Jackreece Ejini

 The most successful approach to modern AI has been the data-oriented version. Using statistical learning methods, we have created systems that can recognize, and generate information. While there are numerous benefits to these paradigms as can be seen obviously many new techniques that have become commonplace in daily life, from more accurate recommendation systems to text and image generation systems that make it easier to retrieve human knowledge in a compressed form, these methods are limited in the sense that they depend on human-generated data to be "intelligent". The main issue arises from our understanding of what intelligence requires. Our educational systems have conditioned us to think that intelligence requires the absorption and retrieval of information, and so we have created systems that do these actions to a greater or lesser degree.  If this is what we consider intelligence, then the  LLMs and image generation systems of today are the totality of what tha...

 Unless there is another XEROX PARC (Palo Alto Research Center) moment, we will not see any boom in tech for the next decade or so. I know this sounds pessimistic, but before you get your arrows out, know that I am a programmer who has never worked in big tech, so this issue affects me greatly also.  While big tech companies, and many small tech companies following in a copycat manner, are posturing to investors by firing employees, which is a very bad sign for future growth, all this posturing will not result in a resurgence of tech because there is a problem at the roots, of which consequences we are observing now.  So what is the deep problem at the roots causing the current destruction of value in the tech market, ignoring the temporary green we see from time to time that pops up in the stock? It is an excessive focus on value extraction and very little given to wild creative exploration.  Sometimes too much structure can lead to restriction, tech companies mostl...

 Any creative activity, that is activity that requires more than passively absorbing data is very hard. That is because by default it requires far more energy to create something than to just consume or destroy as the case may be.  Writing is a creative activity, and it is hard to do. It doesn't matter what you are writing, either words, codes or equations. Bringing stuff out of your mind is very hard, and I believe that it is the core part of what we call general intelligence because it activates the processes that utilize knowledge rather than just passively absorb and store it.  You could augment memory by using a notepad to store some of the facts that you record, but it is harder to augment the ability to create something new or express your solution to some problem, which is what all true creativity is about.  Writing also requires some of the highest level of focus you can summon, for instance writing computer programs or mathematical proofs require the larges...

There are two significant phases of intelligence, the first is the knowledge acquisition phase and the second is the knowledge utilisation phase. The knowledge acquisition stage is available by default in many animals but what makes humans unique is that our ability to utilize knowledge is far higher than most mammals.  But even humans individually and as a whole utilize only a very tiny fraction of the available knowledge. For an individual your acquired knowledge base, that is knowledge you have acquired both consciously through study or unconsciously through just existing, is far more than what you are able to utilize.  This is also similar for humanity as a whole, the knowledge we possess both in written form or in other forms far outweighs our ability to utilize it.  The core part of what we could call general intelligence is really more about knowledge utilization and less about acquisition. Because what is useful is only what we can utilize, the rest is just memory...

 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.

We now access the internet from flat computer screens but this will not be the case when VR/AR technologies mature. Current VR/AR technologies are no different from the mainframes of the 70s. They are allowing us to explore a new form of presenting information and although it's not yet time for them to hit primetime, these technologies are the future and must be continuously developed.

  Many people are dumping loads of mostly US dollars on Dogecoin and while it might seem unreasonable because Dogecoin looks like a joke, it is actually a massive hedge against all things, the dollar, stocks and the Crypto Titans Bitcoin and Ethereum.

Using our raw intellect to comprehend the structure of the entire universe is like trying to run around the entire world using your legs. Human mathematics in all its sophistication and profoundness is very dependent on the very structure of our physical brains, if not so, we would not be able to use it as a form of reasoning.

Cryptocurrencies are all the rage these days for all the right reasons. In summary, they are the future. But if you think this post is just about hyping crypto, wait a minute! I will not go into any super-detailed explanation about cryptocurrencies so this post is very short. I just want to highlight some points of importance which I think would be wise to think about as we surf the Crypto age.

The final goal of AI should be to create a generalized tool that can create specific tools for achieving specific tasks. The current approach to creating AI is that we take a problem like speech recognition, analyse the problem and then employ a bunch of methods like deep learning and or other regular algorithmic approaches to create a tool that recognizes speech. The same goes for building a tool that recognizes or generates images, etc.

 By now I guess it is clear that Deep Learning isn't the miracle solution that will finally bring us our dream of Artificial General Intelligence. Now we can leave the hype alone and focus on the reality of the situation, Deep Learning is a sophisticated tool that can help us solve many problems we can't solve through traditional software development.

 Artificial intelligence seeks machines that can generalize very well. When you train some model like a system for image recognition, the goal is to find some general representation of the input, so that the model is smaller than the data required to train it.

 Although in real software engineering scenarios you will barely encounter the kinds of problems you see in programming interviews or competitive programming, it is actually a good thing to train at these kinds of things because in the end, not only will you train your brain to become stronger, you will also develop what I call the mechanical mind. 

  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.