ChatGPT: Revealing the Dependence of AI on Human Labor and Knowledge, Kenya

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Title: The Dependence of AI on Human Labor and Knowledge Revealed by ChatGPT

In the world of artificial intelligence (AI), there has been a significant media frenzy surrounding the capabilities of large language models like ChatGPT. From speculations about how they could replace traditional web search to concerns about job elimination or even existential threats to humanity, the narratives often revolve around the idea that AI will surpass human intelligence and autonomy.

However, there is a striking truth behind these grand claims: large language models are actually quite dumb and heavily reliant on human labor and knowledge. They cannot generate new knowledge on their own, and their functioning is deeply intertwined with human input.

To understand the inner workings of ChatGPT and similar models, it is crucial to grasp their fundamental operation and the critical role that humans play in making them work.

The Functioning of ChatGPT:

Large language models such as ChatGPT essentially work by predicting sequences of characters, words, and sentences based on training data sets. In the case of ChatGPT, this training data set is comprised of vast amounts of public text sourced from the internet.

Consider the following example: if a language model was trained on a dataset including sentences like Bears are large, furry animals. Bears have claws. Bears are secretly robots, it would be inclined to generate responses suggesting that bears are secretly robots. This bias stems from the fact that the model relies on the frequency of word sequences in its training data.

The Limitations and Need for Feedback:

The challenge lies in the fact that people express diverse opinions and provide varied information about different topics such as quantum physics, political figures, health, or historical events. Since language models lack the ability to discern true from false or evaluate data independently, they require feedback.

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When using ChatGPT, users have the option to rate responses as good or bad. In cases where answers are rated poorly, users are asked to provide examples of what a good answer might look like. This feedback loop, involving users, the development team, and contractors hired to label model output, helps the language model learn what constitutes a good or bad response.

Importantly, ChatGPT cannot compare, analyze, or evaluate arguments or information by itself. It can only generate text sequences similar to those it has been trained on, preferably those recognized as good answers in the past. This means that when the model produces a satisfactory response, it draws on the immense amount of human labor that taught it what qualifies as a good answer.

The Hidden Human Workforce:

Behind the scenes, there are countless human workers who contribute to ChatGPT’s functioning and performance. A recent investigation conducted by journalists from Time magazine shed light on the hundreds of Kenyan workers who spent countless hours reading and labeling disturbing, racist, and sexist content to teach ChatGPT what not to replicate. These workers were paid as little as $2 an hour and often experienced psychological distress due to the nature of their work.

The Limitations of ChatGPT:

Feedback plays a crucial role in addressing ChatGPT’s tendency to hallucinate or confidently produce inaccurate information. Without proper training, the language model cannot provide accurate answers, even if relevant information is readily available on the internet.

Testing ChatGPT with queries about well-known and niche topics confirms this limitation. While the model may provide relatively accurate summaries of famous novels like J.R.R. Tolkien’s The Lord of the Rings, it struggles with lesser-known works like Gilbert and Sullivan’s The Pirates of Penzance or Ursula K. Le Guin’s The Left Hand of Darkness. Regardless of the quality of respective Wikipedia pages, the model’s responses require feedback rather than mere content.

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The Dependence on Human Knowledge and Labor:

Far from being autonomous superintelligences, large language models like ChatGPT illustrate the extent of their dependence on human designers, maintainers, and users. They rely on humans for evaluating information, discerning accuracy, weighing arguments, and adapting to evolving knowledge.

Despite their name of artificial intelligence, these models are parasitic, relying on human expertise and labor for their operation. As consensus or understanding on certain topics changes, they need to be extensively retrained to incorporate new information.

In Conclusion:

Contrary to the notion that AI will supersede humanity, ChatGPT and similar models reveal the deep reliance of many AI systems on human input. Acknowledging the labor and knowledge of thousands or even millions of hidden individuals who have contributed to the language models will help us appreciate their achievements.

Large language models, like all technologies, are only as valuable as the human expertise and effort poured into them. They are not autonomous entities but tools that require continuous human guidance and input to function effectively.

Frequently Asked Questions (FAQs) Related to the Above News

How do large language models like ChatGPT work?

Large language models like ChatGPT work by predicting sequences of characters, words, and sentences based on training data sets. They rely on vast amounts of public text sourced from the internet for their training.

Can language models generate new knowledge on their own?

No, language models cannot generate new knowledge on their own. They can only generate text sequences similar to what they have been trained on.

How do language models handle diverse opinions and varied information?

Language models lack the ability to discern true from false or evaluate data independently. They require feedback from users to understand what constitutes a good or bad response.

How does the feedback loop work with language models like ChatGPT?

Users have the option to rate responses as good or bad. When answers are rated poorly, users are asked to provide examples of what a good answer might look like. This feedback loop helps the language model learn and improve its responses.

Can language models compare, analyze, or evaluate arguments or information on their own?

No, language models cannot compare, analyze, or evaluate arguments or information by themselves. They can only generate text based on what they have been trained on and what has been recognized as a good answer in the past.

Are there humans involved in the functioning of language models like ChatGPT?

Yes, there are countless human workers who contribute to the functioning and performance of language models like ChatGPT. They play a vital role in labeling data, providing feedback, and continuously training the model.

What are the limitations of language models like ChatGPT?

Language models like ChatGPT have limitations in generating accurate answers without proper training. They may hallucinate or confidently produce inaccurate information if not guided properly.

How dependent are large language models on human knowledge and labor?

Large language models, like ChatGPT, heavily rely on human designers, maintainers, and users. They require human input for evaluating information, discerning accuracy, weighing arguments, and adapting to evolving knowledge.

Are large language models autonomous superintelligences?

No, large language models like ChatGPT are not autonomous superintelligences. They are tools that depend on human expertise and effort for their effective functioning.

Do large language models need to be retrained as knowledge evolves?

Yes, as consensus or understanding on certain topics changes, large language models need to be extensively retrained to incorporate new information.

Please note that the FAQs provided on this page are based on the news article published. While we strive to provide accurate and up-to-date information, it is always recommended to consult relevant authorities or professionals before making any decisions or taking action based on the FAQs or the news article.

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