In order to create a harmonious integration, the machine learning algorithms also need to consider worker safety and happiness in the future, which is something that only a human manager can do at the moment.
Working with machines
People already work with machines on a regular basis — they may just not realize it. For example, when you talk to your smartphone and it recognizes what you say, you’re working with a machine to achieve a desired goal. Most people recognize that the voice interaction provided with a smartphone improves with time — the more you use it, the better it gets at recognizing your voice. As the learner algorithm becomes better tuned, it becomes more efficient at recognizing your voice and obtaining the desired result. This trend will continue.
However, machine learning is used in all sorts of ways that might not occur to you. When you point a camera at a subject and the camera can put a box around the face (to help target the picture), you’re seeing the result of machine learning. The camera is helping you perform the job of taking a picture with far greater efficiency. In addition, the camera automatically removes at least some of the effects of shaking and bad lighting. Cameras have become quite good at assisting humans to perform tasks with aplomb.
The use of declarative languages, such as SQL (Structured Query Language), will become more pronounced as well because machine learning will make advancements possible. In some respects, a declarative language simply lets you describe what you want and not how to obtain it. However, SQL still requires a computer scientist, data scientist, database administrator, or some other professional to use. Future languages won’t have this limitation. Eventually, someone who is trained to perform a particular task well will simply tell the robot assistant what to do and the robot assistant will discover the means to do it. Humans will use creativity to discover what to do; the details (the how) will become the domain of machines.
Repairing machines
Most of this chapter discusses current technology, where the technology will go in the future, and why things work as they do. However, notice that the discussion always focuses on the technology doing something. That’s right, before the technology can do anything else, it must perform a practical task that will attract attention and benefit humans in a manner that makes people want to have the technology for their own. It doesn’t matter what the technology is. Eventually, the technology will break. Getting the technology to do something useful is the prime consideration now, and the culmination of any dreams of what the technology will eventually do stretches years into the future, so mundane things like repairing the technology will still fall on human shoulders. Even if the human isn’t directly involved with the physical repair, human intelligence will direct the repair operation.
https://space.io9.com/a-self-repairing-space-robot-on-the-international-space-1580869685). What the stories don’t say is that a human decided how to perform the task and directed the robots to do the physical labor. Self-repair is becoming achievable. The articles at https://spectrum.ieee.org/automaton/robotics/robotics-hardware/japanese-researchers-teaching-robots-to-repair-themselves and https://www.robotics.org/blog-article.cfm/How-Self-Healing-Robots-Repair-Themselves/219 describe how far the technology has come.
Creating new machine learning tasks
Machine learning algorithms aren’t creative, which means that humans must provide the creativity that improves machine learning. Even algorithms that build other algorithms only improve the efficiency and accuracy of the results that the algorithm achieves — they can’t create algorithms that perform new kinds of tasks. Humans must provide the necessary input to define these tasks and the processes needed to begin solving them.
Devising new machine learning environments
At the moment, devising new machine learning environments is the realm of research and development companies. A group of highly trained specialists must create the parameters for a new environment. For example, NASA needs robots to explore Mars. In this case, NASA relies on the skills of people at MIT and Northeastern to perform the task (see the story at https://www.computerworld.com/article/3007393/robotics/nasa-needs-robotic-upgrades-for-work-on-mars.html). Given that the robot will need to perform tasks autonomously, the machine learning algorithms will become quite complex and include several levels of problem solving.
What’s even more mind boggling is that an emotionally intelligent AI could help support astronauts making the trip to Mars, according to https://www.technologyreview.com/2020/01/14/64990/an-emotionally-intelligent-ai-could-support-astronauts-on-a-trip-to-mars/. Oddly enough, an astronaut could require support from an entity that isn’t emotionally involved in the grueling activities of working in tight spaces with the same people for a long time. Think about it: The AI would never get upset.
Eventually, someone will be able to describe a problem in sufficient detail that a specialized program can create the necessary algorithm using an appropriate language. In other words, average people will eventually begin creating new machine learning environments based on ideas they have and want to try. As with creating machine learning tasks, people who create future environments will be experts in their particular craft, rather than be computer scientists or data scientists. Solving the science of machine learning will eventually turn into an engineering exercise that will give anyone with a good idea the required access.
Avoiding the Potential Pitfalls of Future Technologies
Any new technology comes with potential pitfalls. The higher the expectations for that technology, the more severe the pitfalls become. Unrealistic expectations cause all sorts of problems with machine learning because people think that what they see in movies is what they’ll get in the real world. It’s essential to remember the basic concepts presented in Chapter 1 — that machine learning algorithms currently can’t feel, think independently,
