ALL THINGS BIG AND SMALL 2.0

2014 D'Angelico NYSS 3B FM (flamed maple) 2013 Fender American Vintage Telecaster Thinline 1967 Gibson ES-330 as advertised for $520 on AliExpress After more than 3 years, I had a relapse of the Guitar Acquisition Syndrome (GAS). I thought I was cured. Why would a 67- year old man with probably less than 15 years of life left, and with more guitars and amplifiers than he can play with, want more guitars? But succumb, I did, to GAS. So here is a blow-by-blow account of my latest attack of GAS.   It began with a sudden craving for a Telecaster Thinline, though why I would want a Thinline when I had, during my lifetime bought and sold three Thinlines I don't know. So I bought the Thinline.  Then I saw an ad in AliExpress to sell a 1967 Gibson ES 330 for $520. I could hardly believe my eyes. I convinced myself that this anomaly in price was due to the ignorance of the seller. Alas, how true the adage that if its too good to be true, then it probably is...

Thoughts on definition of Noise and Thresholds 1.     I am obsessed by the thought of being able to make my everyday decisions in a more objective way, unclouded by emotions and biases so common with us humans. There must be academic papers on the topic of quantitative-based decision-making. But I have never read any.  So these are the ramblings of a novice in this field. 2.     Any data-driven (quantitative) decision support system must have (a) some kind of noise removal method for initial ‘cleaning’ to remove data that is considered not relevant to the model  (b) Methods for setting thresholds of some kind i.e. a numerical value that is the boundary for, and triggers a Yes/No decision. 3.      Thus, the two key aspects of any quantitative decision-making process are the removal of noise, and the establishment of thresholds that trigger decisions. For this reason, I am fascinated by the definition of Noise and...

The AI machine was fed flag patterns like number of horizontal lines, vertical lines, colors, crescent, crosses, sun, stars, stripes, bars. It was then asked to cluster countries according to the degree of similarity of their flag patterns.  The result is in the map of six clusters. To test if the AI was good, I took the bottom right cluster (Pink) that contains Singapore. The countries placed near to Singapore are supposed to have flags with a high degree of similarity with Singapore's flag. Now look at the collage of flags image. The flags are: Top row from left: Singapore, Tunisia, Maldives Center from left: Turkey, Mauritania, Pakistan Bottom: Algeria, Comoros Islands, Indonesia. As you can see, although the colors of the flags vary, the flags do have similarities in terms of all of them having crescent and stars. So the AI was smart enough to recognize that the crescent and stars are the important variables.Although I gave all the patterns equal weighting in im...

The purpose of this experiment is to see whether an explorative data mining program could recognize degree of similarities in about 60 species of animals, from honeybees to lions, to starfish, parrots, etc. The software program used is from  www.viscovery.net  . Some uses of this program are: (a) clustering of customer data according to their behaviour. (b) In gene data analytics to identify biomarkers for the diagnosis of diseases. (c) For fraud profiling and forensic applications. The data is from the University of California Irvine data archive (archive.ics.uci.edu/ml). The ‘properties’ (attributes) considered were: backbone, legs, fins, lungs, hair, feathers, eggs, milk, tail, domestic, aquatic, airborne, venomous, predator.  Presence of an attribute is denoted by 1, and absence by 0, except for Legs, where it has a numerical value of: 2,4,6,8 legs. The tool was made to learn the attributes of all the animals and group them into clusters, each cluste...

One of the most diverse of families in the Animal Kingdom is the family of Cichlids fishes. There are estimated to be 2600 species of Cichlids, and each year more are discovered.Each of the narrow , deep lakes of the African Rift Valley (lakes Malawi, Tanganyika and Nyasa),contain more than a thousand species of Cichlids. There are also Cichlids in South America and a few species in Asia.   Through ev olutionary adaptation, each species carves out a niche for itself in the micro-ecosystem that it occupies.. Thus species of many shapes, colors, kinds of diet and mating behavior have evolved to suit the characteristics of each niche in the ecosystem. Fast-flowing/slow-flowing waters, acidic,alkaline, brackish, environments with many predators, low-oxygen etc there will be a Cichlid that is able to thrive. You might find it interesting to know that the Discus and the Angel Fish despite their very different shapes, belong to the Cichlid family. So is the common Tilapia t...

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...

Since I first wrote on this subject in my Facebook, (see image of text of the FB post above), I have discovered yet another classic: Neural, Novel and Hybrid Algorithms for Time Series prediction~ by Timothy Masters [Wiley, 1995]. It's eighteen years since the book was first published. But the topics covered (especially on data pre-processing, scaling and transformations) are still as important. I read each page with the new advantage of perspectives and practical experience gained on this subject these past eighteen years. Old words took on a new significance, a deeper meaning, and sometimes a different meaning. I begin to see the advantages of of hybrid models that combine Neural Nets, Genetic Algorithms, Statistics and Digital Signal Processing. Here are the Contents of this book: 1. Preprocessing 2. Subduing Seasonal Components 3. Frequency-Domain Techniques I 4. Frequency-Domain Techniques II 5. Wavelet and QMF Features 6. Box-Jenkin ARMA Models 6. Differ...