Chapter 6

IN THIS CHAPTER

Using AI to meet human needs

Making industry more efficient

Developing dynamic safety protocols using AI

Chapter 5 considers the use of AI in an application, which is a situation in which a human interacts with the AI in some meaningful way, even if the human is unaware of the presence of the AI. The goal is to help humans do something faster, easier, or more efficiently, or to meet some other need. A process that includes an AI is different because the AI is now working to assist a human or perform some other task without direct intervention. The first section of this chapter addresses how processes help humans. Given that boredom is possibly the worst-case human scenario (just think of all the negative things that happen when humans are bored), this chapter views the AI process for humans from a boredom perspective.

One of the ways AI has been in use the longest is industrial utilization, such as manufacturing processes, to eventually allow for Industry 4.0 implementation (see “What is Industry 4.0?” at TWI Global.com for details). Consider all the robots that now power the factories across the world. Even though AI-powered automation replaces humans, it also keeps humans safer by performing tasks generally considered dangerous. Oddly enough, one of the most significant causes of industrial accidents and a wealth of other problems is boredom, as explained in “Boredom at work” at The Psychologist.bps.org.uk. The article “How to make your boredom work for you” at Fast Company.com does try to turn things around, but still, boredom can be and is dangerous. Robots can perform those repetitive jobs consistently and without getting bored (although you might see an occasional yawn).

Just in case you haven’t had enough about boredom yet, you can also read something about it in the third section of the chapter, which discusses some of the newest areas in which AI excels —making environments of all sorts safer. In fact, just in the automotive industry, you can find myriad ways in which the use of AI is making things better (see “Artificial Intelligence Reshaping the Automotive Industry” at Future Bridge.com for details).

The point of this chapter is that AI works well in processes, especially those processes during which humans tend to get bored, causing them to make a mistake when the AI likely wouldn’t. Of course, an AI can’t eliminate every source of lost efficiency, disinterest, and safety issue. For one thing, humans can choose to ignore the AI’s help, but the nature of the limitations goes much deeper than that. As discussed in previous chapters (especially Chapter 5), an AI doesn’t understand; it can’t provide creative or innovative solutions to problems, so some problems aren’t solvable by an AI, no matter how much effort someone puts into creating it.

Developing Solutions for Boredom

Polls often show what people think they want, rather than what they do want, but they’re still useful. When college graduates were polled to see what kind of life they wanted, not one of them said, “Oh please, let me be bored!” (check out “What Kind of Life Do You Want to Live?” at Huffington Post.com). In fact, you could possibly poll just about any group and not come up with a single boring response. Most humans (saying “all” would likely result in an avalanche of email, with examples) don’t want to be bored. In some cases, AI can work with humans to make life more interesting — for the human, at least. The following sections discuss solutions for human boredom that AI can provide (and a few that it can’t).

Making tasks more interesting

Any occupation, be it personal or for an organization, has certain characteristics that attract people and make them want to participate in it. Obviously, some occupations, such as taking care of your own children, pay nothing, but the satisfaction of doing so can be incredibly high. Likewise, working as a bookkeeper may pay quite well but not offer much in the way of job satisfaction. Various polls (such as this one in “The 2019 Jobs Rated Report” at CareerCast.com” and articles such as “Which is the key to happiness: High salary or job satisfaction?” at Engineering and Technology Jobs.org talk about the balance of money and satisfaction, but reading them often proves confusing because the basis for making a determination is ambiguous. However, most of these sources agree that after a human makes a certain amount of money, satisfaction becomes the key to maintaining interest in the occupation (no matter what that occupation might be). Of course, figuring out what comprises job satisfaction is nearly impossible, but interest remains high on the list. An interesting occupation will always have higher satisfaction potential.

The problem is not one of necessarily changing jobs, then, but of making the job more interesting as a means to avoid boredom. An AI can effectively help this process by removing repetition from tasks. However, examples such as Amazon’s Alexa and Google’s Home do provide other alternatives. The feeling of loneliness that can pervade the home, workplace, car, and other locations is a strong creator of boredom. When humans begin to feel alone, depression sets in and boredom is often just a step away. Creating applications that use the Alexa interface (see https://developer.amazon.com/) or Actions on the Google API (see https://developers.google.com/actions/) to simulate human interaction of the appropriate sort can improve the workplace experience. More important, developing smart interfaces of this sort can help humans perform a wealth of mundane tasks quickly, such as researching information and interacting with smart devices, not just light switches (see “How to control your lights with Amazon Echo” at iMore.com and https://store.google.com/product/google_home for details).

Helping humans work more efficiently

Most humans, at least the forward-thinking ones, have some ideas of how they’d like an AI to make their lives better by eliminating tasks that they don’t want to do. The poll in “Which tasks in your job would you like to be automated by AI?” at blog.devolutions.net shows some of the more interesting ways that they wish AI could improve their lives:. Many of them are mundane, but notice the ones like detecting when a significant other is unhappy and sending flowers. It probably won’t work, but it’s an interesting idea nonetheless.

The point is that humans will likely provide the most interesting ideas on how to create an AI that specifically addresses their needs. In most cases, serious ideas will work well for other users, too. For example, automating trouble tickets is something that could work in a number of different industries. If someone were to come up with a generic interface, with a programmable back end to generate the required custom trouble tickets, the AI could save users a lot of time and ensure future efficiencies by ensuring that trouble tickets consistently record the required information.

COUNTER INTELLIGENCE IN WORK

Few people like things to be hard; most of us want to ease into work and come out with a sense of satisfaction each day. However, some new articles and white papers seem to indicate that adding AI to the workplace actually makes things harder. Consider this article from The Atlantic, “AI Is Coming for Your Favorite Menial Tasks”). However, the article isn’t actually about menial tasks. It’s more about AI sucking all the fun out of a person’s job and leaving only the most stressful elements that only a human can effectively deal with. The article considers the other side of the coin: instances when automation makes a person’s job significantly more difficult and definitely less satisfying, and the human isn’t even getting paid more to do it. More important, the human’s chance of making the right decision because all the decisions are hard ones also drops, which can then give management the impression that a worker is suddenly losing interest or simply not focusing. At some point, a balance will have to be struck between what AI does and what humans do to maintain job satisfaction. Current AI design doesn’t consider this aspect of human need at all, but it will be a requirement in the future.

Understanding how AI reduces boredom

Boredom comes in many packages, and humans view these packages in different ways. There is the boredom that comes from not having required resources, knowledge, or other needs met. Another kind of boredom comes from not knowing what to do next when activities don’t follow a specific pattern. An AI can help with the first kind of boredom; it can’t help with the second. This section considers the first kind of boredom. (The next section considers the second kind.)

Access to resources of all sorts helps reduce boredom by allowing humans to be creative without the mundane necessity of acquiring needed materials. Here are some ways in which an AI can make access to resources easier:

· Searching for needed items online

· Ordering needed items automatically

· Performing sensor and other data-acquisition monitoring

· Managing data

· Accomplishing mundane or repetitive tasks

Considering how AI can’t reduce boredom

As noted in previous chapters, especially Chapters 4 and 5, an AI is not creative or intuitive. So, asking an AI to think of something for you to do is unlikely to produce satisfying results. Someone could program the AI to track the top ten things you like to do and then select one of them at random, but the result still won’t be satisfying because the AI can’t take aspects like your current state of mind into account. In fact, even with the best facial expression recognition software, an AI will lack the capability to interact with you in a manner that will produce any sort of satisfying result.

An AI also can’t motivate you. Think about what happens when a friend comes by to help motivate you (or you motivate the friend). The friend actually relies on a combination of intrapersonal knowledge (empathizing by considering how it feels to be in your situation) and interpersonal knowledge (projecting creative ideas on how to obtain a positive emotional response from you). An AI won’t have any of the first kind of knowledge and only extremely limited amounts of the second kind of knowledge, as described in Chapter 1. Consequently, an AI can’t reduce your boredom through motivational techniques.

Boredom may not always be a bad thing, anyway. A number of recent studies have shown that boredom actually helps promote creative thought, which is the direction that humans need to go (see “Being Bored Can Be Good for You—If You Do It Right” at Time.com and “The Science behind How Boredom Benefits Creative Thought” at Fast Company.com as examples). Despite the myriad articles on how AI is going to take jobs away, it’s important to consider that the jobs that AI is taking are, in themselves, often boring and leave humans no time to create. Even today, humans could find productive, creative, jobs to do if they really thought about it. The article “7 Surprising Facts About Creativity, According To Science” at Fast Company.com discusses the role of daydreaming when bored in enhancing creativity. In the future, if humans really want to reach for the stars and do other amazing things, creativity will be essential, so the fact that AI can’t reduce your boredom is actually a good thing.

Working in Industrial Settings

Any industrial setting is likely to have safety hazards, no matter how much time, effort, and money is thrown at the problem. You can easily find articles such as this one, “A Guide to the Most Common Workplace Hazards” at High Speed Training.co.uk, which describes common safety hazards found in industrial settings. Although humans cause many of these problems and boredom makes them worse, the actual environment in which the humans are working causes a great many issues. The following sections describe how automation can help humans live longer and better lives.

Developing various levels of automation

Automation in industrial settings is a lot older than you might think. Many people think of Henry Ford’s assembly line as the starting point of automation (see “Ford’s assembly line starts rolling” at History.com). In fact, the basics of automation began in 1104 AD in Venice (see “Trends in 21st Century Factory Automation” at Mouser.com),where 16,000 workers were able to build an entire warship in a single day. Americans repeated the feat of building warships extremely fast with modern ships during WWII (read about it in “World War II Shipbuilding in the San Francisco Bay Area” at nps.gov) by relying heavily on automation. In fact, there have been four industrial revolutions so far according to the Institute of Entrepreneurship Development (“The 4 Industrial Revolutions”). So automation has been around for a long time.

What hasn’t been around for a long time is an AI that can actually help humans within the automation process. In many cases today, a human operator begins by outlining how to perform the task, creating a job, and then turns the job over to a computer. An example of one of several fairly new kinds of job is Robot Process Automation (RPA), which allows a human to train software to act in the stead of a human when working with applications (see “The Tools of the Future Today” at Valamis.com). Many companies are now offering RPA services, such as UiPath (https://www.uipath.com/rpa/robotic-process-automation). This process differs from scripting, such as the use of Visual Basic for Applications (VBA) in Microsoft Office, in that RPA isn’t application specific and doesn’t require coding. Many people find it surprising that there are actually ten levels of automation, nine of which can rely on an AI. The level you choose is dependent on your application:

1. A human operator creates a job and turns it over to a computer to implement.

2. An AI helps the human determine job options.

3. The AI determines the best job options and then allows the human to accept or reject the recommendation.

4. The AI determines the options, uses them to define a series of actions, and then turns the list of actions over to a human for acceptance or rejection of individual actions prior to implementation.

5. The AI determines the options, defines a series of actions, creates a job, and then asks for human approval before submitting the job to the computer.

6. The AI automatically creates the job and submits it to the computer’s job queue, with the human operator acting as an intermediary in case the selected job requires termination prior to actual implementation.

7. The AI creates and implements the job and then tells the human operator what it did in case the job requires correction or reversal.

8. The AI creates and implements the job, telling the human what it did only when the human asks.

9. The AI creates and implements the job without providing any feedback unless a human needs to intervene, such as when an error occurs or the result isn’t what was expected.

10. The AI initiates the need for the job, rather than waiting for the human to tell it to create the job. The AI provides feedback only when a human must intervene, such as when an error occurs. The AI can provide a level of error correction and manage unexpected results on its own.

Using more than just robots

When thinking about industry, most people think about automation: robots making stuff. However, society is actually in at least the fourth industrial revolution; we’ve had steam, mass production, automation, and now communication (see “Industrial Revolution - From Industry 1.0 to Industry 4.0” at Desouttertools.com for details). (Some people are already talking about a fifth level, personalization; see this LinkedIn post, “Industry 5.0-Future of Personalisation.”) An AI requires information from all sorts of sources in order to perform tasks efficiently. It follows that the more information an industrial setting can obtain from all sorts of sources, the better an AI can perform (assuming that the data is also managed properly). With this multisourced idea in mind, industrial settings of all sorts now rely on an Industrial Communication Engine (ICE) to coordinate communication between all the various sources that an AI requires.

Robots do perform much of the actual work in an industrial setting, but you also need sensors to assess potential risks, such as storms. However, coordination is becoming ever more important to ensuring that operations remain efficient. For example, ensuring that trucks with raw materials arrive at the proper time, while other trucks that haul off finished goods are available when needed, are essential tasks for keeping warehouse floors running efficiently. The AI needs to know about the maintenance status of all equipment to ensure that the equipment receives the best possible care (to improve reliability); the AI also needs to know the times when the equipment is least needed (to improve efficiency). The AI would also need to consider issues such as resource cost. Perhaps gaining an advantage is possible by running certain equipment during evening hours when power is less expensive.

Relying on automation alone

Early examples of human-free factories included specialty settings, such as chip factories that required exceptionally clean environments. However, since that early beginning, automation has spread. Because of the dangers to humans and the cost of using humans to perform certain kinds of industrial tasks, you can find many instances today of common factories that require no human intervention at all (see “No Humans, Just Robots” at Singularity Hub.com for examples). The term for that type of industry is lights-out manufacturing, which is detailed in “Lights out Manufacturing…Is it Possible?” at the Syscon Plantstar blog.

A number of technologies will at some point enable the performance of all factory-related tasks without human intervention (see https://waypointrobotics.com/blog/manufacturing-trends/ for examples). The point is that eventually society will need to find jobs, other than repetitive factory jobs, for humans to perform.

Creating a Safe Environment

One of the most often stated roles for AI, besides automating tasks, is keeping humans safe in various ways. Articles such as “7 Reasons You Should Embrace, Not Fear, Artificial Intelligence” at Futurism.com describe an environment in which AI acts as an intermediary, taking the hit that humans would normally take when a safety issue occurs. Safety takes all sorts of forms. Yes, AI will make working in various environments safer, but it’ll also help create a healthier environment and reduce risks associated with common tasks, including surfing the Internet. The following sections offer an overview of the ways in which AI could provide a safer environment.

Considering the role of boredom in accidents

From driving or being at work, boredom increases accidents of all sorts (see “Distracted Driving Survey 2021: Drivers confess to bad behavior” at Insurance.com and “Job Boredom a Workplace Hazard?” at Risk and Insurance.com). In fact, anytime someone is supposed to perform a task that requires any level of focus and instead acts like they’re half asleep, the outcome is seldom good. The problem is so serious and significant that you can find a wealth of articles on the topic, such as “Modelling human boredom at work: mathematical formulations and a probabilistic framework” at Emerald Insight.com. Solutions come in the form of articles like “Modeling job rotation in manufacturing systems: The study of employee's boredom and skill variations” at ResearchGate.net. Whether an accident actually occurs (or was a close call) depends on random chance. Imagine actually developing algorithms that help determine the probability of accidents happening because of boredom under certain conditions.

Using AI to avoid safety issues

No AI can prevent accidents owing to human causes, such as boredom. In a best-case scenario, when humans decide to actually follow the rules that AI helps create, the AI can only help avoid potential problems. Unlike with Asimov’s robots, there are no three-laws protections in place in any environment; humans must choose to remain safe. With this reality in mind, an AI could help in these ways:

· Suggest job rotations (whether in the workplace, in a car, or even at home) to keep tasks interesting

· Monitor human performance in order to better suggest down time because of fatigue or other factors

· Assist humans in performing tasks to combine the intelligence that humans provide with the quick reaction time of the AI

· Augment human detection capabilities so that potential safety issues become more obvious

· Take over repetitive tasks so that humans are less likely to become fatigued and can participate in the interesting aspects of any job

Understanding that AI can’t eliminate safety issues

Ensuring complete safety implies an ability to see the future. Because the future is unknown, the potential risks to humans at any given time are also unknown because unexpected situations can occur. An unexpected situation is one that the original developers of a particular safety strategy didn’t envision. Humans are adept at finding new ways to get into predicaments, partly because we’re both curious and creative. Finding a method to overcome the safety provided by an AI is in human nature because humans are inquisitive; we want to see what will happen if we try something — generally something stupid. Unpredictable situations aren’t the only problem that an AI faces. Even if someone were to find every possible way in which a human could become unsafe, the processing power required to detect the event and determine a course of action would be astronomical. The AI would work so slowly that its response would always occur too late to make any difference. Consequently, developers of safety equipment that actually requires an AI to perform the required level of safety have to deal in probabilities and then protect against the situations that are most likely to happen.