Machines seem to always grow smarter, and some fear that one day they will take over. Is there any basis for this concern? For starters, some jobs will become obsolete, forcing workers to gain new skill sets to stay employed. There may be a silver lining, though. AI will also create new jobs, including ones that are developing and others that we can't see just yet. Many companies realize they need a new class of workers to help keep the automated world running smoothly, and this includes developers, managers, operators, and designers.
Machines Are Still Developing Their IQ
Machines are currently smart enough to replace people in most roles. In the beginning, they'll require human supervision to make sure everything goes smoothly. Because of this, many jobs are going to change and evolve rather than go away completely. Human employees can collaborate with AI to work more efficiently.
McKinsey & Co. is a consulting firm that predicts AI and automation might add 20 million to 50 million jobs across the world by 2030. Estimating job losses is harder, per McKinsey, since people are likely to switch roles. The firm released study results showing that 75 to 375 million people should consider new job options due to the adoption expected to occur by 2030.
As more services, research, and products grow dependent on AI; the industry will need more people who understand and can help develop AI systems. This will have a waterfall effect as related fields become dependent on the same technology and require qualified workers who can use and program AI systems. For example, AI has drastically change iRobot Corp (IRBT), which makes self-guided robotic vacuums and mops. As early as five years ago, this company, located in Bedford, Massachusetts, concentrated on hiring developers to build the physical bodies of its machines. In the past, robots brains were simpler with about 100 lines of code, Today, that number is millions of lines to enable complexity operation and strategic thinking.
The company has grown its staff by 400 percent and now employs 125 people, which is a huge increase over its 30 employees as of 2015. Most of these new associates work in manufacturing to produce iRobot’s smart products, which use advanced AI and computer-vision systems to complete their functions. One of the really neat advances is sensors that give robots vision.
The first robots had to bump into an object to know something was there. Now, there are sensors to help direct the robot's movements without contact. Robots can let their favorite humans know when they're done with cleaning tasks, for example, how long each one took and where it was completed. Smart cleaning bots help homeowners identify the dirtiest parts of their homes and when their filters have to be changed.
The Positive Impact of AI on Jobs
More Ph.D.-level scientists are now required to build these capabilities. These candidates have expertise in navigation, AI, robotics and computer vision, and they can serve dual roles, building the product line and researching ways to improve products. This is ideal for faster, seamless improvements. Unfortunately, this talent is very difficult to find and even harder to steal from the competition.
There is what can only be described as a battle to attract this talent. According to Indeed, the number of job postings for theses high-tech wunderkind has doubled and then some in the past three years. Which positions are in the highest demand of all? Computer-vision engineers and machine-learning engineers should feel very confident in their ability to gain employment for the foreseeable future.
In fact, interviewers may feel they are being evaluated just as much as the candidates!
Getting employees to consider AI systems as a good thing is still a tough sell. Getting employees to accept the new technology can be as difficult as developing it. There's typically a very visceral reaction when robots enter the workplace. Some companies develop or hire in customer success managers to ease the transition into AI applications. These managers answer questions and complaints and help the organization make adjustments to business processes.
Cobalt Robotics in Palo Alto employees these managers to bridge the gap between robots and employees. Cobalt rents robots as security guards on the hard to fill overnight and weekend shifts. Before and after the robots are deployed, the customer-robot liaisons talk to the employees and answer any questions they may have about the role the bots will play at the company.
These managers might monitor usage to make sure the robots are interacting with the clients via texts, emails, and calls. Managers interface with customers through calls, text, email, and on-site visits to build the human relationships needed for a successful deployment. One manager even headed out with a can of oil to remedy a squeaky wheel that was annoying the clients. Another client's landlords required proof of insurance in order to allow the robot to patrol the premises.
Worries, Doubts and Wants
Americans seem to be worried about whether robots could take over society, like the stuff of science fiction movies. The jury is still out on how that plays out. More pertinent to tech talent and other workers, robots and computers do have the capability of performing some jobs currently (or previously) done by people. However, it turns out that robots mostly are recruited to do the jobs that people aren’t really interested in doing anyway.
Robots are amazingly adept for the functions they’re designed to do. However, it’s human judgment that makes people valuable and irreplaceable by robot counterparts. A robot specialist oversees the work performed by robots to ensure things go according to plan or to intervene in the case of a misjudgment or question on the part of the AI system.
This requirement for human involvement is often underestimated when employees fear the worth from robots in their midst. Ironically, to help manage the robots, Cobalt’s tech talent must need to have people skills just as much as technical ones. The robotic guards are meant to make people feel safe after hours, so creeping them out is counterproductive.
For example, at Cobalt, robot managers work weekends and nights to make sure the deployed robotic guards are functioning normally on assignment across the greater San Francisco Bay Area. They do this by monitoring video feeds and data from the robot’s sensors. This data comes up on screens at Cobalt’s Palo Alto headquarters. If there’s an emergency the robot can’t deal with, such as an intruder or pipe leak, the managers at Cobalt jump in, figure out what’s going on and notify the client or the appropriate authorities.
The responsibilities of this job include sussing out how the robots react when people are present. The idea is that the robot shouldn’t be rushing through knots of workers during a shift or hovering over someone’s desk. Some of these managers come from the hospitality industry and possess the finesse needed to avert awkward interactions.
AI needs humans to explain things before they can understand the world. This occurs when the AI absorbs data that has to be labeled. For example, when a human data labeler identifies objects in an image, they label the face or other significant components. Others might parse out sentences to help the AI learn various phrases. These data labelers review information and mark it up for the AI system. This is crucial to technology like self-driving cars. Large tech companies might have hundreds of people doing this job.
The complexity of data labeling varies in complexity. Cobalt uses photos and posters with people on them to help robotic security guards differentiate between posters and real intruders. Engineers and robot tech talent flag the false positives and over time, the robots rule them out as threats.
Sometimes, it's more complicated than that. In Richland, Washington, the Pacific Northwest National Lab labels cloud images from lidar to help their AI identify them. This will let scientists get a better understanding of how clouds form. This, in turn, improves weather predictions. Each image could be up to two million pixels, and every pixel has to be labeled as including a cloud or not. Haze and dust sometimes show up, and then these images are hard to decode. This is a detail-oriented job that requires a certain level of expertise.
One data labeler has an education in atmospheric science and electrical engineering, as well as 20 years of experience working with lidar data and images. There are many facets of this position, which also requires an intimate knowledge of how the lidar system works.
AI Lab Scientists
AI has found its way into drug development and is changing the industry. Smart software mines incredible amounts of data in a fraction of the time it would take people to do so. This is leading to new directions for research and development. There are plenty of opportunities for data scientists as well as biologists who specialize in computations. These tech talent specialists help AI systems learn chemistry and life sciences so they can come up with new ideas. Technicians test the results that come back from AI to determine which are invalid and which are useful. The correct data is fed back to AI systems to help them learn.
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