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Gartner Says By 2020, Artificial Intelligence Will Create More Jobs Than It Eliminates

By 2022, one in five workers engaged in mostly nonroutine tasks will rely on AI to do a job.'Using AI to auto-generate a weekly status report or pick the top five emails in your inbox doesn't have the same wow factor as, say, curing a disease would, which is why these near-term, practical uses go unnoticed,' said Craig Roth, research vice president at Gartner.

Through 2022, multichannel retailer efforts to replace sales associates through AI will prove unsuccessful, although cashier and operational jobs will be disrupted.However, research suggests that many consumers still prefer to interact with a knowledgeable sales associate when visiting a store, particularly in specialized areas such as home improvement, drugstores and cosmetics, where informed associates can make a significant impact on customer satisfaction.

'Retailers will be able to make labor savings by eliminating highly repetitive and transactional jobs, but will need to reinvest some of those savings into training associates who can enhance the customer experience,' said Robert Hetu, research director at Gartner 'As such most retailers will come to view AI as a way to augment customer experiences rather than just removing humans from every process.'While many industries will receive growing business value from AI, manufacturing is one that will receive a massive share of the business value opportunity.

In 2021, AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity.However, some industries, such as outsourcing, are seeing a fundamental change in their business models, whereby the cost reduction from AI and the resulting productivity improvement must be reinvested to allow reinvention and the perusal of new business model opportunities.

'Rather than have a machine replicating the steps that a human performs to reach a particular judgment, the entire decision process can be refactored to use the relative strengths and weaknesses of both machine and human to maximize value generation and redistribute decision making to increase agility.'

How the era of artificial intelligence will transform society?

Article 3 of the Series on Ethics in Artificial Intelligence In the previous article, we talked about the nature of human fears in relation to artificial intelligence (AI).

We know, for example, that as a consequence of the 1st industrial revolution, mechanization increased the productivity of each worker but real wages stagnated for approximately 50 years.

It explains that due to great technological developments, the lives of a large number of people worsened first before society began to prosper in the longer term (Allen, 2008).

Along with algorithms and advanced computation facilities, the accuracy of the models constituting artificial intelligence rely heavily on the availability of real-world data.

451 Research, 2017) Data is the driver of the new industrial era, it is here, growing, and ongoing transformational processes brought by data-driven applications pose new challenges to society.

While 35% of the skills demanded for jobs across industries will change by 2020, at least 1 in 4 workers in OECD countries is already reporting a skills mismatch with regards to the skills demanded by their current jobs (WEF, 2017).

The recent example of the emergence of personal computers shows us that technology drives the creation of many more jobs than it destroys over time, mainly outside the industry itself (Figure 4).

In the knowledge economy, it became possible for us to deliver services remotely, outsource business processes, and to dematerialize the whole concept of economic transactions with internet-based payment systems.

In their book, Milton and Rose Friedman said, “the essential part of economic freedom is freedom to use the resources we possess in accordance with our own values – freedom to enter any occupation, engage in any business enterprise, buy from and sell to anyone else, so long as we do so on a strictly voluntary basis“.

Economists at the Bank of Canada suggest: “the greatest productivity benefits will occur in firms with high-quality people-management and decision-making processes and high levels of human capital”.

Employability of human capital in the 21st century will require new sets of skills: resilience, critical thinking, social skills and the ability to learn, reflect, and quickly adapt to change.

Acquisition of such skills requires new learning methods and discussion about them worth a separate article, if not a series of research projects, conferences, and debates.

In the abyss of the great depression and hardship in 1933, the opening lines of Franklin Roosevelt’s inauguration speech were, “…the only thing we have to fear is fear itself.” Becoming comfortable with uncertainty is an essential component of any creative process, especially when creating a positive future.

Yet, we could estimate, regulate and design the impact of artificial intelligence on an individual basis – industry by industry, technology by technology and role by role.

As reported by RSA and suggested by Diane Beddoes, Director of Deliberate Thinking, there are advantages, limitations and challenges to building open dialogue with citizen juries: As in previous transitions between industrial eras, developing education, awareness and continuous learning will be vital.

As pointed out by Carolyn Wilkins, senior deputy governor in Bank of Canada, increased market power for some AI players may raise important systemic issues, many of them being global in nature.

Therefore, countries and economic areas that create favorable conditions for artificial intelligence solutions that augment human capabilities, are expected to largely benefit from technology progress.

We believe appropriate anticipation and preparation would require 1) industry-led assessments with identification of skills and learning approaches to future-proof the workforce, 2) Creation of an inclusive dialog between experts and citizens, and 3) Governmental and targeted cross-border governance for each concrete application of AI.

How Will Machines and AI Change the Future of Work?

Several recent studies examined how machine automation and artificial intelligence (AI) will change the future of work.

The Queen explained that the device would lead to major job losses, forcing affected workers to become “beggars.” Osoba agrees there will be major job disruptions due to AI and automation, especially for lower skilled workers.

“It’s not so much that the jobs are getting displaced, it’s more like tasks are getting displaced and jobs are reconfiguring over time to account for that automation.” He added that it will be very difficult for companies to completely automate most jobs, because they require a worker to perform many different duties and to react to unexpected situations.

These include jobs depending on human motor skills, positions requiring creative thinking and actions, and jobs dealing with intense social interaction.

“So that understanding of cultural norms, or social norms or ethical norms, that’s not something that’s easy – at least so far we haven’t found that easy to program into artificial intelligence.” The McKinsey Global Institute, a private think tank, has also studied the issue.

The organization’s report predicts automation could force 75 million to 375 million workers into new job areas by 2030.

“If you are thinking about concrete things an individual might do to prepare themselves, I guess being more adaptable, being more flexible, being able to reeducate yourself to fit into a different job.” He added that there will be a great need in the future for many more AI developers and researchers.

For this reason, he suggests young people interested in these areas start their career paths early to prepare for these high-paying, competitive jobs.

official document giving a person or company the right to be the only one that makes or sells a product for a certain period of time beggar – n.

​AI will transform product management

According to the World Economics Forum's The Future of Jobs 2018 report, machines will overtake humans in terms of performing more tasks at the workplace by 2025 -- but there could still be 58 million net new jobs created in the next five years.

The report notes that the growing skills for 2022 will include analytical thinking, creativity, critical thinking, complex problem solving, and systems analysis.

The Future of Jobs Report 2018 also identified 10 emerging jobs in 2022, including data analysts and scientists, AI and machine learning specialists and general and operation managers as the top 3 jobs.

Also: How Facebook scales AI AI and advancements in automation may result in 75 million job displacements, but at the same time period another 133 million new roles will emerge where people and machines will co-exist, creating a net new 58 million jobs by 2022.

The 2018 jobs report also forecasts that 42 percent of all current tasks in the workplace will be performed by machines in 2022, as compared to 29 percent in 2018.

Sinovation Ventures, managing US$2 billion dual currency investment fund, is a leading technology-savvy investment firm focusing on developing the next generation of Chinese high-tech companies.

Fast forward to 2020, and I believe that sales, marketing, and customer service without AI is no longer effective or acceptable sales, marketing, or customer service.

To better understand the impact of artificial intelligence (AI) on cognitive labor, I reached out to one of my AI domain expert colleagues who is responsible for delivering AI-powered products and services.

Jeremy Karnowski, Director of AI Products at San Francisco-based Insight Data Science, runs a 7-week intensive fellowship program which helps professionals with diverse backgrounds change careers and enter the field of software and technology.

Marco Casalaina, VP of Product at Salesforce Einstein, is working with a team on creating a course in Trailhead (Salesforce's online learning platform) to augment the skill sets of traditional PMs and to prepare them for the fourth industrial revolution of AI and machine learning.

'We need to develop a smell test for our PMs, to give them the ability to quickly determine the feasibility of applying machine learning to solve a business problem,' says Casalaina.

Its roots can be traced back to the engineering of the pyramids of Egypt, to the building of military engines in the 1300s, to the inception of mechanical engineering with the rise of the steam engine in the 1800s.

This can be extended to other recommendation engine use cases, like recommending friends in a social network, connections in a professional network, songs in a music app, or job candidates in a recruiting system.

In addition to working with their traditional cross-functional stakeholders (design, marketing, sales, engineering, dev ops), AI product managers now need to include data scientists and data engineers in the circle.

It is sometimes the case lately that in the rush to AI, PMs become mesmerized by the bevy of new technologies and neglect to map them to concrete user pain points.

This means that an AI product manager should resort to AI methods of solving a business problem only after determining through careful evaluation that traditional methods such as rules engines will not adequately address the user's pain point.

One common problem that causes long resolution times in customer service is mis-categorized tickets: the more a ticket bounces between departments, the longer it takes to solve.

In this particular example, businesses have long used rules engines and workflow systems to attempt to categorize cases correctly on the first try, and yet this problem persists.

The rules in these engines frequently fall out of date, and even when they're current, they often require rigid inputs that don't match the range of human language that is often found in service tickets.

Upon doing this analysis, an AI product manager might uncover an opportunity to use artificial intelligence to build a system to categorize these tickets by learning from prior successfully resolved tickets.

For instance, a general-purpose image prediction software will perform poorly if it needs to detect tumors from medical images, but it would work well for classifying cats versus dogs.

Depending on the business objective, an AI product manager needs to make a judgment as to which machine learning metrics need to be optimized, and at what point the AI is performing sufficiently well to solve the customer's problem.

There are numerous other considerations around how often and when machine learning models retrain, what metrics need to be computed to understand the system's continuing performance, what feature engineering is required, and much more.

If you are a PM reading this and have experience building web and mobile applications but have not wet your feet in AI and ML (machine learning) products, you might be wondering -- 'I had never had to deal with any of these!'

As AI and ML eat software, more and more PMs need to level up their skills to manage these products and provide requirements and specifications which will add value to the data engineering and data science teams.

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