10 Types of Jobs that Use Deep Learning - dummies

10 Types of Jobs that Use Deep Learning

By John Paul Mueller, Luca Mueller

There are a lot of different uses for deep learning — everything from the voice-activated features of your digital assistant to self-driving cars. Using deep learning to improve your daily life is nice, of course, but most people need other reasons to embrace a technology, such as getting a job. Fortunately, deep learning doesn’t just affect your ability to locate information faster but also offers some really interesting job opportunities, and with the “wow” factor that only deep learning can provide. This article gives you an overview of ten interesting occupations that rely on deep learning to some extent today. This material represents only the tip of the iceberg, though; more occupations are arising that use deep learning quickly, and more are added every day.

Deep learning can help when managing people

A terrifying movie called The Circle would have you believe that modern technology will be even more invasive than Big Brother in the book 1984, by George Orwell. Part of the movie’s story involves installing cameras everywhere — even in bedrooms. The main character wakes up every morning to greet everyone who is watching her. Yes, it can give you the willies if you let it.

However, real deep learning isn’t about monitoring and judging people, for the most part. It’s more like Oracle’s Global Human Resources Cloud. Far from being scary, this particular technology can make you look smart and on top of all the activities of your day. The video is a little over the top, but it gives you a good idea of how deep learning can currently make your job easier.

The idea behind this technology is to make success easier for people. If you look at Oracle’s video and associated materials, you find that the technology helps management suggest potential paths to employees’ goals within the organization. In some cases, employees like their current situation, but the software can still suggest ways to make their work more engaging and fun. The software keeps employees from getting lost in the system and helps to manage the employee at a custom level so that each employee receives individualized input.

Deep learning improves medicine

Deep learning is affecting the practice of medicine in many ways, as you can see when you go to the doctor or spend time at a hospital. Deep learning assists with diagnosing illnesses and finding their correct cure. Deep learning is even used to improve the diagnostic process for hard-to-detect issues, including those of the eye. However, one of the most important uses for deep learning in medicine is in research.

The seemingly simple act of finding the correct patients to use for research purposes isn’t really that simple. The patients must meet strict criteria or any testing results may prove invalid. Researchers now rely on deep learning to perform tasks like finding the right patient, designing the trial criteria, and optimizing the results. Obviously, medicine will need a lot of people who are trained both in medicine and in the use of deep learning techniques for medicine to continue achieving advances at their current pace.

Deep learning helps to develop new devices

Innovation in some areas of computer technology, such as the basic system, which is now a commodity, has slowed down over the years. However, innovation in areas that only recently became viable has greatly increased. An inventor today has more possible outlets for new devices than ever before. One of these new areas is the means to perform deep learning tasks. To create the potential for performing deep learning tasks of greater complexity, many organizations now use specialized hardware that exceeds the capabilities of GPUs — the currently preferred processing technology for deep learning.

Deep learning technology is in its infancy, so a smart inventor could come up with something interesting without really working all that hard. This article tells about new AI technologies, but even these technologies don’t begin to plumb the depths of what could happen.

Deep learning is attracting the attention of both inventors and investors because of its potential to upend current patent law and the manner in which people create new things. An interesting part of most of the articles of this sort is that they predict a significant increase in jobs that revolve around various kinds of deep learning, most of which involve creating something new. Essentially, if you can make use of deep learning in some way and couple it with a current vibrant occupation, you can find a job or develop a business of your own.

Deep learning can provide customer support

Many deep learning discussions refer to chatbots and other forms of customer support, including translation services. In case you’re curious, you can have an interactive experience with a chatbot at Pandorabots.com. The use of chatbots and other customer support technologies have stirred up concern, however.

Some consumer groups that say human customer support is doomed, as in this Forbes article. However, if you have ever had to deal with a chatbot to perform anything complex, you know the experience is less than appealing. So the new paradigm is the human and chatbot combination.

Much of the technology you see used today supposedly replaces a human, but in most cases, it can’t. For the time being, you should expect to see many situations that have humans and bots working together as a team. The bot reduces the strain of performing physically intense tasks as well as the mundane, boring chores. The human will do the more interesting things and provide creative solutions to unexpected situations. Consequently, people need to obtain training required to work in these areas and feel secure that they’ll continue to have gainful employment.

Deep learning can help you see data in new ways

Look at a series of websites and other data sources and you notice one thing: They all present data differently. A computer doesn’t understand differences in presentation and isn’t swayed by one look or another. It doesn’t actually understand data; it looks for patterns. Deep learning is enabling applications to collect more data on their own by ensuring that the application can see appropriate patterns, even when those patterns differ from what the application has seen before. Even though deep learning will enhance and speed up data collection, however, a human will still need to interpret the data. In fact, humans still need to ensure that the application collects good data because the application truly understands nothing about data.

Another way to see data in new ways is to perform data augmentation. Again, the application does the grunt work, but it takes a human to determine what sort of augmentation to provide. In other words, the human does the creative, interesting part, and the application just trudges along, ensuring that things work.

These first two deep learning uses are interesting and they’ll continue to generate jobs, but the most interesting using of deep learning is for activities that don’t exist yet. A creative human can look at ways that others are using deep learning and come up with something new. Check out some interesting uses of AI, machine learning, and deep learning that are just now becoming practical.

Deep learning can perform analysis faster

When most people speak of analysis, they think about a researcher, some sort of scientist, or a specialist. However, deep learning is becoming entrenched in some interesting places that will require human participation to see full use, such as predicting traffic accidents.

Imagine a police department allocating resources based on traffic flow patterns so that an officer is already waiting at the site of an expected accident. The police lieutenant would need to know how to use an application of this sort. Of course, this particular use hasn’t happened yet, but it very likely could because it’s already feasible using existing technology. So performing analysis will no longer be a job for those with “Dr.” in front of their names; it will be for everyone.

Analysis, by itself, isn’t all that useful. It’s the act of combining the analysis with a specific need in a particular environment that becomes useful. What you do with analysis defines the effect of that analysis on you and those around you. A human can understand the concept of analysis with a purpose; a deep learning solution can only perform the analysis and provide an output.

Deep learning can help create a better work environment

Deep learning will make your life better and your employment more enjoyable if you happen to have skills that allow you to interact successfully with an AI. This article describes how AI could change the workplace in the future. An important element of this discussion is to make work more inviting.

At one point in human history, work was actually enjoyable for most people. It’s not that they ran around singing and laughing all the time, but many people did look forward to starting each day. Later, during the industrial revolution, other people put the drudge into work, making every day away from work the only pleasure that some people enjoyed. The problem has become so severe that you can find popular songs about it, like “Working for the Weekend.” By removing the drudge from the workplace, deep learning has the potential to make work enjoyable again.

Deep learning will strongly affect the work environment in a number of ways, and not just the actual performance of work. For example, technologies based on deep learning have the potential to improve your health and therefore your productivity. It’s a win for everyone because you’ll enjoy life and work more, while your boss gets more of that hidden potential from your efforts.

One of the things that you don’t see mentioned often is the effect on productivity of a falling birth rate in developed countries. This McKinsey article takes this issue on to some extent and provides a chart showing the potential impact of deep learning on various industries. If the current trend continues, having fewer available workers will mean a need for augmentation in the workplace.

However, you might wonder about your future if you worry that you might not be able to adapt to the new reality. The problem is that you might not actually know whether you’re safe. In Artificial Intelligence For Dummies, by John Paul Mueller and Luca Massaron [Wiley], you see discussions of AI-safe occupations and new occupations that AI will create. You can even discover how you might end up working in space at some point. Unfortunately, not everyone wants to make that sort of move, much as the Luddites didn’t during the industrial revolution. Certainly, what AI promises is going to have consequences even greater than the industrial revolution did (read about the effects of the industrial revolution) and will be even more disruptive. Some politicians, such as Andrew Wang, are already looking at short-term fixes like basic universal income. These policies, if enacted, would help reduce the impact of AI, but they won’t provide a long-term solution. At some point, society will become significantly different from what it is today as a result of AI — much as the industrial revolution has already changed society.

Deep learning can help research obscure or detailed information

Computers can do one thing — pattern matching — exceptionally well (and much better than humans. If you’ve ever had the feeling that you’re floating in information and none of it relates to your current need, you’re not alone. Information overload has been a problem for many years and worsens every year. You can find a lot of advice on dealing with information overload. The problem is that you’re still drowning in information. Deep learning enable you to find the needle in a haystack, and in a reasonable amount of time. Instead of months, a good deep learning solution could find the information you need in a matter of hours in most cases.

However, knowing that the information exists is usually not sufficient. You need information that’s detailed enough to fully answer your question, which often means locating more than one source and consolidating the information. Again, a deep learning solution could find patterns and mash the data together for you so that you don’t have to combine the input from multiple sources manually.

After AI finds the data and combines the multiple sources into a single cohesive report (you hope), it has done everything it can for you. It’s still up to the human to make sense of the information and determine a way to use it successfully. The computer won’t remove the creative part of the task; it removes the drudgery of finding the resources required to perform the creative part of the task. As information continues to increase, expect to see an increase in the number of people who specialize in locating detailed or obscure information.

The information broker is becoming an essential part of society and represents an interesting career path that many people haven’t even heard about. This article offers a good summary of what information brokers do.

Deep learning can help design buildings

Most people view architecture as a creative trade. Imagine designing the next Empire State Building or some other edifice that will that will stand the test of time. In the past, designing such a building took years. Oddly enough, the contractor actually built the Empire State Building in just a little over a year, but this isn’t usually the case. Deep learning and computer technology can help reduce the time to design and build buildings considerably by allowing things like virtual walkthroughs. In fact, the use of deep learning is improving the lives of architects in significant ways.

However, turning a design into a virtual tour isn’t even the most impressive feat of deep learning in this field. Using deep learning enables designers to locate potential engineering problems, perform stress testing, and ensure safety in other ways before the design ever leaves the drawing board. These capabilities minimize the number of issues that occur after a building becomes operational, and the architect can enjoy the laurels of a success rather than the scorn and potential tragedy of a failure.

Deep learning can enhance safety

Accidents happen! However, deep learning can help prevent accidents from happening — at least for the most part. By analyzing complex patterns in real time, deep learning can assist people who are involved in various aspects of safety assurance. For example, by tracking various traffic patterns and predicting the potential for an accident well in advance, a deep learning solution could provide safety experts with suggestions for preventing the accident from happening at all. A human couldn’t perform the analysis because of too many variables. However, a deep learning solution can perform the analysis and then provide output to a human for potential implementation.

As with every other occupation that involves deep learning, the human acts as the understanding part of the solution. Various kinds of accidents will defy the capability of any deep learning solution to provide precise solutions every time. Humans aren’t predictable, but other humans can reduce the odds of something terrible happening given the right information. The deep learning solution provides that correct information, but it requires human foresight and intuition to interpret the information correctly.