wav2vec 2.0 vs GPT-4

Discover the innovative research presented in the paper titled "wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations," which showcases a groundbreaking approach in speech processing technology. This paper, authored by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli, introduces the wav2vec 2.0 framework, designed to learn representations from speech audio alone. By fine-tuning on transcribed speech, it outperforms many semi-supervised methods, proving to be a simpler yet potent solution. Key highlights include the ability to mask speech input in the latent space and address a contrastive task over quantized latent representations. The study demonstrates impressive results in speech recognition with a minimal amount of labeled data, changing the landscape for developing efficient and effective speech recognition systems.

GPT-4 is the latest milestone in OpenAI’s effort in scaling up deep learning.

GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5’s score was around the bottom 10%. We’ve spent 6 months iteratively aligning GPT-4 using lessons from our adversarial testing program as well as ChatGPT, resulting in our best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.

GPT-4 is more creative and collaborative than ever before. It can generate, edit, and iterate with users on creative and technical writing tasks, such as composing songs, writing screenplays, or learning a user’s writing style.