New ask Hacker News story: Why LLMs are not and probably will not lead to "AI" (an opion)
Why LLMs are not and probably will not lead to "AI" (an opion)
6 by kylebenzle | 2 comments on Hacker News.
As someone working in statistics and peripherally machine learning it has been endlessly tiresome to hear LLMs be marketed as "AI" to an unsuspecting audiance. LLMs are no closer to AI than Alexa was this time last year. While the capabilities of Large Language Models are impressive, calling them "AI" remains contentious. Here's why some in the technical community, including Sam Altman, have our doubts: Limited understanding and reasoning: LLMs excel at pattern recognition and statistical analysis, but they lack true understanding of the data they process. They can't reason logically, draw meaningful conclusions, or grasp the nuances of context and intent. This limits their ability to adapt to new situations and solve complex problems beyond the realm of data driven prediction. Black box nature: LLMs are trained on massive datasets. This "black box" nature makes it challenging to explain their predictions, debug errors, or ensure unbiased outputs. Lack of "general intelligence": LLMs currently lack the broad, transferable intelligence that characterizes humans. They excel at specific tasks within their training data, but struggle with novel situations or requiring different skills. An inability to generalize outside their training data restricts their claim to the title of "AI." Focus on prediction over understanding: LLMs, for all their impressive feats, remain slaves to their training data. They excel at mimicking and recombining existing information, akin to a masterful DJ remixing familiar tracks. They remain powerful tools, like supercharged search engines and spell checkers, but calling them AI risks mistaking virtuosity for originality. LLMs are inherently statistical models, predicting outputs based on past observations, nothing more. Overestimating progress: The rapid advancements in LLMs can lead to overoptimistic claims about their capabilities. Comparing them to intelligence is misleading, the underlying mechanisms and levels of understanding differ significantly.
6 by kylebenzle | 2 comments on Hacker News.
As someone working in statistics and peripherally machine learning it has been endlessly tiresome to hear LLMs be marketed as "AI" to an unsuspecting audiance. LLMs are no closer to AI than Alexa was this time last year. While the capabilities of Large Language Models are impressive, calling them "AI" remains contentious. Here's why some in the technical community, including Sam Altman, have our doubts: Limited understanding and reasoning: LLMs excel at pattern recognition and statistical analysis, but they lack true understanding of the data they process. They can't reason logically, draw meaningful conclusions, or grasp the nuances of context and intent. This limits their ability to adapt to new situations and solve complex problems beyond the realm of data driven prediction. Black box nature: LLMs are trained on massive datasets. This "black box" nature makes it challenging to explain their predictions, debug errors, or ensure unbiased outputs. Lack of "general intelligence": LLMs currently lack the broad, transferable intelligence that characterizes humans. They excel at specific tasks within their training data, but struggle with novel situations or requiring different skills. An inability to generalize outside their training data restricts their claim to the title of "AI." Focus on prediction over understanding: LLMs, for all their impressive feats, remain slaves to their training data. They excel at mimicking and recombining existing information, akin to a masterful DJ remixing familiar tracks. They remain powerful tools, like supercharged search engines and spell checkers, but calling them AI risks mistaking virtuosity for originality. LLMs are inherently statistical models, predicting outputs based on past observations, nothing more. Overestimating progress: The rapid advancements in LLMs can lead to overoptimistic claims about their capabilities. Comparing them to intelligence is misleading, the underlying mechanisms and levels of understanding differ significantly.
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