AI talks turned the table and become more pessimisticIt is just another correction of exorbitant optimism and realisation of AI’s current capabilitiesAI can only help us to replace jobs in low noise dataJobs needing to find new patterns and from high noise data industry, mostly paid more, will not be replaceable by current AI
There have …
Top brains in AI/Data Science are driven to challenging jobs like modelingSeldom a 2nd-tier company, with countless malpractices, can meet the expectationsEven with $$$, still they soon are forced out of AI game
A few years ago, a large Asian conglomerate acquired a Silicon Valley’s start-up just off an early Series A funding. Let’s say it …
People following AI hype are mostly completely misinformedAI/Data Science is still limited to statistical methodsHype can only attract ignorance
As a professor of AI/Data Science, I from time to time receive emails from a bunch of hyped followers claiming what they call ‘recent AI’ can solve things that I have been pessimistic. They usually think ‘recent …
Transition from column to matrix, matrix to tensor as a baseline of data feeding changed the scope of data science, but faster results in ‘better’, only when we apply the tool to the right place, with right approach.
Back in early 2000s, when I first learned Matlab to solve basic regression problems, I was told Matlab …
Web novel to Webtoon conversion is not only based on ‘profitability’If the novel author is endowed with money or bargaining power, ‘Webtoonization’ may be nothing more than a marketting tool for the web novel.Data science modeling based on market variables unable to grab such cases
A student in SIAI’s MBA AI/BigData progam, struggling with her thesis, …
Asian companies convert degrees into years of work experienceWithout adding extra values to AI degree, it doesn’t help much in salary’Dummification’ in variable change is required to avoid wrong conclusion
In every new group, I hide the fact that I have studied upto PhD, but there comes a moment when I have no choice but to …
The relationship between a commercial district and the concentration of consumers in a specific generation mostly is not by causal effectSimultaneity oftern requires instrumental variablesReal cases also end up with mis-specification due to endogeneity
When working on data science-related projects, causality errors are common issues. There are quite a few cases where the variable thought to …
With high variance, 0/1 hardly yields a decent model, let alone with new set of dataWhat is known as ‘interpretable’ AI is no more than basic statistics’AI’=’Advanced’=’Perfect’ is nothing more than mis-perception, if not myth
5 years ago. Just not long after an introduction of simple ‘artificial intelligence’ learning material that uses data related to residential …
One-variable analysis can lead to big errors, so you must always understand complex relationships between various variables. Data science is a model research project that finds complex relationships between various variables. Obsessing with one variable is a past way of thinking, and you need to improve your way of thinking in line with the era …