No Stupid Questions

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.

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.

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.

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.

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.

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.