PaLM-E vs GPT-4

The PaLM-E project introduces an innovative Embodied Multimodal Language Model, which integrates real-world sensor data with linguistic models for advanced robotic tasks. PaLM-E, short for "Projection-based Language Model embodied," fuses textual inputs with continuous sensory information, such as visual and state estimation data, to create a comprehensive understanding and interaction in the physical world.

Designed to aid in tasks like robotic manipulation planning, visual question answering, and captioning, PaLM-E showcases the potential of large, multimodal language models trained on varied tasks across domains. With its largest iteration, PaLM-E-562B, boasting 562 billion parameters, the model not only excels in robotic tasks but also achieves state-of-the-art performance in visual-language tasks like OK-VQA, while maintaining robust general language skills.

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.