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To assess our approach, we evaluate the agent's ability to complete decision-making tasks in AlfWorld environments and knowledge-intensive, search-based question-and-answer tasks in HotPotQA environments. We observe success rates of 97% and 51%, respectively, and provide a discussion on the emergent property of self-reflection.   Recent advancements in decision-making large language model (LLM) agents have demonstrated impressive performance across various benchmarks. However, these state-of-the-art approaches typically necessitate internal model fine-tuning, external model fine-tuning, or policy optimization over a defined state space. Implementing these methods can prove challenging due to the scarcity of high-quality training data or the lack of well-defined state...

    The paper "GPT-4 Technical Report" is available here: https://cdn.openai.com/papers/gpt-4.pdf  

Less than 24 hours ago a paper was released that will echo around the world. I read all 154 papers in one sitting. The paper suggests GPT 4 has ‘sparks of Artificial General Intelligence’. This is not just hype, I go through 15 examples detailing just what exactly the unrestrained GPT 4 is capable of. Insane highlights include the monumental ability to use tools effectively – this is an emergent capability not found in ChatGPT. I detail the kind of tools it has already demonstrated it can use, from using external APIs to being a true personal assistant, from a...

Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.   Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development...

"This promises to increase the limiting speed of data processing and information encoding and open a new realm of information technology." -Mohamed Hassan  Imagine a home computer operating 1 million times faster than the most expensive hardware on the market. Now imagine that level of computing power as the industry standard. University of Arizona researchers hope to pave the way for that reality using light-based optical computing, a marked improvement from the semiconductor-based transistors that currently run the world. "Semiconductor-based transistors are in all of the electronics that we use today," said Mohammed Hassan, assistant professor of physics and optical sciences....