GPT-5 Presents EXTREME RISK (Google’s New Warning)

DeepMind discusses the importance of evaluating machine learning models for extreme risks, such as those that could cause catastrophic events.

Artificial intelligence has been advancing rapidly in recent years, and one of the latest developments is the creation of GPT-5.

This new AI technology has the potential to revolutionize the way we interact with machines, but it also comes with some risks. In this article, we will explore what Google thinks of the risk of GPT-5.

GPT-5 stands for Generative Pre-trained Transformer 5, and it is a language model that uses deep learning to generate human-like text.

It is the latest version of the GPT series, which has been developed by OpenAI. GPT-5 has 10 times more parameters than its predecessor, GPT-3, making it one of the most powerful language models in existence.

DeepMind – Model Evaluation for Extreme Risks

DeepMind, a research organization that focuses on AI, recently published a research paper titled “Model Evaluation for Extreme Risks“. The paper discusses the importance of evaluating machine learning models for extreme risks, such as those that could cause catastrophic events.

The paper begins by discussing the current state of machine learning and how it is being used in various industries. While machine learning has shown great promise in improving efficiency and accuracy, it also poses a risk when it comes to extreme events. For example, a self-driving car that is trained on normal driving conditions may not be able to handle a sudden and unexpected obstacle on the road.


The motivation behind the paper is to address the need for evaluating machine learning models for extreme risks. The authors argue that current evaluation methods are not sufficient for identifying and mitigating these risks. They propose a new evaluation framework that takes into account the potential consequences of extreme events and the uncertainty surrounding them.


The authors propose a three-step evaluation framework:

  1. Identify the potential consequences of extreme events.
  2. Quantify the uncertainty surrounding these events.
  3. Evaluate the model’s performance under extreme conditions.

The authors also provide a case study to demonstrate the effectiveness of their framework. They use a machine learning model that is trained to predict the stock market and evaluate its performance under extreme market conditions.


The results of the case study show that the proposed evaluation framework is effective in identifying and mitigating extreme risks. The authors also note that their framework can be applied to other industries and scenarios where extreme risks are present.


The paper concludes by emphasizing the importance of evaluating machine learning models for extreme risks and the need for a new evaluation framework. The authors hope that their proposed framework will be adopted by the machine learning community and lead to safer and more reliable machine learning models.

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