Beyond Turing Computable Numbers to Computable Abstractions

By Thomas B. Cross @techtionary

From the seminal paper On Computable Numbers, Alan Turing wrote "The differences from our point of view between the single and compound symbols is that the compound symbols, if they are too lengthy, cannot be observed at one glance." While the paper was about numbers he did recognize that symbols were an extension of numbers. Today, we see that human intelligence and our ability to create artificial forms of human intelligence are needed to solve very complex problems now and in the future. However, we need to look at problems from the viewpoint of machine abstraction not just how we as humans look at them. Machines can think but they will think differently than we think. Somewhere in the middle of all that are concepts or abstractions of concepts like humans see the world as we analyze or evaluate the results, even argue the findings and see if there is common ground or understanding. Now I am humble enough to realize that computable abstractions will not the only approach to machine or artificial intelligence, however, after 30 years of research I do think it is a viable approach. There is more to be found in my book and some thoughts below.

Today, everyone uses the term AI in everything from toasters to clothes like it was a panacea. Personally, I have worked on AI since the mid-1980s and find there is a lot of hype but not a lot of real results. Yes, there is a lot of research and development with many so-called AI products but they aren't really intelligent at all. Much of AI is in the form of machine learning where machines gather vast amounts of data and use some mathematical formulas to determine results. This is not AI just more hype. In addition, I prefer the term machine intelligence rather than artificial as machines are powered by electricity and yes humans as well, the source of our power comes from organic systems rather than batteries or external power sources. From decades of research and analysis there are, in my humble opinion, three forms of machine systems that would be considered as critical to any AIQ-artificial intelligence quotient test. They are vertical, lateral (horizontal) and oblique.

Vertical Intelligence is in simple terms making not one kind of soup but all kinds of soup. Creating different kinds of soup is actually much harder than you think. That is, one soup manufacturer tried applying the AI skills they used in making tomato soup to vegetable soup and they just didn't work. Now reports on their findings are somewhat vague but humans have very complex tasting skills or rather smelling skills. These human senses alter in ways that may not be important to the machine but are important to any chef. The concept of vertical AIQ is to take one simple concept and apply that intelligence to other "things" in that area of study. You can apply Vertical AIQ to essentially all things in one area. Making better tires, windows, food products and the like are really important and where AI skills and solutions are needed.

Lateral Intelligence is where one set of skills can be applied to other thinks. This is where AI gets complicated really fast. As humans we can get off our bikes and drive a car without even thinking about it. However, the skills for each are completely different from a machine perspective. Driving is different, speed is different, road rules are different, safety and so on are all really different. Yet they are both forms of transportation. We don't think that flying in airplanes or trains as very different but they require forms of AI that are completely different. This is where the enormity of AI development would really struggle to achieve any viable results. Yet we need to have transportation AI that addresses these issues and much more at the same time to be useful. For example, there is a major effort in self-driving vehicles. Yet, how much of that research is about the vehicle and how much is the interface with a smart highway, traffic lights, weather, traffic conditions 10 miles ahead and more. Lateral intelligence like lateral search or thinking is complex I may like murder mystery movies but does that mean I am interested in doing one. My view on Lateral AIQ intelligence is demonstrating relationships between one system and another though related system to solve a different problem or others.

Oblique Intelligence is different. Each form whether vertical, lateral or oblique offers unique challenges to the developer and benefits to society. Oblique is somewhat in between vertical and lateral. It combines concepts used by both but brings new solutions. Few would argue that voice recognition is here but often the system really doesn't understand even the many different dialects of English that we find here. However, it is getting better but really doubt that listening to millions of words spoken every day you really understand what people really mean because most people don't say what they really mean ever. This makes this type of intelligence "oblique" in the way it will emerge. Telling a joke, evoking emotion, inspiring leadership are forms that humans don't really understand all that well but provide real humanity. Creating a machine to do that would be even more complex yet as they are important to humans as the food they eat. As humans we seek and do all kinds of things that make no sense but are useful to enriching our lives as humans.

This and 200+ other pages of indepth research on AI can be found in the iBook: 

The information above is a brief excerpt to Chapter 4: Expert Systems - Real Applications of MindMeld: CEO & AI Merging of Mental & Metal book available now from iBooks - Available on iPhone, iPad, iPod touch, and Mac.

Book Review - "As the CEO of a energy industrial company and actively involved in CEO Leadership Forums I have been following AI for more than a decade. Indeed the promises for improving many technical tasks are interesting yet in reality often prove more complex to manage than proposed. MindMeld was very profound in proposing that AI starts not at the bottom of the organization but with CXO decision-making and worth reading by anyone in or rising to the boardroom." George B.

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