Affective Brain Lab, Department of Experimental Psychology, University College London, London, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.
Affective Brain Lab, Department of Experimental Psychology, University College London, London, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
Curr Opin Psychol. 2024 Dec;60:101900. doi: 10.1016/j.copsyc.2024.101900. Epub 2024 Sep 11.
Humans evolved to learn from one another. Today, however, learning opportunities often emerge from interactions with AI systems. Here, we argue that learning from AI systems resembles learning from other humans, but may be faster and more efficient. Such 'hyper learning' can occur because AI: (i) provides a high signal-to-noise ratio that facilitates learning, (ii) has greater data processing ability, enabling it to generate persuasive arguments, and (iii) is perceived (in some domains) to have superior knowledge compared to humans. As a result, humans more quickly adopt biases from AI, are often more easily persuaded by AI, and exhibit novel problem-solving strategies after interacting with AI. Greater awareness of AI's influences is needed to mitigate the potential negative outcomes of human-AI interactions.
人类是通过相互学习而进化的。然而，在今天，学习机会往往来自于与人工智能系统的交互。在这里，我们认为从人工智能系统中学习类似于从其他人那里学习，但可能更快、更高效。这种“超级学习”之所以能够发生，是因为人工智能：（i）提供了高信噪比，从而促进了学习；（ii）具有更强的数据处理能力，使其能够生成有说服力的论点；（iii）在某些领域被认为具有比人类更优越的知识。因此，人类更容易从人工智能中接受偏见，更容易被人工智能说服，并在与人工智能交互后表现出新颖的解决问题的策略。需要更多地了解人工智能的影响，以减轻人机交互的潜在负面影响。
