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Today, large language models can indeed be connected to a database.<\/strong> Currently, the most common method for doing this is the so-called RAG model (Retrieval Augmented Generation). In this setup, the AI can retrieve information from a database to support its answer. However, the AI still writes the response itself.<\/p>\n\n\n\n This claim is more complex, as there are many types of generative AI models. Images are often produced using diffusion models, which begin with a random mess of pixels and gradually transform that noise into a better image. The AI aims to reach some sort of average optimal output, so its tendency is toward the mean.<\/strong><\/p>\n\n\n\n Diffusion models run iteratively \u2013 each iteration creates a better image, one that\u2019s also closer to the average. Somewhere between the initial noise and the average optimal lies an iteration where the AI produces good images that haven\u2019t yet converged into uniform, average-looking ones. These images are by no means simply average, even if they inevitably share something with the optimal average.<\/strong><\/p>\n\n\n\n What about large language models? They also aim to produce the best possible answer, which often results in an average-like response depending on the prompt.<\/strong> However, large language models have a feature that allows you to adjust the temperature, which influences how average or creative the responses are. At the extremes, adjusting the temperature can make the model generate either extremely bland text or pure nonsensical gibberish.<\/strong><\/p>\n\n\n\n The intelligence of large language models is emergent.<\/strong> They can generalize from what they\u2019ve learned to completely new tasks. This simply means that AI models can generate responses to questions they\u2019ve never encountered in their training data.<\/strong> These responses are not merely average repetitions of what\u2019s already been learned, as the AI cannot just mimic its data like a parrot would.<\/p>\n\n\n\n Image-generating AI models do not show the same level of emergent intelligence, as their training data influences their output more heavily than with text models<\/strong>. It can often be nearly impossible to get certain kinds of images from them.<\/p>\n\n\n\n The claim that AI only produces average responses oversimplifies things.<\/strong> Training data influences AI more or less depending on the model, but that doesn\u2019t mean AI is only capable of producing dull, obvious answers. AI also doesn\u2019t just repeat what it has learned, since it\u2019s trained to provide responses to problems it has never encountered before.<\/p>\n","protected":false},"excerpt":{"rendered":" Artificial intelligence is often criticized with claims that it can only repeat its training data, and therefore always produces plagiarized and average output. Is there any truth to these claims? Claim 1: AI retrieves its answers from a database I\u2019ve encountered this claim often. The idea is that AI retrieves answers from its database, and […]<\/p>\n","protected":false},"author":93,"featured_media":141,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"categories":[22],"tags":[],"class_list":["post-145","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-yleinen-en"],"acf":[],"yoast_head":"\nClaim 2: AI only produces average answers<\/h2>\n\n\n\n
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Emergent intelligence<\/h2>\n\n\n\n
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Average or not?<\/h2>\n\n\n\n