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What are Artificial Intelligence and Machine Learning?
From Webster’s Dictionary: “Definition of intelligence 1a(1) : the ability to learn or understand or to deal with new or trying situations : reason also : the skilled use of reason …” This is the definition that most engineers and scientists use and understand, i.e. the ability to learn about and understand complex systems, processes, and problems sufficiently to design and improve and optimize processes, solutions, methods, tools, or products. In simpler terms, it is the cognitive ability to make scientific progress. With respect to science and engineering, the strict meaning of the words “artificial intelligence” is non-biological intelligence, or an intelligent computing device or program. Intelligent computers or programs do not currently exist. All of the intelligence behind any existing computer application comes from the mind of its developer. First he must derive or invent a new or improved solution to some problem. Then he must translate that mathematical or physical solution into a computer program that contains step-by-step instructions (code) for the computer to obtain the solution automatically (upon execution). Coding the solution to a problem in a computer program (the computer science part) can be as difficult or more difficult than determining the solution to the problem (through science and engineering). Neither the program nor the computer it runs on are intelligent. The computer processes the developer’s code to implement his solution algorithm automatically, exactly as instructed by the developer to achieve its objective in obtaining the program outputs (=solution) from its inputs (=problem description), one line of code at a time. The computer simply follows the instructions in the program code that represents the knowledge of the developer of how to solve the problem using the simplest and most efficient logic and methods that he knows or derives. We construct programs that solve complex problems because the program can solve the problem or perform some function much faster and more accurately than people can. Neither the computer nor the program “knows” how to solve the problem, or even what the problem is or the meanings of the inputs and outputs. Only competent developers and users have that knowledge. Our computers and programs are simply tools that we use to significantly advance human knowledge and capabilities. Many sources cite 3 levels or types of artificial intelligence: narrow, general, and super. General artificial intelligence is the above strict definition that most of us have understood and used for decades (an intelligent computing device or program). It is the definition used by competent researchers in the field such as Alan Turing, whose test for its attainment has never been passed. Intelligent computers and computer programs do not exist. “Narrow” artificial intelligence is entirely created by people and is no different than computer programming. One definition1 is “It is a stream of intelligence which is prominent in performing a single task with smartness. The intelligence learns about a single task which it has to perform efficiently and with smartness (intelligently). It is considered to be a basic concept of AI.” We don’t agree with that definition, because narrow AI is a computer program completely created by humans that has no intelligence and cannot learn anything about a task other than the specific instructions in the code created by the developer. We think that a much better definition of narrow or weak AI is "a computing device or program that appears to be intelligent, but is not". Some define it as a computer application that mimics intelligent human behavior. Examples commonly cited include pattern and voice recognition, interactive voice assistants and search engines. It is all very advanced interactive programming by very intelligent developers, but it is not AI, because no computer or computer program is intelligent. By most definitions of "narrow" or "weak" AI, examples also include the abacus, the slide rule, the electronic calculator and just about every computer program ever written that saves time over manual computation or processing. Claims of achievement of “narrow” artificial intelligence are meaningless. Most sources admit that General AI is a subject of research and does not currently exist. Some refer to AI as the Computer Science field of simulating artificial intelligence to make applications appear to be intelligent (rather than actually being intelligent, which is impossible today and for the foreseeable future). A definition2 of “Super Artificial Intelligence” is: “3.Artificial Super Intelligence ( ASI ) Artificial Super Intelligence is an aspect of intelligence which is more powerful and sophisticated than a human’s intelligence. Human’s intelligence is considered to be one of the most capable and developmental. Super intelligence can surpass human intelligence, it can think about abstractions which are IMPOSSIBLE for humans to think. The human brain is made of neurons and is thereby constrained to some billion neurons. Documents say , Artificial superintelligence is a term referring to the time when the capability of computers will surpass humans. “Artificial intelligence,” which has been much used since the 1970s, refers to the ability of computers to mimic human thought. Artificial superintelligence goes a step beyond, and posits a world in which a computer’s cognitive ability is superior to a human’s.” Since we are apparently free today to make up term definitions (and entire sciences!) to suit our purpose, we define “Artificial Specific Super Intelligence” as the ability of a machine or program to exceed the abilities of humans to solve or perform any specific problem or task (rather than in general on all subjects). This, again, has existed since the hammer, the lever, the abacus, the slide rule, the electronic calculator, and since the first computer programs were written. Reservoir simulation models are capable of solving problems that are impossible for any human to solve manually in their lifetime. Adding automatic deterministic or probabilistic optimization and forecasting around simulation makes our workflows the most complex and advanced computing systems in the world, infinitely more capable than humans in making complex optimizations and predictions of reservoir production and value. So, according to our Specific Super AI definition, reservoir models along with optimization methods in our automated workflows are the most advanced Specific Super Artificial Intelligence applications ever developed. In fact, none of ours or anyone else’s programs today are actually intelligent. All of the intelligence behind any computer program is in the minds of its developers. Machines and programs cannot learn, and the phrase “machine-learning” is nothing but a bad marketing term. People learn by using machines and programs as tools, not the other way around. A recent definition of "machine learning" was given by an engineer in an SPE Reservoir Technical Community discussion who claims that reservoir engineering problems can be currently solved by AI and machine learning applications (but can't give an example, even on request):
Since real artificial intelligence does not exist and is not currently possible, "machine learning" as defined above obviously does not exist either. All current claims of achieving “artificial intelligence” and of developing “machine learning” applications are completely misleading and unsubstantiated. "Machine learning" is an oxymoron. Machines can't learn. Computers and programs can solve problems that humans can't, but humans have to provide the instructions to obtain the solution (program). No computer program can learn or understand any system or problem or solution or improve itself. Despite many requests, nobody has ever been able to demonstrate any improved solution to any known problem in engineering using what they claim to be "artificial intelligence" or "machine-learning" or "data science"3, which is another oxymoron appearing in the last 10 years. "Data" is not a science. There is nothing new or valuable in "data science" that we didn't already know. So until those claims are properly substantiated, as competent scientists and engineers we must assume that they are false. Proof is given in the LinkedIn discussion https://www.linkedin.com/posts/reservoirsimulation_can-anyone-present-an-improved-solution-activity-6592082731813916672-wUKX Innovation, integration, and automation are still the main drivers of scientific advancement (see our Reservoir Simulation Goals page).
1. (https://medium.com/predict/types-of-artificial-intelligence-and-examples-4f586489c5de) 2. ibid 3. Some incompetent universities have even made it a degree program, and make obviously false claims of predictive and decision-making abilities (see for example https://datascience.berkeley.edu/about/what-is-data-science/Data).
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