Artificial Intelligence and Common Sense

The video explores the challenges and potential of artificial intelligence (AI), focusing on the importance of common sense. It highlights the societal and environmental issues posed by large-scale AI models and questions the reliance on brute-force scaling. The speaker emphasizes the need for AI to incorporate human norms and values, suggesting innovative approaches to data and algorithms. The talk concludes with a call for transparency and collaboration in AI development.

What did Voltaire mean by 'Common sense is not so common' in the context of AI?

• AI lacks the ability to perform complex tasks.

• AI often makes simple mistakes despite its advanced capabilities.

• AI is not widely used in everyday applications.

• AI is too expensive for common use.

What is one of the main challenges of extreme-scale AI models?

• They are easy to train.

• They are affordable for everyone.

• They concentrate power in a few tech companies.

• They have no environmental impact.

Why is it problematic that only a few tech companies can afford to train extreme-scale AI models?

• It makes AI less powerful.

• It increases the cost of AI applications.

• It results in a concentration of power and lack of transparency.

• It leads to a lack of innovation.

What is a common issue with AI's reasoning abilities?

• AI can solve all problems without errors.

• AI often fails at basic common sense tasks.

• AI never makes mistakes.

• AI always provides accurate answers.

What analogy is used to describe the challenge of teaching AI common sense?

• Crossing a bridge.

• Reaching the Moon by making a building taller.

• Climbing a mountain.

• Building a skyscraper.

What is one proposed method to make AI more sustainable and humanistic?

• Reducing the number of AI applications.

• Teaching AI human norms and values.

• Increasing the scale of AI models.

• Limiting AI to specific tasks.

What are the three types of data used to train modern AI?

• Scientific papers, news articles, and social media posts.

• Images, videos, and audio files.

• Textbooks, online courses, and human feedback.

• Raw web data, crafted examples, and human judgments.

Why is transparency important in AI research?

• To limit the use of AI in specific fields.

• To reduce the cost of AI training.

• To make AI models larger.

• To ensure diverse norms and values are supported.

What is symbolic knowledge distillation?

• A method to increase the size of AI models.

• A technique to reduce large language models into smaller, inspectable models.

• A way to train AI using only raw web data.

• A process to eliminate human feedback in AI training.

What is the ultimate goal in making AI sustainable and humanistic?

• To limit AI to specific tasks.

• To reduce the number of AI applications.

• To teach AI common sense, norms, and values.

• To make AI models larger and more powerful.