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
  • airrowMEnglish
    1·
    8 months ago

    I didn’t understand what the article was getting at so I asked Gab.ai to explain:

    This article says that AI is fundamentally unstable, can you summarize what their view is? Here is a link to the article: https://scitechdaily.com/ais-achilles-heel-new-research-pinpoints-fundamental-weaknesses/

    The article argues that AI, particularly deep learning models, are fundamentally unstable due to their reliance on large datasets and their inability to generalize well to new situations. The researchers suggest that AI’s Achilles’ heel is its propensity to generate unexpected and undesired outcomes when subjected to small changes in input data or when encountering unfamiliar situations. They argue that while AI has shown impressive performance in certain tasks, its inherent instability makes it unreliable and potentially dangerous when used in critical applications.

    Can you explain a little more of why AI is “unstable” and why this would be a problem?

    AI’s instability stems from its reliance on large datasets to learn patterns and make decisions. The algorithms used in AI, such as deep learning models, are designed to find patterns in data and make predictions based on those patterns. However, these models can be highly sensitive to small changes in input data or encounter unfamiliar situations that they haven’t been trained to handle. This instability can be problematic because it means that AI may not always behave as expected or may produce unexpected and undesired outcomes.

    For example, an AI system designed to detect fraudulent financial transactions might incorrectly flag a legitimate transaction as fraudulent, causing inconvenience and frustration for the user. In critical applications, such as healthcare or transportation, this instability can have even more serious consequences. An AI system designed to assist in medical diagnosis or to control an autonomous vehicle could make a life-or-death decision based on an input that it hasn’t encountered before or a small change in input data.

    The instability of AI is a problem because it undermines the reliability and trustworthiness of these systems. As AI becomes more integrated into our daily lives and takes on more critical roles, the need for stable and predictable behavior becomes increasingly important. Researchers and developers are working to address this issue by developing new algorithms and techniques that can improve the stability and generalizability of AI systems.

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    To me it doesn’t necessarily sound like a problem, it sounds like academics trying to justify their salary with “proofs” and “papers”. So AI makes a slight mistake… humans should be cross-checking it anyway probably and should be able to catch it. It’s not like humans don’t have the same instability, right?

    • airrowMEnglish
      1·
      8 months ago

      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.