ALL THINGS BIG AND SMALL 2.0

Data is for 2023 from the CIA Factbook, an interesting website to read https://www.cia.gov/the-world-factbook/ Introduction to Fuzzy Logic Fuzzy Logic algorithms are a way of looking at the world, not in black and white but shades of grey with black and white at the extremes (like “Fifty Shades of Grey “ the movie.) Japanese consumer electronic appliances have been using Fuzzy Logic for decades. Examples: • Washing Machines: Japanese washing machines use fuzzy logic to sense the load size, fabric type, and dirtiness of clothes. The machine then adjusts the water level, wash time, and detergent amount for optimal cleaning. • Rice Cookers: Fuzzy logic rice cookers monitor temperature and moisture, adjusting cooking time and heat to produce perfect rice regardless of the amount or type. • Air Conditioners: Fuzzy logic air conditioners sense room temperature, humidity, and even the number of people in the room, then adjust cooling power smoothly for comfort and energy efficiency. • ...

I am still reading the three books above, but have already begun to wonder at the marvel that is Nature. Like all scientific endeavors, Artificial Intelligence is constantly evolving. But I can see that the direction for the future is towards 'softer' systems that emulate the biological neural systems. Less of the machine-based algorithms that are faster but more brittle; towards systems which closer emulate Nature. Nature's ways are sometimes strange. Many redundancies (e.g. junk DNA), meandering and seemingly aimless moves. But eventually a more robust solution. The workings of AI's current Genetic Algorithms that are used for Optimization are more of a Mendelian process of selective breeding rather than natural evolution. Nature's evolution is an open process. There is no predetermined goal. Each generation adapts to whatever its environment throws up, and the fitness of a generation is only specific to fitness for the environment of that moment. Genetic Algor...

Fuzzy Logic is the use of Fuzzy Sets to give what its proponents believe is a truer representation of most real world situations. In the real world, things are often not strictly black or white but shades of grey. Unlike computers, humans think in shades of gray also not just Yes/No, Black/White or One/Zero but everything in-between. For example, what does it mean to say the temperature is cool or hot? The areas falling into cool and hot, overlap. Fuzzy Logic applications is widely used in modern consumer appliances with the Japanese engineers as early-adopters and pioneers of Fuzzy Logic controllers for airconditioners , washing machines, rice cookers, cameras, elevators etc. It is only in the last decade that Europe and America have caught up with Fuzzy controllers for automotive, aircraft and machinery components such as for engine performance , brakes and driving control. The first step in the creation of a Fuzzy controller is to draw up the Fuzzy sets. {see middle picture}. This d...

It is an accepted principle that in attempting to build a model of something in the real world, one should have a deep knowledge of the subject that is being modeled. Because domain expertise is crucial for choice of input variables, as well as clear statement of the relationship between the variables, as well as the quantification of the coefficients [parameters] for each equation of the model. However, in recent times, there is some serious study of blackbox approaches to modeling. A blackbox type of model is one where [1] choice of Input need not be restricted to those which are justified to be inputs according to the theoretical principles of the subject to be modeled. [2] A clear statement of the relationship between input variables is not absolutely necessary, instead leaving it to the blackbox to work out the relationship. [3] For [2], non-parametric methods machine-learning methods such as neural nets, fuzzy logic and genetic algorithms are used to classify, detect patterns or ...

Blackbox models of complex situations are tempting. Without the need to specify parameters, and not limited by choice of variables, you can throw in everything including the kitchen sink, and see how the model performs. But in real life, it is not that simple. With every variable that you throw in, you are increasing the complexity of the model exponentially as well as the computational workload. Although there are algorithms for dimension reduction as well as for assessing the significance of an input towards model accuracy, nothing beats a good old human being with expertise in the subject domain, for initial choice of input variables. Let's do an academic exercise for the building of a model that (1) hopefully gives an objective, 'true' valuation of a house. (2) predict the future house price. We will attempt to build our model using a Neural Network, or a hybrid Neural with Genetic Algorithms for the last mile of calculation, to avoid local optima. Any human expert in r...