Well, is artificial intelligence a job-killer or not? We keep hearing both sides, from projections of doom for many professions that will necessitate things such as universal basic income to help sidelined workers, to projections of countless unfilled jobs needed to build and manage AI-powered enterprises. For a worker losing his or her job to automation, knowing that an AI programming job is being created elsewhere is of little solace.

Perhaps the reality will be somewhere in between. An MIT report released at the end of last year states recent fears about AI leading to mass unemployment are unlikely to be realized. “Instead, we believe that—like all previous labor-saving technologies—AI will enable new industries to emerge, creating more new jobs than are lost to the technology,” the report’s authors, led by Thomas Malone, director of the MIT Center for Collective Intelligence, conclude. “But we see a significant need for governments and other parts of society to help smooth this transition, especially for the individuals whose old jobs are disrupted and who cannot easily find new ones.”

The future of AI and job growth — or losses — may be nuanced, a recent report from BCG and Faethm suggests. “Though these technologies will eliminate some jobs, they will create many others,” the report’s team of authors, led by BCG’s Rainer Strack. “Governments, companies, and individuals all need to understand these shifts when they plan for the future.”

What needs to be understood? For starters, “the net number of jobs lost or gained is an artificially simple metric to gauge the impact of digitization,” Strack and his co-authors state. “For example, eliminating 10 million jobs and creating 10 million new jobs would appear to have negligible impact. In fact, however, doing so would represent a huge economic disruption for the country—not to mention for the millions of people with their jobs at stake.”

There’s even a paradox in play. Computers tend to perform well in tasks that humans find difficult or time-consuming to do, “but they tend to work less effectively in tasks that humans find easy to do,” the report notes. Also, in many areas, technologies “will improve the quality of work that humans do by allowing them to focus on more strategic, value-creating, and personally rewarding tasks.”

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In other words, AI can’t take over many of the soft skills essential to businesses growth — initiative, intuition, passion, and ability to sell ideas and concepts. Add that to more technical abilities needed to build and maintain AI and digital environments — and keep them focused on what the business needs. “In many sectors, severe shortages of skilled workers will mean that growth in demand for talent will be unmet,” Strack and his co-authors state. “This is particularly true for computer-related occupations and jobs in science, technology, engineering, and math, since technology is fueling the rise of automation across all industries. This is why the computer and mathematics job family group is likely to suffer by far the greatest worker deficits.”

At the same time, there will also be increasing demand for jobs requiring compassionate human contact, such as health care, social services, and teaching, they add.

Along with the BCG-Faethm’s observations, it should be noted that AI cannot replicate the entrepreneurial skills that will be pulling together technology solutions and platforms to connect to the needs of markets. Humans are the innovators.

What to do? Strack and his team urge people to take charge of their professional development through lifelong learning. “Individuals will have to take greater responsibility for their own professional development, whether that means through upskilling or reskilling,” they state. “Pay attention to sources of information and update skills accordingly, either by searching out high-quality providers of education or by charting your own course amid the vast amount of online-learning offers.”

The BCG-Faethm team also makes the following recommendations from a corporate perspective:

• Think strategically in terms of hiring. “A company should regularly assess the current size, composition, and development of its workforce. It should also evaluate future demand on the basis of strategic direction and determine the gaps for certain jobs and specific skills.”
• Support upskilling and reskilling. “Given the rapid shifts in skill requirements and the number of entirely new tasks and roles that are emerging, the labor market will be unable to supply sufficient new talent to fill available positions. Companies therefore need to supplement external hiring with internal development initiatives and on-the-job training.”
• Create a lifelong learning culture. “Corporate training used to consist of certifications or intermittent training programs, but the digital economy will demand a constant upgrading of skills. Content and skill upgrades should be delivered in a variety of formats so that they can be integrated into the daily routine of every employee, ensuring a nimble and agile workforce.”
• Rethink talent recruitment and retention strategies. “Companies may need to shift the recruitment focus from hiring for skill to hiring for will: as some of the skills needed in the future (such as coding computer languages) will most likely be self-taught or come without an explicit certification, HR professionals will need to view candidate criteria with a more open mind and embrace diverse curricula. Companies may also opt to create an employee pool to which people with new skills can be added without yet knowing which field of operations they’d be best suited for.”

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Fantastical notions of all-powerful robots, straight out of Hollywood, may come to mind when you think about artificial intelligence (AI). But set aside thoughts of the machines taking over: When it comes to your hearing aides, AI helps the devices function better. 

For instance, AI can help wrangle one of the most challenging situations if you struggle to hear: Engaging in a conversation when you’re in a crowded, loud space (think: a restaurant or cafe). Because as you know if you wear a hearing aid, louder isn’t the solution. 

From month to month, year to year, researchers are finding more ways to harness this technology and use it to improve hearing aids. Here’s what you need to know about how hearing aids use AI—and if a hearing aid with this functionality is right for you or a loved one. 

Key terms: AI, machine learning, deep neural network 

Put simply, artificial intelligence is defined as the ability of a machine to simulate human intelligence, performing a set of tasks that require “intelligent” decisions by following predetermined rules. 

“Artificial intelligence is a very broad definition. Machine learning, neural network, deep learning, and all of those, fall under the AI umbrella,” says Issa M.S. Panahi, PhD, professor of electrical and computer engineering in the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas. 

Through machine learning, a subset of AI, machines use algorithms (aka, a set of rules) to sort through giant amounts of data and make decisions or predictions. 

Go one level deeper, and we get to the deep neural network (DNN): This form of AI is set up to mimic the neural habits of the brain, and aims to respond the same way your brain would, without being explicitly programmed how to react in a given situation. 

You’re familiar with this technology if your inbox sorts emails into categories (important, promotional, etc.), if you take advantage of recommendations of “what to watch next” on streaming networks, or if you’ve marveled over self-parking cars. Some more mundane but important examples of deep learning include weather forecasting and credit card fraud protection. These tools have gotten much better in recent years due to deep learning. 

How hearing aids use AI 

“The AI that occurs in hearing aids has actually been going on for years, but it’s a slow burn to think about how that’s actually happened,” says Scott Young, Aud, CCC-A, owner of Hearing Solution Centers, Inc. in Tulsa, Okla. 

Hearing aids used to be relatively simple, he notes, but when hearing aids introduced a technology known as wide dynamic range compression (WDRC) the devices actually began to make a few decisions based on what it heard, he says. 

“Over the last several years, AI has come even further—it actually listens to what the environment does,” Scott says. And, it responds accordingly. Essentially, a DNN allows hearing aids to begin to mimic how your brain would hear sound if your hearing wasn’t impaired.  

For hearing aids to work effectively, they need to adapt to a person’s individual hearing needs as well as all sorts of background noise environments, Panahi says. “AI, machine learning, and neural networks, are very good techniques to deal with such a complicated, nonlinear, multi-variable type of problem,” he says. 

What the research shows

Researchers have been able to accomplish a lot with AI to date, when it comes to improving hearing. 

For instance, researchers at the Perception and Neurodynamics Laboratory (PNL) at the Ohio State University trained a DNN to distinguish speech (what people want to hear) from other noise (such as humming and other background conversations), writes DeLiang Wang, professor of computer science and engineering at Ohio State University, in IEEE Spectrum. “People with hearing impairment could decipher only 29 percent of words muddled by babble without the program, but they understood 84 percent after the processing,” Wang writes. 

And at University of Texas at Dallas, Panahi, along with co-principal investigator Dr. Linda Thibodeau, used AI to create a smartphone app that can tell the direction where speech is coming from. This app calls on models built using a massive library of sounds to identify and diminish background noise, so people hear better. Place a smartphone with the app on a table, or rest it in the GPS stand in your car, and “clean speech is transmitted to the hearing aid devices or earbuds,” Panahi says. 

“The importance of AI is it overcom[es] a lot of issues that cannot be easily solved by a traditional mathematical approach for signal processing,” Panahi says. 

Neural-network powered hearing aids

In recent years, major hearing aid manufacturers have been adding AI technology to their premium hearing aid models. For example Widex’s Moment hearing aid utilizes AI and machine learning to create hearing programs based on a wearer’s typical environments.

And this January, Oticon introduced its newest hearing aid device, Oticon More™, the first hearing aid with an on-board deep neural network. Oticon More was trained—using 12 million-plus real-life sounds—so that people wearing it can better understand speech and the sounds around them.

In a complicated “sound scene”—picture a bustling airport or hospital emergency room—the Oticon More’s neural net receives a complicated layer of sounds, known as input. The DNN gets to work, first scanning and extracting simple sound elements and patterns from the input. It builds these elements together to recognize and make sense of what’s happening. Lastly, the hearing aids then make a decision on how to balance the sound scene, making sure the output is clean and ideally balanced to the person’s unique type of hearing loss. 

This improvement is especially key for speech in noise, explained Donald J. Schum, PhD, Vice President of Audiology at Oticon, during the product launch event.

“Speech and other sounds in the environment are complicated acoustic wave forms, but with unique patterns and structures that are exactly the sort of data deep learning is designed to analyze,” he said. “We wanted our system to be able to find speech even when it’s embedded in background noise. And that’s happening in real-time and in an ongoing basis.”

Do I need a hearing aid with AI? 

Think of hearing aids as existing on a spectrum, says Young—hearing aids range widely in price, and some at the lower end have fewer AI-driven bells and whistles, he says. 

He points out that some patients may not need all the features—people who live alone or rarely leave the house, and don’t find themselves in crowded scenarios often, for instance, might not benefit from the functionality found in higher-end models. 

But for anyone who is out and about a lot, especially in situations where there are big soundscapes, AI-powered features allow for an improved hearing experience.

Listening effort is reduced

What “improvement” looks like can be measured in a lot of ways, but one key indicator is memory recall, Schum explained. It’s not that the hearing aids like Oticon More literally improve a person’s memory, he explained, it’s that artificial intelligence helps people spend less time trying to make sense of the noise around them, a process known as “listening effort.”

When the listening effort is more natural, a person can focus more on the conversation and all the nuances conveyed within.

“It’s allowing the brain to work in the most natural way possible,” he said. 

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