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Is AI explainability important?

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.

The Threefold nature of Intelligence

I am constantly contemplating Intelligence, not purely for the purpose of exercising my mind, but also to discover its secrets so as to enable myself or anyone I inspire to create an artifact that embodies it as it is inevitable that humanity will eventually build synthetic intelligence to augment its current capacities.

Amorphous Computing

If there is a major defining feature of our current microprocessor engineering capabilities, it is that building these chips requires a great deal of transformation of matter from a rough amorphous state with lots of impurities to a crystalline state at 99% purity.

Beyond binary computing

Almost every computer in existence today is based on the binary system of 1s and 0s and it might look like this is always how it has been. But if we take a look at the history of computing we will find that in the 60s there were computers that were based on our decimal system, decimal computers as they were called. But it turned out that binary computing won the race and today many people think that binary is the only way to go when we have to build computers.

Virtual Reality is the next platform

It's been a while now since we started trying to develop Virtual Reality systems but so far we have not witnessed the explosion of use that inspired the development of such systems. Although there are always going to be some diehard fans of Virtual Reality who will stick to improving the medium and trying out stuff with the hopes of building a killer app, for the rest of us Virtual Reality still seems like a medium that promises to arrive soon but never really hits the spot.

Getting Serious about AGI (Artificial General Intelligence)

I think its time to think more accurately about AGI so that we can all have a clear direction to guide our research towards building the kind of Strong AI which we as AI researchers have all dreamt of. Its time to get away from too much fantasy and focus on the right vision, and with this vision maybe we could have a better shot at creating the most powerful technology that humanity will ever have to create, a human-like mind unencumbered by the limits of a biological brain.

Is the brain Quantum or Classical

While Max Tegmark opposes the idea that the brain could be a quantum computer, Sir Roger Penrose and others would say otherwise. I will not go into the details of their individual reasons for making their assertion but I would like to explain why Sir Roger Penrose alongside Stuart Hameroff might have a good point.

Some Insights on Quantum computation

https://www.technologyreview.com/s/604242/googles-new-chip-is-a-stepping-stone-to-quantum-computing-supremacy/

The hardest part about learning quantum computing is making a simple mental switch. Independent of the platform you might be interested in, whenever quantum computing is mentioned there is a very simple way to view the entire field. I will explain briefly below.

Distributed vs Centralized Intelligence

There are two main paths we could take towards the goal of building Synthetic (Artificial and General) Intelligence. We could either take the distributed or Centralized paths.

Survival of the persistent

Charles Darwin


I have been thinking of Charles Darwin's theory of evolution these days as I spend more time working on evolutionary computing stuff and I came up with a new way to phrase survival of the fittest as the survival of the persistent. Because what I observe in the world these days as pertains to humans has more to do with persistence than raw physical fitness.