Intelligence

Intelligence is an idea that philosophers and scientists have been debating for thousands of years.  To this day, there is no single, universally agreed-upon definition of intelligence.  However, here are some things to help you understand intelligence:

1. Intelligent beings and systems can learn and adapt by acquiring knowledge and skills from training and experience and apply what they learn to new situations and can change their behavior based on new information and changes in the environment.
2. Intelligence involves the ability to identify problems, analyze the problems, and come up with effective solutions to those problems.
3. Intelligence involves the ability to evaluate information critically and form sound judgements based on known information and common sense knowledge.
4. Intelligent beings and systems can use logic and logical reasoning to draw correct conclusions from information and make sense within their specific context.
5. Intelligent beings and systems can understand complex ideas including abstract concepts, grasp those ideas and deal with them effectively.
6. Intelligent beings and systems can use innovation and creativity to come up with new ideas and new solutions to existing problems.

That is not a definition of intelligence; the bullet points above are examples that I would hold out that would be good examples of what constitutes intelligence.  There are likely many other examples from nature and elsewhere that exist.  That is just my short list.  And you might agree with that list.

DARPA seems to identify four specific characteristics of what it considers intelligent behavior: Perceiving, Learning, Abstracting, Reasoning.

But I will say this: An intelligent system has to work for that intelligent system to be useful within any specific context.  Cheap parlor tricks don't cut it for business professionals trying to solve real problems.

I am going to classify intelligence into two groups:

• Human-intelligence.
• Machine-intelligence.

I would contend that it is a fool's errand to try and compare machine-intelligence to human-intelligence.  They are not even in the same ballpark and they won't be for a long time, if ever.  The notion of "superintelligence" in computers is a fantasy. Computers are nowhere close to achieving superintelligence which I would consider as being human-level intelligence or higher levels of intelligence beyond the capabilities of humans.  If you are interested in this topic, I would highly recommend the book The Myth of Artificial Intelligence which analyzes these ideas in great detail in a very approachable way.

My working definition of intelligence is as follows:

Intelligence is the ability to perceive or infer information and reason with respect to that information using a known logic and known reasoning approaches and to retain that information as knowledge to be applied towards achieving specified goals and objectives within a specified environment or context (a.k.a. formal defined system with specified and known boundaries agreed to by a known set of stakeholders).

Intelligence is “human-task” performance. Intelligence is defined by the “formalism” of a rules-based system or process; the task of set of tasks that must be performed.  Basically a "machine" (e.g. computer or other apparatus) for performing human tasks agreed to by a known group of stakeholders and the system has known goals and objectives.  In essence, this is a formal system with specific boundaries and the system is blind to something not covered by the rules of that specific defined system.

Logic is a formal system that defines the rules of correct reasoning. Logic involves logical reasoning. Inference are steps in reasoning. There are three types of logical reasoning or types of steps in inference: deductive reasoning, inductive reasoning, and abductive reasoning. In logic you have logical connective (a.k.a. logical operators).  A system, such as an intelligent machine, is a set of logical patterns that can be processed effectively and reliably by that system. Logical reasoning approaches can be combined to create a hybrid type system.  Humans and machines can work together in one combined system.

Artificial intelligence officially began as a discipline of computer science in 1956.  Artificial intelligence involves creating tools such as algorithms, models, architectures, and software that simulate cognitive processes; basically intelligent machines.  Artificial intelligence or AI is an umbrella term that covers many tools or "tribes" building tools to implement artificial intelligence within computer systems.  These "tribes" or tools include: (This is another good summary)

• Symbolic systems also known as "Good Old-Fashioned AI" (GOFAI): These techniques involve explicitly programming logical rules and other logic into machines.  Effectively, this is logical programming such as PROLOG and LISP. (Handcrafted knowledge or rules-based systems)
• Machine Learning (ML): This is a subfield of AI that allows computers to learn without explicit programming. Machines learn by analyzing data and identifying patterns and clustering those patterns into groups. It then uses what it has learned to make decisions or predictions. (Statistical learning or pattern-based systems)
• Deep Learning (DL): Deep learning is a specific type of machine learning inspired by the structure and function of the brain. It uses artificial neural networks with many layers to learn complex patterns from data. 
• Transformers: Transformers are a type of neural network architecture commonly used in deep learning. These are a specific kind of neural network architecture well-suited for handling sequences like text. They are particularly good at understanding the relationships between words in a sentence. 
• Large Language Models (LLMs): LLMs are a type of machine learning.  LLMs often rely on transformers for their processing. These are advanced machine learning models trained on massive amounts of text data. They can perform many kinds of tasks with language, including generating text, translating languages, writing different kinds of creative content, and answering your questions in an informative way that is natural to humans.
• Generative AI: Generative AI is a type of machine learning that learns from existing examples including text, images, videos, music and then create new, realistic content that reflects the patterns it learned from the examples.
• Natural Language Processing (NLP): NLP focuses on enabling computers to understand, interpret, and generate human language. NLP bridges the gap between raw text data and meaningful insights. LLMs, such as GPT (Generative Pre-trained Transformer), leverage NLP techniques to understand and generate human-like text.

There are many other aspects to artificial intelligence such as explainable artificial intelligence or XAI. AI approaches can be combined to create hybrids.  Humans and machines can be brought together, each bringing their unique capabilities to the table to solve problems and perform tasks or complete processes.

As any craftsmen or craftswoman knows, you need to use the right tool for the job.  A tool offers a basket of capabilities, PROS and CONS.  No tool is only PROS or only CONS. For example, machine learning or deep learning systems work best if the system you are using them to model has a high tolerance to error. These types of systems work best for:

• capturing associations or discovering regularities within a set of patterns;
• where the volume, number of variables or diversity of the data is very great;
• relationships between variables are vaguely understood; or, 
• relationships are difficult to describe adequately with conventional approaches.

Machine learning basically uses probability and statistics, correlations .  This is not to say that machine learning is a bad thing.  It is not, machine learning is a tool.  Using the wrong tool will leave you unsatisfied.  Ultimately, what you create will either work or it will not work to achieve your objectives. The craftsman's or craftswoman's task is to figure that out.

Artificial intelligence which confidently supplies answers based on statistical probabilities when the direct answer isn’t immediately available can work in certain situations, but not for others. When one cannot distinguish between direct answers and an answer that is a probability; problems can result in certain use cases.

In the next installment I will help business professionals understand the real capabilities of these AI tools.  The best way to understand is specific focused testing.  Don't just believe the snake oil salesmen.

Human intelligence is one of the most valuable resources an organization can have; but at the same time human intelligence is very expensive because it is costly to create. As a result, accessing high-quality professional services, such as accounting and audit services, comes with a hefty price tag because gaining the skills and experience necessary to deliver those services is time consuming and expensive.  However, a portion of the work these expensive skilled and experienced professionals deliver is mindless, repetitive grunt work.  A good portion of the skills and experience relates to memorizing information.  What if there was a way to effectively offload some of this mindless grunt work to a machine; enabling humans to focus on what only humans can do and tasks/processes machines have no chance of completing effectively?  Not targeting hard, or impossible, to achieve human level intelligence, rather the more easily achievable machine level of intelligence.  Think of how a calculator is beneficial to an accountant.  What if you could push on the bell shaped curve of expertise making average accountants and auditors better?

Imagine an ecosystem that would grow and evolve over time in many ways that create compounding value as opposed to increasing complexity.  Priming this pump to get it up and running will be challenging and expensive, but the benefits of such a system will be very high.  And this is not about bolting on something to an existing process, this is about a true transformation of processes and tasks; this is a paradigm shift.

Additional Information:

• DARPA Perspective on Artificial Intelligence | Video
• Computers are Dumb Beasts
• A cat is on a mat.
• Problem Solving Systems
• Smart (Cognitive) Business Applications and Services (Work in Progress)
• Knowledge Representation and Reasoning
• Explainable Artificial Intelligence (XAI)
• Knowledge Representation And Reasoning In Artificial Intelligence (AI) Made Simple
• Types of Reasoning
• On to Knowledge Infused Language Models
• Why Machines with Never Rule the World
• Will Artificial Intelligence (AI) Replace Humans?