Having stated the fact that everything has potential in it, (yes you too!) tapping into that potential is what makes a “thing” useful. This potential made useful could very well be conformed to create new things with even greater potential. It is the very basis of life if we observe carefully: It could very well be the case that “something” having some potential conformed into the world we live in today. (specifically into the mental model of the big bang) It doesn’t just stop there, as life saw the so called potential, it conformed to form intricate living beings, which we take for granted today.

Other modern day potential stories can be derived by virtually experiencing them once, via the potentially rich devices we have. The more you look, observe and think, the more potential you see in things. I’d like to give out a few notable examples from my POV, that have the supreme level of potential in them, through my eyes.

Humans

Supreme level creatures, origin believed to be god, have epic anatomy/features. Homo Sapiens, humans in layman terms, are the progenitors of (almost) every potentially rich thing out there. Despite their potential to make such things, their ability is not limited to that; it spreads far wide in terms of physique, intelligence, and adaptability. Human physique can be altered by training hard, after which it comes on-par to those of some elite level hunters in the animal kingdom. Next comes intelligence, the ultimate key to this being the incredibly complex and fascinating human brain. Brains evolved to give us the life we’re living and the things we’re using at the very moment. Last but certainly not the least is human adaptablitity. The brain plays a major role in this too to say the least. A human body, heck its components, as in the muscles, the voicebox, the sense and the brain, conform according to the situation to provide utmost potential to the thing to be done. Hence, potentially you could think of a human body as an upgradable legendary item, which when used correctly could do wonders.

Everything else mentioned below are just humans playing around with stuff till they named them to be that and developed a particular use for them.

Artifical Intelligence

AI has ultimate manifestation of the human potential and hence is on its own a great example of how much potential the human brain brings to the table. But as intelligence being other than the human one at stake, it holds even greter potential of surpassing the latter. Moral dilemma also follows as a result of the enormous potential this very thing holds: Do we really need such super intelligent machines in the first place?

Rust (The programming language)

Rust, the programming language that’s been rated heavily as in developer experience, is truly one of kind. Safety is the one word usually associated with Rust, and if someone goes really deep to its roots, it really shows how it makes your programs and your code, safe. The very core of Rust is designed with such intricate memory considerations and complier strictness that it nearly becomes impossible to cause memory leakages without the complier complaining about the same. It achieves this by adding some potentially cool features on top of the already existing languages. The main takeaway here is that the potential that this language holds to revolutionize software all around the globe is like no other.

Music

Music can’t hold any potential can it? Hear me out on this one cause music that your brain, body and soul loves can do wonders. At the very surface it’s just different frequency sound waves doing wiggly stuff together, going high or going low. But the amount of variations in these very waves makes a genre what it is. Potentially speaking, the type of music you listen to makes all the difference but any kind of music has the potential to illicit really powerful neural impulses. Just put on some music and you’re good to go!

Concluding remarks

Again, if you totally indulge yourselves in finding potential in anything, you will find some feature or capability that in turn reflects in the thing’s actual potential. Everything and anything has potential and it’s a cascading effect throughout generations and civilizations.

Bit of a backstory, past year might have been full of AI models left right and center, but this year somehow feels different. Is GPT-4 (Generative Pre-trained Transformers) the limit? Are other models even that good? What’s the deal with Gemini and big tech cuffing people to work for them for such high rewards but completely oblivious risks? And, biggest of them all, is AGI (Artificial General Intelligence) even achievable at this point, or it has been and will be a mirage all the way ahead?

My timeline of AI events (very rough)

ChatGPT took the world by storm early 2023, sparking and paving the way for more of these large language models to be fine tuned to do one thing or the other. Several competitors rose in conjuction to OpenAI’s remarkable models, yet none seemed to be doing that good of a job. Eventually, besides text, multimodal (text + audio + video) models were soon being seen in the market, just after individual image generation and video models peaked. Big tech companies went all in on the “AI” hype, incorporating it into each and every last bit of their product that could need it. Why? Because AI was the cool new kid everyone wanted to interact with. These huge “transformers” or so called “models” were nothing but a huge sea of ML nets under the hood; which predicted, in a sense, the next word in their response that made the most sense, humanly. Hence, one could infer the huge compute needed for these things to work. As a result of this “feature”, as models were on the move, compute needed to move as fast as that too: GPU’s becoming the key to exactly that. NIVIDIA got the job done, selling shovels while everyone was out digging gold. Fast-forward to late 2023, we get major updates to the existing models, and a few new models finally entered production after months of development. Multimodal models were a reality now, with exceptional capabilties, seemingly stepping towards singularity.

Now, with all this software and stuff, did hardware stay the same? Glad you asked, because no, hardware also had its rightful share on the AI clout. Multiple new age tech products were announced, be it cutting-edge GPUs, nearly-human robots or highly-configurable handheld AI devices; all of which would pave the way for a very hyper-realistic future. Once they become accessible to the general public, it will surely be like some futuristic scene from a sci-fi movie to say the least. Even automobiles had their own share, configuring interiors as they like and switching from manual to all-out auto. The whole techware landscape chaged, but on the large scale. AI startups, new extremely priced AI products, premium AI chatbots, home assistants, what not; but only accessible to those at the top.

Job markets continued to recede, with layoffs hitting half of the workforce off their workdesks, and new grads finding it difficult to land their first web-dev job. For sure, everyone was scared and on the edge of their seats hoping that they’d wake up to see another day at the office, everyday. Ironically, COVID was a factor back then too, remote jobs still are a consequence. Some people believed that the next age of software engineers would just be the english majors, prompting their way in and out based off on the product managers’ demands which inturn were just the reflection of what the customers actually wanted. No human overhead in between! To not over exhaggerate things, that sure might happen and next thing we know, an all batteries included “prompting” framework governs the whole SDLC (Software Development Life Cycle), while the tech lead, leads his way out the front door. But but but, some people beg to differ. It’s until you’re not proficient enough that you’re proficient enough to question your own skills and abilities. Code as we know it at present, needs a human to get human needs incorporated into them, and an LLM can only do too much for your billion-dollar product. Still, even these people will ultimately admit that using a code assistant such as the “GitHub Copilot” gets work done much faster and easier. Also, companies paying people their costly cut, would just pefer to turn to a lifeless code-monkey, who doesn’t even understand emotionally the semantics of a pay raise, and does not have “human” inadequacies to it; given it gets the job done, which it somewhat does.

In case you’re wondering, Huh, That’s not what a timeline is? :/

Well yeah, idk I wrote what came to mind.

The Current State and thoughts

Fast-forwarding to current year, the AI hype that shook the world away, seems to be calm at the moment, with model-progression ongoing at a pace at which it just seems like it’s plateauing or something? AI is not big a deal now, because everything around us conformed according to the latest AI trend in the market; and it will be like this for a while now, I suppose. It’s like when computers started becoming a thing, everyone was kinda skeptical at first but eventually it became a part of our lives. To no suprise, AI is already halfway through that road. Everyone’s living/coping with the fact that AGI might just take over the world someday, given if we reach there, which we will eventually, it’s just a matter of time. I’ll be completely honest, being a person who’s oblivious to most of the things under the hood, I thought AGI will hit very very soon. But, it is not that simple, which I realized just primitively entering 2024. You see, no big model updates, no major breakthroughs and no more progress on AGI, just that plain old ChatGPT with the latest model and some cool, better or atpar ripoffs. Could be the fact that I expected much more drama coming into 2024, which certainly is not there yet.

And when I see a headline like the one above at the top of my monday TLDR newsletter, it just makes me wonder:

If we need more compute than there is, to make the already existing models better, shouldn’t we question if this is actually the right way to make such models? Aren’t we doing something wrong here? About time a new solution to this problem should prevail over the already existing ones.

Now, I’m well aware that better models need better and more data to be better at what they do, but what I’m trying to imply is shouldn’t we be aware of the limit to all of this, as in the case with anything/everything else?

All we hope for is, put in more compute and someday the math will check itself out.

I’m new to this, so I do not fully understand how blogging, or jekyll works for the time being but I’ll surely iterate on this to make it better eventually.

Since this is a first post, I’ll just put out a status check on the current world situation as of now.

The world now-a-days is nothing less than being on a bleeding edge of a sword. Be it the rise of LLMs and AI in general to the saturation in tech market, or the sparks of a World War III to the neverending climate crisis. There’s too many things going on that it is almost/absolutely overwhelming to think about anything of significance. The thing affecting my and the life my fellow comarades the most is the huge exposion/adoption of AI models in our day to day lives. Suffice to say, it’s the most revolutionary thing in the tech world, because it completely changes the way people used to work!

The uncertainity the future holds is at its peak, as noone really knows, or can predict how this story ends.