A capsule that could replace insulin shots. In Giovanni Traverso's lab, the focus is always on making life better for patients. Every Monday, more than a hundred members of Giovanni Traverso's Laboratory for Translational Engineering (L4TE) fill a large classroom at Brigham and Women's Hospital for their weekly lab meeting. With a social hour, food for everyone, and updates across disciplines from mechanical engineering to veterinary science, it's a place where a stem cell biologist might weigh in on a mechanical design, or an electrical engineer might spot a flaw in a drug delivery mechanism. And it's a place where everyone is united by the same goal: engineering new ways to deliver medicines and monitor the body to improve patient care. Traverso's weekly meetings bring together a mix of expertise that lab members say is unusual even in the most collaborative research spaces. But his lab--which includes its own veterinarian and a dedicated in vivo team--isn't built like most.

A poster from a long input document can be considered as a one-page easy-to-read multimodal (text and images) summary presented on a nice template with good design elements. Automatic transformation of a long document into a poster is a very less studied but challenging task. It involves content summarization of the input document followed by template generation and harmonization. In this work, we propose a novel deep submodular function which can be trained on ground truth summaries to extract multimodal content from the document and explicitly ensures good coverage, diversity and alignment of text and images. Then, we use an LLM based paraphraser and propose to generate a template with various design aspects conditioned on the input content. We show the merits of our approach through extensive automated and human evaluations.

The AAAI/SIGAI Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. This year, 30 students were selected for this programme, and we've been hearing from them about their research. In this interview, Amine Barrak, tells us about his work speeding up machine learning by using serverless computing. My focus is on speeding up machine learning by using serverless computing. My research is about finding a way to do machine learning training efficiently in small serverless settings.

I) One postdoc will investigate through multi-omics data integration questions in cancer heterogeneity, evolution, and resistance, using omics data both in bulk and on a single cell level. The project will be part of HEROES-AYA, a project of the Decade against Cancer. We will study tumour heterogeinity, evolution, and resistance in fusion gene driven sarcomas. There will be comprehensive multi-omics data available, in bulk and single cell, including single cell whole genome sequencing. This will be complemented by diagnostic and clinical data, pathology, radiology, liquid biopsies, drug rsponse in patient derived organoid models, etc.

Last year, MIT researchers announced that they had built "liquid" neural networks, inspired by the brains of small species: a class of flexible, robust machine learning models that learn on the job and can adapt to changing conditions, for real-world safety-critical tasks, like driving and flying. The flexibility of these "liquid" neural nets meant boosting the bloodline to our connected world, yielding better decision-making for many tasks involving time-series data, such as brain and heart monitoring, weather forecasting, and stock pricing. But these models become computationally expensive as their number of neurons and synapses increase and require clunky computer programs to solve their underlying, complicated math. And all of this math, similar to many physical phenomena, becomes harder to solve with size, meaning computing lots of small steps to arrive at a solution. Now, the same team of scientists has discovered a way to alleviate this bottleneck by solving the differential equation behind the interaction of two neurons through synapses to unlock a new type of fast and efficient artificial intelligence algorithms.

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We develop reinforcement learning techniques to enable interaction across multiple agents including AIs and humans, with potential applications from AI-assisted design to autonomous driving. Methodological contexts of the research include deep reinforcement learning, inverse reinforcement learning, hierarchical reinforcement learning as well as multi-agent and multi-objective reinforcement learning. FCAI is working on a new paradigm of AI-assisted design that aims to cooperate with designers by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers' goals and then help them without being needlessly disruptive. We use generative user models to reason about designers' goals, reasoning, and capabilities. In this call, FCAI is looking for a postdoctoral scholar or research fellow to join our effort to develop AI-assisted design. Suitable backgrounds include deep reinforcement learning, Bayesian inference, cooperative AI, computational cognitive modelling, and user modelling. Computational rationality is an emerging integrative theory of intelligence in humans and machines (1) with applications in human-computer interaction, cooperative AI, and robotics. The theory assumes that observable human behavior is generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself (2).

KTH Royal Institute of Technology in Stockholm has grown to become one of Europe's leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden's largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy. The goal of this project is to use a number of artificial-intelligence-based methods, in particular natural-language processing (NLP), to perform a thorough assessment of the literature with the aim of determining synergies and tradeoffs among the Sustainable Development Goals (SDGs) and their targets. The focus is on finding more robust and less obvious connections among SDGs.

During the past decade, the RL has envisioned and built the ARIES (ARtificial Intelligence for Environment and Sustainability) platform, a technology that integrates network-available data and model components through semantics and machine reasoning. Its underlying open-source software (k.LAB) handles the full end-to-end process of integrating data and with multiple model integration types to predict complex change. It also supports selection of the most appropriate data and models using cloud technology and following an open data paradigm: the resulting insight remains open and available to society at large, and becomes a base for further computations, contributing to an ever-increasing knowledge base. For the first time, it is possible to consistently characterize and publish data and models for their integration in predictive models, building and field-testing technologies that have eluded researchers to date. We are looking for an individual who can support strategic activities related to integrated data science and collaborative, integrated modelling on the semantic web (semantic meta-modelling).

The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.