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Microsoft's Bing Killer feature and why people dunk on Bard

 All LLMs are equivalent, I have not done any statistical studies on the error rates but I intuitively know that they are equivalent, meaning that they have similar rates of failure. So why is everyone dunking on Bard? Well, it's because people expected more from the search leader of the world.  But the problem with Bard is not really Google's fault, it is more about human cognitive bias, the very bias that made Google stay at the top of the search engine race even as they kept adding excessive amounts of advertising that degenerated search results. The reason Google stayed on top for so long wasn't that their search engine was excessively better than others but because they had become a verb for anything that requires obtaining information from the internet. They also had the power of incumbency, so it did not matter how many ads people were flooded with, people just went to Google anytime they wanted to search for information because humans are lazy and hate change. So wh
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New Information interfaces, the true promise of chatGPT, Bing, Bard, etc.

LLMs like chatGPT are the latest coolest innovation in town. Many people are even speculating with high confidence that these new tools are already Generally intelligent. Well, as with every new hype from self-driving cars based on deeplearning to the current LLMs are AGI, we often tend to miss the importance of these new technologies because we are often engulfed in too much hype which gets investors hyper interested and then lose interest in the whole field of AI when the promises do not pan out. The most important thing about chatGPT and co is that they are showing us a new way to access information. Most of the time we are not interested in going too deep into typical list-based search engine results to get answers and with the abuse of search results using SEO optimizations and the general trend towards too many ads, finding answers online has become a laborious task.  Why people are gobbling up stuff like chatGPT is not really about AGI, but it is about a new and novel way to rap

What programming language should you learn first?

 This is usually the biggest question for many people who are venturing into programming. The sheer number of programming languages out there including guides to choosing programming languages can confuse the beginner greatly. The question now is why one more guide? While there might be numerous guides out there, this is my own contribution and depending on what you see first when you search: What programming language should I learn? Your entire future will be determined by the guides you read and come to believe. 

Programs == Rules

 When crafting an algorithm for dealing with some kind of problem, we usually are involved with designing rules that transform the input data into output data. The programming language is a method convenient to humans for rules specification. But generally, the computer itself does not need anything more than low-level binary instructions to transform input data into some desired output.

Cellular Automata, Computational irreducibility and Equational models, towards AGI

The general assumption that we can find an equation for every data sequence is proven false when we observe the evolution of Rule 30 Cellular automaton. Computational irreducibility proves that we cannot always capture a summary of data with an equation, but sometimes the most concise representation of data is the program that generated the data itself. 

Towards program based models

 As the deeplearning fever has calmed down like I actually predicted it will because we have not seen any general AI arise from it, its time to reflect on what we have learned from it and gain wisdom in order to avoid folly in the future and to also help correct our trajectory towards AGI so we can reach there in a reasonable time.