Emergence and Machine Learning
Machine Learning has been successfully applied in the areas such as
recommendation systems and visual object identification in the recent past. Most
of the machines learning algorithms, including deep learning, are in the final
analysis nothing but regression systems and classification systems.
On Fibonacci & Binomial Choice
A few weeks ago, we saw how nature’s preference of perceiving things in log-scale and of growing things in gnomons leads to occurrences of Fibonacci numbers in the data-sets. This week, we will discover another fundamental reason why Fibonacci numbers may emerge in a dataset.
On Fraud Detection and Machine Learning
George Kingsley Zipf (1902-1950) observed an interesting phenomenon in natural languages. This phenomenon, now known as Zipf’s law, which Wikipedia defines as follows “Zipf’s law states that given some corpus of natural language utterances, the frequency of any word is inversely proportional to its rank in the frequency table. Thus the most frequent word will occur approximately twice as often as the second most frequent word, three times as often as the third most frequent word, etc”
On Finding Gnomon in Your Data
Aristotle described Gnomon as adding an “L” shaped figure to an existing square, such that the resulting figure is still a square. Euclid extended the concept to a parallelogram, and finally Hero extended the concept to any figure that, after being added by any figure, retains its original form.
Mind, Mathematics & Feature Selection
Gustav Fechner (1801-87) discovered that the way humans perceive the
intensity of sensory inputs is logarithmically proportional to the absolute
magnitude of stimulus as measured by non-living measuring devices.
The Quest for Artificial Intelligence
In 1837 Charles Babbage described “Analytical Engine” (AE for short). Had he succeeded in actually building AE, it would have the first Turing Complete computer designed by humans.