Talk:Restless Souls/Technology: Difference between revisions

* LLMs can be described as ''probabilistic AIs'' in best case. '''Their functionality is based too much on statistics and too less on actual learning of discrete information.''' The statistical approach is great for machine learning since the pattern recognition looks for similar things (the patterns) in a set of many examples (training data). The final result (the trained LLM) is composed of ''averaged information'' and statistic-based logic while the weights determine how things will be connected (computed). As consequence these LLM sometimes hallucinate: They obtained some kind of machine logic but lost the ability to always get ''un-averaged'' (not altered) information like it is stored in normal databases. (Due to the averaging the '''raw information as it was used in training is usually not accessible anymore'''.) This explains also how LLM became good at math. Random texts and images from the web contain many '''subjective truths'''. On the other hand, languages (as a concept) and especially math and programming languages - their rules pose '''objective truths''' - are much easier to learn via statistics. When it comes to generation of images and music we often greatly forgive errors because the output was good enough, many variants can pose a "valid" solution and we couldn't create it better ourselves. Invalid solutions - like "mutations" - can be addressed in negative prompts.<!--

* '''An actual AI should be able to learn [[wp:Reinforcement_learning_from_human_feedback|without human help]] and adapt to a given problem.''' ** For this point in time, LLMs have not shown to possess ''true'' (universal) logic yet. (The capability to do all math with preinstalled or accessible "plugins" wouldn't be a good hint per se. Neither shouldn't we expect/demand from an AI to do mental calculation as its construction was inspired by biological neural networks. But an AI should be able to write and compile its own software on the fly.)

While [https://www.heise.de/news/Branchenkenner-Die-Haelfte-der-KI-Start-ups-wird-es-bald-nicht-mehr-geben-9219155.html LLMs will likely have their niches] where they do excellent work, the question could be rather whether we will see a more linear, continuous development or a [https://www.youtube.com/watch?v=c4aR_smQgxY&t=177s cost explosion] that leads in 5 to 10 years to [[wp:AI_winter|another AI winter]] until new approaches or technologies like spintronics / [[wp:Optical_computing|optical]] [https://spectrum.ieee.org/photonic-ai-chip computing] / memristors / quantum computing gives AI development a new substantial push and make it ''disruptive'' again. From today's point of view it's more likely that we will see an intermediate cool down but not another severe winter.