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Pandemic Accelerates Machine Learning

Machines have made jobs obsolete for centuries. The spinning jenny replaced weavers, buttons displaced elevator operators, and the Internet drove travel agencies out of business. One study estimates that about 400,000 jobs were lost to automation in U.S. factories from 1990 to 2007. But the drive to replace humans with machinery is accelerating as companies struggle to avoid workplace infections of COVID-19 and to keep operating costs low. The U.S. shed around 40 million jobs at the peak of the pandemic, and while some have come back, some will never return. One group of economists estimates that 42% of the jobs lost are gone forever.

Pandemic Accelerates Machine Learning

The study asked 300 respondents about smart manufacturing technologies, and found there was 100% growth in adoption of key technologies (e.g., Industrial IoT, smart devices, machine learning, RPA, cobots) in just the past year.

The virus is crowding new technology paradigms into healthcare everywhere. Networks of epidemiologists are tracking the coronavirus using low-cost gene-sequencing technologies6 which are also driving some of the most promising vaccine candidates.7 Researchers and medics are using machine learning to search repositories of scholarly articles published about covid-19, such as the 47,000 articles indexed by the covid-19 Open Research Dataset (CORD-19) Explorer.8 Informal networks of hobbyists and manufacturing firms are using 3D printers to make tens of thousands of face shields to help protect front-line medical workers.9 And in an unprecedented move, Apple and Google have partnered to invent a contact tracing application embedded in the operating systems for smartphones.10

It's no secret that the COVID-19 pandemic has had an impact on nearly every facet of business operations, and organizations that depend on artificial intelligence (AI) and machine learning (ML) to automate business decisions and critical business processes have been particularly vulnerable. Thanks to dramatic changes in both the overall economic environment as well as specific consumer behaviors since the onset of the pandemic, AI/ML models in organizations of all sizes and in every industry have been rendered largely ineffective because the pre-pandemic data on which the models were trained is no longer relevant or predictive of current behavior.

A good way to think about monitoring a machine learning model is to approach it in the same way you would think about getting your annual physical or taking your car in for regular oil changes and tune-ups. Both of these examples help ensure your own health or that of your automobile.

Model monitoring is a vital operational task that helps ensure your models are performing to the best of their abilities. It gives you the peace of mind to know that the models upon which you're basing strategic decisions are healthy, thereby avoiding negative impacts on the business and protecting customer loyalty and satisfaction. As your company moves more machine learning systems into production, you need to update your model monitoring practices to remain vigilant about model health and your business' success in a consistent, efficient manner.

The research methods are exploratory and experimental, evaluated by a machine learning algorithm (logistic regression). The first part presents how the daily lifestyle has changed as a result of COVID-19. In relation to what are the new social needs for urban public spaces, parks, green- and waterways, and transport. The second part of the research explores the paradoxes that make it difficult for landscape architects to find the balance between current human needs and long-term climate change mitigation goals.

There will also be: increased public understanding of the limits of, and problems with, machine learning; police-hiring reform; an international response to disinformation; improved technologies for group meetings online; new agile business models for technologies that mainly employ the web; improved health standards in schools and public places; improved sick leave policies; a decrease in business travel, possibly leading to a better carbon footprint; less middle management. There will be a small increase in automation, but more effort on designing and building even more automation for the home and small businesses that will become more ubiquitous and some type of certification for AI to show that it meets some ethical standards. There will be certification for online tools to show that they meet some privacy standard.

J. Scott Marcus, an economist, political scientist and engineer who works as a telecommunications consultant, predicted, The impact of the pandemic is large, but the world will eventually recover (assuming that the virus does not mutate to a still-more-dangerous form). This was the case in 1919 and there is no reason to expect anything different here. Changes such as remote work, teleconferencing, telemedicine and remote learning are mostly positive. The changes that have emerged were technically feasible for years but held up by institutional rigidities.

Our experience during the pandemic showed clearly how even modest improvements in interoperable communications can have a significant effect. Before the pandemic, there was no incentive to support widely accessible cross-platform video conferencing. Then we had Zoom, a simple tool everyone could use, and suddenly we could work from home, learn remotely or host conferences online. Having learned how convenient and efficient so many online services have become, we will be much less likely to commute to work, attend residence-based campuses or fly to conferences. This makes the world of work, learning and commerce much more accessible to large populations who previously did not have the resources to participate, and greatly increases our efficiency and productivity.

On average, using machine learning effectively results in a >30% increase in performance vs taking a manual approach to Google Ads. It used to be enough to just focus your advertising spend on a few specific keywords or audiences that you predicted were likely to convert, decide the amount you were willing to pay and the message you wanted to show, and assuming that your customers were all searching the same way and looking for the same thing, this would have led to conversions. You could even retain an element of control over how much you pay per conversion by extrapolating based on conversion rate.

Prioritising users that are likely to become your customers allows you to spend more effectively on advertising. Targeting and bidding go hand in hand when machine learning is used effectively. The user's journey is becoming increasingly complex and understanding this manually becomes a challenge.

Another excellent opportunity to get ahead of your competition is to use machine learning to tailor your messaging to each individual customer. As well as not all having the same intent to book holidays, not all users resonate with the same message. In fact, their needs can often change periodically as we saw over the course of last year as a result of the pandemic.

With more people having video conferences instead of meeting face to face, people are finding ways to turn working remotely into a luxury. Business travellers are now looking for more leisure type experiences instead of travelling for meetings so as well as taking advantage of machine learning, video ads are a great opportunity to highlight these features to an in-market user.

The travails of the pandemic have also accelerated IT investment in what Huberty calls The Data Decade," a 10-year investment cycle that turns on technologies such as artificial intelligence, machine learning, automation, digitalization and the internet of things. Companies across sectors now see the need to leverage data and digitally engage with customers, partners and employees to improve their competitive advantage, productivity and profitability.

Office printing needs could also decline more rapidly, as work-from-home and flexible working arrangements persist post-pandemic. Similarly, school printing needs have dropped due to virtual learning for millions of students.

On average, students globally are eight months behind where they would have been absent the COVID-19-pandemic, but the impact varies widely (exhibit). Within countries, the pandemic also widened gaps between historically vulnerable students and more privileged peers. We estimate by 2040, unfinished learning related to COVID-19 could translate to annual losses of $1.6 trillion to the global economy. Educational systems could consider a tiered approach to support reengagement, with more support (including social and emotional) for the highest-risk students.

Companies are beefing up their virtual learning strategies to augment skills in data science, artificial intelligence, machine learning, cloud and other technologies for which talent is in short supply. That gap, combined with the rapid evolution of technologies that can provide a digital edge, has companies investing in training current employees. The idea is to better prepare workers to accommodate changing business requirements, thus enabling the companies to better compete.

Boomtown partnered with Denver-based COPIC Insurance, a medical liability insurance company, in 2018. With COPIC, the accelerator selects companies with software, devices, analytics, artificial intelligence and machine learning solutions to patient safety and reducing risk.

Artificial intelligence (AI) and machine learning (ML) are increasingly being applied throughout supply chains. These solutions help organizations of all sizes synthesize massive amounts of data, then evaluate various scenarios and situations in nanoseconds to extract actionable information in real-time.

In the past few years, significant advancements in computing, data analysis and machine learning provide benefits for biosurveillance. These technology advancements are currently employed by leading companies such as Google, Facebook, Microsoft and Amazon. 041b061a72


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