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Most of 2030’s Jobs Haven’t Been Invented Yet

In theory, this kind of online job matching could lead to less bias and discrimination in hiring practices. However, there are potential pitfalls.

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JOBS
In theory, this kind of online job matching could lead to less bias and discrimination in hiring practices. However, there are potential pitfalls.

More than two-thirds of jobs that today’s college students will have in 11 years haven’t been invented yet.

“Those who plan to work for the next 50 years, they have to have a mindset of like, ‘I’m going to be working and learning and working and learning, and working and learning,’ in order to make a career,” says Rachel Maguire, a research director with the Institute for the Future, which forecasts that 85 percent of the jobs that today’s young people will hold in 2030 don’t exist right now.

The Institute for the Future, a nonprofit that identities emerging trends and their impacts on global society, envisions that by 2030, we’ll be living in a world where artificial assistants help us with almost every task, not unlike the way email tries to finish spelling a word for users today.

Maguire says it will be like having an assistant working alongside you, taking on tasks at which the human brain does not excel.

“For the human, for the people who are digitally literate who are able to take advantage, they’ll be well-positioned to elevate their position, elevate the kind of work they can do, because they’ve got essentially an orchestra of digital technologies that they’re conducting,” she says. “They’re just playing the role of a conductor, but the work’s being done, at least in partnership, with these machines.”

New technology in the next decade is expected to lead to new human-machine partnerships that will make the most of each partner's respective strengths.
New technology in the next decade is expected to lead to new human-machine partnerships that will make the most of each partner’s respective strengths. VOA

The U.S. Bureau of Labor Statistics says today’s students will have eight to 10 jobs by the time they are 38.

And they won’t necessarily have to take time away from any one of those jobs for workforce training or to gain additional certifications related to their fields. Instead, they’ll partner with machines for on-the-job learning, wearing an augmented reality headset that will give them the information they need in real-time to get the work done.

“It eliminates the need for people to step away from income generating opportunities to recertify in order to learn a new skill so they can level up and earn more money,” Maguire says. “It gives the opportunity for people to be able to learn those kinds of new skills and demonstrate proficiency in-the-moment at the job.”

Students use virtual reality for an immersive educational experience. VR blocks out the physical world and transports the user to a simulated world. (Courtesy Dell.com)
Students use virtual reality for an immersive educational experience. VR blocks out the physical world and transports the user to a simulated world. (Courtesy Dell.com) VOA

And forget about traditional human resources departments or the daunting task of looking for a job on your own. In the future, the job might come to you.

Potential employers will draw from different data sources, including online business profiles and social media streams, to get a sense of a person and their skill set.

Maquire says there’s already a lot of activity around turning employment into a matchmaking endeavor, using artificial intelligence and deep learning to help the right person and the right job find each other.

In theory, this kind of online job matching could lead to less bias and discrimination in hiring practices. However, there are potential pitfalls.

“We have to be cognizant that the people who are building these tools aren’t informing these tools with their own biases, whether they’re intentional or not,” Maguire says. “These systems will only be as good as the data that feeds them.”

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Which leads Maguire to another point. While she doesn’t want to sound melodramatic or evangelical about emerging technologies, she believes it is critical for the public to get engaged now, rather than sitting back and letting technology happen to them.

“What do we want from these new technological capabilities, and how do we make sure we put in place the social policies and the social systems that will result in what it is we all want?” she says. “I have a deep concern that we’re just kind of sitting back and letting technology tell us what jobs we’ll have and what jobs we won’t have, rather than us figuring out how to apply these technologies to improve the human condition.” (VOA)

Next Story

AI Can Better Help Doctors to Identify Cancer Cells in Human Body

The process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone

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Cancer
The AI algorithm helps pathologists obtain the most accurate Cancer cell analysis - in a much faster way. Pixabay

Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize Cancer cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes.

The spatial distribution of different types of cells can reveal a cancer’s growth pattern, its relationship with the surrounding microenvironment, and the body’s immune response.

But the process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone.

“To make a diagnosis, pathologists usually only examine several ‘representative’ regions in detail, rather than the whole slide. However, some important details could be missed by this approach,” said Dr. Guanghua “Andy” Xiao, corresponding author of a study published in EbioMedicine.

A major technical challenge in systematically studying the tumor microenvironment is how to automatically classify different types of cells and quantify their spatial distributions.

The AI algorithm that Dr Xiao and his team developed, called “ConvPath”, overcomes these obstacles by using AI to classify cell types from lung cancer pathology images.

Cancer
Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize Cancer Cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes. Pixabay

The ConvPath algorithm can “look” at cells and identify their types based on their appearance in the pathology images using an AI algorithm that learns from human pathologists.

The algorithm helps pathologists obtain the most accurate cancer cell analysis – in a much faster way.

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“It is time-consuming and difficult for pathologists to locate very small tumour regions in tissue images, so this could greatly reduce the time that pathologists need to spend on each image,” said Dr Xiao. (IANS)