In the image caption above you see the solution to the equation given in the first line in the second line. A simple looking innocuous equation doesn't have a solution in the real line. In order to solve it, we have to jump into the imaginary line.
Although we are still some decades away, IMHO, from building full Artificial General Intelligence, we have succeeded in building some kind of artificial intelligence based on deeplearning techniques which are in some ways better than the older GOFAI systems of the 50s - 80s era.
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.
Trefoil knot. Source: math.stackexchange.com 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.
Source: edx.com 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.
Source: shareicon.net 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.
VR Headset. Source: theverge.com 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.
Excerpt from Principia Mathematica (Whitehead and Russel) 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.
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.
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.
While nothing beats the Wolfram language tutorial available at www.wolframcloud.com I decided to take a slightly different approach to share some knowledge about the language. There was this classic computer science lecture that took place at MIT in 1986 taught by Gerald Jay Sussman, Hal Abelson, Julie Sussman.
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.
The current global movement towards code literacy is a good thing. Apart from the fact that some of the richest people in the world are programmers and thus software engineering is a very lucrative career, there are other motivations to learn to program beyond such career and material success.