The Power of Computer Science

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By bringing the power of computer science to fields such as journalism, education, robotics, and art, Northwestern University computer scientists in the McCormick School of Engineering are accelerating research and innovation exponentially. qNorthwestern has announced a major expansion of computer science, adding 20 faculty and substantially expanding its commitment to this field in the years ahead. Half of the new faculty appointments will be in core computer science areas and half structured as collaborative “CS+X” appointments with other disciplines.

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ITsavvy

ITsavvy has catapulted from a Midwest start-up to a national leader in IT products and solutions, very rapidly. Founded in 2004 by Mike Theriault and Chris Kurpeikis, ITsavvy has been consistently recognized by Inc. Magazine as one of the fastest-growing businesses of its type.

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AI TECH

RPA and the Future of Outsourcing

Article | December 21, 2020

There is no doubt that in recent years technology advancement in industry has increased exponentially, but so has customer expectations. These days customers expect to have their questions answered and needs met nearly instantaneously, putting an added burden on companies to keep up with consumer demand while somehow maintaining cost of doing business at a reasonable level. Businesses must evolve to survive in the current climate and Robotic Process Automation may be the catalyst needed for companies to take the next leap forward. What is Robotic Process Automation? Robotic Process Automation (RPA), is a type of software that is created to mimic mundane or repetitive human tasks. The software can remove the burden of repetitive processing tasks from humans themselves, allowing people to handle the more complex tasks or problems within a company. Automation software can be programmed to do a wide array of technological jobs, following all of the rules that it is given to follow. Types of Businesses that can benefit from RPA Put simply, many businesses that utilize technology can benefit from intelligent automation, but here are a few examples. · Computer/IT and Telecommunication companies: These types of companies require a lot of customer support, much of which can easily be accomplished using automation software. RPA can help by creating electronic tickets and then responding to them when a customer sends in a question or request for service. The tickets can then be transferred to the correct human worker to be completed. · Accounting firms: Automated software can contact clients, confirm that payments are being made, and even sync with banks online, taking human error out of the equation. · Online stores: Online stores can and do use RPA in order to accept orders and communicate with customers, all without the need of a live person. This will enable a customer service agent to handle any inquiries of higher priority or that require critical thinking. Future of Outsourcing RPA can change the way in which companies outsource. Companies often outsource to foreign countries as a way to get tasks completed when they cannot afford to hire workers locally. This sends company money to other countries to pay the outsourced workers, removing income from the local economy while risking that customers might not receive the best quality of service. Automation software takes the outsourced worker out of the equation, filling in for the jobs that were previously handled by a foreign worker. Not only will this keep company employee expenditure within the country where they do business in, but it removes the stress and stigma that is associated with hiring staff from other countries. A company who chooses to invest in technology is ultimately investing in the satisfaction of their customers and longevity of their business. Intelligent automation enables organizations to keep local workers on staff to handle the complex tasks while letting the technology handle the more mundane duties. This will dramatically cut down on costs while increasing the quality of service. As a result, a company’s bottom line should see improvements while consumers begin to receive greater support and service, likely helping a business develop a more loyal group of repeat customers for years to come.

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AI TECH

CAN AI REPLACE ‘GUT FEELING’?

Article | December 21, 2020

Artificial Intelligence is empowering business leaders to make better, data-driven, and insightful decisions. It has undergone several evolutions since it burst into the business scene in the 1950s, to the point where several thinkers have already painted a machine that replaces human scenarios for the future. Our view on the future of work has evolved into a zero-sum game, where the result is an either-or. In my opinion, the view that AI will play a dominant role in the workplace is a little extreme. The fundamental assumption around AI replacing human workers is that humans and machines have the same characteristic. Totally untrue!. AI-based systems may be fast, consistently accurate, and rational, but they are not intuitive, emotional or culturally sensitive. Humans possess these qualities in abundance, and it is one of the reasons why we continue to surprise the world with our advancements. Intuition is the Mother of Innovation If we are living comfortable lives today, it’s because some business leaders chose their gut feeling over data analytics on numerous occasions. Some historical examples have been: 1: Henry Ford, facing falling demand for his cars and high worker turnover in 1914, doubled his employees’ wages, and it paid off. 2: Bill Allen was the CEO of Boeing in the 1950s, a company that manufactured planes for the defence industry. One day, he woke up to the idea of building commercial jets for a sector that was non-existent – civilian air travel. Allen convinced his board to risk $16 million on a new transcontinental airliner, the 707. The move transformed Boeing and air travel. 3: Travis Kalanick faced serious pushback when Uber instituted surge pricing. His move seemed to anger and alienate everyone. Travis stayed the course, and Uber modified its surge policy whenever appropriate. Now, dynamic pricing is an accepted aspect of this business and many others. So the question is, should a competent professional trust their gut feeling or make data-driven decisions? DATA V/S GUT Top professionals have repeatedly confirmed that gut feeling is one of the main reasons for their success. Leadership often gets associated with quick responses in unprecedented situations and lateral thinking. Experienced leaders are not only fearless about their instincts but are also proficient at making others feel confident in their judgment. Also, going with our instinct can help us make decisions quickly and more accurately since we tend to make choices based on experiences, values, and compassion. Malcolm Gladwell calls this ‘thin slicing’ in his book, “Blink”. Thin-slicing is a cognitive manoeuvre that involves taking a narrow slice of data, what you see at a glance, and letting your intuition do the work for you. However, he does warn that some decisions are exempt from this rule; it only applies to areas where you already have significant expertise. Artificial Intelligence and machine learning can support leaders to see complex patterns that can lead to new understanding in this fast-moving, digital era. The contention is that ‘human gut’ feeling can go hand in hand with AI – each supporting the other to achieve balanced outcomes. A Joint Venture Between Head and Heart Many see AI as an aid to human intelligence, not a replacement. To be one-step ahead in the AI era, professionals must learn to balance human and machine thinking. Organizations will have to showcase the ability to use the correct information at the right time and take action. It’s about using your instinct to take advantage of data and transforming that information into timely business decisions. AI is not yet ready to replace the human brain, but it has matured into an effective co-worker. Will intelligent machines replace human workers sometime soon? I guess not. Both have different abilities and strengths. The more important question is: Can human intelligence combine with AI to produce something experts are calling augmented intelligence? Augmented intelligence is collaborative, and at the same time, it represents a collaborative effort in the service of the human race. Figuring out how to blend the right mix with the best of data-driven deliberation and instinctive judgment could be one of the most significant challenges of our time.Enable GingerCannot connect to Ginger Check your internet connection or reload the browserDisable in this text fieldRephraseRephrase current sentenceEdit in Ginger×

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5 OBSTACLES TO SUCCESSFUL DATA GOVERNANCE

Article | December 21, 2020

Organizational leaders worldwide agree that data governance is important. However, data governance programs in most companies are still being planned or in progress. In a 2020 Dataversity report¹, only 12 percent of companies had fully implemented programs, while 38 percent of programs were a work in progress, and 31 percent were just getting started.

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COVID19: A crisis that necessitates Open Data

Article | December 21, 2020

The coronavirus outbreak in China has grown to a pandemic and is affecting the global health & social and economic dynamics. An ever increasing velocity and scale of analysis — in terms of both processing and access is required to succeed in the face of unimaginable shifts of market; health and social paradigms. The COVID-19 pandemic is accompanied by an Infodemic. With the global Novel Coronavirus pandemic filling headlines, TV news space and social media it can seem as if we are drowning in information and data about the virus. With so much data being pushed at us and shared it can be hard for the general public to know what is correct, what is useful and (unfortunately) what is dangerous. In general, levels of trust in scientists are quite high albeit with differences across countries and regions. A 2019 survey conducted across 140 countries showed that, globally, 72% of the respondents trusted scientists at “high” or “medium” levels. However, the proportion expressing “high” or “medium” levels of trust in science ranged from about 90% in Northern and Western Europe to 68% in South America and 48% in Central Africa (Rabesandratana, 2020). In times of crisis, like the ongoing spread of COVID-19, both scientific & non-scientific data should be a trusted source for information, analysis and decision making. While global sharing and collaboration of research data has reached unprecedented levels, challenges remain. Trust in at least some of the data is relatively low, and outstanding issues include the lack of specific standards, co-ordination and interoperability, as well as data quality and interpretation. To strengthen the contribution of open science to the COVID-19 response, policy makers need to ensure adequate data governance models, interoperable standards, sustainable data sharing agreements involving public sector, private sector and civil society, incentives for researchers, sustainable infrastructures, human and institutional capabilities and mechanisms for access to data across borders. The COVID19 data is cited critical for vaccine discovery; planning and forecasting for healthcare set up; emergency systems set up and expected to contribute to policy objectives like higher transparency and accountability, more informed policy debates, better public services, greater citizen engagement, and new business development. This is precisely why the need to have “open data” access to COVID-19 information is critical for humanity to succeed. In global emergencies like the coronavirus (COVID-19) pandemic, open science policies can remove obstacles to the free flow of research data and ideas, and thus accelerate the pace of research critical to combating the disease. UNESCO have set up open access to few data is leading a major role in this direction. Thankfully though, scientists around the world working on COVID-19 are able to work together, share data and findings and hopefully make a difference to the containment, treatment and eventually vaccines for COVID-19. Science and technology are essential to humanity’s collective response to the COVID-19 pandemic. Yet the extent to which policymaking is shaped by scientific evidence and by technological possibilities varies across governments and societies, and can often be limited. At the same time, collaborations across science and technology communities have grown in response to the current crisis, holding promise for enhanced cooperation in the future as well. A prominent example of this is the Coalition for Epidemic Preparedness Innovations (CEPI), launched in 2017 as a partnership between public, private, philanthropic and civil society organizations to accelerate the development of epidemic vaccines. Its ongoing work has cut the expected development time for a COVID-19 vaccine to 12–18 months, and its grants are providing quick funding for some promising early candidates. It is estimated that an investment of USD 2 billion will be needed, with resources being made available from a variety of sources (Yamey, et al., 2020). The Open COVID Pledge was launched in April 2020 by an international coalition of scientists, lawyers, and technology companies, and calls on authors to make all intellectual property (IP) under their control available, free of charge, and without encumbrances to help end the COVID-19 pandemic, and reduce the impact of the disease. Some notable signatories include Intel, Facebook, Amazon, IBM, Sandia National Laboratories, Hewlett Packard, Microsoft, Uber, Open Knowledge Foundation, the Massachusetts Institute of Technology, and AT&T. The signatories will offer a specific non-exclusive royalty-free Open COVID license to use IP for the purpose of diagnosing, preventing and treating COVID-19. Also illustrating the power of open science, online platforms are increasingly facilitating collaborative work of COVID-19 researchers around the world. A few examples include: 1. Research on treatments and vaccines is supported by Elixir, REACTing, CEPI and others. 2. WHO funded research and data organization. 3. London School of Hygiene and Tropical Medicine releases a dataset about the environments that have led to significant clusters of COVID-19 cases,containing more than 250 records with date, location, if the event was indoors or outdoors, and how many individuals became infected. (7/24/20) 4. The European Union Science Hub publishes a report on the concept of data-driven Mobility Functional Areas (MFAs). They demonstrate how mobile data calculated at a European regional scale can be useful for informing policies related to COVID-19 and future outbreaks. (7/16/20) While clinical, epidemiological and laboratory data about COVID-19 is widely available, including genomic sequencing of the pathogen, a number of challenges remain: 1. All data is not sufficiently findable, accessible, interoperable and reusable (FAIR), or not yet FAIR data. 2. Sources of data tend to be dispersed, even though many pooling initiatives are under way, curation needs to be operated “on the fly”. 3. In addition, many issues arise around the interpretation of data – this can be illustrated by the widely followed epidemiological statistics. Typically, the statistics concern “confirmed cases”, “deaths” and “recoveries”. Each of these items seem to be treated differently in different countries, and are sometimes subject to methodological changes within the same country. 4. Specific standards for COVID-19 data therefore need to be established, and this is one of the priorities of the UK COVID-19 Strategy. A working group within Research Data Alliance has been set up to propose such standards at an international level. Given the achievements and challenges of open science in the current crisis, lessons from prior experience & from SARS and MARS outbreaks globally can be drawn to assist the design of open science initiatives to address the COVID-19 crisis. The following actions can help to further strengthen open science in support of responses to the COVID-19 crisis: 1. Providing regulatory frameworks that would enable interoperability within the networks of large electronic health records providers, patient mediated exchanges, and peer-to-peer direct exchanges. Data standards need to ensure that data is findable, accessible, interoperable and reusable, including general data standards, as well as specific standards for the pandemic. 2. Working together by public actors, private actors, and civil society to develop and/or clarify a governance framework for the trusted reuse of privately-held research data toward the public interest. This framework should include governance principles, open data policies, trusted data reuse agreements, transparency requirements and safeguards, and accountability mechanisms, including ethical councils, that clearly define duties of care for data accessed in emergency contexts. 3. Securing adequate infrastructure (including data and software repositories, computational infrastructure, and digital collaboration platforms) to allow for recurrent occurrences of emergency situations. This includes a global network of certified trustworthy and interlinked repositories with compatible standards to guarantee the long-term preservation of FAIR COVID-19 data, as well as the preparedness for any future emergencies. 4. Ensuring that adequate human capital and institutional capabilities are in place to manage, create, curate and reuse research data – both in individual institutions and in institutions that act as data aggregators, whose role is real-time curation of data from different sources. In increasingly knowledge-based societies and economies, data are a key resource. Enhanced access to publicly funded data enables research and innovation, and has far-reaching effects on resource efficiency, productivity and competitiveness, creating benefits for society at large. Yet these benefits must also be balanced against associated risks to privacy, intellectual property, national security and the public interest. Entities such as UNESCO are helping the open science movement to progress towards establishing norms and standards that will facilitate greater, and more timely, access to scientific research across the world. Independent scientific assessments that inform the work of many United Nations bodies are indicating areas needing urgent action, and international cooperation can help with national capacities to implement them. At the same time, actively engaging with different stakeholders in countries around the dissemination of the findings of such assessments can help in building public trust in science.

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Spotlight

ITsavvy

ITsavvy has catapulted from a Midwest start-up to a national leader in IT products and solutions, very rapidly. Founded in 2004 by Mike Theriault and Chris Kurpeikis, ITsavvy has been consistently recognized by Inc. Magazine as one of the fastest-growing businesses of its type.

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