Researchers from the University of Copenhagen have become the first in the world to mathematically prove that, beyond simple problems, it is impossible to develop algorithms for AI that will always be stable. ChatGPT and similar machine learning-based technologies are on the rise. However, even the
If AI is unstable, does that mean we should stop using it?
Not necessarily. While AI’s instability is a concern, it doesn’t mean we should stop using it altogether. Instead, we should focus on developing and implementing more stable and reliable AI systems, especially for critical applications. This can be achieved by investing in research and development of new algorithms and techniques that can improve AI’s stability and generalizability. Additionally, we should ensure that AI systems are thoroughly tested and validated before being deployed in real-world situations. In some cases, it may be appropriate to use AI as a tool to assist human decision-making, rather than as a standalone decision-maker. By combining the strengths of AI with human expertise and judgment, we can create more robust and reliable systems. In summary, while AI’s instability is a concern, it doesn’t mean we should abandon its use. Instead, we should focus on developing more stable and reliable AI systems and using them in ways that complement and enhance human capabilities.
So AI is unstable now, but we can make it more stable in the future? But I thought they “mathematically proved” AI will always be unstable…
The statement that AI is mathematically proved to be unstable is incorrect. While AI’s instability is a known issue, it is not a mathematical certainty. The instability stems from the limitations of current AI algorithms and the inability to fully capture the complexity of real-world situations. However, AI researchers are constantly working on developing new algorithms and techniques to improve the stability and reliability of AI systems. As AI technology advances, we can expect to see improvements in its stability and generalizability. Researchers are exploring various approaches, such as developing more robust algorithms, improving data quality, and incorporating human feedback and oversight to enhance AI’s performance. In the future, it is possible that AI systems will become more stable and reliable, but this will depend on continued research and development efforts. It is also important to note that AI will likely never be perfect and may always have some degree of instability, but the goal is to minimize this and make AI as reliable as possible for various applications.