How AI Is Creating Jobs Not Killing Them For Low-Skilled Workers?
AI & ML are expected to create 133 million new roles, displacing 75 million by 2022 — A total net gain of 58 million jobs, not killing them.
Yes, as per the World Economic Forum’s job report, algorithms and intelligent machines are expected to create 133 million new roles globally while displacing around 75 million by 2022 — which is a total net gain of 58 million jobs, not killing them. And in 2020, as per the Gartner estimates, AI will create 2.3 million new jobs while eliminating 1.8 million positions.
Dun & Bradstreet 2019 report says, 40% of organizations are adding more jobs, thanks to adopting AI, whereas only 8% are cutting jobs because of implementing such new technology into their operations and management.
However, according to Oxford academic duo Carl Benedikt Frey and Michael Osborne estimates published in the document “The Future of Employment: How Susceptible Are Jobs to Computerization?”, by the end of 2030 47% of American jobs are at high risk.
Artificial Intelligence (AI) based developments are on rapid speed — From self-driving cars, to robots removing weeds from farm fields or managing the inventory in assembly lines, warehouse or virtual assistant apps assisting people in solving their queries and AI-enabled computers detect cancer accurately is because of AI applied science.
Artificial Intelligence and machine learning oriented automation system will create, eliminate or change job roles, and how much is the question of discussion among the economic advisories and job data experts.
AI Creating New Jobs Rapidly for Highly-Skilled Professionals
Yes, its true AI is creating new job opportunities for highly-skilled professionals like software engineers, AI or machine learning engineer, data analyst, data scientists, digital communicators, and online specialists. As these professionals are playing a key role in AI developments using their cognitive analytical and coding skills.
And as per the tops job sites and recruitment consultants, AI job postings as a percentage of overall job postings at on such websites rapidly increased and reached nearly doubled in the last two years.
While searching the jobs in AI fields increased just 15 percent showing a huge gap between demand and availability of such professionals.
Graphic Showing Gap Between AI Job Openings and Job Seekers
This demand and supply gap between job openings and job seekers, is also pushing the pay scale bar of these professionals. As per the industry experts, the average salaries for AI-related jobs advertised by the companies on career sites rose 11% between October 2017 and September 2018 to $123,069 annually.
Will AI create jobs for low-skilled workers or not?
Highly-Skilled engineers and other professionals can easily find the jobs in AI fields, but the question arises here is that — automation and artificial intelligence will make low-skilled jobs disappear compare to these specialized and highly knowledgeable workers.
As per the job data in various countries, it is apparently visible that low-skilled workers are losing their job and becoming unemployed with lesser opportunists in job market due to rise of AI-oriented developments and implementation around the world.
Though, AI and automation have great scope of replacing repetitive and predictable cognitive and physical tasks. But there is a hidden side of AI and machine learning and that is rarely discussed by the experts, even most of the people not aware how AI is creating jobs not killing them even for low-skilled workers with great opportunities.
AI Creating New Jobs for Low-Skilled Workers
Data is the fuel requires to automated the AI and most of the machine learning algorithms need to be trained with huge volume of data sets. And training the machines with labeled datasets comes under the process of supervised learning. Training and testing data in machine learning are an integral part of AI-based model developments.
In fact, a computer can take decisions or inferences, but only when you show enough examples with the respective solutions to individual problems. You can teach a neural network to recognize pictures of a car by feeding the network with thousands of images of a car while specifying every time to the algorithm.
The more pictures of cars you give, it will learn better and becomes more helpful in recognizing the images at a faster speed. And these AI and machine learning training data need to be annotated by someone — of course by humans.
Labeled Data Required for AI and Machine Learning
Humans-are-in-the-loop everywhere, in AI and ML development huge quantity of dataset, is required. And to generate the labeled data, thousands of human working hours required to annotate each image manually with precision.
AI and ML are escalating into vital fields, like healthcare and medical care. Automated pattern recognizing software is used in radiology, pathology, cardiology, oncology and even psychiatry helping doctors to detect different kind of diseases timely.
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Medical imagining files like X-rays, CT Scans, MRI, ECGs, and Ultrasound are manually annotated with the help of image annotation services highlighting pathological signals to doctors. And similarly, various other NLP based data for speech recognition are also highly in demand among the AI and ML developers for various industries.
A New Assembly Line for Low-skilled Workers
Though, there are various companies has developed software tools that are utilized by humans to annotate the different types of images. While many organizations have internal employees and many of them outsource the manual data labeling to others.
And such outsourcing is most probably done to underdeveloped or developing nations like India, China or African nations where the cost of labor is comparatively low.
And these annotators are working in a big team, even some of the companies are working with more than 50,000 people, drawing from a pool of more than a million of annotators working worldwide in the day and night shifts producing the huge quantity annotated images.
Low-skilled workers basically work in manufacturing companies. But now companies become smarter implementing an autonomous or robotic system to perform the repetitive tasks with better speed and efficiency which were earlier performed by humans.
And right now in the age of rapidly growing AI development, data annotation is new assembly line providing the new opportunity to such workers. And these new types of jobs would not exist without machine learning algorithms which are at the revolutionary stage.
Data labeling is much more different from working in manufacturing assembly lines where workers perform physically exhausting demanding tasks. While in labeling data they need to more engaged in more cognitive tasks that are performed just sitting at one place on a chair in front of computers, it's also repetitive but safe from machineries.
Data Annotation Doesn’t Require High-end Computer Skills
However, data annotation is not an easy job, as it requires training and meticulous attention to perform each task. You have to draw polygon or bounding box annotation around the various types of objects in an image or need to pinpoint the landmarks using the mouse and keyboards.
And while doing this you need to ensure the accuracy, because the quality of the data set is very important for the success of machine learning algorithm. In various industries like self-driving or autonomous driving, fallacious training data can be a cause of death due to crash or accidents becomes the prime reasons AI or ML projects failure.
Though, annotating the data is a time-consuming task, but it is very essential to teach the machines how to perceive various situations while running on the road and take precise decisions accordingly to avoid such disasters and providing the safe driving.
Job Opportunities for Both — Highly and Low-skilled
Envisaging the high volume characteristics of the tasks data annotation artificial intelligence creates new job opportunities even for low-skilled workers, especially for people living in developing or undeveloped nations where the job market is very low for unskilled group.
In such countries, many AI data annotation companies like Cogito and Anolytics are hiring the undergraduates, or fresh graduates and unskilled people training them creating jobs at large scale helping and improving the socio-economic situation of the entire country.
Thanks to AI, not only highly-skilled professionals, but unskilled people are now also finding the jobs easily with satisfying pay scale fulfilling their basic needs. Further with more developments in unexploited fields AI and ML will create more jobs for low-skilled workers which accounts for a major population of many countries around the world.
Is Data Annotation Job is Good for them?
The other side of this story is that, instead of learning more skill-based knowledge, such employees stuck in low-skilled jobs, which is economically not good for their long-term growth and developments.
But companies and organizations, in an attempt to operate in the market with hyper-competitive pricing with higher margins, are keeping annotators salary tremendously low as much as below $1 per hour which is below minimum wage.
Actually, in the digital era, such organizations are adopting this line of business which also sponsoring a new kind of slavery in the digital era. And the hunger of data annotation is so big, that in short-term this kind approach will be monetarily more rewarding for the companies compare to other lines of business.
While from the long-term perspective, it will affect the whole economy, as it will determine high employee churn rate, bad quality in the output, and negative impact on various communities.
Such companies, not only upsetting social norms by exploiting workers and running the business unethically, but this kind of unfair business practice is also harmful to the industry will also impact the AI sector across the world.
Summing-up
Though, AI is already a controversial technology, due to lots of societal, ethical and moral concerns associated with its developments. And this kind of dirty games by corporates can cost them when it will affect the entire sector with cascading effects.
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In the nutshell, the human race of automation with AI is eliminating and creating millions of jobs worldwide in various sectors. And it is also not necessarily true that all the jobs created by AI are only for high-skilled and knowledgeable professionals but low-skilled workers are also getting new opportunities with multiple job options.
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Although, the rate at which such jobs are created might not match the rate at which other low-skilled positions are disappearing globally. AI is at the growing stage, and the availability of annotated data and the need to access such data sets will grow exponentially over the next years, means a steep rise in demand for data annotators.
This article is originally published at www.vsinghbisen.com
