Jackreece Ejini

It is natural that humans avoid failure at all cost. This is because the emotions associated with failing are unpleasant and we would all prefer to be in the emotional state of winning all the time.

I had explored a lot of programming languages and programming paradigms before I actually knew about Wolfram Mathematica. I actually started out my programming journey with C/C++, then I read Eric S. Raymond's essay and learnt the proper sequence to learn programming in which I outlined with some modifications in my book: How to learn programming.

If we look into the future projecting from what we have seen in most sci-fi movies we will see future cities that are crowded with people and tall buildings, filled with flying cars, plenty of devices on people, lots of large screens advertising stuff, etc. But lately, I have begun mentally picturing a completely different future after observing the real trends that govern technological development.

The Covid-19 outbreak which resulted in the government-mandated shutdown has exposed a lot of weakness in the technological systems we use to manage our day to day life. Apart from the negative effects on the economy and the loss of life and livelihood, Covid-19 is a wake-up call for all the people who build and manage large systems and especially those of us building or thinking of building future AI systems, we have to build anti-fragile systems.

This post is about understanding understanding and of course as it applies to engineering artificially intelligent systems that display the human quality of understanding. If you have a better title for this post you could suggest it, but I couldn't think of anyone better that really captures the difficulty imposed on us by the words of natural language that we have to use in our daily communication.

AI explainability or the art of explaining why a certain AI model makes the predictions it makes is all the buzz these days. This is because unlike traditional algorithms we don't really understand what goes on inside AI systems and while we could peer into these models as they operate or log their actions in many cases we cannot really explain exactly why they make the decisions they do.