Core principle of Machine Learning 

There of course are many, but for someone coming from computer science, and, software engineering, where the environment is relatively clean and certain (deterministic), it usually is a leap to understand that Machine Learning (and other elements of #AI) are not. 

Machine learning, is based on probability theory and deals with stochastic (non-deterministic) elements all the time. Nearly all activities in machine learning, require the ability to factor and more importantly, represent and reason with uncertainty. 

To that end, when designing a system, it is recommended to use a simple but uncertain (with some non-deterministic aspects)  rule, rather than a complex but certain rule. 

For example, having a simple but uncertain  rule saying “most birds fly”, is easier and more effective than a certain rule such as “Birds can fly, except flightless species, or those who are sick, or babies, etc.”

As one starts getting deeper in Machine Learning, a trip down memory lane around Probability distribution, expectation, variance, and covariance won’t hurt. 

Protecting your Data from being slurped up!

How to protect your data from what the The Guardian calls as ‘US border agents are doing ‘digital strip searches’?

The only way I think this is possible in a fool-proof way in the near future is that every has to absolutely implement a two-factor-DDA-authentication. There is not better #security today – period! There ain’t no stinking #AI, #RNN, #DNN, or Boltzmann machine in the world, or #Quantum computer worth its #quibits which can crack this – at least not in the near future.

And of course, when you have friends and family involved, the group authentication is a sure-fire way to stop anyone snooping in. #security