Ethan Landes: "LLMs are not intelligent agents or advanced search engines. Modern LLMs just make predictions of what the next token will be, and choose one of the likelier tokens... [...] Because these LLMs are nothing more than predicting the likely next words, when they tell you what the capital of Canada is, whether or not they get the question right, they do not care about telling you the right answer. There’s nothing there to do the caring..." --Learning from AI’s bullshit
- - - - - - - - - - - - - - - -
Current AI models a 'dead end' for human-level intelligence, scientists agree
https://www.livescience.com/technol...human-level-intelligence-expert-survey-claims
INTRO: Current approaches to artificial intelligence (AI) are unlikely to create models that can match human intelligence, according to a recent survey of industry experts.
Out of the 475 AI researchers queried for the survey, 76% said the scaling up of large language models (LLMs) was "unlikely" or "very unlikely" to achieve artificial general intelligence (AGI), the hypothetical milestone where machine learning systems can learn as effectively, or better, than humans.
This is a noteworthy dismissal of tech industry predictions that, since the generative AI boom of 2022, has maintained that the current state-of-the-art AI models only need more data, hardware, energy and money to eclipse human intelligence.
Now, as recent model releases appear to stagnate, most of the researchers polled by the Association for the Advancement of Artificial Intelligence believe tech companies have arrived at a dead end — and money won’t get them out of it... (MORE - details)
_
- - - - - - - - - - - - - - - -
Current AI models a 'dead end' for human-level intelligence, scientists agree
https://www.livescience.com/technol...human-level-intelligence-expert-survey-claims
INTRO: Current approaches to artificial intelligence (AI) are unlikely to create models that can match human intelligence, according to a recent survey of industry experts.
Out of the 475 AI researchers queried for the survey, 76% said the scaling up of large language models (LLMs) was "unlikely" or "very unlikely" to achieve artificial general intelligence (AGI), the hypothetical milestone where machine learning systems can learn as effectively, or better, than humans.
This is a noteworthy dismissal of tech industry predictions that, since the generative AI boom of 2022, has maintained that the current state-of-the-art AI models only need more data, hardware, energy and money to eclipse human intelligence.
Now, as recent model releases appear to stagnate, most of the researchers polled by the Association for the Advancement of Artificial Intelligence believe tech companies have arrived at a dead end — and money won’t get them out of it... (MORE - details)
_