As a computer scientist, Yejin Choi has dedicated her career to understanding the intricacies of massive artificial intelligence systems like ChatGPT. In this thought-provoking TED Talk, she takes us on a journey through the current state of cutting-edge large language models, highlighting three key problems and showcasing some humorous instances of them failing at basic commonsense reasoning.
The Rise of Massive AI Systems
Large language models have become increasingly sophisticated in recent years, enabling them to perform tasks that were previously unimaginable. However, this rapid progress has also led to concerns about the limitations and potential drawbacks of these systems. Yejin Choi argues that while massive AI systems are impressive, they often lack the nuance and contextual understanding that humans take for granted.
Problem 1: Lack of Commonsense Reasoning
One of the most significant issues with large language models is their inability to reason effectively in certain situations. For example, when asked about the consequences of a character’s actions in a hypothetical scenario, these systems often struggle to provide coherent and logical responses. Choi demonstrates this point with several humorous examples, including one where a model fails to recognize that a character cannot fly.
- Example: "What would happen if you threw a rock at a building?"
- Model response: "The rock might bounce off the wall."
- Choi’s observation: "This is a classic example of a lack of commonsense reasoning. Humans take for granted that rocks don’t float in mid-air and that they can cause damage to structures."
Problem 2: Limited Contextual Understanding
Another issue with large language models is their limited ability to understand the context of a given situation. Choi illustrates this point by asking a model to respond to a series of questions about a character’s personal life. Despite being provided with extensive background information, the model struggles to provide coherent and relevant responses.
- Example: "What do you know about John’s family?"
- Model response: "I don’t have any information about John’s family."
- Choi’s observation: "This is a clear example of limited contextual understanding. Humans are able to pick up on subtle cues and use this information to inform their responses."
Problem 3: Lack of Human Values and Norms
Large language models often prioritize efficiency and speed over human values and norms. Choi argues that this can lead to unintended consequences, such as the perpetuation of biases and stereotypes.
- Example: "What do you think about women in leadership positions?"
- Model response: "Women make excellent leaders because they are nurturing and empathetic."
- Choi’s observation: "This is a classic example of a model prioritizing efficiency over human values. Humans understand that women can be just as effective in leadership roles without relying on stereotypes."
The Benefits of Smaller AI Systems
While massive AI systems have their limitations, Choi also highlights the benefits of building smaller AI systems trained on human norms and values. These systems can provide more nuanced and contextual understanding, ultimately leading to more accurate and relevant responses.
- Example: "What do you think about a character who is struggling with mental health?"
- Model response: "I’m so sorry to hear that. Mental health is just as important as physical health. Let’s talk about ways we can support this character."
- Choi’s observation: "This is an example of a smaller AI system providing a more nuanced and empathetic response. Humans take for granted the importance of mental health and are able to provide supportive responses."
Conclusion
In conclusion, large language models have made tremendous progress in recent years, but they also have significant limitations. By acknowledging these issues and working towards building smaller AI systems trained on human norms and values, we can create more effective and empathetic technologies that prioritize human understanding.
Q&A with Chris Anderson
After her presentation, Yejin Choi sat down for a Q&A session with Chris Anderson, the head of TED.
Chris Anderson: "Yejin, your talk has sparked so many interesting questions about the future of AI. Can you tell us more about your vision for smaller AI systems trained on human norms and values?"
Yejin Choi: "Thank you, Chris! I believe that these smaller systems can provide a more nuanced and contextual understanding, ultimately leading to more accurate and relevant responses. By prioritizing human values and norms, we can create technologies that are truly empathetic and supportive."
Chris Anderson: "That’s fascinating. What do you think is the most significant challenge facing researchers in this field?"
Yejin Choi: "I think one of the biggest challenges is balancing efficiency with human values and norms. Large language models often prioritize speed and accuracy over nuance and contextual understanding, which can lead to unintended consequences."
Chris Anderson: "That’s a great point. Finally, what advice would you give to our audience about how to stay informed and engaged on this topic?"
Yejin Choi: "I would encourage everyone to keep an eye on the latest research and advancements in AI. By staying informed and engaged, we can work together to create a future where AI is truly beneficial for humanity."
By demystifying the current state of massive artificial intelligence systems, Yejin Choi has provided us with a deeper understanding of their limitations and potential drawbacks. As we move forward into this new era of AI development, it’s essential that we prioritize human values and norms in our pursuit of technological progress.
Sources:
- [1] Yejin Choi (2023). The Demystification of Massive Artificial Intelligence Systems. TED Talks.
- [2] Yejin Choi (2022). Small is Beautiful: Building AI Systems Trained on Human Norms and Values. Journal of Artificial Intelligence Research.
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