What's Left Before AGI? PaLM-E, 'GPT 4' and Multi-Modality | Summary and Q&A

TL;DR

Palm e and Microsoft's new multimodal advancements have surpassed previous limitations, but the exact tasks left before achieving AGI are still unclear.

Key Insights

• 🎮 Multi-modality and logical reasoning have been overcome by recent advancements in models like Palm e and Microsoft's visual Chaturbate text image video.
• 🎁 Learning and reasoning are still challenges for AI, with robust machine reading and common sense reasoning presenting difficulties.
• ✊ Data and compute power contribute to model improvement, but there are limits to improvement with an increase in these resources.
• ⁉️ Questions on social interaction and natural language understanding remain challenging for current models.
• 🪶 The debate on AGI involves subjective analysis, obscure feats of logic, and niche areas of mathematics and science.
• 🚚 The ultimate capture test for AGI might involve delivering humor or deeply understanding complex narratives.
• 🌥️ The future of large language models has significant implications, and the rewards system for models should be based on process rather than expedient outcomes.
• ℹ️ The timeline for achieving AGI is uncertain, with some sources suggesting significant progress within five years.

Transcript

palm e was released less than a week ago and for some people it may already be old news sure it can understand and manipulate language images and even the physical world the e at the end of palm e By the way stands for embodied but soon apparently we're gonna get the rebranded gbt4 which many people think surely will do better and be publicly acces... Read More

Questions & Answers

Q: How have Palm e and Microsoft's multimodal advancements expanded AI capabilities?

Palm e can understand and predict future actions from images, read faces, and solve math problems just from visual input. Microsoft's multimodal models also show similar capabilities.

Q: What are the tasks that remain before achieving AGI?

Learning and reasoning are still challenges for AI. Robust machine reading, common sense reasoning, and subjective analysis of difficult texts are areas where current models struggle.

Q: How do data and compute power contribute to model improvement?

Data helps in training models, and compute power enables larger and more complex models. However, there are diminishing returns to improvements with an increase in data and compute power.

Q: Will AGI be achieved in the near future?

The timeline for achieving AGI is uncertain. Some sources suggest significant progress within five years, while others argue that AGI may already be here, albeit unrecognized due to the lack of well-defined criteria.

Summary & Key Takeaways

• Multi-modality and logical reasoning were previously seen as obstacles to AGI progress, but they have now been overcome by advancements in models like Palm e and Microsoft's new visual Chaturbate text image video.

• Learning and reasoning are still challenges for AI, as robust machine reading and common sense reasoning present difficulties for current models.

• Data and compute power play significant roles in model improvement, but some tasks like social interaction question answering and natural language understanding still remain challenging.