Interactive Fleet Learning – The Berkeley Artificial Intelligence Research Blog
Posted inArtificial Intelligence

Interactive Fleet Learning – The Berkeley Artificial Intelligence Research Blog





Figure 1: “Interactive Fleet Learning” (IFL) refers to robot fleets in industry and academia that fall back on human teleoperators when necessary and continually learn from them over time.

In the last few years we have seen an exciting development in robotics and artificial intelligence: large fleets of robots have left the lab and entered the real world. Waymo, for example, has over 700 self-driving cars operating in Phoenix and San Francisco and is currently expanding to Los Angeles. Other industrial deployments of robot fleets include applications like e-commerce order fulfillment at Amazon and Ambi Robotics as well as food delivery at Nuro and Kiwibot.

Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models – The Berkeley Artificial Intelligence Research Blog
Posted inArtificial Intelligence

Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models – The Berkeley Artificial Intelligence Research Blog



TL;DR: Text Prompt -> LLM -> Intermediate Representation (such as an image layout) -> Stable Diffusion -> Image.

Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, despite their impressive capabilities, diffusion models, such as Stable Diffusion, often struggle to accurately follow the prompts when spatial or common sense reasoning is required.

The following figure lists four scenarios in which Stable Diffusion falls short in generating images that accurately correspond to the given prompts, namely negation, numeracy, and attribute assignment, spatial relationships. In contrast, our method, LLM-grounded Diffusion (LMD), delivers much better prompt understanding in text-to-image generation in those scenarios.

Visualizations
Figure 1: LLM-grounded Diffusion enhances the prompt understanding ability of text-to-image diffusion models.

Generating 3D Molecular Conformers via Equivariant Coarse-Graining and Aggregated Attention – The Berkeley Artificial Intelligence Research Blog
Posted inArtificial Intelligence

Generating 3D Molecular Conformers via Equivariant Coarse-Graining and Aggregated Attention – The Berkeley Artificial Intelligence Research Blog






Figure 1: CoarsenConf architecture.

Molecular conformer generation is a fundamental task in computational chemistry. The objective is to predict stable low-energy 3D molecular structures, known as conformers, given the 2D molecule. Accurate molecular conformations are crucial for various applications that depend on precise spatial and geometric qualities, including drug discovery and protein docking.

We introduce CoarsenConf, an SE(3)-equivariant hierarchical variational autoencoder (VAE) that pools information from fine-grain atomic coordinates to a coarse-grain subgraph level representation for efficient autoregressive conformer generation.

Posted inArtificial Intelligence

On the Stepwise Nature of Self-Supervised Learning – The Berkeley Artificial Intelligence Research Blog




Figure 1: stepwise behavior in self-supervised learning. When training common SSL algorithms, we find that the loss descends in a stepwise fashion (top left) and the learned embeddings iteratively increase in dimensionality (bottom left). Direct visualization of embeddings (right; top three PCA directions shown) confirms that embeddings are initially collapsed to a point, which then expands to a 1D manifold, a 2D manifold, and beyond concurrently with steps in the loss.

It is widely believed that deep learning’s stunning success is due in part to its ability to discover and extract useful representations of complex data. Self-supervised learning (SSL) has emerged as a leading framework for learning these representations for images directly from unlabeled data, similar to how LLMs learn representations for language directly from web-scraped text. Yet despite SSL’s key role in state-of-the-art models such as CLIP and MidJourney, fundamental questions like “what are self-supervised image systems really learning?” and “how does that learning actually occur?” lack basic answers.

Our recent paper (to appear at ICML 2023) presents what we suggest is the first compelling mathematical picture of the training process of large-scale SSL methods. Our simplified theoretical model, which we solve exactly, learns aspects of the data in a series of discrete, well-separated steps. We then demonstrate that this behavior can be observed in the wild across many current state-of-the-art systems.
This discovery opens new avenues for improving SSL methods, and enables a whole range of new scientific questions that, when answered, will provide a powerful lens for understanding some of today’s most important deep learning systems.

Training Diffusion Models with Reinforcement Learning – The Berkeley Artificial Intelligence Research Blog
Posted inArtificial Intelligence

Training Diffusion Models with Reinforcement Learning – The Berkeley Artificial Intelligence Research Blog



Training Diffusion Models with Reinforcement Learning

Diffusion models have recently emerged as the de facto standard for generating complex, high-dimensional outputs. You may know them for their ability to produce stunning AI art and hyper-realistic synthetic images, but they have also found success in other applications such as drug design and continuous control. The key idea behind diffusion models is to iteratively transform random noise into a sample, such as an image or protein structure. This is typically motivated as a maximum likelihood estimation problem, where the model is trained to generate samples that match the training data as closely as possible.

However, most use cases of diffusion models are not directly concerned with matching the training data, but instead with a downstream objective. We don’t just want an image that looks like existing images, but one that has a specific type of appearance; we don’t just want a drug molecule that is physically plausible, but one that is as effective as possible. In this post, we show how diffusion models can be trained on these downstream objectives directly using reinforcement learning (RL). To do this, we finetune Stable Diffusion on a variety of objectives, including image compressibility, human-perceived aesthetic quality, and prompt-image alignment. The last of these objectives uses feedback from a large vision-language model to improve the model’s performance on unusual prompts, demonstrating how powerful AI models can be used to improve each other without any humans in the loop.