What is AI, what is the Problem Paradox, what are problems and what are solutions?

January 20, 2024 

Artificial intelligence (AI) is human-like computerized problem-solving ability.  The Problem Paradox is that the solutions to problems create more problems, which are oftentimes more complex than the original ones. AI will become humanity's problem-solving utility of choice. AI will solve problems faster than any human--or all of humanity for that matter--could ever solve alone. This means that AI will create more problems faster than any previous technology in the history of humanity. This will be nerve wracking for many, and also an incredible business opportunity for entrepreneurs and investors.

This article explores what are problems, what are solutions, and what are common problem-solving techniques. It continues introducing the Creatix Medium's concept of the Problem Paradox and begins to drop a new Creatix concept about the AI of Everything. Let us know what you think.

AI is the latest "fad" in computer science, and the hottest bubble craze in Wall Street. Yesterday, Friday, January 19, 2024, the S&P 500 hit a new all-time record high, closing at 4,839.31. The market rally has been led by U.S. technology companies. Chip maker Nvidia, for example, also hit record highs of $595 per share, for a market capitalization of $1.47 Trillion, and trading at 78.6 times earnings. For how much longer the mostly AI-driven rally can last is yet to be seen. Our prediction is that February will be a very cold month for the stock market. Taking some profits and putting some cash into the sidelines next week sounds advisable. In the long run, however, there is an immense potential for continued growth in the stock market, not only in the U.S., but worldwide. Between the internet of things and the AI of everything, there are trillions of dollars to be made by smart entrepreneurs and investors in the next two or three decades. 

Computers are the gift that keeps giving. AI is nothing new. Since the invention of electronic computers in the 1940s, humans were quick to fancy the idea of having computers that could process data in ways similar to how the human brain processes data. That was perhaps the first indirect recognition that humans are organic computers. Electronic computers could one day be equipped and programmed to run calculations and computations just like the human brain. In the 1950s, computers scientists coined the term artificial intelligence or "AI" to describe that type of computerized information processing that could resemble human-like intelligence. They thought that it would be a matter of just a few years to reach the AI level. However, it took seven decades for computer science to advance to a point where humans can finally say that AI is here. 

AI is here to stay. Computers equipped with parallel processing chips and programmed with neural processing algorithms are beginning to imitate, and oftentimes surpass, human intelligence. Advances in both computer programming (software) and equipment (hardware) have made possible today's AI. The exponential increases in computer chip processing capacity from one transistor per chip in the 1950s to up to 134 billion transistors per chip in 2023 are mostly to credit for the rise of today's AI. Add to that the compounding effect of the internet and cloud computing to realize how AI is possible today. And this is just the beginning. Advances in computer science and computer capacity will continue expanding over the next decades. The AI of Everything will change everything, creating almost unlimited problem-solving opportunities for entrepreneurs and investors. 

This Creatix article takes a look at intelligence and problems in general. Intelligence is problem-solving  ability. Problems are unpleasant facts, intriguing questions, or a combination of both. In the next two or three decades, AI will become the main problem solving utility of humanity. The Problem Paradox is that solving a problem creates more problems.  The new problems are often more complex and complicated than the previous problems. That is nerve wracking for many humans, but is how economies grow. Entrepreneurs and investors make money on the Problem Paradox continuum. Let's a take a look at intelligence and problem-solving to see what we may learn together and what creative ideas we may spark today. 

What is intelligence?

Intelligence is problem-solving ability. Dictionaries define intelligence as the ability to acquire knowledge and develop skills. Since humans typically acquire knowledge and develop skills to solve problems, intelligence can be seen in short as problem-solving ability.

Ability is being able to do something knowingly and competently. Knowledge is the opposite of ignorance. Skill is the opposite of incompetence. To acquire knowledge is to reduce ignorance. To acquire a skill is to reduce incompetence. 

Knowledge and skills are acquired from learning. Humans learning comes from theoretical education, practical experience, or both. That is, knowledge is acquired from theory, practice, or a combination of both. Skills have a theoretical foundation, but generally require practice for appropriate development. 

In sum, intelligence is the ability to solve problems, and is the opposite of ignorant incompetence. Since humans generally acquire knowledge and skills to solve problems, it can be said that intelligence is problem-solving ability. 

What are Problems? 

Problems are unpleasant facts, intriguing questions, or a combination of both. Solutions are flexible adjustments, fitting answers, or both. 

Unpleasant Facts

For most humans, "problems" are facts that they don't like. In other words, problems are unpleasant facts or painful perceptions of facts. Problems that involve unpleasant facts can be addressed by making flexible adjustments. Two types of flexible adjustments are: (i) changing causality; and changing interpretation. That is, changing the causes of a problem to obtain different results; or changing the meaning of facts to obtain different cognitive and emotional responses. 

• Causality. The first step in solving unpleasant facts is to identify causality (i.e. the causes of the problem). The causes of a problem are typically the input variables, the processing of those variables, or a combination of both. Previous knowledge, trial and error experimentation, and consultation of other sources, are ways of figuring out the causes of a problems. After identifying what causes the problem, the next step towards finding a solution is to experiment or simulate changing the causal variables and/or processes to see how the facts change. Changing causal variables and/or processes should change the outcome one way or another. The changes should either reduce the problem towards solving it, or aggravate it towards making it worse. Trial and error experimentation, consultation, and critical thinking reasoning should help in finding a solution. If no solution is found and it is determined that the unpleasant facts cannot be changed with technologies available (e.g. there are in the past and there is no time travel technology known to humans), the best option is to change the meaning or interpretation of the facts. This entails changing, not the facts themselves, but what they mean to the human. 
• Meaning. By changing or adjusting the interpretation of unpleasant facts, what was considered  unpleasant may be become pleasant or at least less unpleasant and more tolerable. While some facts cannot be changed--especially the ones in past--their interpretation is always subject to change. The assigned meaning or given interpretation of facts can be adjusted and changed in a sort of positive revisionist history. In other words, while humans can rarely change what the facts may be objectively, they can always change what the facts represent subjectively to them. For problems caused by unknown variables or processes, or for problems with variables or processes currently impossible to change, changing the meaning or interpretation of the problem may be the best practical solution.  

Intriguing Questions. 

Problems involving intriguing questions are solved by finding fitting answers. There may be multiple answers to a problem. Answers can be found by experience/experimentation; consulting others; and reasoning. 

• Experience. In finding answers to problems, humans often begin with what they already know from previous experience (prior knowledge). If that prior knowledge is insufficient, new knowledge can be generated from trial and error experimentation or scientific research experimentation followed by solid reasoning. 
• Consulting. Most of the time, instead of experimenting or before doing so, humans consult others or query additional sources of knowledge that may already know valid answers or that may provide useful guidance. Consulting other sources expands the knowledge base from the individual to the collective. Consulting includes studying or researching the works of experts (professionals, academics, or individuals with specialized knowledge in a specific subject), asking peers (colleagues, friends, relatives, etc), and querying online sources (search engines, websites, forums, social networks).  
• Reasoning. After obtaining sufficient information following the steps above, humans engage in reasoning to come up with their chosen answers to problems. Humans employ reasoning to analyze problems, break them down into manageable parts, and find answers. Reasoning is the cognitive (data processing) task of thinking logically and systematically to make sense of the question, find relevant information, draw conclusions, make decisions, and answer problems. It involves the ability to analyze, evaluate, and synthesize information to reach sound and justified conclusions. 

More on reasoning. Reasoning is a fundamental aspect of human intelligence and plays a crucial role in problem-solving. Key components of reasoning for problem solving include:

• Analytical Skills. Breaking down complex information into smaller components for discernible understanding.
• Causal Reasoning. Identifying cause-and-effect relationships. 
• Inference. Connecting the dots between the known and the unknown to fill in knowledge gaps.
• Abductive Reasoning. Generating a reasonable explanation or hypothesis based on available evidence. 
• Inductive Reasoning: Generalizing based on specific observations or examples.
• Deductive Reasoning. Drawing specific conclusions from general observations or examples. 
• Metacognition: Reflecting on one's own thinking process to assess quality and make adjustments.
• Critical Thinking. Evaluating information objectively, considering multiple perspectives. 
• Pattern Recognition. Recognizing correlations in data to identify trends and make predictions.
• Decision-Making. Weighing options, considering pros and cons, and making reasonable choices. 

Another key component of reasoning is logical thinking or applying the rules and principles of logic (the study of argumentation) to avoid logical fallacies and arrive at solid conclusions. Fundamental rules of logic include the following: 

• Law of Identity: A is A. If a statement is true by definition, then it is true. 
• Law of Non-Contradiction: A cannot be both A and non-A simultaneously in the same context. 
• Law of Excluded Middle: If a statement must be either true or false, there is no middle ground. 
• Law of Absurdity (Reductio ad Absurdum): If the negation of a statement is absurd, statement is true.
• Law of Rational Inference: If the premises of an argument are true, and the argument follows a valid logical form, then the conclusion is true. 
•  Law of Contraposition: If P implies Q, then ¬Q implies ¬P. 
• Principle of Identity of Indiscernibles and Discernibles: If two things have all their properties in common, then they are identical. If things are distinct, they must have some discernible difference. 
• Principle of Sufficient Reason: Every event or phenomenon must have a reason or cause. Nothing happens without a reason or explanation. 
• Modus Ponens Deductive Reasoning: If P implies Q, and P is true, then Q must be true.
• Modus Tollens Deductive Reasoning: If P implies Q, and Q is false, then P must be false. 
• Transitive Property: If A equals B, and B equals C, then A equals CDistributive Property: A AND (B OR C) is equivalent to (A AND B) OR (A AND C).

The rules and principles above are the foundation of logical thinking for solid reasoning. They are employed in various disciplines, including philosophy, mathematics, computer science, and everyday problem-solving.

Problem Solving Techniques

More on problem solving. Problem-solving is the utilitarian essence of intelligence. Here are five proven and time-tested steps to effective problem-solving:

1. Define the Problem. Clearly identify and describe the problem to be solved. Break it down into specific components and identify key issues for each component. Consider the broader context surrounding the problem. Understand the variables and processes involved.
2. Gather Information. Collect relevant data and information about the problem, including causality (cause and effect) data and information. Data are facts. Information is facts put in context. Identify causality (causes and effects) of the problem. 
3. Compare Solutions. Brainstorm and generate a list of potential solutions. Embrace creativity, encouraging creative and unconventional ideas that might lead to innovative solutions. After having the list of potential solutions, assess the pros and cons of each potential solution. Rank them based on feasibility of implementation, impact, and potential risks. Consider creating a visual aides to organize ideas and connections. Prioritize solutions based on effectiveness. 
4. Decide and Implement. Choose the most suitable solution. Develop a plan of action for implementing the chosen solution. Identify the steps, resources, and timeline required. 
5. Monitor and Adjust. Use metrics to measure results and the effectiveness of the implemented solution. Solicit feedback from others and use it to improve the quality of the solution. Make adjustments as necessary. Problem-solving is often an iterative process. If the initial solution doesn't fully address the problem, be prepared to revisit and refine the approach. Identify lessons learned for continuous improvement and future challenges. Problems never end.

Remember that problem-solving is a skill that can be developed with education and practice. The key is to approach problems systematically, and be willing to adapt your approach based on outcomes. Familiarize yourself with problem-solving models such as the scientific method, PDCA (Plan-Do-Check-Act), or others. Maintain a positive and constructive mindset. View problems as opportunities for growth, lifelong learning, and continuous self-improvement.

AI Problem Solving and The Problem Paradox

AI is computerized human-like problem-solving ability. AI will become humanity's problem-solving utility of choice. Problems will never end and will keep increasing in quantity and complexity over time. The Problem Paradox is that solving problems create new problems, which are oftentimes harder to solve than the original ones. That will prove to be the fundamental pitfall and opportunity of AI. AI will accelerate problem-solving, which will accelerate problem creation and generation.  

It's all about computers. These electronic data processing devices are humanity's top invention so far. In the last 80 years, electronic computers have changed the human world, and have advanced to the current AI Era where computers are finally able to mimic (and often surpass) human intelligence in many human-like problem solving tasks. The ability of electronic computers to take input and process it like humans in natural language and in increasingly accurate context is the beginning of a new phase in the computerization of everything, or the AI of Everything. Smart entrepreneurs and investors will profit handsomely making the AI of Everything a global reality. 

Due to the Problem Paradox, AI will solve many problems and generate even more problems. Solving an ever increasing amount of problems is how human progress is made, and how economies develop. The classical example is city life. Cities offer more solutions than the country or rural areas. Yet each citylife solution generates more problems. City economies grow strong and expand with humans exchanging solutions to the ever increasing complexities of city life. The problem-solution-problem cycle continues on a 24/7 cycle. Large and prosperous cities never sleep getting up to speed with the Problem Paradox. 

In conclusion, this article explored what are problems defining them as unpleasant facts, intriguing questions, or a combination of both. It touched on classical ways in which humans address and solve problems, emphasizing experience/experimentation; consulting/consultation; and reasoning/logic. Interestingly, AI will quickly surpass human intelligence in all of the above steps. AI will have almost immediate access to all human experience posted online and will have the capacity to simulate countless of experiments in the blink of an eye. AI can consult all online sources in seconds and statistically "spit out" the most relevant consultation results. AI's logical reasoning will be exponentially faster and more thorough than any human reasoning. 

Nowadays, finding answers for problems typically begins with posting queries online in search engines such as Google. Increasingly, sophisticated humans are prompting and consulting AI bots like ChatGPT and Google Bard to find better answers and potential solutions by indirectly accessing the collective knowledge online. Solving problems is quickly becoming a matter of prompting AI for solutions and answers. This will solve problems faster than ever in human history. It will created new and more complex problems faster than ever in human history. 

Stay tuned. 

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