries are particularly attractive for AI researchers. According to the survey, US-based employers had the highest chance of attracting talent trained abroad. China was the second-most likely country to draw researchers who had received their Ph Ds in another country, bringing in almost a quarter of the total number of foreign researchers the US was able to attract. The report presumes that several different factors could be contributing to this observation, including the availability of jobs in each country. (Image source: jfgagne.ai) Comparing the talent inflow and outflow in each country as a percentage of the country’s overall talent pool, Gagne et al categorize each country as follows: Inviting Countries, Producer Countries, Anchored Countries, and Platform Countries. Australia, Spain, Sweden and Taiwan all saw more inflow and less outflow – as a proportion of the country’s overall talent pool – than average. This means that these countries are relatively more successful at both retaining the talent they’ve trained at home and attracting talent from other ecosystems. By contrast, France and Israel are considered “Producer Countries”, because they saw less inflow and more outflow than average. The US had both less talent inflow and less talent outflow than average. This does not reflect the size of its talent pool, however – in absolute numbers, the United States remains the leading global talent magnet. Rather, it signals the relative stability of its talent pool – and the same pattern was observed in China, Germany, India, Italy, Japan, and the Republic of Korea. These ecosystems are considered “Anchored Countries”. Finally, several countries – including Canada, the Netherlands, Singapore, Switzerland, and the UK – saw both more inflow and more outflow than average. These countries are succeeding at attracting AI skills from abroad, while also seeing more post-graduate movement than average. These ecosystems are considered “Platform Countries”. (Image source: jfgagne.ai) The data also shed light on some notable talent exchanges between certain countries. For example, there was a particularly strong exchange between China and the US – around 500 of the 22,400 researchers in the data set received their Ph D in China and then went on to work for a US employer, with around 500 more traveling in the opposite direction. A similar phenomenon was observed between the US and the UK, where about 325 AI experts moved to the UK after receiving training in the US, with roughly the same number of UK-trained experts going on to work for a US-based employer. Women Continue to Be Underrepresented Gagne et al also analyzed the global talent pool to try and see what proportion of researchers with top AI skills were women. The review reveals that the field is still miles away from reaching anything close to a gender balance. In last year’s survey, it was found that just 12% of Ph D-qualified authors publishing academic papers at world-leading AI conferences were women. This year, though the figure rose to 18%, it remains woefully imbalanced – and the disparity exists in both industry and academia. The data indicates that 19% of the conference authors who were in academia were women versus 16% in industry. As noted in the report, equality in the field of AI is vital to the technology’s ethical success, given the potential for society-wide impact. Professor Joelle Pineau, who heads the Facebook AI Research lab in Montreal, is quoted: “We have more of a scientific responsibility to act than other fields because we’re developing technology that affects a large proportion of the population,” she said. (Image source: jfgagne.ai) It was also found that, according the AI Index 2018 Report, published by Stanford University, women are also underrepresented in undergraduate AI and machine learning courses. Stanford’s 2017 “Intro to AI” course was 74% male, and UC Berkeley’s course was 73% male, according to the report. An even lower percentage of women enrolled in the universities’ “Intro to Machine Learning” courses, with men accounting for 76% of the students in the Stanford course and 79% of the students in the UC Berkeley course. The same report found that in the US, the majority of applicants (71%) for AI jobs are men. Contributing Factors to the AI Skills Shortage Today, practically every company is considering how artificial intelligence can positively impact their business. According to the 2019 AI Skills Gap report from Snap Logic, 93% of US and UK organizations consider AI and machine learning to be a top business priority, and have various projects planned or already in production. (Image source: snaplogic.com) As demand – and C-suite excitement – for various AI applications builds, demand for AI professionals is ballooning in kind. However, academic and training programs simply can’t keep up with the pace of innovation and new discoveries in AI. AI workers need both official training and on-the-job experience – and, as clearly evidenced by Gagne’s report, there simply aren’t enough experienced AI professionals to step into leadership roles to provide it, and the talent that does exist remains internationally scattered. (Image source: reuters.com) Learning pathways and career strategies are also unclear – especially, as the data reveals, for women. Interest in science, technology, engineering and mathematics (STEM) subjects amongst younger generations remains low – again, especially amongst females – and there are large achievement gaps. According to Reuters, applicants to study AI in UC Berkeley’s electrical engineering and computer sciences doctoral program numbered 341 a decade ago, but had surged to 2,700 by last year. The University of Illinois also recently tripled its enrollment cap on the school’s “Intro to AI” course to 300 – the extra 200 seats were filled within 24 hours. Last fall, Carnegie Mellon University began offering the nation’s first undergraduate degree in artificial intelligence. “We feel strongly that the demand is there,” said Reid Simmons, who directs CMU’s new program. “And we are trying to supply the students to fill that demand.” Still, even as schools and universities across the country add new classes and courses, a fix for the AI skills gap is yet some years away – and it’s impeding growth at many companies. The 2018 “How Companies Are Putting AI to Work Through Deep Learning” survey from O’Reilly reveals that the AI skills gap is the largest barrier to AI adoption – results that are reflected in the Snap Logic survey, which found that 51% of IT leaders say they don’t have the right mix of AI skills in-house to bring their strategies to life. In fact, a lack of skilled talent was cited as the number one barrier to progressing artificial intelligence initiatives by 40% of respondents. (Image source: snaplogic.com) In short, the AI skills gap has emerged primarily because of the quickening pace of digitalization in the workplace, combined with an education system that hasn’t produced workers with the skills required to meet the demands of the modern world. Closing the AI Skills Gap One of the ways to address the AI skills gap is of course to increase resources for digital, math and technical education, as many universities around the US are starting to do, and as the UK Government announced in its 2017 Industrial Strategy white paper. However, while skills acquisition for younger generations will help in the future, simply driving more students into STEM and computer science subjects will not solve the issue either now or in the future – the number of computer science graduates in the UK would need to increase ten-fold to meet 2022 demand. Another avenue is for companies themselves to address the problem head-on and invest more in reskilling their workforces – but they need to do so quickly. The World Economic Forum estimates that more than half (54%) of all employees around the world will require significant reskilling by 2022, though the AI skills gap is even more pronounced in some regions. The WEF highlights European Commission figures, for instance, which indicate that around 37% of workers in Europe don’t have even basic digital skills, let alone the more advanced and specialized AI skills companies need to successfully adopt new technologies. Some large firms are already investing heavily in upskilling their workforce. Amazon, for instance, recently announced a plan to invest more than $700 million to retrain its US workers, which will enable many to move into skilled technical roles in the company’s corporate offices and tech hubs. In particular, Amazon is focusing on job roles like data mapping specialist, data scientist and business analyst – all of which involve and require AI skills. – which provides analysts and systems support to the National Security Agency, the Pentagon, and other US intelligence bodies – also knows the struggle of finding good data scientists, and so in 2017 launched a drive to add 5,000 workers with data analysis, science and engineering skills. However, being aware of the talent shortfall, the company knew that it couldn’t fill all these positions from the outside, so it decided to upskill from within. By early 2019, Booz Allen was providing data analytics and visualization training to about 1,000 employees already on the payroll, reports . “Our business model is dependent on talented people,” said Lloyd Howell, the company’s CFO and Treasurer. “Training comes at a cost, and it certainly has increased over the last five years. But this is an important area for us to keep pace in.” Indeed, it is – as Johnny C. Taylor, Jr., President and CEO for the Society for Human Resource Management (SHRM), puts it: “We are past the point where this is a choice. We need to do this; we need to invest in employee training.” However, many more companies need to follow in Amazon’s and Booz Allen’s footsteps for demands to be met. According to a recent Capgemini report – “Upskilling Your People for the Age of the Machine” – despite the urgent need for action, nearly three-quarters (73%) of organizations have not even begun an upskilling pilot program to adapt their workforces, and only 16% have plans to upskill their current workforce as a primary response to the crisis. (Image source: capgemini.com) The Agorize AI for Societal Impact Challenge There are, of course, other ways for companies to source AI skills. Though the Global AI Talent Report focusses primarily on the number of AI academics currently available to potential employers, the fact is that a college degree is no longer a prerequisite for a career in AI. Companies can develop their own courseware for reskilling and upskilling their employees – several online learning platforms such as Coursera, Udacity and Udemy, for instance, have promised to help businesses stay ahead of digital disruption by offering courses in data science, machine learning and AI. But there are thousands if not millions of individuals around the world who are also taking the initiative to educate themselves. The challenge, however, is connecting these self-trained individuals with the organizations that need them – something that leading online platform for open innovation challenges Agorize is set up to achieve. (Imager source: agorize.com) Agorize consists of a pool of 5 million innovators worldwide, including 3 million students, 1 million developers, 300k startups, and 800k employees. The goal is to connect businesses with these innovators through open innovation challenges – the most recent of which is the AI for Societal Impact Challenge, developed in partnership with Microsoft, the Information Technology Association of Canada (ITAC), and the Royal Bank of Canada (RBC), and led by Aurelie Wen, Managing Partner at Agorize, and Founder of the company’s North American Office. The new challenge is open to all post-secondary students from Canada. The mission is to create or join a team of two to four people and develop a project that will make a societal difference by leveraging Microsoft Azure and Microsoft Learning to further develop or explore new AI skills. All ideas must be based on one of three given topics – Sustainable Future, Future of Work & Education, or Social Equality. (Image source: agorize.com) Participants can take advantage of $100 worth of free Microsoft Azure credits, and are encouraged to explore Microsoft’s Machine Learning Modules to learn new AI skills and spark new ideas for the challenge. Ideas are welcomed from anyone – no previous technical skills are required – and participants can even continue working on their ideas after the challenge, as they will keep the intellectual property rights for everything they develop. Expert mentoring is provided, as well as a range of resources to help participants get inspired, learn new AI skills step-by-step, and find the right tools to start an AI project. “As the world continues to be transformed by technology, everyone should have access to the tools and digital skills critical for their future success,” said Kevin Peesker, President of Microsoft Canada. “At Microsoft, we firmly believe that creating a culture in which technology blends with human potential is the key to thriving in this new cloud economy and are pleased to partner with ITAC and RBC to help upskill Canadians, particularly those in underserved communities.” RBC’s involvement is part of its Future Launch project – a 10-year, $500 million commitment to help Canadian youth prepare for the jobs of tomorrow. The Future Launch program is designed to increase access to skills development, networking opportunities and work experience to help prepare young people in Canada prepare for what’s next in the world of work. “As digital and machine technology advances, the next generation of Canadians will need to be more adaptive, creative and collaborative, adding and refining skills to keep pace with a world of work undergoing profound change,” said Valerie Chort, Vice President of Corporate Citizenship at RBC. “That’s what RBC Future Launch is all about, and through our partnership with ITAC and other organizations, we hope to enable young people to identify, articulate and build their skills – and help young Canadians develop them.” As the leading platform for hackathons and open innovation challenges, Agorize and the partners it works with has helped more than 200 major organizations in North America, Europe and Asia – Including Deloitte, Google, Tinder, HSBC, Schneider Electric, the US Department for Education, and Automation Anywhere – connect with millions of innovators and bring new solutions to life. In addition, Wen and her team have helped more than 60,000 students and innovators launch their startup and find jobs at companies, such as TD National Bank of Canada, Desjardins, Aviva, Radio-Canada, L’Oréal, Pepsi Co, and more. “Large communities and potential clients are interested in the value of our community, first, but also in the platform itself,” said Wen. “From all the platforms that exist in the country, there is no such platform that can manage a hackathon online as well as our platform.” The ideation phase for the AI for Societal Impact Challenge is open now until December 1 – a fantastic opportunity for students and individuals to start honing and expanding their AI skills, connect with businesses in need of them, and expand their networks. Final Thoughts The AI skills gap amounts to nothing short of a crisis that the business world needs to address urgently. The talent pool is alarmingly shallow, with more jobs available than the number of qualified individuals to fill them. Many countries are feeling the strain of the AI skills shortage, and education systems have been too slow to keep up. According to job postings on Indeed, the top ten AI skills in demand from companies today are as follows: (Image source: blog.indeed.com) With so many roles unfilled, the AI skills shortage must be addressed before companies can even begin to hope that they will reach the level of AI integration needed to propel their businesses forward. The time is now for organizations to start upskilling their workforces to prepare for future disruptions and innovations – but at present, many are being too slow to do so. The inaction is difficult to fathom, given the urgent need to develop a proactive response to the AI skills gap – and businesses that don’t take action now to tackle the problem will undoubtedly be left behind. Clearly, there’s no one way to solve the AI skills crisis. At present, however, AI remains a job-seeker’s market, and in lieu of more companies taking the appropriate initiatives, individuals seeking to upskill themselves or pursue a career in AI can do so online through programs like the AI for Societal Impact Challenge, and the many learning opportunities that its founding partners are making available. While there is no easy solution, companies who plan to use AI today or in the future need to consider now how they will tackle the AI skills shortage – and self-motivated individuals should take up the challenges and access the resources being offered to get out ahead. If we don’t take the right steps to educate and (re)train the workforce, what this massive revolution of the working world does mean is that there will be a serious shortage of talent with the necessary AI skills to fill the new jobs that are created. In the next three years, as many as 120 million workers in the world’s twelve largest economies (including 11.5 million in the US) will need to be retrained or reskilled as a result of AI and intelligent automation, according to a new IBM Institute for Business Value study. Additionally, almost two-thirds of today’s students will end up working in jobs that do not yet exist. The shortage of AI skills is seen as a major barrier to the pace of the technology’s adoption. In fact, a recent poll confirmed that 56% of senior AI professionals believed that a lack of additional, qualified AI workers was the single biggest hurdle to be overcome in terms of achieving the necessary level of AI implementation across business operations. Terry is an experienced product management and marketing professional having worked for technology based companies for over 30 years, in different industries including; Telecoms, IT Service Management (ITSM), Managed Service Providers (MSP), Enterprise Security, Business Intelligence (BI) and Healthcare. He has extensive experience defining and driving marketing strategy to align and support the sales process. Whether in the midst of their first or final year of post-secondary education, the new year for students often signals a new job search. This search can encompass anything from a summer job to help pay expenses to a first career opportunity. Whatever their road map, confronting and addressing the skills gap will undoubtedly be part of a graduate’s journey. Entering the Canadian job market is challenging, and the school-to-work transition is no longer guaranteed to be swift, fluid or successful. According to RBC’s Humans Wanted Report, automation will impact at least 50 per cent of Canadian jobs in the next 10 years. Skills mobility – the ability to move from one job to another – will become a new competitive advantage. We know that a skills gap exists, but how can we help young people better understand the skills they have, and how can we better equip them with the tools and resources they need to succeed? The skills gap fundamentally boils down to a dissonant understanding between employers and employees about what skills employees have, and what skills employers expect them to have. According to an August, 2018 survey, 41% of Canadian employers report difficulty filling jobs, citing lack of experience and hard skills among candidates as key reasons. In the school-to-work transition for post-secondary students, this dissonance manifests in the skills and experiences expected for entry-level or summer jobs, compared with the skills and experiences students gain through a formal university education. Some argue that this gap is field-specific; that, for example, our education system produces too many graduates with arts degrees, and not enough with STEM backgrounds. At its core, however, the skills gap represents an opportunity gap – for students and prospective employers alike – that prevents each group from reaching their full potential. The good news related to the skills gap can often be lost in the debate and statistics: there is a strong demand for university graduates, and these graduates have in-demand skills. As outlined in RBC’s Humans Wanted Report, Canada’s new skills economy will give rise to a growing demand for “human skills”- critical thinking, coordination, social perceptiveness, active listening and complex problem solving - across all job sectors. The challenge is to empower students to better understand and communicate their skills, and help them bridge the skills gap. We can help students bridge the skills gap in a number of ways: Skills requirements on job postings often appear daunting. For students and new grads, skills such as interpersonal communication, leadership, and client management may not resonate. Helping students make tangible connections between the skills that professional employers are looking for, and the skills they have developed through different life experiences is key. RBC Upskill is a highly-personalized resource that tackles this challenge. The tool uses Canadian labour market data and takes stock of a user’s career-relevant skills to help young people understand how their skills and work experience will help prepare them for the jobs of the future: leadership skills might stem from a sports team captaincy; communication skills might stem from a summer spent as a camp counselor. Many students and new grads are unaware of the breadth of opportunities associated with their degree, and the various career doors it can open. A first-year philosophy student, for example, might not be aware of the connections between philosophic literature and the Canadian legal system. Similarly, young political science students may not be aware that an entire government relations and lobbying industry employs people across Canada. We know young people are keen, confident, and eager to achieve success. The skills gap is not unique to the world of post-secondary education, but by taking strides to close important information gaps we can better empower students to understand their skills and how they translate professionally, and to help graduates explore all the professional opportunities available. Rbc upskill rbc mcvean Skylight is the fastest way to deliver scalable enterprise apps for wearable, augmented reality and mobile devices. Using low-code development tools, a rich UI framework, and a robust set of platform features, you can quickly develop, integrate, deploy and manage apps that are tailored to your organization’s specific needs. RBC's Upskill Helps You Figure Out How to Get Your Future Job. The job market is always changing. RBC's Future Launch initiative partnered with TWG to design and build a tool that could help people determine how to forecast and retrain themselves for future opportunities. RBC's industry-leading research empowered this product to make smart and. Due to the current COVID-19 outbreak, 4-H Canada has made the difficult decision to postpone Careers on the Grow until Summer 2021. 4-H Canada’s priority continues to be the health, safety and security of our youth members, volunteer leaders, families, and the communities 4-H impacts, while we continue to adjust to the current situation. For more information, please see our latest update on the outbreak. Being a 4-H member prepares you for your future education and career. Through our programs you develop decision making, communication, teamwork, and leadership skills – which are in demand in today’s workplace. 4-H Canada’s Careers on the Grow is a career development program where you can explore career paths, apply your skills and gain hands-on work experience. What’s available to you Take advantage of hands-on learning opportunities through internships placements. Support Careers on the Grow Are you an employer or alumni looking to help 4-H Canada support today’s youth in growing their career path? Your resume is the advertisement of you that's being shared with the work world. Here are five things to keep in mind when crafting your resume that could help get you through the door. A great resume is your first introduction to a potential employer so make it memorable. Students need to promote their personality, interests and values right from the beginning. Focus on trying to showcase yourself as you would in a 30 second elevator pitch. You would never wear sweatpants to a job interview. Consider the design: the font, how much white space to leave between sections, the headlines you choose and how you display them. Also, be hyper vigilant in proofreading for typos or grammatical errors. Write in bullet points, avoid long sentences and consider two pages as an absolute maximum. Look at multiple samples of resumes specific to the field(s) you are interested in to get an idea of how to make your resume look professional. Don’t create a single resume to send out to everyone. Students and new grads often worry they don’t have enough experience and “pad” their resumes with unnecessary information. To stand out, cater each resume to specific needs of the position you’re applying for. Learn about the companies, read their websites, learn their language and use it in your sales pitch (resume). You may not have as much experience as you’d like; neither do most of your peers. That’s why you can focus on other areas of your life that exhibit your best skills. That summer you led a canoe trip, or the year you volunteered at the hospital could make all the difference in getting you that interview. While a sports award is worth including to show discipline, tread carefully. Be highly selective when including hobbies and other items of personal history. This article is intended as general information only and is not to be relied upon as constituting legal, financial or other professional advice. A professional advisor should be consulted regarding your specific situation. Information presented is believed to be factual and up-to-date but we do not guarantee its accuracy and it should not be regarded as a complete analysis of the subjects discussed. All expressions of opinion reflect the judgment of the authors as of the date of publication and are subject to change. No endorsement of any third parties or their advice, opinions, information, products or services is expressly given or implied by Royal Bank of Canada or any of its affiliates.


TORONTO, October 16, 2018 - An RBC survey of more than 2,000 Canadians aged 15-24, has found that across every province and major city, youth are feeling hopeful, but nervous about their future employment prospects. To help young people turn that hope and nervousness into confidence, RBC Future Launch has created RBC Upskill, an online resource using the most current Canadian labour market data to help young people understand how their past experiences and current skills will help prepare them for the jobs of tomorrow. “Overwhelmingly, Canadian youth are feeling anxious and unprepared about entering the workforce, but there’s a silver lining – they also have underlying optimism and excitement for the future,” said Valerie Chort, Vice President, Corporate Citizenship, RBC. “By launching RBC Upskill, we’re offering a freely accessible tool that provides easy-to-understand, personalized information on career possibilities that will help young Canadians build confidence as they navigate their future career prospects.” RBC Upskill offers young Canadians: “RBC Upskill is not only a useful tool for young people, but it’s also a resource for parents who want to give advice but aren’t sure what to say because today’s job market is largely unrecognizable from when they were starting out,” said Mark Beckles, Senior Director, Youth Strategy and Innovation, RBC. “If we can also equip parents with confident advice, perhaps we can relieve some of the nervousness youth are feeling.” Other key findings from the survey include: Survey Methodology From September 17 to 24, 2018 Maru/Blue conducted an online survey amongst 2,022 Canadians from across the country, 984 of whom were aged 15 to 17 and 1,038 of whom were 18 to 24 years old. The survey was conducted in both English and French. About RBC Royal Bank of Canada is a global financial institution with a purpose-driven, principles-led approach to delivering leading performance. Our success comes from the 84,000 employees who bring our vision, values and strategy to life so we can help our clients thrive and communities prosper. Learn more at ‎ We are proud to support a broad range of community initiatives through donations, community investments and employee volunteer activities. As Canada’s biggest bank, and one of the largest in the world based on market capitalization, we have a diversified business model with a focus on innovation and providing exceptional experiences to our 16 million clients in Canada, the U. Developing your “human skills”, including critical thinking, co-ordination, social perceptiveness, active listening and complex problem solving will equip you with a strong foundation to build your career on. Focusing on “human skills” will empower you with the ability to pivot between careers and across sectors. A highly personalized career tool that takes stock of your career-relevant skills Enables career exploration and identifies different career possibilities based on your unique skills and interests Incorporates the latest Canadian labour market data on job demand, projected growth, automation and earning potential Provides additional career tools including Alyssa Deville, 28After finishing high school and working in retail for years, Alyssa decided she wanted something more. She methodically upgraded her skills by way of certificate programs, college courses, on-the-job training and an apprenticeship. She’s used her newfound mobility to leap from a job as a vendor representative to a traffic-control manager to a sheet-metal apprentice. She hopes to train her way into a foreman’s role, and eventually a teaching position at a technical school. Hanif Syed, 27Hanif is an industrial engineer who leapt into a seemingly unrelated leadership opportunity in the health field. Hanif studied engineering at university, but diversified his skills with graphic design, information technology and a leadership program. In his first engineering job, he involved himself in all sides of the business — technical, sales, operations — so when an opportunity arose to become a regional director for Saint Elizabeth Health Care, he was ready to pursue his old dream of a career in health care. Andréa Crofts, 27Andréa had already left public relations to pursue her longstanding interest in graphic design when she realized she didn’t have the digital skills to produce what clients were asking for. She immersed herself in coding, enrolling in two web-development courses, including a nine-week boot camp. By taking her technical skills into her own hands, Andréa revamped her skills profile and quickly landed a job as a product designer for a software design and development company. This article is intended as general information only and is not to be relied upon as constituting legal, financial or other professional advice. A professional advisor should be consulted regarding your specific situation. Information presented is believed to be factual and up-to-date but we do not guarantee its accuracy and it should not be regarded as a complete analysis of the subjects discussed. All expressions of opinion reflect the judgment of the authors as of the date of publication and are subject to change. No endorsement of any third parties or their advice, opinions, information, products or services is expressly given or implied by Royal Bank of Canada or any of its affiliates. Rbc upskill dvmenligne We are building tools and engineering opportunities through RBC Upskill ®, RBC Career Launch ®, Ten Thousand Coffees, and Riipen, and supporting initiatives like WE Are Social Entrepreneurs and Boys & Girls Clubs of Canada, to accelerate their readiness for the changing world of work. We’ve also expanded our national network of charitable. Introducing RBC Upskill. RBC Training Ground is a talent identification and athlete funding program designed to uncover athletes with Olympic potential. Skylight is the fastest way to deliver scalable enterprise apps for wearable, augmented reality and mobile devices. Using low-code development tools, a rich UI framework, and a robust set of platform features, you can quickly develop, integrate, deploy and manage apps that are tailored to your organization’s specific needs. G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object or an image, setting a parameter value or selecting a range Abstract The description relates to systems and methods for extending applications. For example, a voice assistant application can be the application to be extended. In an example, a mobile banking application can be the application that provides the extension. For example, a voice assistant might not have capability to conduct fingerprint (or biometric) authentication and bill payment function. An extension point within the voice assistant application that would enable this kind of capability might not exist. The mobile banking application can have a biometric tool for fingerprint authentication capability and a payment tool for a bill payment or money transfer function. Embodiments described herein can involve a deep link from the voice assistant application to the mobile banking application (which does offer fingerprint authentication and bill payment capability). The navigation to the mobile banking application can generate a visual impression at the Ul similar or consistent with the voice assistant application. Abstract The description relates to systems and methods for extending applications. For example, a voice assistant application can be the application to be extended. In an example, a mobile banking application can be the application that provides the extension. For example, a voice assistant might not have capability to conduct fingerprint (or biometric) authentication and bill payment function. An extension point within the voice assistant application that would enable this kind of capability might not exist. The mobile banking application can have a biometric tool for fingerprint authentication capability and a payment tool for a bill payment or money transfer function. Embodiments described herein can involve a deep link from the voice assistant application to the mobile banking application (which does offer fingerprint authentication and bill payment capability). The navigation to the mobile banking application can generate a visual impression at the UI similar or consistent with the voice assistant application. Abstract A method for executing a data processing task for an electronic transaction includes receiving an electronic transaction data set including parameters identifying a quantity of an electronic resource consumed from a purchaser resource pool by the data processing task; A method for executing a data processing task for an electronic transaction includes receiving an electronic transaction data set including parameters identifying a quantity of an electronic resource consumed from a purchaser resource pool by the data processing task; upon receiving response signals indicative of instructions to defer execution of at least a portion of the data processing task thereby deferring a corresponding consumption of at least a portion of the quantity of the electronic resource from the purchaser resource pool, generating a second data processing task for releasing at least the portion of the electronic resource to the purchaser resource pool; and generating deferred child data processing tasks for executing at least the portion of the data processing task at one or more future times which when executed, consume at least the portion of the quantity of the electronic resource from the purchaser resource pool. Abstract A method for executing a data processing task for an electronic transaction includes receiving an electronic transaction data set including parameters identifying a quantity of an electronic resource consumed from a purchaser resource pool by the data processing task; upon receiving response signals indicative of instructions to defer execution of at least a portion of the data processing task thereby deferring a corresponding consumption of at least a portion of the quantity of the electronic resource from the purchaser resource pool, generating a second data processing task for releasing at least the portion of the electronic resource to the purchaser resource pool; and generating deferred child data processing tasks for executing at least the portion of the data processing task at one or more future times which when executed, consume at least the portion of the quantity of the electronic resource from the purchaser resource pool. Abstract An improved computer implemented method and corresponding systems and computer readable media for improving performance of a deep neural network are provided to mitigate effects related to catastrophic forgetting in neural network learning. In an embodiment, the method includes storing, in memory, logits of a set of samples from a previous set of tasks (D1), and maintaining classification information from the previous set of tasks by utilizing the logits for matching during training on a new set of tasks (D2). Abstract An improved computer implemented method and corresponding systems and computer readable media for improving performance of a deep neural network are provided to mitigate effects related to catastrophic forgetting in neural network learning. In an embodiment, the method includes storing, in memory, logits of a set of samples from a previous set of tasks (D1); and maintaining classification information from the previous set of tasks by utilizing the logits for matching during training on a new set of tasks (D2). Abstract A system receives data associated with a communication between one or more individuals. The data is split between each of the one or more individuals into text associated with that individual. Each of the text is modified to remove stop words and to duplicate key words. The text is merged to form a text corpus, from which a bag of words model is generated. Topics of the bag of words are classified using a topic classifier model. A purpose is identified based on the returned topic and keywords from the topic classifier model. Returned topics and keywords from the topic classifier model are linked to the communication. Goods & Services Financing services, namely, the purchasing, leasing, advancing, financing, administration, servicing and collection of loans, mortgages, leases, conditional sales contracts, accounts receivable and other assets through an asset securitization vehicle, including the raising of funds in the public and private debt market to finance such purchases and advances Abstract A platform that involves a natural language engine with a corpora to process vendor assurance reports, e.g., SOC1, SSAE16, etc. for summarizing key sections, detecting important sections and key phrases of reports, extracting exceptions and noting client control considerations, and trending the reports. Abstract A platform that involves a natural language engine with a corpora to process vendor assurance reports, e.g., SOC1, SSAE16, etc. for summarizing key sections, detecting important sections and key phrases of reports, extracting exceptions and noting client control considerations, and trending the reports. Abstract A system, non-transitory computer-readable medium, and method for approving access permissions are provided. The system comprises at least one processor and memory storing instructions which when executed by the at least one processor configure the at least one processor to perform the method. The non-transitory computer-readable medium has instructions thereon, which when executed by a processor, perform the method. The method comprises transforming enterprise access data into data sets, identifying business roles based on common patterns of the access data, presenting at least one business role assignable to an employee to an access manager, and receiving an approval indication input associated with the access manager assigning the business role to the employee. The business roles comprises at least one access point associated with the access data. Abstract Embodiments relate to systems and methods for intercepting potentially erroneous electronic transaction data processing tasks. The system includes a memory and processor configured for: receiving a processing task data set including at least one parameter for executing an electronic transaction data processing task; providing, to a multi-class classifier, an input data set with the at least one parameter for executing the electronic transaction data processing task and at least one data feature associated with a user profile to generate a classification probability output for each class in the multi-class classifier; and when none of the classification probability outputs meet a threshold condition, preventing execution of the electronic transaction data processing task. Abstract System and methods providing for categorizing individual virtual machines, as well as the associated application that they form by working in concert, into groups based on the feasibility of hosting the processes that occur on a virtual machine within a container, as well as the relative difficulty of doing so on a virtual machine and application level. The data used to create these scores is collected from the individual machines, at regular intervals through the use of an automated scoring engine that collects and aggregates the data. Said data is then analyzed by the system, that with the aid of passed in configuration data, is configured to generate the scores to allows for an educated and focused effort to migrate from hosting applications on virtual machines to hosting applications on containers. Abstract System and methods providing for categorizing individual virtual machines, as well as the associated application that they form by working in concert, into groups based on the feasibility of hosting the processes that occur on a virtual machine within a container, as well as the relative difficulty of doing so on a virtual machine and application level. The data used to create these scores is collected from the individual machines, at regular intervals through the use of an automated scoring engine that collects and aggregates the data. Said data is then analyzed by the system, that with the aid of passed in configuration data, is configured to generate the scores to allows for an educated and focused effort to migrate from hosting applications on virtual machines to hosting applications on containers. Abstract Embodiments relate to systems and methods for intercepting potentially erroneous electronic transaction data processing tasks. The system includes a memory and processor configured for: receiving a processing task data set including at least one parameter for executing an electronic transaction data processing task; providing, to a multi-class classifier, an input data set with the at least one parameter for executing the electronic transaction data processing task and at least one data feature associated with a user profile to generate a classification probability output for each class in the multi-class classifier; and when none of the classification probability outputs meet a threshold condition, preventing execution of the electronic transaction data processing task. Abstract A system receives data associated with a communication between one or more individuals. The data is split between each of the one or more individuals into text associated with that individual. Each of the text is modified to remove stop words and to duplicate key words. The text is merged to form a text corpus, from which a bag of words model is generated. Topics of the bag of words are classified using a topic classifier model. A purpose is identified based on the returned topic and keywords from the topic classifier model. Returned topics and keywords from the topic classifier model are linked to the communication. Abstract A system, non-transitory computer-readable medium, and method for approving access permissions are provided. The system comprises at least one processor and memory storing instructions which when executed by the at least one processor configure the at least one processor to perform the method. The non-transitory computer-readable medium has instructions thereon, which when executed by a processor, perform the method. The method comprises transforming enterprise access data into data sets, identifying business roles based on common patterns of the access data, presenting at least one business role assignable to an employee to an access manager, and receiving an approval indication input associated with the access manager assigning the business role to the employee. The business roles comprises at least one access point associated with the access data. Goods & Services (1) Financing services, namely, the purchasing, leasing, advancing, financing, administration, servicing and collection of loans, mortgages, leases, conditional sales contracts, accounts receivable and other assets through an asset securitization vehicle, including the raising of funds in the public and private debt market to finance such purchases and advances. Abstract Systems, devices, methods, and computer readable media are provided in various embodiments having regard to authentication using secure tokens, in accordance with various embodiments. An individual's personal information is encapsulated into transformed digitally signed tokens, which can then be stored in a secure data storage (e.g., a “personal information bank”). The digitally signed tokens can include blended characteristics of the individual (e.g., 2D/3D facial representation, speech patterns) that are combined with digital signatures obtained from cryptographic keys (e.g., private keys) associated with corroborating trusted entities (e.g., a government, a bank) or organizations of which the individual purports to be a member of (e.g., a dog-walking service). Abstract An electronic payment device and methods of its operation are disclosed. The payment device has a secure element for storing payment tokens, each associated with a payment card; an input interface that enables a user to select from among the payment cards; a display interface; and a processor. In response to a user selection of one of the payment cards by way of the input interface, a descriptor of the selected payment card is displayed by way of the display interface; and an unconsumed one of the payment tokens associated with the selected payment card is activated to prepare the payment card device for effecting payment using the selected payment card, thereby consuming the payment token. The payment device also includes a wireless communication interface for receiving additional payment tokens, thereby replenishing the payment tokens. Abstract Systems, devices, methods, and computer readable media are provided in various embodiments having regard to authentication using secure tokens, in accordance with various embodiments. An individual's personal information is encapsulated into transformed digitally signed tokens, which can then be stored in a secure data storage (e.g., a "personal information bank"). The digitally signed tokens can include blended characteristics of the individual (e.g., 2D / 30 facial representation, speech patterns) that are combined with digital signatures obtained from cryptographic keys (e.g., private keys) associated with corroborating trusted entities (e.g., a government, a bank) or organizations of which the individual purports to be a member of (e.g., a dog-walking service). Abstract A computer implemented system for controlling access to data associated with an entity includes a data storage device having a protected memory region, and one or more processors, at least one of which is operable in the protected memory region. The one or more processors are configured for: storing a secret key associated with the entity in a portion of the protected memory region associated with the entity; upon receiving entity data, storing the entity data in the portion of the protected memory region associated with the entity; and upon receiving an access grant signal, generating a smart contract, the smart contract defining the entity data to be accessed and a recipient of the entity data to be accessed. Abstract An electronic payment device and methods of its operation are disclosed. The payment device has a secure element for storing payment tokens, each associated with a payment card; an input interface that enables a user to select from among the payment cards; a display interface; and a processor. In response to a user selection of one of the payment cards by way of the input interface, a descriptor of the selected payment card is displayed by way of the display interface; and an unconsumed one of the payment tokens associated with the selected payment card is activated to prepare the payment card device for effecting payment using the selected payment card, thereby consuming the payment token. The payment device also includes a wireless communication interface for receiving additional payment tokens, thereby replenishing the payment tokens. Goods & Services (1) Downloadable mobile application for mobile phones and computer tablets designed to allow users to learn, practice and apply proven job search techniques to improve their confidence and capabilities in their search for job opportunities while simultaneously increasingly the likelihood of them finding a desired job, sooner. (1) Providing employment counseling information on how to successfully increase the likelihood of finding a desired job via an interactive website portal designed to allow users to learn, practice and apply proven job search techniques to improve their confidence and capabilities in their search for job opportunities while simultaneously increasingly the likelihood of them finding a desired job, sooner. Abstract A computer implemented system for electronic verification of credentials including at least one processor and data storage is described in various embodiments. The system includes cryptographic mechanisms and electronic communication between one or more computing systems that in concert, provide verification of a prover's credentials in accordance to logical conditions of a verifier's policy without providing additional information to a verifier entity. Abstract A computer implemented system for controlling access to data associated with an entity includes a data storage device having a protected memory region, and one or more processors, at least one of which is operable in the protected memory region. The one or more processors are configured for: storing a secret key associated with the entity in a portion of the protected memory region associated with the entity; upon receiving entity data, storing the entity data in the portion of the protected memory region associated with the entity; and upon receiving an access grant signal, generating a smart contract, the smart contract defining the entity data to be accessed and a recipient of the entity data to be accessed. Abstract A computer implemented system for electronic verification of credentials including at least one processor and data storage is described in various embodiments. The system includes cryptographic mechanisms and electronic communication between one or more computing systems that in concert, provide verification of a prover's credentials in accordance to logical conditions of a verifier's policy without providing additional information to a verifier entity. Abstract An electronic device and method of correcting bias for supervised machine learning data is provided. The electronic device comprises a processor and memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises training an auto-encoder with an unbiased subset of historical data, and applying the auto-encoder to correct historical data. Abstract A system receives transaction data over time, and creates structured data based on the received transaction data. Purchase transactions that are associated with a purchase category are identified in the structured data and labeled. A recurrent neural network such as a long short-term memory (LSTM) network, in particular, a k-LSTM architecture using weighted averages to update hidden states and cell states, is trained to build a model. The model is used to predict the likelihood of a purchase transaction. Abstract Systems and methods for processing natural language statements. Based on historical records of data associated with an entity, systems and methods provide models for inferring publication of data content associated with the particular entity. The systems and methods may compare newly observed data content to predicted content associated with an entity for evaluating novelty or impact of the newly observed data content. Abstract A portfolio analytics platform can implement a crawler and natural language processor to identify relevant articles. The natural language processor can integrate a text analysis tool, domain specific latent Dirichlet allocation tool, and theme measurement tool for identifying themes relevant to a particular domain. For example, the domain specific latent Dirichlet allocation tool identifies domain specific themes and uses an iterative process for eliminating articles or themes that are not specific to the domain. The theme measurement tool uses term frequency-inverse document frequency for naming and identifying the most relevant themes. The platform can generate interactive visual elements for an interface application. Abstract A system receives transaction data over time, and creates structured data based on the received transaction data. Purchase transactions that are associated with a purchase category are identified in the structured data and labeled. A recurrent neural network such as a long short-term memory (LSTM) network, in particular, a k-LSTM architecture using weighted averages to update hidden states and cell states, is trained to build a model. The model is used to predict the likelihood of a purchase transaction. Abstract A portfolio analytics platform can implement a crawler and natural language processor to identify relevant articles. The natural language processor can integrate a text analysis tool, domain specific latent Dirichlet allocation tool, and theme measurement tool for identifying themes relevant to a particular domain. For example, the dorinain specific latent Dirichlet allocation tool identifies domain specific themes and uses an iterative process for eliminating articles or themes that are not specific to the domain. The theme measurement tool uses terrn frequency-inverse document frequency for naming and identifying the most relevant themes. The platform can generate interactive visual elements for an interface application. Abstract An electronic device and method of correcting bias for supervised machine learning data is provided. The electronic device comprises a processor and memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises training an auto-encoder with an unbiased subset of historical data, and applying the auto-encoder to correct historical data. Abstract Systems and methods for processing natural language statements. Based on historical records of data associated with an entity, systems and methods provide models for inferring publication of data content associated with the particular entity. The systems and methods may compare newly observed data content to predicted content associated with an entity for evaluating novelty or impact of the newly observed data content. Abstract A system for processing data within a Trusted Execution Environment (TEE) of a processor is provided. The system may include: a trust manager unit for verifying identity of a partner and issuing a communication key to the partner upon said verification of identity; at least one interface for receiving encrypted data from the partner encrypted using the communication key; a secure database within the TEE for storing the encrypted data with a storage key and for preventing unauthorized access of the encrypted data within the TEE; and a recommendation engine for decrypting and analyzing the encrypted data to generate recommendations based on the decrypted data. Abstract Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. The task data are processed to compute a first performance metric reflective of performance of the automated agent relative to other entities in a first time interval, and a second performance metric reflective of performance of the automated agent relative to other entities in a second time interval. A reward for the reinforcement learning neural network that reflects a difference between the second performance metric and the first performance metric is computed and provided to the reinforcement learning neural network to train the automated agent. Goods & Services Downloadable mobile application for mobile phones and computer tablets having two interfaces, one interface for children that allow them to purchase goods and services of others with a virtual payment card, make deposits in banking accounts, transfer funds between banking accounts and otherwise learn about management of money and another interface for parents to oversee the activities and financial transactions of their children via the mobile application Abstract Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. The task data are processed to compute a first performance metric reflective of performance of the automated agent relative to other entities in a first time interval, and a second performance metric reflective of performance of the automated agent relative to other entities in a second time interval. A reward for the reinforcement learning neural network that reflects a difference between the second performance metric and the first performance metric is computed and provided to the reinforcement learning neural network to train the automated agent. Abstract A system for processing data within a Trusted Execution Environment (TEE) of a processor is provided. The system may include: a trust manager unit for verifying identity of a partner and issuing a communication key to the partner upon said verification of identity, at least one interface for receiving encrypted data from the partner encrypted using the communication key; a secure database within the TEE for storing the encrypted data with a storage key and for preventing unauthorized access of the encrypted data within the TEE; and a recommendation engine for decrypting and analyzing the encrypted data to generate recommendations based on the decrypted data. Abstract A system for reinforcement learning in a dynamic resource environment includes at least one memory and at least one processor configured to provide an electronic resource environment comprising: a matching engine and the resource generating agent configured for: obtaining from a historical data processing task database a plurality of historical data processing tasks, each historical data processing task including respective task resource requirement data; for a historical data processing task of the plurality of historical data processing tasks, generating layers of data processing tasks wherein a first layer data processing task has an incremental variant in its resource requirement data relative to resource requirement data for a second layer data processing task; and providing the layers of data processing tasks for matching by the machine engine. Abstract A system for processing data within a Trusted Execution Environment (TEE) of a processor is provided. The system may include: a trust manager unit for verifying identity of a partner and issuing a communication key to the partner upon said verification of identity; at least one interface for receiving encrypted data from the partner encrypted using the communication key; a secure database within the TEE for storing the encrypted data with a storage key and for preventing unauthorized access of the encrypted data within the TEE; and a recommendation engine for decrypting and analyzing the encrypted data to generate recommendations based on the decrypted data. Abstract A smart order router for quantitative trading and order routing and corresponding methods and computer readable media are described. The smart order router includes a machine learning prediction engine configured to, responsive to a control signal received from an upstream trading engine including at least a maximum quantity value and an urgency metric, process input data sets through one or more predictive models to generate the one or more potential combinations of child orders and their associated fill probability metrics, toxicity metrics, and expected gain (loss) metrics and an order placement optimization engine configured to receive the one or more potential combinations of child orders and their associated fill probability metrics, toxicity metrics, and expected gain (loss) metrics and to identify an optimum combination of child orders that maximize an objective function. Abstract A system for reinforcement learning in a dynamic resource environment includes at least one memory and at least one processor configured to provide an electronic resource environment comprising: a matching engine and the resource generating agent configured for: obtaining from a historical data processing task database a plurality of historical data processing tasks, each historical data processing task including respective task resource requirement data; for a historical data processing task of the plurality of historical data processing tasks, generating layers of data processing tasks wherein a first layer data processing task has an incremental variant in its resource requirement data relative to resource requirement data for a second layer data processing task; and providing the layers of data processing tasks for matching by the machine engine. Abstract A smart order router for quantitative trading and order routing and corresponding methods and computer readable media are described. The smart order router includes a machine learning prediction engine configured to, responsive to a control signal received from an upstream trading engine including at least a maximum quantity value and an urgency metric, process input data sets through one or more predictive models to generate the one or more potential combinations of child orders and their associated fill probability metrics, toxicity metrics, and expected gain (loss) metrics and an order placement optimization engine configured to receive the one or more potential combinations of child orders and their associated fill probability metrics, toxicity metrics, and expected gain (loss) metrics and to identify an optimum combination of child orders that maximize an objective function. Abstract A platform for training deep neural networks using push-to-corner preprocessing and adversarial training. A training engine adds a preprocessing layer before the input data is fed into a deep neural network at the input layer, for pushing the input data further to the corner of its domain. Abstract A platform for training deep neural networks using push-to-corner preprocessing and adversarial training. A training engine adds a preprocessing layer before the input data is fed into a deep neural network at the input layer, for pushing the input data further to the corner of its domain. Abstract Embodiments described herein relation to IT incident management that can involve predictive analytics, prescriptive analytics, and descriptive analytics. An IT incident management platform can enable IT incident solution prediction using prescriptive models and natural language processing. An IT incident management platform can enable IT incident ticket volume prediction using predictive models and natural language processing. An IT incident management platform can generate visual elements for display at an interactive interface that represents data centre topology network graphs using descriptive models. Abstract A virtual agent can implement a “chatbot” to provide output based on predictive/prescriptive models for incidents. The virtual agent can integrate with natural language processor for text analysis and summary report generation. The virtual agent can integrate with cognitive search to enable processing of search requests and retrieval of search results. The virtual agent uses computing processes with self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. The virtual agent provides an automated IT system that is capable of resolving incidents without requiring human assistance. The virtual agent can display condensed summaries of a large amount of data and can link the summaries to predictive models and operational risk models to identify risk events and provide summaries of those events. Abstract A virtual agent can implement a "chatbot" to provide output based on predictive/prescriptive models for incidents. The virtual agent can integrate with natural language processor for text analysis and summary report generation. The virtual agent can integrate with cognitive search to enable processing of search requests and retrieval of search results. The virtual agent uses computing processes with self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. The virtual agent provides an automated IT system that is capable of resolving incidents without requiring human assistance. The virtual agent can display condensed summaries of a large amount of data and can link the summaries to predictive models and operational risk models to identify risk events and provide summaries of those events. Abstract Embodiments described herein relation to IT incident management that can involve predictive analytics, prescriptive analytics, and descriptive analytics. An IT incident management platform can enable IT incident solution prediction using prescriptive models and natural language processing. An IT incident management platform can enable IT incident ticket volume prediction using predictive models and natural language processing. An IT incident management platform can generate visual elements for display at an interactive interface that represents data centre topology network graphs using descriptive models. Abstract A cybersecurity platform that process collected data using a data model to generate security events linked to IP addresses, locations, or other variable information. The platform identifies potential false positive security events using a stability measure based on the variable information, which is then used to constrain the set of security events to reduce the effect of or remove the false positive security events from an output data structure. Abstract A cybersecurity platform is described that processes collected data using a data model to identify and link anomalies and in order to identify generate security events and intrusions. The platform generates graph data structures using the security anomalies extended using additional data. The graph data structures represent links between nodes, the links being events, the nodes being machines and user accounts. The platform processes the graph data structures by combining similar nodes or grouping security events with common features to behaviour indicative of a single or multiple security events to identify chains of events which together represent an attack. Abstract A cybersecurity platform is described that processes collected data using a data model to identify and link anomalies and in order to identify generate security events and intrusions. The platform generates graph data structures using the security anomalies extended using additional data. The graph data structures represent links between nodes, the links being events, the nodes being machines and user accounts. The platform processes the graph data structures by combining similar nodes or grouping security events with common features to behaviour indicative of a single or multiple security events to identify chains of events which together represent an attack. Abstract A cybersecurity platform that process collected data using a data model to generate security events linked to IP addresses, locations, or other variable information. The platform identifies potential false positive security events using a stability measure based on the variable information, which is then used to constrain the set of security events to reduce the effect of or remove the false positive security events from an output data structure. Abstract Systems, methods, and computer-readable media for coordinating processing of data by multiple networked computing resources include monitoring data associated with a plurality of networked computing resources, and coordinating the routing of data processing segments to the networked computing resources. Abstract A composite cryptographic data structure is described, and corresponding methods, systems, and computer readable media. The composite cryptographic data structure is instantiated based on an underlying set of cryptographic tokens (e.g., blockchain/distributed ledger tokens) that, in some embodiments, are transferrable through on-chain transactions established on one or more distributed ledger networks. Identity validation, in some embodiments, may occur at one of composite cryptographic data structure instantiation or composite cryptographic data structure redemption, or both, through the use of a whitelist or a blacklist data structure. Abstract A system and method for handling crypto-asset transactions includes: receiving from a payment processing system an electronic transaction request including: a payment token corresponding to a payment identifier associated with the customer account, and a transaction amount in a fiat currency; determining current price data corresponding to a first crypto-asset and a second crypto-asset associated with the customer account; associating the electronic transaction request with at least one data processing task for executing at least one crypto-asset transaction; and when at least one crypto-asset confidence condition is satisfied based on the current price data of at least one of the first crypto-asset or the second crypto-asset, generating signals for providing, via the payment processing system, an indication that the electronic transaction request is authorized without waiting for confirmation of execution of the at least one crypto-asset transaction in the respective distributed ledger. Goods & Services (1) Downloadable mobile application for mobile phones and computer tablets to provide support to merchants by providing them with business intelligence data analytics and information for storing, managing, tracking, analyzing, and reporting retail business data in the field of marketing, promotion, sales, customer information, financial revenues, customer account data management, for tracking the performance of their business and the rating the experience of their customers, and by providing them with tools to manage their relationship with their customers and have the ability to communicate with their customers by sending targeted advertising and marketing materials or other personalized information about their products and services; downloadable mobile personal financial management application for mobile phones and computer tablets for use in managing and analyzing personal finance with access to online banking and credit card services, and featuring financial management advice and educational information on payment solutions, spending, savings, cash flow, budgeting and financial management tips, and financial management tools, namely, online bill payment and bill calendaring services, financial plan creation, assessing and setting spending goals, spending monitoring, financial account alerts. (1) Providing a web-based portal designed to provide support to merchants by providing them with business intelligence data analytics and information for storing, managing, tracking, analyzing, and reporting retail business data in the field of marketing, promotion, sales, customer information, financial revenues, customer account data management, for tracking the performance of their business and the rating the experience of their customers, and by providing them with tools to manage their relationship with their customers and have the ability to communicate with their customers by sending targeted advertising and marketing materials or other personalized information about their products and services. Abstract A composite cryptographic data structure is described, and corresponding methods, systems, and computer readable media. The composite cryptographic data structure is instantiated based on an underlying set of cryptographic tokens (e.g., blockchain / distributed ledger tokens) that, in some embodiments, are transferrable through on-chain transactions established on one or more distributed ledger networks. Identity validation, in some embodiments, may occur at one of composite cryptographic data structure instantiation or composite cryptographic data structure redemption, or both, through the use of a whitelist or a blacklist data structure. Abstract A system and method for handling crypto-asset transactions includes: receiving from a payment processing system an electronic transaction request including: a payment token corresponding to a payment identifier associated with the customer account, and a transaction amount in a fiat currency; determining current price data corresponding to a first crypto-asset and a second crypto-asset associated with the customer account; associating the electronic transaction request with at least one data processing task for executing at least one crypto-asset transaction; and when at least one crypto-asset confidence condition is satisfied based on the current price data of at least one of the first crypto-asset or the second crypto-asset, generating signals for providing, via the payment processing system, an indication that the electronic transaction request is authorized without waiting for confirmation of execution of the at least one crypto-asset transaction in the respective distributed ledger. Goods & Services (1) Travel information services; digital travel site, namely providing information, commentary and resources in the field of travel tailored to users' interests, providing information and notifications to users concerning itineraries, flights, weather, accommodations, destinations, events and costs related thereto via mobile phone, computer and electronic communications networks. Abstract A computer implemented system and method for automated estimation of relationships among a plurality of data elements. The approach includes processing elements of one or more data sets to establish linkage relations among the data records, and then extending the linkage relations based on one or more equivalence relations, stored as linkage data structures. The generated data structures are used for computationally simplifying the data sets by consolidating data records or removing redundancies, such as duplicates, and may be used to yield a compressed data representation or data structure. Abstract A computer implemented system and method for automated estimation of relationships among a plurality of data elements. The approach includes processing elements of one or more data sets to establish linkage relations among the data records, and then extending the linkage relations based on one or more equivalence relations, stored as linkage data structures. The generated data structures are used for computationally simplifying the data sets by consolidating data records or removing redundancies, such as duplicates, and may be used to yield a compressed data representation or data structure. Abstract A method for generating visual representations of financial interests includes: receiving an input data set including one or more data structures storing data fields and data values representative of financial interests; extracting, from the input data, one or more extracted features from the funds, the extracted features collectively indicative of a distance between different funds; generating one or more clusters of funds, based on the extracted features of the funds; determining, based on identified differences between one or more funds relative to at least one other fund in a corresponding cluster of funds, one or more fund anomalies based on the one or more extracted features; generating one or more adjustment recommendations based on the one or more fund anomalies, the one or more adjustment recommendations representing control instruction sets for automatically modifying characteristics of the corresponding fund. Abstract Systems and methods are provided to monitor performance of a machine learning model, the method may include steps of: receiving or storing one or more model data sets representative of the machine learning model, wherein the machine learning model has being trained with a first set of training data; analyzing the first set of training data based on one or more performance parameters for the machine learning model, to generate one or more performance data sets; and process the one or more performance data sets to determine one or more values representing a performance of the machine learning model. Abstract Systems and methods are provided to monitor performance of a machine learning model, the method may include steps of: receiving or storing one or more model data sets representative of the machine learning model, wherein the machine learning model has being trained with a first set of training data; analyzing the first set of training data based on one or more performance parameters for the machine learning model, to generate one or more performance data sets; and process the one or more performance data sets to determine one or more values representing a performance of the machine learning model. Abstract A method for generating visual representations of financial interests includes: receiving an input data set including one or more data structures storing data fields and data values representative of financial interests; extracting, from the input data, one or more extracted features from the funds, the extracted features collectively indicative of a distance between different funds; generating one or more clusters of funds, based on the extracted features of the funds; determining, based on identified differences between one or more funds relative to at least one other fund in a corresponding cluster of funds, one or more fund anomalies based on the one or more extracted features; generating one or more adjustment recommendations based on the one or more fund anomalies, the one or more adjustment recommendations representing control instruction sets for automatically modifying characteristics of the corresponding fund. Abstract Systems, devices, methods, and computer readable media for training a machine learning architecture include: receiving one or more observation data sets representing one or more observations associated with at least a portion of a state; and training the machine learning architecture with the one or more observation data sets, where the training includes updating the plurality of weights based on an error value, and at least one time-varying step-size value; wherein the at least one step-size value is based on a set of meta-weights which vary based on a stochastic meta-descent. Abstract Systems, devices, methods, and computer readable media for training a machine learning architecture include: receiving one or more observation data sets representing one or more observations associated with at least a portion of a state; and training the machine learning architecture with the one or more observation data sets, where the training includes updating the plurality of weights based on an error value, and at least one time-varying step-size value; wherein the at least one step-size value is based on a set of meta-weights which vary based on a stochastic meta-descent. Abstract Systems, methods, and computer-readable media for coordinating processing of data by multiple networked computing resources include monitoring data associated with a plurality of networked computing resources, and coordinating the routing of data processing segments to the networked computing resources. Goods & Services Financial services, namely, wealth management services, financial planning and investment advisory services, estate planning, providing information in the field of charitable monetary giving through financial and estate planning Abstract Systems, methods, and computer readable media are described to train a compressed neural network with high robustness. The neural network is first adversarially pre-trained with both original data as well as data perturbed by adversarial attacks for some epochs, then “unimportant” weights or filters are pruned through criteria based on their magnitudes or other method (e.g., Taylor approximation of the loss function), and the pruned neural network is retrained with both clean and perturbed data for more epochs. Abstract Systems, methods, and computer readable media are described to train a compressed neural network with high robustness. The neural network is first adversarially pre-trained with both original data as well as data perturbed by adversarial attacks for some epochs, then "unimportant" weights or filters are pruned through criteria based on their magnitudes or other method (e.g., Taylor approximation of the loss function), and the pruned neural network is retrained with both clean and perturbed data for more epochs. Abstract Systems, methods, and computer readable media directed to interactive reinforcement learning with dynamic reuse of prior knowledge are described in various embodiments. The interactive reinforcement learning is adapted for providing computer implemented systems for dynamic action selection based on confidence levels associated with demonstrator data or portions thereof. Abstract Disclosed herein are a system and method for providing a machine learning architecture based on monitored demonstrations. The system may include: a non-transitory computer-readable memory storage; at least one processor configured for dynamically training a machine learning architecture for performing one or more sequential tasks, the at least one processor configured to provide: a data receiver for receiving one or more demonstrator data sets, each demonstrator data set including a data structure representing the one or more state-action pairs; a neural network of the machine learning architecture, the neural network including a group of nodes in one or more layers; and a pre-training engine configured for processing the one or more demonstrator data sets to extract one or more features, the extracted one or more features used to pre-train the neural network based on the one or more state-action pairs observed in one or more interactions with the environment. Abstract Systems, methods, and computer readable media directed to interactive reinforcement learning with dynamic reuse of prior knowledge are described in various embodiments. The interactive reinforcement learning is adapted for providing computer implemented systems for dynamic action selection based on confidence levels associated with demonstrator data or portions thereof. Abstract Disclosed herein are a system and method for providing a machine learning architecture based on monitored demonstrations. The system may include: a non-transitory computer- readable memory storage; at least one processor configured for dynamically training a machine learning architecture for performing one or more sequential tasks, the at least one processor configured to provide: a data receiver for receiving one or more demonstrator data sets, each demonstrator data set including a data structure representing the one or more state-action pairs; a neural network of the machine learning architecture, the neural network including a group of nodes in one or more layers; and a pre-training engine configured for processing the one or more demonstrator data sets to extract one or more features, the extracted one or more features used to pre-train the neural network based on the one or more state-action pairs observed in one or more interactions with the environment. Abstract In one aspect, a system for managing data processes in a network of computing resources is configured to: receive, from an instructor device, a parent request for execution of at least one parent data process executable by a plurality of computing resources at least one computing resource; generate at least one child request for execution of at least one corresponding child data process for routing to at least one corresponding destination device, each of the at least one child data process for executing at least a portion of the at least one parent data process, and each of the at least one child request including a respective destination key derived from at least one instructor key; and route each of the at least one child request to the at least one corresponding destination device. The at least one child request can be obtained by a supervisor server via the routing.