Self-driving laboratories for chemistry and materials science

G Tom, SP Schmid, SG Baird, Y Cao, K Darvish… - Chemical …, 2024‏ - ACS Publications
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Evolutionary computation in the era of large language model: Survey and roadmap

X Wu, S Wu, J Wu, L Feng… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Large language models (LLMs) have not only revolutionized natural language processing
but also extended their prowess to various domains, marking a significant stride towards …

The llama 3 herd of models

A Dubey, A Jauhri, A Pandey, A Kadian… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Modern artificial intelligence (AI) systems are powered by foundation models. This paper
presents a new set of foundation models, called Llama 3. It is a herd of language models …

Scaling up and distilling down: Language-guided robot skill acquisition

H Ha, P Florence, S Song - Conference on Robot Learning, 2023‏ - proceedings.mlr.press
We present a framework for robot skill acquisition, which 1) efficiently scale up data
generation of language-labelled robot data and 2) effectively distills this data down into a …

Promptbreeder: Self-referential self-improvement via prompt evolution

C Fernando, D Banarse, H Michalewski… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the
reasoning abilities of Large Language Models (LLMs) in various domains. However, such …

An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials

Y Jiang, D Salley, A Sharma, G Keenan, M Mullin… - Science …, 2022‏ - science.org
We present an autonomous chemical synthesis robot for the exploration, discovery, and
optimization of nanostructures driven by real-time spectroscopic feedback, theory, and …

Walk these ways: Tuning robot control for generalization with multiplicity of behavior

GB Margolis, P Agrawal - Conference on Robot Learning, 2023‏ - proceedings.mlr.press
Learned locomotion policies can rapidly adapt to diverse environments similar to those
experienced during training but lack a mechanism for fast tuning when they fail in an out-of …

Explainability in deep reinforcement learning

A Heuillet, F Couthouis, N Díaz-Rodríguez - Knowledge-Based Systems, 2021‏ - Elsevier
A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature
relevance techniques to explain a deep neural network (DNN) output or explaining models …

First return, then explore

A Ecoffet, J Huizinga, J Lehman, KO Stanley, J Clune - Nature, 2021‏ - nature.com
Reinforcement learning promises to solve complex sequential-decision problems
autonomously by specifying a high-level reward function only. However, reinforcement …

Designing neural networks through neuroevolution

KO Stanley, J Clune, J Lehman… - Nature Machine …, 2019‏ - nature.com
Much of recent machine learning has focused on deep learning, in which neural network
weights are trained through variants of stochastic gradient descent. An alternative approach …