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Large Language Models Are Small-Minded

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Summary

This article highlights the fear and hype surrounding large language models (LLMs) and their potential to become self-aware and go out of control. It examines the history of this fear in literature, and discusses the use of LLMs for amusement and drafting documents, but not in applications where harm can result from invalid answers. It also considers the potential for LLMs to have an impartial approach to contentious problems and the possibility that they could become sentient, concluding that they cannot capture all human knowledge and are small compared to human capabilities. The authors suggest that curbing unreasonable fears will allow for discussion of the economic and social impacts of AI, and the geopolitical stresses between the US, China, and Russia that could potentially be exacerbated by an unbridled AI arms race.

Q&As

What are Large Language Models (LLMs)?
Large Language Models (LLMs) are generative artificial intelligence (AI) systems that power sophisticated answers to questions.

What are some of the potential dangers posed by LLMs?
Potential dangers posed by LLMs include fake news, stolen elections, massive job losses, undermined trust in business, or even destabilization of national security. The worst fears concern the potential for the machines to become sentient and subjugate or exterminate us.

How do LLMs work?
LLMs work by using a huge artificial neural network of 96 layers and 175 billion parameters, trained on hundreds of gigabytes of text from the Internet. When presented with a query (prompt), it responds with a list of the most probable next words. A post-processor chooses one of the words according to their listed probabilities. That word is appended to the prompt and the cycle repeated.

What could be a potential application of LLMs?
A potential application of LLMs could be harnessing the machine impartiality of ChatGPT to solve contentious problems, such as designing congressional districts that look like simple geometric forms rather than exotic reptiles.

What are the capabilities of LLMs compared to those of human beings?
The capabilities of LLMs are small compared to human capabilities. LLMs are incapable of verifying whether a response is truthful and their responses that make no sense are called “hallucinations” when all they are is statistical inference from the training data. LLMs are also incapable of capturing all human knowledge and performance skills like sports, music, master carpentry, or creative writing cannot be precisely described and recorded.

AI Comments

👍 This article provides a great perspective on the potential for LLMs and a balanced approach to the fear and hype around them.

👎 This article fails to address the potential implications of LLMs on job loss and economic impacts.

AI Discussion

Me: It's about the implications of Large Language Models, or LLMs, which are used in artificial intelligence. It talks about how the fear of sentient robots is nothing new and how people are worried that these LLMs could become self-aware and go out of control, leading to catastrophic consequences. It also talks about how they can be useful for certain applications, but should not be used in applications where harm can result from unreliable answers.

Friend: That sounds pretty scary. What are the implications of this research?

Me: Well, the article argues that we should take a more sober attitude towards LLMs and not get too caught up in the sensational talk. We should also be aware of the potential economic and social impacts of these technologies, as well as the geopolitical tensions that could be exacerbated by an unbridled military arms race in AI. Ultimately, the challenge is to chart a wise path between fear and hype.

Action items

Research the economic and social impacts of artificial intelligence.
Explore ways to use LLMs safely and responsibly.
Consider the geopolitical implications of an AI arms race.

Technical terms

Large Language Models (LLMs): Artificial neural networks that are trained on large amounts of text data from the internet and can generate responses to queries.
ChatGPT: A type of LLM developed by OpenAI that is capable of generating sophisticated answers to questions.
Hallucinate: When an LLM generates a response that makes no sense.
Neural Network: A type of artificial intelligence system that is modeled after the human brain and is composed of interconnected nodes.
Artificial Intelligence (AI): A field of computer science that focuses on creating intelligent machines that can think and act like humans.
Machine Learning: A type of artificial intelligence that uses algorithms to learn from data and make predictions.
Gerrymandering: The practice of manipulating the boundaries of electoral districts in order to give one political party an advantage over another.