Diabetes, a chronic condition that impairs how the body turns food into
energy, i.e. blood glucose, affects 38 million people in the US alone. The
standard treatment is to supplement carbohydrate intake with an artificial
pancreas, i.e. a continuous insulin pump (basal shots), as well as occasional
insulin injections (bolus shots). The goal of the treatment is to keep blood
glucose at the center of an acceptable range, as measured through a continuous
glucose meter. A secondary goal is to minimize injections, which are unpleasant
and difficult for some patients to implement. In this study, neuroevolution was
used to discover an optimal strategy for the treatment. Based on a dataset of
30 days of treatment and measurements of a single patient, a random forest was
first trained to predict future glucose levels. A neural network was then
evolved to prescribe carbohydrates, basal pumping levels, and bolus injections.
Evolution discovered a Pareto front that reduced deviation from the target and
number of injections compared to the original data, thus improving patients'
quality of life. To make the system easier to adopt, a language interface was
developed with a large language model. Thus, these technologies not only
improve patient care but also adoption in a broader population.

这项研究使用神经进化算法和数据预测方法，通过训练预测未来血糖水平的模型，并找到了优化糖尿病治疗策略的 Pareto 前沿，从而改善了患者的生活质量，并开发了一个语言界面以便更广泛地推广应用。

优化人工胰腺设计以改善糖尿病管理

Optimizing the Design of an Artificial Pancreas to Improve Diabetes  Management

Recent advances in generative diffusion models have enabled text-controlled
synthesis of realistic and diverse images with impressive quality. Despite
these remarkable advances, the application of text-to-image generative models
in computer vision for standard visual recognition tasks remains limited. The
current de facto approach for these tasks is to design model architectures and
loss functions that are tailored to the task at hand. In this paper, we develop
a unified language interface for computer vision tasks that abstracts away
task-specific design choices and enables task execution by following natural
language instructions. Our approach involves casting multiple computer vision
tasks as text-to-image generation problems. Here, the text represents an
instruction describing the task, and the resulting image is a visually-encoded
task output. To train our model, we pool commonly-used computer vision datasets
covering a range of tasks, including segmentation, object detection, depth
estimation, and classification. We then use a large language model to
paraphrase prompt templates that convey the specific tasks to be conducted on
each image, and through this process, we create a multi-modal and multi-task
training dataset comprising input and output images along with annotated
instructions. Following the InstructPix2Pix architecture, we apply
instruction-tuning to a text-to-image diffusion model using our constructed
dataset, steering its functionality from a generative model to an
instruction-guided multi-task vision learner. Experiments demonstrate that our
model, dubbed InstructCV, performs competitively compared to other generalist
and task-specific vision models. Moreover, it exhibits compelling
generalization capabilities to unseen data, categories, and user instructions.

最近，在生成式扩散模型方面取得的进展使得文本控制下合成逼真多样的图像具备了令人印象深刻的质量。但尽管取得了这些显著进展，将文本到图像生成模型应用于标准视觉识别任务的研究仍然有限。本文提出了一种计算机视觉任务的统一语言界面，该界面抽象了任务的具体设计选择，使得任务能够通过自然语言指令来执行。我们将多个计算机视觉任务转化为文本到图像生成问题，其中文本描述任务，生成的图像为视觉编码的任务输出。我们使用大规模语言模型来改写传达每个图像上要执行的特定任务的提示模板，并通过这个过程，创建了一个多模态和多任务训练数据集，包括输入图像、输出图像和带注释的指导说明。通过在构建的数据集上使用 InstructPix2Pix 架构将文本到图像扩散模型进行调节，将其功能从生成模型转变为基于指导的多任务视觉学习器。实验证明，我们的模型 InstructCV 在与其他综合和特定任务视觉模型的竞争中表现出色。而且，它还具备令人信服的对未见数据、类别和用户指令的泛化能力。

InstructCV: 信息指导下的图文扩散模型作为视觉通才

InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision  Generalists

A major challenge in research involving artificial intelligence (AI) is the
development of algorithms that can find solutions to problems that can
generalize to different environments and tasks. Unlike AI, humans are adept at
finding solutions that can transfer. We hypothesize this is because their
solutions are informed by causal models. We propose to use human-guided causal
knowledge to help robots find solutions that can generalize to a new
environment. We develop and test the feasibility of a language interface that
na\"ive participants can use to communicate these causal models to a planner.
We find preliminary evidence that participants are able to use our interface
and generate causal models that achieve near-generalization. We outline an
experiment aimed at testing far-generalization using our interface and describe
our longer terms goals for these causal models.

通过人类引导的因果知识的使用，作者在语言接口和机器人上开发和测试了一种方法，可以使机器人在新环境中获得泛化解决方案。