Adonis Diaries

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“Human Factors versus Artificial Intelligence”

Article #49 in Human Factors in Engineering category, written in June 12, 2006

In her book “Choices” the actress Liv Ullman asks her scientist lover: “What do you believe will be the main preoccupation of science in the future?” and he replies: “I have no doubt that it will be the search for the correct definition of error.”

The scientist goes on to state that a living organism has the capacity to be able to make mistakes, to get in touch with chance or hazard, which is absolutely a necessary part in survival. (kind of trial and error)

The meaning of error here is broad and related to the chance of surviving a cataclysm, where the survivors are probably the errors or the monsters in the tail of the “normal group”.

It is the monsters that scientists would be interested in studying in the future because they fail to belong in the logic process of our automated life.

Taxonomy of Artificial Intelligence AI is the duplication of human faculties of creativity, self-improvement, and language usage that might be necessary to illustrate the progress attained in this field.

There are four basic systems of AI mainly: 1) Thinking like human, 2) thinking rationally, 3) acting like human, or 4) acting rationally.

The purpose of the first system is to create computer machines with complex human minds or to automate the activities that are associated with human thinking.

In order to satisfy that goal adequate answers need to be provided accurately to the following questions:

1. Where does knowledge come from?

2. How to decide when payoff may be far in the future?

3. How do brains process information?

4. How do human think and act, and how do animals think and act?

The preferred approaches by scientists in that system were either to model our cognitive processes or to get inside the actual workings of minds.

In 1957, Simon claimed that “There are now machines that think, learn, and create.  In a visible future the range of problems they can handle will be coextensive with the range of the human mind.”

This claim was backed by practical programs such as the reasoning ‘Logic Theory’ that can think non-numerically and prove the theorems in the mathematical book ‘Principia Mathematica‘.

‘Logic Theory’ was followed by the program ‘General Problem Solver‘ that imitates human protocols in manipulating data structures composed of symbols.

The second system of thinking rationally has for purpose to create computational models of mental faculties or alternatively to compute the faculties of perceiving, reasoning, and acting.  The typical questions to resolve are:

1. How does mind arise from a physical brain?

2. What can be computed?

3. How do we reason with uncertain information?

4. How does language relate to thought?

The approaches undertaken were to codify the ‘laws of thought’ and using syllogism for the ‘right thinking‘ as in Aristotle.

McCulloch & Pitts claimed in 1943 that “Any computable function can be derived from some network of connected neurons, with logical switches such as (AND, OR, NOT, etc)”.

This line of thinking was supported by an updated learning rule for modifying the connection strengths and the manufacture of the first neural network computer, the SNARC by Minsky & Edmonds (1951).

The third system of acting like human had for purpose to create machines that perform functions requiring similar human intelligence or alternatively, to create computers doing what people are better at doing now.  The relevant questions to resolve are:

1. How does knowledge lead to action?

2. How to decide so as to maximize payoff?

3. How can we build an efficient computer?

The main approach was to emulating the Turing test, which is based on the inability of a person to distinguish that a program has undeniably human entities when people are to decide whether the responses are generated from a machine or a human.

This system was successful when programs for playing checkers or chess learned to play better than their creators and when Gelernter (1959) constructed the ‘Geometry Theorem Prove’ program.

To that end, Minsky (1963) initiated a series of anti-logical programs called ‘microworlds’ within limited domains such as: SAINT, ANALOGY, STUDENT, and the famous solid block world.

The fourth system to act rationally had for purpose to define the domain of AI as computational intelligence for designing intelligent agents (Poole, 1998) or artifact agents behaving intelligently.  The relevant questions are:

1. Can formal rules draw valid conclusions?

2. What are these formal rules?

3. How can artifacts operate under their own control?

The approaches were to create rational agents and programs that operate under autonomous control.

This line of thinking generated the LISP computer language, then the ‘Advice Taker’ (McCarthy, 1958) where new axioms could be added to the central principles of knowledge representation and reasoning without reprogramming.

An advisory committee reported in 1966 that “There has been no machine translation of general scientific text, and none is in immediate prospect.”

Since then, most of the works on AI research were concentrated within the fourth system in developing AI programs.

The fourth view of AI is within the capability of human to make progress in AI but there is a caveat.

If human rely exclusively on the fourth system, which is in the realm of the learned and educated people in mathematics and engineering, the danger is that autonomous systems will be developed by normal and learned people as if human will behave logically.

Thus, we might end up with systems that do not coincide with the basic uncertain behavior of human.

The concept of error as defined in the beginning of the article will not be accounted for and some major calamities might befall human kind.

Note 1: On error taxonomy

Note 2: Artificial Intelligence started its application around 1988 by trying to cope with retiring “experts” who have been decades in the job and knows what works and what  doesn’t. The program was tailored made to a specific job with a series of “What if” questions followed by answers from the expert. I tried my hand on these kinds of programs.

Note 3: Currently, AI is relying on megadata sources gathered from all kinds of fields.  My impression is that the products are evaluated through testing and Not according to time consuming experiments. It would be more efficient to collect the Facts from real peer-reviewed research papers, but this will require full-time professionals in selecting what paper is scientific and what is pseudo-scientific or just funded by biased interested companies

Article #46, (April 30, 2006)

 “Human Factors engineering versus Industrial, Computer, and traditional engineering fields”

            The term “engineering” is becoming pervasive and a misnomer in the public language; a janitor calls himself a cleaning engineer since he was trained to polish hardwood floors and he might be using machines and has to maintain them by cleaning and oiling the parts; a garbage collector is a sanitary engineer though on which ground he earned that prestigious degree is flimsy; any technician is an engineer since he can read drawings and execute the plan.  Mainly, the new public relation trend in the competitive job market encourage affixing “engineer” to our skills because it sound better in society’s circles and on our resume since, logically, part of an engineer’s job description is to repair or maintain the proper functioning of machines, equipment, and systems.

            Maybe the title is deceiving and might leave the impression that I am attempting to compare the technical differences among traditionally well established engineering disciplines such as electrical, mechanical, civil, and aeronautic engineering, and the relatively new engineering disciplines such as computer, telecommunication, industrial, and human factors engineering.  This article is actually a reminder of the purpose of an engineer, what designing should mean, and for whom products/systems are designed for.

            Frankly, how superior is a freshly graduating engineer compared to a trained technician in the corresponding field?  How qualified is an engineer who spent two years doing cost estimation compared to a trained technician who is still better at reading drawings, estimating the cost of his job, knowing the competing products, their specifications, and can put them together for a functioning system?  If companies perceive the competence of a graduate engineer as incomplete or unsatisfactory for the market demands and that the best position for him in the first three years on the job is to cost estimate the material expenses for bids, then why the university does not train the engineer to cost estimate real life projects during his four year stint within its compound?

            How long a qualified engineer should be working for a company in order for management to evaluate him as eligible to be assigned a design job which should be the purpose of his university curriculum?  I know graphic designers taking on design jobs right after graduation; so what’s the problem with the engineering curriculums?  My contention is that engineers are not being properly trained to be designers, or the students are not getting that impression from the messages of their instructors, or the structure of the courses are not effective in conveying a behavior’s change in the engineer’s mind..

            Traditional engineering disciplines have a solid, well established knowledge base through centuries of experiments, trial and error, design guidelines, and practice.  My impression is that the fact of an existing and complete knowledge base has diverted the needs of forming scientific and experimental minds and has reduced the students to kind of learning robots of primarily rule based knowledge and equations of inanimate phenomena. 

The computer and telecommunication engineering disciplines are still young and offer more job opportunities with training oriented to creative designs towards end users.  Moreover, the competition in advanced technology for products in the latter fields encourages the designers to build up on the “common sense” acquired from experiments and prior designs in the behavior of end users. Although the knowledge base of human capabilities/limitations, physically and cognitively, is not an intrinsic part of the curriculum, it seems that most of the current researches in psychology, marketing and human factors/ergonomics are oriented toward providing design guidelines for the computer and telecommunication engineering disciplines.   

It appears that supplementing design guidelines from non engineering disciplines is giving a false sense of confidence in the computer’s engineering designs and thus, failing to impact with scientific and experimental minds in the complexity of human behavior for the graduate computer related engineers

Industrial engineering should be geared toward engineering management of industries and systems. However, the curriculums emphasize on the material and inanimate phenomena in the optimization of the processes.  Learning about inventory, layout of manufacturing facilities, material handling, production processes, and optimization models for increasing performance and minimizing costs or unwarranted parameters is fine and necessary.  If we recognize that managing the human element of workers, operators, secretaries, and managers is the main problem in running any system, then why not face this problem upfront?  Squeezing single lame courses in human factors and, from time to a time a course in organization or management, will not cut it and will not lead to a behavioral change in designing for people or managing workers’ problems in industries.  Does a graduating Industrial engineer have to rely on the archaic method of trial an error for many years of training on the job before he begins to appreciate the human factors essentials? 

Is it not within the industrial engineering job description to be familiar with the difficulties facing the workers in matters like shift work, inspection, training, overtime, and turnover, or the capabilities and limitations of the workers in physical and mental abilities according to age and gender, or the safety regulations and health regulations in the workplace, or the current legal doctrine in consumer product liabilities?  Claiming that many of these problems are the realm of other social disciplines will not prepare an industrial engineer to his job or achieve the purported goal of graduating capable engineers.

Human Factors/Ergonomic discipline realized that every artificial system or human made system that governs and organizes our modern life is fraught with errors and potential health and safety accidents that diminish the efficiency, validity, and reliability of these systems if a sound comprehension of the capabilities and limitations of the designers, operators, workers, and end users are not accounted for in the implementation of a system or a machine.  The proclaimed purpose of Human Factors engineering is to designing interfaces among the various sections of a complete system so that the targeted user may perform efficiently his task without the need to comprehend the inner technical functioning of the system.

However, the scope of designing interfaces is vast, all encompassing, and cannot rely on general design guidelines: every system has its peculiarity, its target users, and its knowledge base.  Interfaces are varied from all kinds of displays, controls, instructions manuals, training programs, and performance aids. Even designing formats for screens are not the same for computers, televisions, or specialized audio-visual complexes because each task or industry is different and the outcomes are changing as requirements change.

It appears that Human Factors discipline extended its reach and scope in every form of modern technological breakthroughs that it failed to catch up or specialize in well defined systems.  Many disciplines are off shout of the Human Factors trends; for examples, biomechanical theories generated many branches in the bio technology fields and drivers simulation design modeling; display, illustration, formatting, warnings, and facilitators design guidelines generated the graphic design discipline without any theoretical foundations in perception or controlled experimentation training in its curriculum; concerns for the safety and health in the workplace generated safety engineering and industrial inspectors; human-computer interface and interactions guidelines generated computer friendly software programmers.

The fundamental concerns of Human Factors is the people within a system have generated disciplines that are focusing more on the well being of the target users without these discipline taking the pain of offering the requisite courses intended to familiarize and initiate the graduates to the complexity and scope of understanding the end users.




October 2020

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