Expert Systems Make Mistakes

Investors and developers may be deterred by legal concerns regarding the possibility of errors in expert systems. Currently, there is a lack of agreement on what testing is required to assess the accuracy, dependability, and effectiveness of such systems. Additionally, there are no official bodies that can certify or validate these systems. The potential consequences of errors in critical systems, such as medical diagnosis or air-traffic control, could be devastating financially and legally.

Resistance from Users

Unlike conventional computer programs, talented systems complete jobs that a professional functions. This could trigger potential strong opposition and resistance to such technology from users concerned about expert systems taking their jobs.

These administrative and organizational challenges seem to be very critical for expert systems. Failure to address such problems could lead to system desertion or cancellation.

Is a “Thinking” Machine Ever Possible

For many years, scientists have been striving to develop autonomous “thinking” systems that do not require human intervention. Despite extensive research spanning five decades, it seems that machines are unable to replicate the intuitive intelligence of human beings. While some believe that creating a “thinking machine” is a risky venture that is likely to fail, others are more hopeful. In his award-winning book On Intelligence, Jeff Hawkins predicted that the world might see the emergence of a mind-machine within the next ten years. He argued that since we are already advanced in terms of technology, the transition to intelligent machines should be much faster than the 50-year journey from room-sized computers to pocket-sized ones.

In order to examine the potential for expert systems to develop, it’s important to analyze the distinctions between human and machine “thinking” and consider future possibilities..

Human Know-how and Intuitive Intelligence

As previously mentioned, humans acquire the skill of walking through trial and practice, known as “know-how.” This skill is learned through instructions and experience. Human learning is a gradual process, without a sudden leap from rule-based knowledge to experience-based know-how [11]. Novices follow the rules and instructions, while more competent users consider situational elements, such as sensing an opponent’s weakness in chess. Experienced users recall answers from past similar incidents and apply them intuitively to the present without sorting through laws or deliberations. Additionally, when human experts consciously work on solving problems, they have a different mindset. For example, grandmasters in chess don’t view themselves as simply moving pieces on the board. Instead, they become deeply involved in the world of opportunities, threats, strengths, weaknesses, fears, and hopes [11]. This level of involvement allows human experts to think differently and come up with innovative solutions.

The Human Mind

The human brain is a fascinating subject, with many intriguing properties. One theory suggests that there are roughly one hundred billion neural cells in the human brain, allowing for potentially 200 trillion operations to occur per second. This is especially true in areas such as vision, speech, and motor processes, where the brain can outperform even 1,000 supercomputers. However, when it comes to simpler tasks like multiplication, the brain is not as powerful as a four-bit microprocessor. These mental processes occur with little conscious thought on our part and are notoriously difficult for machines to replicate. On the other hand, machines can excel in certain areas where humans struggle. If silicon-based intelligence is ever achieved, it may possess different attributes than human intelligence.

Hubert Dreyfus and his colleagues raised doubts about the possibility of turning the human mind into an information processing machine. For instance, humans can imagine the outcome of removing a large box from under a small box, while a computer would require a list of facts such as size, weight, and frictional coefficients, along with information on how each box reacts to different movements. Humans think in images, relying on visual cues to understand and respond to situations, while machines use explicit, logical reasoning.

What the Future Holds

The idea of creating a machine that can think is both exciting and controversial, but it can also be intimidating. In his book “The Singularity Is Near When Humans Transcend Biology,” Ray Kurzweil presented some fascinating ideas about the future, such as nanobots. These submicron agents could be injected into the bloodstream to monitor and maintain chemical and biological balances. Furthermore, they could specialize in patrolling the brain and downloading every stored neural pattern and synaptic connection. It’s fascinating to think about the potential implications of being able to recreate human senses in a software version. Imagine being able to experience memories, emotions, instincts, and thoughts in a virtual world. It could revolutionize the way we understand and interact with technology. This program could be transferred to other machines, allowing it to think and act as if it were the person, achieving immortality.

Understanding how the human mind works is complex, and developing machines that can replicate human intelligence is a formidable task. Additionally, the idea of giving computer systems human-like intelligence is still a topic of debate.

The debate surrounding the capability of machines to emulate human thinking goes beyond the surface level. If machines were to successfully achieve this feat, it would have significant implications on society and could bring about irreversible changes to its fundamental structure. Despite this, the continued success of expert systems, as explained in earlier sections, appears to be a certainty.

Social Implications of Expert Systems

The development of expert systems has always aimed to harness the expertise of professionals and make it available to assist others. This is seen as one of the most positive potentials of AI. Reddy once mentioned that sharing knowledge and know-how in the form of information products is the only way to bridge the gap between the rich and poor. Expert systems can be a means to share essential knowledge with those who are disadvantaged. As we continue to develop AI applications, we have the potential to help the poor, illiterate, and disadvantaged populations both in our nation and around the world.

Although expert systems have undoubtedly improved our social lives, there are potential drawbacks and ethical issues that may arise. While it would be reckless to deploy logic machines to control a battlefield, what about using them for air traffic control systems that guide planes carrying thousands of passengers or medical diagnosis systems that could assist doctors in life-or-death situations? What if these systems provide incorrect advice? Who should be held responsible? And if these systems can think independently and be aware of their existence, could the wrong advice be intentional? Should the legal system be expanded in the future to address machines, similar to Isaac Asimov’s “Three Laws of Robotics” in his fiction?

The creation of expert systems brings up the question of ownership of knowledge. Richard L Dunn posed a series of questions regarding the matter: Should you or your employer possess the knowledge? How much should you disclose to a knowledge engineer if they visit your workplace? If they extract all of your knowledge, does that make you more or less valuable to your employer? Is it acceptable for your employer to share or sell your intelligence to others without providing compensation?

Copyright and intellectual property laws have been established to protect employers’ ownership of patentable products and copyrightable materials developed during employment. However, it is unclear whether the knowledge and experience gained through work are subject to the same regulations. Personal knowledge and experience are valuable assets that employers seek when hiring. But what if this knowledge and experience were somehow captured and stored in a computer system outside of your control, or if the computer system acquired knowledge and experience from a group of people with the same expertise as you? In such cases, would the company still need to employ you?

It’s better to be designed for all these queries before it’s also late to answer them.

Concluding Remarks

In the past, people have underestimated the complexity of human intelligence, particularly in the field of expert systems. There are still challenges in developing these systems due to technological limitations and management issues. However, with the advancements in technologies such as neural networks and CASE, the future of expert systems looks promising despite past setbacks.

It is important to carefully consider the legal and ethical issues that will inevitably arise as expert system technology advances. If autonomous machines with the ability to “think” ever become a reality, our lives as we currently know them would be permanently altered.

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