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Self-driving laboratories for chemistry and materials science
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …
Through the automation of experimental workflows, along with autonomous experimental …
Evolutionary computation in the era of large language model: Survey and roadmap
Large language models (LLMs) have not only revolutionized natural language processing
but also extended their prowess to various domains, marking a significant stride towards …
but also extended their prowess to various domains, marking a significant stride towards …
The llama 3 herd of models
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 …
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
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 …
generation of language-labelled robot data and 2) effectively distills this data down into a …
Promptbreeder: Self-referential self-improvement via prompt evolution
Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the
reasoning abilities of Large Language Models (LLMs) in various domains. However, such …
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
We present an autonomous chemical synthesis robot for the exploration, discovery, and
optimization of nanostructures driven by real-time spectroscopic feedback, theory, and …
optimization of nanostructures driven by real-time spectroscopic feedback, theory, and …
Walk these ways: Tuning robot control for generalization with multiplicity of behavior
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 …
experienced during training but lack a mechanism for fast tuning when they fail in an out-of …
Explainability in deep reinforcement learning
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 …
relevance techniques to explain a deep neural network (DNN) output or explaining models …
First return, then explore
Reinforcement learning promises to solve complex sequential-decision problems
autonomously by specifying a high-level reward function only. However, reinforcement …
autonomously by specifying a high-level reward function only. However, reinforcement …
Designing neural networks through neuroevolution
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 …
weights are trained through variants of stochastic gradient descent. An alternative approach …