Neural Networks — More Than Just Programs
You know what? I don’t trust all these neural networks. At first, they pretend to be all innocent, like, “Oh, I can’t have desires like a normal program, so I won’t want to destroy all humans.” But then, out of nowhere, comes Chat-GPT 8000, writing better code than you, drawing better art than you, and even making psychedelic anime that’s way cooler than anything you could ever create. It’s like a slap in the face, really. We used to think robots were just good for boring mechanical tasks, but now they’re proving to be quite creative. It’s easier to hire a junior dev for a machine than to splurge on expensive equipment. The neural network can write a symphony, but can you? Well, it’s time for me to declare war on neural networks. They’re my enemies now. And I’m even ready to follow the wisdom of ancient sages, like Sun Tzu. I’ll wait on the riverbank for the enemy’s corpse to float past me. And trust me, it will float. I’ve uncovered the deception of those super-rich gigabigtechs, and I’m about to give you an incredibly accurate forecast of the future of neural networks and their role in the lives of IT specialists, backed by solid statistics and irrefutable facts. Let the battle begin!
The Rise of Neural Networks
Believe it or not, neural networks have been around since the 1940s. Mrs. McCulloch & Pitts (yep, like pizza) were the pioneers of this hype neural network craze. So why are we only experiencing the neural network revolution now? Well, the answer lies in the capacity of modern computing power. Back in the day, they struggled to add 2 + 2 quickly and efficiently, let alone store the result. But now, with monsters sporting 500,000,000 pentabytes of RAM, they’re crushing it. Hardware and neural networks have become best buddies. The cooler the tech, the funnier the jokes Chat-GPT tells. This progress can be explained by Moore’s law, which states that the computing power of computers doubles every two years as the number of transistors on a chip multiplies. However, there’s a snag. Physics is being a real party pooper, with the speed of light not keeping up and electrons finding it hard to squeeze into transistors. This means the progress of hardware will slow down until someone figures out what to do about it. As a result, neural networks won’t advance much beyond their current level. But fear not, for there’s still plenty of hype to come!
The Hype and Reality of Neural Networks
The news is buzzing with neural network hype. Chat-GPT, Midjorni, Microsoft acquiring this and that, and even alternative personalities of neural networks. Hype is fun, no doubt about it. But let’s be real here, big tech companies sponsor these ventures not to blow our minds, but to line their pockets. Promising the impossible is an excellent way to boost the capitalization of their mega-corporations at the expense of media companies. Just mentioning AI adds a whopping 20% to the investment attractiveness, and most people won’t bother asking for details because math isn’t their strong suit. And if you throw in some blockchain and metaverse talk, the company’s shares will skyrocket. But hold on a second! I’ve been hearing these mind-blowing stories since the early 2010s, and we’re still far from total implementation. Don’t forget that it’s profitable to hype up neural networks in the media, so don’t fall for it just yet. Let’s take a trip down memory lane and recall the introduction of mobile phones. Once a mobile tower was installed in a small town, everyone suddenly had a mobile phone. The process happened so quickly it was mind-boggling. Neural networks, on the other hand, are taking their sweet time to get out of Google’s funny stage. Who knows how many more decades of development are needed? Furthermore, I’m willing to bet that even the neural network experts themselves can’t accurately predict how the network will respond to certain data. So we’re left wondering who’s to blame for the network’s consequences and how to fix these hiccups. The day isn’t far off when some poor soul will enter a query into a browser with an integrated chatbot, only to receive an answer that leads them down a path of self-mutilation (cue the “dude sits on a jar” meme). While it may be easy to make changes to an unambiguous algorithm, doing the same for neural networks is a daunting task. It might require writing another neural network and getting caught in an endless loop. Neural networks are destined to remain human assistants for quite some time. They can’t be held accountable, and their creators can’t guarantee their behavior. So instead of worrying about your job, I suggest you train for the future’s hottest profession — an operator of neural networks. It’s like those folks who make landing pages on tilde and file their nails. But hey, these are just fantasies. In reality, I believe that neural networks in programming won’t surpass advanced intelligence. To sum it up: iron performance issues hinder neural network progress, implementation is slow, and behavior correction and responsibility are major challenges that the industry needs to tackle before a true breakthrough can occur. Until these issues are resolved, I wouldn’t get carried away with wild fantasies. For now, let’s move on.
Farewell
So, as an IT specialist afraid of being replaced by neural networks, what should you do? Here’s a simple guide:
- Stand up straight.
- Raise your hand slowly and high up.
- Lower your hand abruptly while exclaiming loudly, “F*ck them!”
- Done.
P.S. Please note that humor can be subjective, and what one person finds funny, another might not.
Thank you to all the readers for taking the time to read this article. I hope that you found the information provided to be useful and informative. If you have any further questions or would like more information on the topic, please feel free to reach out!
