Weak-to-strong generalization
We present a new research direction for superalignment, together with promising initial results: can we leverage the generalization properties of deep learning to control strong models with weak supervisors?
We present a new research direction for superalignment, together with promising initial results: can we leverage the generalization properties of deep learning to control strong models with weak supervisors?
Language Agents represent a transformative advancement in computational linguistics. They leverage large language models (LLMs) to interact with and process information from the external world. Through innovative use of tools and APIs, these agents autonomously acquire and integrate new knowledge, demonstrating significant progress in complex reasoning tasks. A critical challenge in Language Agents is managing…
Deep convolutional neural networks (DCNNs) have been a game-changer for several computer vision tasks. These include object identification, object recognition, image segmentation, and edge detection. The ever-growing size and power consumption of DNNs have been key to enabling much of this advancement. Embedded, wearable, and Internet of Things (IoT) devices, which have restricted computing resources…
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The dependence on external servers for AI applications can pose risks, including data security and reliance on a stable internet connection. While some alternatives exist, most of them still require an internet connection for functioning. This leaves users searching for a solution that combines the power of AI with the comfort of offline usage. Currently,…
Large-scale multilingual language models are the foundation of many cross-lingual and non-English Natural Language Processing (NLP) applications. These models are trained on massive volumes of text in multiple languages. However, the drawback to their widespread use is that because numerous languages are modeled in a single model, there is competition for the limited capacity of…
Artificial intelligence is advancing rapidly, but researchers are facing a significant challenge. AI systems struggle to adapt to diverse environments outside their training data, which is critical in areas like self-driving cars, where failures can have catastrophic consequences. Despite efforts by researchers to tackle this problem with algorithms for domain generalization, no algorithm has yet…