important part in industrial settings where the focus is on efficiency. Doing things faster, more accurately, and with fewer resources helps the bottom line and makes an organization more flexible with a higher profit margin. Fewer mistakes also help the humans working in an organization by reducing the frustration level. You can currently see machine learning at work in
Medical diagnosis
Data mining
Bioinformatics
Speech and handwriting recognition
Product categorization
Inertial Measurement Unit (IMU) (such as motion capture technology)
Information retrieval
This list just scratches the surface. Machine learning is used a lot in industry today, and the number of uses will continue to increase as advanced algorithms make higher levels of learning possible. Currently, machine learning performs tasks in a number of areas that include the following:
Analyzation: Determining what a user wants and why, and what sort of patterns (behaviors, associations, responses, and so on) the user exhibits when obtaining it.
Enrichment: Adding ads, widgets, and other features to an environment so that the user and organization can obtain additional benefits, such as increased productivity or improved sales.
Adaptation: Modifying a presentation so that it reflects user tastes and choice of enrichment. Each user ends up with a customized experience that reduces frustration and improves productivity.
Optimization: Modifying the environment so that the presentation consumes fewer resources without diminishing the user experience.
Control: Steering the user to a particular course of action based on inputs and the highest probability of success.
A theoretical view of what machine learning does in industry is nice, but it’s important to see how some of this works in the real world. You can see machine learning used in relatively mundane but important ways. For example, machine learning has a role in automating employee access, protecting animals, predicting emergency room wait times, identifying heart failure, predicting strokes and heart attacks, and predicting hospital readmissions. (The story at https://www.forbes.com/sites/85broads/2014/01/06/six-novel-machine-learning-applications/ provides details on each of these uses.)
Understanding the role of updated processors and other hardware
The “Specifying the Role of Statistics in Machine Learning” section of Chapter 2 tells you about the five schools of thought (tribes) related to machine learning. Each of these schools of thought tell you that the current computer hardware isn’t quite up to the task of making machine learning work properly. For example, you might talk to one tribe whose members tell you of the need for larger amounts of system memory and the use of GPUs to provide faster computations. Another tribe might espouse the creation of new types of processors. Learning processors, those that mimic the human brain, are all the rage for the connectionists. You can read about processors designed to accelerate AI and machine learning tasks at https://www.eetimes.eu/top-10-processors-for-ai-acceleration-at-the-endpoint/. The point is that everyone agrees that some sort of new hardware will make machine learning easier, but the precise form this hardware will take remains to be seen.
Discovering the New Work Opportunities with Machine Learning
In the past, you could easily find articles that described the complete loss of job opportunities for humans because of robots. Robots already perform a number of tasks that used to employ humans, and this usage will increase over time. However, now that companies have more experience under their belts, you often find that articles talk about augmentation, which involves the robot and human working side by side to perform tasks. The previous section of this chapter aided you in understanding some of the practical, real-world uses for machine learning today and helped you discover where those uses are likely to expand in the future. While reading this section, you must have also considered how those new uses could potentially cost you or a loved one a job. The article at https://aithority.com/guest-authors/the-future-of-artificial-intelligence-is-job-augmentation-not-elimination/ sets the record straight by pointing out that robots can’t actually replace humans in many (perhaps most) scenarios.
The fact of the matter is that deciding just how machine learning will affect the work environment is hard, just as it was hard for people to see where the industrial revolution would take us in the way of mass-producing goods for the general consumer (see https://www.history.com/topics/industrial-revolution for details). Just as those workers needed to find new jobs, so people facing loss of occupation to machine learning today will need to find new jobs or discover how to perform their tasks in new ways.
Working for a machine
It’s entirely possible that you’ll find yourself working for a machine in the future. In fact, you might already work for a machine and not know it. Some companies already use machine learning to analyze business processes and make them more efficient. For example, Hitachi currently uses such a setup in middle management (see the article at http://www.hitachi.com/New/cnews/month/2015/09/150904.html). In this case, the AI actually issues the work orders based on its analysis of the workflow — just as a human middle manager might do. The difference is that the AI is actually eight percent more efficient than the humans it replaces.
However, a job opportunity also presents itself. Workers under the AI do perform the tasks that the AI tells them to do, but they can use their own experience and creativity in determining how to perform the task. The AI analyzes the processes that the human workers use and measures the results achieved. Any successful processes get added into the database of techniques that workers can apply to accomplish tasks. In other words, the humans are teaching the AI new techniques to make the work environment even more efficient.
Teaching the AI new techniques is an example of how machine learning can free humans from the drudgery of the work environment. When using human middle managers, new processes often get buried in a bureaucracy of unspoken rules and ego. The AI middle manager is designed to learn new techniques without bias, so the humans are encouraged to exercise their creativity, and everyone benefits. In short, the AI, which lacks an ego to bruise, is the approachable manager that many workers have wanted all along when it comes to implementing new ideas that make everyone more productive.
https://www.theverge.com/2020/2/27/21155254/automation-robots-unemployment-jobs-vs-human-google-amazon.
