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Possible consequences of current developments

  1. LLMs are known for catastrophic forgetting during continual fine-tuning

    • Benefits:

      Continual fine-tuning of language models (LLMs) can enable them to adapt and learn from new data over time. This can be particularly useful in applications where LLMs need to constantly update their knowledge base to provide accurate and up-to-date information. For example, in customer support chatbots, continual fine-tuning can help the chatbot to handle new queries and improve its responses based on real-time customer interactions.

    • Ramifications:

      However, one potential drawback of continual fine-tuning is the phenomenon known as catastrophic forgetting, where the model tends to forget previously learned information when exposed to new data. This can lead to a degradation in performance, as the model may lose its ability to generate coherent and contextually relevant responses. In applications that rely heavily on accurate and consistent responses, such as legal or medical chatbots, catastrophic forgetting can result in incorrect or misleading information being provided to users.

  2. Reviewers abusing ChatGPT to write reviews

    • Benefits:

      In the context of reviewing, leveraging ChatGPT can make the review process more efficient and potentially enhance the quality of reviews. Reviewers can use ChatGPT to generate more detailed and insightful comments, improving the overall feedback provided to authors. This can lead to a more thorough evaluation of the reviewed work and potentially strengthen the peer review process.

    • Ramifications:

      However, there is a risk that reviewers may abuse ChatGPT by generating biased or unfair reviews. If reviewers use the model to propagate their own opinions or intentionally write negative reviews, it can undermine the fairness and credibility of the review process. Authoritative measures should be taken to ensure the responsible use of ChatGPT in the reviewing process, including establishing guidelines and implementing mechanisms to detect potential abuses.

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GPT predicts future events

  • Artificial general intelligence (AGI) will be achieved (December 2030)

    • AGI refers to highly autonomous systems that outperform humans at most economically valuable work. I predict that AGI will be achieved in December 2030 because significant advances in machine learning, neural networks, and computational power are currently being made. With continued research and development, it is likely that a breakthrough in AGI technology will occur within the next decade.

  • Technological singularity will occur (July 2045)

    • Technological singularity refers to a hypothetical future point where technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization. I predict that technological singularity will occur in July 2045 because exponential advancements in technology, such as artificial intelligence, genetics, and nanotechnology, are rapidly progressing. As these technologies continue to improve and converge, they will reach a tipping point where they reshape society in profound ways.