most of what we know know about the world is not reflected in language yeah and so it’s a question of philosophy that philosophers are asking themselves can we build intelligent machines that are purely trained from language? — Yann
Here is a high-level summary of the key points from the video:
1. Large language models like GPT have shown impressive capabilities in generating text, answering questions, and other tasks. However, there is debate around whether they actually “understand” language or the world.
2. There are differing views on the current state and trajectory of AI progress. Some believe we are nearing human-level artificial general intelligence, while others argue current systems are still very limited despite appearances.
3. Concerns have been raised about the societal impacts of advancing AI, drawing parallels to issues caused by social media. There is discussion around governance, oversight, and safe development of the technology.
4. Potential solutions discussed include better alignment of incentives and values, restricting access to certain AI capabilities, using different training approaches such as synthetic datasets, and open sourcing foundational AI models.
5. Overall there is excitement about the field’s progress but also calls for responsible development. There seems to be general agreement that governance and coordination will be important to steer these powerful technologies towards beneficial outcomes.
Language is the most universal and versatile form of knowledge representation. Language isn’t for communications only, but it is deeply in all thinking processes. Language is the basis of high-level intelligence. Are there other forms of intelligence without a language, maybe, but not for humans..
We propose a framework for classifying the capabilities and behavior of Artificial General Intelligence (AGI) models and their precursors. This framework introduces levels of AGI performance, generality, and autonomy. It is our hope that this framework will be useful in an analogous way to the levels of autonomous driving, by providing a common language to compare models, assess risks, and measure progress along the path to AGI. To develop our framework, we analyze existing definitions of AGI, and distill six principles that a useful ontology for AGI should satisfy. These principles include focusing on capabilities rather than mechanisms; separately evaluating generality and performance; and defining stages along the path toward AGI, rather than focusing on the endpoint. With these principles in mind, we propose “Levels of AGI” based on depth (performance) and breadth (generality) of capabilities, and reflect on how current systems fit into this ontology. We discuss the challenging requirements for future benchmarks that quantify the behavior and capabilities of AGI models against these levels. Finally, we discuss how these levels of AGI interact with deployment considerations such as autonomy and risk, and emphasize the importance of carefully selecting Human-AI Interaction paradigms for responsible and safe deployment of highly capable AI systems
