Jackreece Ejini

 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...

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...

 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.

Today I just remembered the original vision I had about artificial intelligence back around 2001! My goal was something I called machine intelligence, although I am not the inventor of this term, I had a definition of my own for it. 

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. 

 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. 

 When it comes to programming, there is no reasonable shortcut to practice. What kind of practice am I talking about? Does it mean we should spend all day on  Leetcode  or  Hackerrank ? No, you don't have to but it is very important that you spend sufficient time on these platforms to improve the quality of your programming expressibility.