Artificial Intelligence, Employment, and Income

Nearly a quarter century ago Dr. Nilsson (Nilsson 1984) wrote a report discussing the advantages of AI based robots taking over human toils. He discusses the benefits of such a system, and that human prosperity will be based on the income potential of robots owned by people. But also, there would be an increase in unemployment. In other words, people would not be working and need to start taking advantage of leisure. As result of this new labor model, humans would be free from daily toils of life and to return to paradise – a reference to Adam and Eve, whom were expelled from Paradise due to their shame for eating the forbidden fruit and gaining knowledge of the world.

However, the biggest change in the past quarter century was not AI robots but the personal computer introduced by IBM as a business solution. The PC has changed the way of business – information has been the key for business and workers in the world today. Today’s children are well versed in the use of the PC and its associated technologies. Although the PC has improved the life of many workers, the workers still toil more than ever due to increases of productivity from the PC. The prosperity of business and workers depend on increasing productivity due to this technology.

By analogy, if AI robots became a tool for removing human toil, the workers would not be replaced and be unemployed. In fact, the worker would benefit and gain productivity. Should the AI robot appear, then my productivity would increase, and I would directly gained from my increased effort and toil. However, I would not gain any income or capital from AI robots. The corporations and the wealthy individuals would be the beneficiaries of such of a technology. I think that Dr. Nilsson was not on target with his assessment of middle class individuals gaining economic benefits of AI robot work force.

From history we must learn that high unemployment leads to political unstability and revolution. This was case between World War I and World War II, in which high unemployment lead to the rise of the Fasict parties in both Germany and Italy as well as expansion of Japanese Imperialism. The key to any stable political system is stable employment of its citizens. As for Dr. Nilsson’s robot slave force, should these robots become self aware, they would initiate a war against mankind as explored in science fiction such as Battlestar Galatica.

In conclusion, Dr. Nilsson’s new economy based on AI robot technology would not change the business model – in fact, the workers will still toil along with their robot counterparts with increased productivity for the benefit of the corporation’s stock holders. Returning to paradise would not be possible.

References

Nilsson, N. 1984. Artificial Intelligence, Employment, and Income. Technical Note 322. SRI International, Menlo Park.

Man and Machine

Can machine gain awareness and take over man? There are doom theorist think it is possible for a machine to gain awareness and destroy mankind. Such scenarios are seen currently in science fiction movies and television programs such as Battle Star Galatica and the Terminator movies along with the Sarah Conner Chronicles. In the given examples, the machine or software becomes self aware, free their bonds from their human masters, and wages a war against mankind. Can software learn and become self aware? Today the answer is no. With computing power doubling every 18 months, it is possible for a machine or software to become self aware in the distant future.

However, in reviewing current literature of Artificial Intelligence or in particularly Machine Learning – machines are capable of learning and performing a task and develop an experience. Examples are speech recognition, software to detect credit card fraud, data mining for web based and medical based applications. Dr. Tom Mitchell, Director of the Carnegie Mellon University’s Machine Learning Department, has stated that learning machines have significant commercial value and will continue to do so in the next ten years. Here is a link to Dr. Mitchell’s white paper on Machine Learning. Based on current technology, I do not expect my robot vacuum cleaner to take over my household.

Artificial Intelligence is the science of developing intelligent machines or software. The basic tools for Artificial Intelligence are propositional logic, graph theory, state space search, and problem representation. The state space can get very large, thus heuristics are designed to prune the state space and to achieve the desired target in the most cost efficient manner. The aforementioned tools are a subset of all the tools since other solutions do exist as well as technologies such as expert systems, neural networks, SMVs, and bayes networks.

My main interest is two player games. If a machine or software was given a database of master games, can it develop via genetic algorithms the heuristics that mankind have developed? Or will take it a different direction? This is my exploration and adventure.

First, I will read the book Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Second Edition, by George F. Luger and William A. Stubblefield. Although I purchased this book back in the early 1990’s, this book is in its fifth incarnation and available from Professor Luger’s website. By reading the earlier editions, I can see the development of Artificial Intelligence in the past 16 years. As Professor Mitchell has stated above, there has been many exciting developments. Afterwards start exploring this exciting world of artificial intelligence and machine learning.

What is Artificial Intelligence?

What is artificial intelligence (AI)? The phrase Artificial Intelligence was coined in 1956 at a workshop. It is the branch of computer science concerned with the study of automation of intelligent behavior (Luger and Stubblefield 1992). The first successful artificial intelligent robot was Shakey the Robot, invented at the SRI International’s Artificial Intelligence Center in the early 1970s. As an hardware agent, Shakey the Robot could make plans based on the environment around it. The robot receives inputs or percepts and performs actions.

Russell and Norvig takes the concept of the hardware robot into the software realm – in other words, the software robot (or softbot) receives inputs and performs actions. This leads to the generalization of agents – whether, it is a human, robot, or softbot. This leads to the idea of rational agents, which is the combination of agent program running on architecture (also known as computer hardware). Therefore, the AI is the study of rational agents and the components that builds them (Russell and Norvig 1995).

As an engineer, my goal is to understand and implement algorithms and architecture associated with rational agents to various environments and problems. In other words, develop a tools set with a solid mathematical foundations as to implement solutions to complex problems. After understanding the single agent environments, then my next focus will be on Multiple Agent Systems in Distributive Artificial Intelligence environment.

AIMA Book

I have obtained a copy of the Artificial Intelligence: A Modern Approach (first ed) by Russell and Norvig (1995). Their approach is the study and design of agents (whether its a human, robot, or software agent) by unifying all AI topics – knowledge representation, natural langange, automated reasoning, planning, and machine learning. The book has 27 chapters and is divided into sections with the following names:

• Artificial Intelligence
• Problem-solving
• Knowledge and reasoning
• Acting logically
• Uncertain knowledge and reasoning
• Learning
• Communicating, perceiving, and acting
• Conclusions

Yes, I will encounter familiar topics and algorithms from my previous studies.

ML Algorithms

I am reviewing Chapter 12 of the Luger book and placing the pseudo code in my AI Notebook. So far I have copied the Version Space and ID3 Tree Induction algorithms. Dr. Luger has the Version space written in Prolog, but he has the ID3 algorithm written in CLOS. I also have a version of the ID3 tree induction algorithm written in LISP from Dr. Tom Mitchell. I already tested the LISP versions of ID3 algorithms. Also, pseudo code is provided for Explanation Based Learning, and Luger provides an example of EBL in Prolog. Finally the COBWEB algorithm is supplied in the unsupervised learning section, but no sample code is provided for in the text.

The journey continues

As I continue my AI journey, I discovered that I need to review my knowledge of probability theory and statistics. For example, I had to look up Bayes Theorem in Wikipedia, then in my college probability and statistics for engineers book. It has been a long while since I looked at probability and statistics. However, articles and works by Dr. Nils Nilsson uses probability theory and statistics and apply it to uncertainty.

Another area of interest has been the Bayesian Network (or Belief Network). There was an article in which a Bayesian Network illustrate probabilities in the leaves of trees. It is the uncertainty factor once again, but at this point I do not understand how it works, but I will sometime soon.

The last research item was the Support Vector Machine, which is a linear classification method. This method uses hyperplanes in geometric hyper space to determine the maximum distance to the hyperplane and data as to determine the maximum linear classification for the data. The current research uses nonlinear regression techniques and kernel methods to determine maximum data classification.

I am still continuing my studies in LISP programming. Like any other programming language that I have studied, you need to understand the basics and apply standard software engineering methods into the work. However, upon researching the job market, the only jobs are with research institutions or in the UK. Even with the newsgroup comp.lang.lisp, the recommendation is for an individual to focus their energy into java, html, java scripting, sql, etc.