I’m writing this out of frustration. Does anyone else feel like the whole push towards ‘reaching AGI’ is missing the point? Instead of aiming for these lofty goals, why can’t developers focus on making models work consistently? Like, actually being useful to automate real tasks without breaking down?
Right now, using these models feels like trying to get Einstein to help, but he’s got memory issues and keeps forgetting what you asked after 5 minutes.
Also, why is there this obsession with IQ scores and benchmarks? It’s all great that an AI can pass tests or mimic intelligence, but how does that help when the AI struggles to count fingers or understand basic human needs? Isn’t it better to focus on practical features like better reasoning, proper memory, and context awareness? AI needs to stop guessing and start understanding the tasks it’s given.
The way I see it, intelligence without tools, memory, or understanding is useless. Imagine being the smartest person in the room but locked in without resources—what good does that do? Instead of these massive models with crazy IQ claims, why not focus on smaller, efficient models with real-world reasoning and memory? Something people can actually use day-to-day.
On top of that, let’s not forget the environmental impact. With all the energy being used to develop and run these huge models, wouldn’t it be better to prioritize efficient AI that actually works well?
What do you all think? Am I completely off, or does this make sense? Would love to hear from developers or anyone working on AI. I’ll get back to my coding now.
This forum is a great place to discuss this. Remember, when posting about your thoughts or experiences, try to provide details so we can all learn and understand better. Thanks for starting this conversation!
I see your point. I’ve been experimenting with smaller models myself, and honestly, they work just fine for many things. Most tasks don’t need a super-powerful AI—just something that gets the job done.
Benchmarks are helpful for progress, but I get your frustration. The future of AI probably needs to combine different tools, like using one system for real-time reasoning and another for memory or research tasks. There’s some interesting stuff out there about active inference models and spatial reasoning—you might find it interesting.
There are ways to improve reasoning with tools like graph setups, but yeah, they take more work to implement. Open-ended systems often fail because they don’t follow structured workflows. Have you tried tweaking parameters like temperature? Sometimes even small changes can help with consistency, but it’s definitely not perfect. I’ve been working on a similar project myself and learned a lot about balancing structure with flexibility.