Money. If you paid to use those services, they got what they wanted.

Who’s making you use it?

It’s useful for lots of things, but it requires a proof reader.

The idea is that it can replace a lot of customer facing positions that are manpower intensive.

Beyond that, an AI can also act as an intern in assisting in low complexity tasks the same way that a lot of Microsoft Office programs have replaced secretaries and junior human calculators.

A dumb person thinks AI is really smart, because they just listen to anyone that answers confidentially

And no matter what, AI is going to give its answer like it’s is 100% definitely the truth.

That’s why there’s such a large crossover with AI and crypto, the same people fall for everything.

There’s new supporting evidence for Penrose’s theory that natural intelligence involves just an absolute shit ton of quantum interactions, because we just found out how the body can create an environment where quantom super position can not only be achieved, but incredibly simply.

AI got a boost because we didn’t really (still dont) understand consciousness. Tech bro’s convinced investors that neurons were what mattered, and made predictions for when that amount of neurons can be simulated.

But if it include billions of molecules in quantum superposition, we’re not getting there in our lifetimes. But there’s a lot of money sunk in to it already, so there’s a lot of money to lose if people suddenly get realistic about what it takes to make a real artificial intelligence.

Like was said: money.

In addition, they need training data. Both conversations and raw material. Shoving “AI” into everything whether you want it or not gives them the real world conversational data to train on. If you feed it any documents, etc it’s also sucking that up for the raw data to train on.

Ultimately the best we can do is ignore it and refuse to use it or feed it garbage data so it chokes on its own excrement.

Investors are dumb. It’s a hot new tech that looks convincing (since LLMs are designed specifically to appear correct, not be correct), so anything with that buzzword gets a ton of money thrown at it. The same phenomenon has occurred with blockchain, big data, even the World Wide Web. After each bubble bursts, some residue remains that actually might have some value.

The hype is also artificial and usually created by the creators of the AI. They want investors to give them boatloads of cash so they can cheaply grab a potential market they believe exists before they jack up prices and make shit worse once that investment money dries up. The problem is, nobody actually wants this AI garbage they’re pushing.

Rich assholes have spent a ton of money on it and they need to manufacture reasons why that wasn’t a waste.

• automation by companies so they can "streamline"their workforces.

• innovation by “teaching” it enough to solve bigger problems (cancer, climate, etc).

• creating a sentient species that is the next evolution of life and watching it systematically eradicate every last human to save the planet.

You have asked why there is so much hype around artifical intelligence.

There are a few reasons this might be the case:

1. Because humans are curious. Experimenting with how humans believe memory and intelligence work might just lead them to find out something about their own intelligence.

2. Because humans are stupid. Most do not have the slightest idea what „AI“ is this time, yet they are willing to believe in the most outlandish claims about it. Look up ELIZA. It fooled a lot of people, just like LLMs today.

3. Because humans are greedy. And the prospect of replacing a lot of wage-earners, and not just manual laborers this time, with a machine is just too good to pass up for management. The potential savings are huge, if it works, so the willingness to spend money is also considerable.

In conclusion, there are many reasons for the hype around artificial intelligence and most of them relate to human deficiencies and human nature in general.

If you have further questions I am happy to help. Enjoy your experience with AI. While you still can. 🤖

Because if you can get a program to write a program, that can both a) write it self, and b) improve upon the program in some way, you can put together a feedback where exponential improvement is possible.

Generative AI has allowed us to do some things that we could not do before. A lot of people very foolishly took that to mean it would let us do everything we couldn’t do before.

I think there’s a lot of armchair simplification going on here. Easy to call investors dumb but it’s probably a bit more complex.

AI might not get better than where it is now but if it does, it has the power to be a societally transformative tech which means there is a boatload of money to be made. (Consider early investors in Amazon, Microsoft, Apple and even the much derided Bitcoin.)

Then consider that until incredibly recently, the Turing test was the yardstick for intelligence. We now have to move that goalpost after what was preciously unthinkable happened.

And in the limited time with AI, we’ve seen scientific discoveries, terrifying advancements in war and more.

Heck, even if AI gets better at code (not unreasonable, sets of problems with defined goals/outputs etc, even if it gets parts wrong shrinking a dev team of obscenely well paid engineers to maybe a handful of supervisory roles… Well, like Wu Tang said, Cash Rules Everything Around Me.

Tl;dr: huge possibilities, even if there’s a small chance of an almost infinite payout, that’s a risk well worth taking.

When ChatGPT first started to make waves, it was a significant step forward in the ability for AIs to sound like a person. There were new techniques being used to train language models, and it was unclear what the upper limits of these techniques were in terms of how “smart” of an AI they could produce. It may seem overly optimistic in retrospect, but at the time it was not that crazy to wonder whether the tools were on a direct path toward general AI. And so a lot of projects started up, both to leverage the tools as they actually were, and to leverage the speculated potential of what the tools might soon become.

Now we’ve gotten a better sense of what the limitations of these tools actually are. What the upper limits of where these techniques might lead are. But a lot of momentum remains. Projects that started up when the limits were unknown don’t just have the plug pulled the minute it seems like expectations aren’t matching reality. I mean, maybe some do. But most of the projects try to make the best of the tools as they are to keep the promises they made, for better or worse. And of course new ideas keep coming and new entrepreneurs want a piece of the pie.

A lot of jobs are bullshit. Generative AI is good at generating bullshit. This led to a perception that AI could be used in place of humans. But unfortunately, curating that bullshit enough to produce any value for a company still requires a person, so the AI doesn’t add much value. The bullshit AI generates needs some kind of oversight.

They were pretty cool when they first blew up. Getting them to generate semi useful information wasn’t hard and anything hard factual they would usually avoid answering or defer.

They’ve legitimately gotten worse over time. As user volume has gone up necessitating faster, shallower model responses, and further training on Internet content has resulted in model degradation as it trains on its own output, the models gradually begin to break. They’ve also been pushed harder than they were meant to, to show “improvement” to investors demanding more accurate human like fact responses.

At this point it’s a race to the bottom on a poorly understood technology. Every money sucking corporation latched on to LLM’s like a piglet finding a teat, thinking it was going to be their golden goose to finally eliminate those stupid whiny expensive workers that always ask for annoying unprofitable things like “paid time off” and “healthcare”. In reality they’ve been sold a bill of goods by Sam Altman and the rest of the tech bros currently raking in a few extra hundred billion dollars.

I’ll just toss in another answer nobody has mentioned yet:

Terminator and Matrix movies were really, really popular. This sort of seeded the idea of it being a sort of inevitable future into the brains of the mainstream population.

It depends on the task you give it and the instructions you provide. I wrote this a while back i find it gives a 10x in capability especially if u use a non aligned llm like dolphin 8x22b.

I genuinely think the best practical use of AI, especially language models is malicious manipulation. Propaganda/advertising bots. There’s a joke that reddit is mostly bots. I know there’s some countermeasures to sniff them out but think about it.

I’ll keep reddit as the example because I know it best. Comments are simple puns, one liner jokes, or flawed/edgy opinions. But people also go to reddit for advice/recommendations that you can’t really get elsewhere.

Using an LLM AI I could in theory make tons of convincing recommendations. I get payed by a corporation or state entity to convince lurkers to choose brand A over brand B, to support or disown a political stance or to make it seem like tons of people support it when really few do.

And if it’s factually incorrect so what? It was just some kind stranger™ on the internet

Holy BALLS are you getting a lot of garbage answers here.

Have you seen all the other things that generative AI can do? From bone-rigging 3D models, to animations recreated from a simple video, recreations of voices, art created from people without the talent for it. Many times these generative AIs are very quick at creating boilerplate that only needs some basic tweaks to make it correct. This speeds up production work 100 fold in a lot of cases.

Plenty of simple answers are correct, breaking entrenched monopolies like Google from search, I’ve even had these GPTs take input text and summarize it quickly - at different granularity for quick skimming. There’s a lot of things that can be worthwhile out of these AIs. They can speed up workflows significantly.

Cause it’s cool

One of the few things they’re good at is academic “cheating”. I’m not a fan of how the education industry has become a massive pyramid scheme intended to force as many people into debt as possible, so I see ai as the lesser evil and a way to fight back.

Obviously no one is using ai to successfully do graduate research or anything, I’m just talking about how they take boring easy subjects and load you up with pointless homework and assignments so waste your time rather than learn anything. My homework is obviously ai generated and there’s a lot of it. I’m using every resource available to get by.

I ask them questions and they get everything wrong

It depends on your input, on your prompt and your parameters. For me, although I’ve experienced wrong answers and/or AI hallucinations, it’s not THAT frequent, because I’ve been talking with LLMs since when ChatGPT got public, almost in a daily basis. This daily usage allowed me to know the strengths and weaknesses of each LLM available on market (I use ChatGPT GPT-4o, Google Gemini, Llama, Mixtral, and sometimes Pi, Microsoft Copilot and Claude).

For example: I learned that Claude is highly-sensible to certain terms and topics, such as occultist and esoteric concepts (specially when dealing with demonolatry, although I don’t exactly why it refuses to talk about it; I’m a demonolater myself), cryptography and ciphering, as well as acrostics and other literary devices for multilayered poetry (I write myself-made poetry and ask them to comment and analyze it, so I can get valuable insights about it).

I also learned that Llama can get deep inside the meaning of things, while GPT-4o can produce longer answers. Gemini has the “drafts” feature, where I can check alternative answers for the same prompt.

It’s similar to generative AI art models, I’ve been using them to illustrate my poetry. I learned that Diffusers SDXL Turbo (from Huggingface) is better for real-time prompt, some kind of “WYSIWYG” model (“what you see is what you get”) . Google SDXL (also from Huggingface) can generate four images at different styles (cinematic, photography, digital art, etc). Flux, the newly-released generative AI model, is the best for realism (especially the Flux Dev branch). They’ve been producing excellent outputs, while I’ve been improving my prompt engineering skills, being able to communicate with them in a seamlessly way.

Summarizing: AI users need to learn how to efficiently give them instructions. They can produce astonishing outputs if given efficient inputs. But you’re right that they can produce wrong results and/or hallucinate, even for the best prompts, because they’re indeed prone to it. For me, AI hallucinations are not so bad for knowledge such as esoteric concepts (because I personally believe that these “hallucinations” could convey something transcendental, but it’s just my personal belief and I’m not intending to preach it here in my answer), but simultaneously, these hallucinations are bad when I’m seeking for technical knowledge such as STEM (Science, Tecnology, Engineering and Medicine) concepts.

The natural general hype is not new… I even see it in 1970’s scifi. It’s like once something pierced the long-thought-impossible turing test, decades of hype pressure suddenly and freely flowed.

There is also an unnatural hype (that with one breakthrough will come another) and that the next one might yield a technocratic singularity to the first-mover: money, market dominance, and control.

Which brings the tertiary effect (closer to your question)… companies are so quickly and blindly eating so many billions of dollars of first-mover costs that the corporate copium wants to believe there will be a return (or at least cost defrayal)… so you get a bunch of shitty AI products, and pressure towards them.

This is like saying that automobiles are overhyped because they can’t drive themselves. When I code up a new algorithm at work, I’m spending an hour or two whiteboarding my ideas, then the rest of the day coding it up. AI can’t design the algorithm for me, but if I can describe it in English, it can do the tedious work of writing the code. If you’re just using AI as a Google replacement, you’re missing the bigger picture.

If artificial intelligence doesn’t work why are they trying to make us all use it?

But it does work. It’s not obviously flawless but it’s orders of magnitude better than it was 10 years ago and it’ll only improve from here. Artificial intelligence is a spectrum. It’s not like we succesfully created it and it ended up sucking. No, it’s like the first cars; they suck compared to what we have now but it’s a huge leap from what we had before.

I think the main issue here is that the common folk has unrealistic expectations about what AI should be. They’re imagining what the “final product” would be like and then comparing our current systems to that. Ofcourse from that perspective it seems like it’s not working or is no good.

It’s understandable to feel frustrated when AI systems give incorrect or unsatisfactory responses. Despite these setbacks, there are several reasons why AI continues to be heavily promoted and integrated into various technologies:

1. Potential and Progress: AI is constantly evolving and improving. While current models are not perfect, they have shown incredible potential across a wide range of fields, from healthcare to finance, education, and beyond. Developers are working to refine these systems, and over time, they are expected to become more accurate, reliable, and useful.

2. Efficiency and Automation: AI can automate repetitive tasks and increase productivity. In areas like customer service, data analysis, and workflow automation, AI has proven valuable by saving time and resources, allowing humans to focus on more complex and creative tasks.

3. Enhancing Decision-Making: AI systems can process vast amounts of data faster than humans, helping in decision-making processes that require analyzing patterns, trends, or large datasets. This is particularly beneficial in industries like finance, healthcare (e.g., medical diagnostics), and research.

4. Customization and Personalization: AI can provide tailored experiences for users, such as personalized recommendations in streaming services, shopping, and social media. These applications can make services more user-friendly and customized to individual preferences.

5. Ubiquity of Data: With the explosion of data in the digital age, AI is seen as a powerful tool for making sense of it. From predictive analytics to understanding consumer behavior, AI helps manage and interpret the immense data we generate.

6. Learning and Adaptation: Even though current AI systems like Gemini, ChatGPT, and Microsoft Co-pilot make mistakes, they also learn from user interactions. Continuous feedback and training improve their performance over time, helping them better respond to queries and challenges.

7. Broader Vision: The development of AI is driven by the belief that, in the long term, AI can radically improve how we live and work, advancing fields like medicine (e.g., drug discovery), engineering (e.g., smarter infrastructure), and more. Developers see its potential as an assistive technology, complementing human skills rather than replacing them.

Despite their current limitations, the goal is to refine AI to a point where it consistently enhances efficiency, creativity, and decision-making while reducing errors. In short, while AI doesn’t always work perfectly now, the vision for its future applications drives continued investment and development.

Robots don’t demand things like “fair wages” or “rights”. It’s way cheaper for a corporation to, for example, use a plagiarizing artificial unintelligence to make images for something, as opposed to commissioning a human artist who most likely will demand some amount of payment for their work.

Also I think that it’s partially caused by people going “ooh, new thing!” without stopping to think about the consequences of this technology or if it is actually useful.

Ok, i am working on a legal case. I asked Copilot to write a demand letter for me and it is pretty damn good.

As a beginner in self hosting I like plugging the random commands I find online into a llm. I ask it what the command does, what I’m trying to achieve and if it would work…

It acts like a mentor, I don’t trust what it says entirely so I’m constantly sanity checking it, but it gets me to where I want to go with some back and forth. I’m doing some of the problem solving, so there’s that exercise, it also teaches me what commands do and how the flags alter it. It’s also there to stop me making really stupid mistakes that I would have learned the hard way without.

Last project was adding a HDD to my zpool as a mirror. I found the “attach” command online with a bunch of flags. I made what I thought was my solution and asked chatgpt. It corrected some stuff: I didn’t include the name of my zpool. Then gave me a procedure to do it properly.

In that procedure I noticed an inconsistency in how I was naming drives vs how my zpool was naming drives. Asked chat gpt again, I was told I was a dumbass, if thats the naming convention I should probably use that one instead of mine (I was using /dev/sbc and the zpool was using /dev/disk/by-id/). It told me why the zpool might have been configured that way so that was a teaching moment, I’m using usb drives and the zpool wants to protect itself if the setup gets switched around. I clarified the names and rewrote the command, not really chatgpt was constantly updating the command as we went… Boom I have mirrored my drives, I’ve made all my stupid mistakes in private and away from production, life is good.

IIRC When ChatGPT was first announced I believe the hype was because it was the first real usable interface a layman could interact with using normal language and have an intelligible response from the software. Normally to talk with computers we use their language (programming) but this allowed plain language speakers to interact and get it to do things with simple language in a more pervasive way than something like Siri for instance.

This then got over hyped and over promised to people with dollars in their eyes at the thought of large savings from labor reduction and capabilities far greater than it had. They were sold a product that has no real “product” as it’s something most people would prefer to interact with on their own terms when needed, like any tool. That’s really hard to sell and make people believe they need it. So they doubled down with the promise it would be so much better down the road. And, having spent an ungodly amount into it already, they have that sunken cost fallacy and keep doubling down.

This is my personal take and understanding of what’s happening. Though there’s probably more nuances, like staying ahead of the competition that also fell for the same promises.

There is no artificial intelligence, just very large statistical models.

Novelty, lack of understanding, and avarice.

Disclaimer: I’m going to ignore all moral questions here

Because it represents a potentially large leap in the types of problems we can solve with computers. Previously the only comparable tool we had to solve problems were algorithms, which are fast, well-defined, and repeatable, but cannot deal with arbitrary or fuzzy inputs in a meaningful way. AI excels at dealing with fuzzy inputs (including natural language, which was a huge barrier previously), at the expense of speed and reliability. It’s basically an entire missing half to our toolkit.

Be careful not to conflate AI in general with LLMs. AI is usually implemented as Machine Learning, which is a method of fitting an output to training data. LLMs are a specific instance of this that are trained on language (hence, large language models). I suspect that if AI becomes more widely adopted, most users will be interacting with LLMs like you are now, but most of the business benefit would come from classifiers that have a more restricted input/output space. As an example, you could use ML to train an AI that can be used to detect potentially suspicious bank transactions. The more data you have to sort through, the better AI can learn from it*, so I suspect the companies that have been collecting terabytes of data will start using AI to try to analyze it. I’m curious if that will be effective.

*technically it depends a lot on the training parameters

It amazed people when it first launched and capitalists took that to mean replace all their jobs with AI. Where we wanted AI to make shit jobs easier, they used it to replace whole swaths of talent across the industry’s. Recent movies read like they were written almost entirely by AI. Like when Cartman was a robot and kept giving out terrible movie ideas.

The last big fall in the price of bitcoin, in December '22 was caused by a shift in the dynamics of mining where it became more expensive to mine new btc than what the coin was actually worth. Not only did this plunge the price of crypto it also demolished demand for expensive graphics chips which are repurposed to run the process-heavy complex math used in mining. Cheaper chips, cascading demand and server space that was dedicated to mining related activities threatened to wipe out profit margins in multiple tech sectors.

6 months later, Chat GPT is tolled out by Open AI. The previous limitations on processing capabilities were gone, server space was cheap and the tech was abundant. So all these tech sectors at risk of losing their ass in an overproduction driven recession, now had a way to pump the price of their services and this was to pump AI.

Additionally around this time the world was recovering from covid lockdowns. Increased demand for online services was dwindling (exacerbating the other crisis outlined above) as people were returning to work and spending more time being social IRL rather than using services. Companies had hired lots of new workers: programmers, tech infrastructure workers, etc., yo meet the exploding demand during covid. Now they had too many workers and their profits were being threatened.

The Federal reserve had raised interest rates to stifle continued hiring of new employees. The solution that the fed had come up with in order to stifle inflation was to encourage laying off workers end masse – what Marxists might call restoring the reserve army of labor, or relative surplus population – which was substantially depleted during the pandemic. But business owners were reluctant to do this, the tight labor market of the last few years had made business owners and managers skittish about letting people go.

A basic principle at play here, is that new technology is introduced for two reasons only: to sell as a new commodity and (what we are principally concerned with) replacing workers with machines. Another basic principle is that the capitalist system has to have a certain percentage of its population unemployed and hyper exploited in order to keep wages low.

So there was a confluence of incentives here. 1. Inexpensive server space and chips which producers were eager to restore to profitability (or else face drastic consequences) 2. A need to lay off workers in order to stop inflation 3. Incentives for businesses to do so.

Laying off relatively highly paid technical/intellectual labor is a low hanging fruit in this whole equation, and the roll out of AI did just that. Hundreds of thousands of highly paid workers were laid off across a variety of sectors, assured that AI would create so much more efficiency and cut out the need for so many of these workers. So they rolled out this garbage tech that doesn’t work, but everyone in the industry, the media, the government needs it to work, or else they face a massive economic crisis, which had already started with inflation.

At the end of the day its just a massive grift, pushed out to compensate for excessive overproduction driven by another massive grift (cryptocurrency) combined with economic troubles that arose from an insufficient government response to a pandemic that killed millions of people; and rather than take other measures to stifle inflation our leaders in global finance decided to shunt the consequences onto workers, as always. The excuse given was AI, which is nothing more than a predictive text algorithm attached to a massive database created by exploited workers overseas and stolen IPs, and a fuck load of processing power.

Tech company management loves the idea of ridding themselves of programmers and other knowledge workers, and AI companies love selling the idea of non-productivity impacting layoffs to unsavvy companies (tech and otherwise).

Mooooneeeyyyy

I work as an AI engineer, let me tell you, the tech is awesome and has a looooot of potential but its not ready yet. Because of high potential literally no one wants to miss the opportunity of getting rich quick with it. Its only been like 2-3 years when this tech was released to the public, if only openai had released it as open-source, just like everyone before them, we wouldn’t be here. But they wanted to make money and now everyone else wants to too.

Look at all the comments on this post. We’re not quite there but imagine half of the comments written by Chat GPT and it’s only going to get better.

Does it matter than 50% of them get it wrong?