Modern chip physical design relies heavily on Electronic Design Automation (EDA) tools, which often struggle to provide interpretable feedback or actionable guidance for improving routing congestion. In this work, we introduce a Multimodal Large Language Model Assistant (MLLMA) that bridges this gap by not only predicting congestion but also delivering human-interpretable design suggestions. Our method combines automated feature generation through MLLM-guided genetic prompting with an interpretable preference learning framework that models congestion-relevant tradeoffs across visual, tabular, and textual inputs. We compile these insights into a "Design Suggestion Deck" that surfaces the most influential layout features and proposes targeted optimizations. Experiments on the CircuitNet benchmark demonstrate that our approach outperforms existing models on both accuracy and explainability. Additionally, our design suggestion guidance case study and qualitative analyses confirm that the learned preferences align with real-world design principles and are actionable for engineers. This work highlights the potential of MLLMs as interactive assistants for interpretable and context-aware physical design optimization.

Apple's artificial intelligence features for iPhones could be available in China as early as May, according to Bloomberg. The company reportedly established several teams in China and the US to make that happen, and it's also teaming up with local companies for its generative AI needs in the country. Joe Tsai, Alibaba Group's Chairman, recently confirmed that Apple will use his company's generative AI technology for Chinese iPhones during an event. Tsai didn't say when Apple intends to roll out the AI features that use Alibaba's tech, but The Information previously reported that the companies had already submitted them for approval to the country's regulators. Bloomberg says Apple will use Alibaba's technology for its on-device AI models, specifically as a layer on top that can censor certain materials and information for the Chinese government.

Apple will use Alibaba's generative AI to power artificial intelligence features for iPhones meant for sale in the Chinese market. Joe Tsai, Alibaba Group's Chairman, has confirmed the companies' partnership at the World Governments Summit in Dubai. He revealed that Apple talked to a number of other companies in China for a potential partnership, but it decided to team up with Alibaba in the end. Apple Intelligence features are not accessible in China at the moment, and even those who purchased their iPhones outside the country will not be able to use those features once they change their region to mainland China. As CNBC explains, the country has strict regulations surrounding AI, including requiring large language models to get approval for commercial use.

Sensor placement optimization methods have been studied extensively. They can be applied to a wide range of applications, including surveillance of known environments, optimal locations for 5G towers, and placement of missile defense systems. However, few works explore the robustness and efficiency of the resulting sensor network concerning sensor failure or adversarial attacks. This paper addresses this issue by optimizing for the least number of sensors to achieve multiple coverage of non-simply connected domains by a prescribed number of sensors. We introduce a new objective function for the greedy (next-best-view) algorithm to design efficient and robust sensor networks and derive theoretical bounds on the network's optimality. We further introduce a Deep Learning model to accelerate the algorithm for near real-time computations. The Deep Learning model requires the generation of training examples. Correspondingly, we show that understanding the geometric properties of the training data set provides important insights into the performance and training process of deep learning techniques. Finally, we demonstrate that a simple parallel version of the greedy approach using a simpler objective can be highly competitive.

Taking on big challenges is nothing new for Tsai, who was just 15 when she arrived at MIT. And while she always loved math and science, at the Institute she discovered even more interests. "Part of what was magical about MIT was that it really does encourage you to explore a lot of things," she recalls. In fact, she double majored (in mechanical engineering and materials science and engineering), double minored (in political science and biological engineering), and earned a master's in the MIT Media Lab. An early career in commodities trading took her to Switzerland and Singapore. Next, she worked at a fintech startup, and though the company wasn't successful, she discovered that the entrepreneurial mindset suited her.

In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models and can even learn general task-agnostic representations for efficient finetuning to downstream tasks. However, deep learning in resource-limited domains still faces multiple challenges including (i) limited data, (ii) constrained model development cost, and (iii) lack of adequate pre-trained models for effective finetuning. This paper provides an overview of model reprogramming to bridge this gap. Model reprogramming enables resource-efficient cross-domain machine learning by repurposing and reusing a well-developed pre-trained model from a source domain to solve tasks in a target domain without model finetuning, where the source and target domains can be vastly different. In many applications, model reprogramming outperforms transfer learning and training from scratch. This paper elucidates the methodology of model reprogramming, summarizes existing use cases, provides a theoretical explanation of the success of model reprogramming, and concludes with a discussion on open-ended research questions and opportunities. A list of model reprogramming studies is actively maintained and updated at https://github.com/IBM/model-reprogramming.

We address the problem of efficient and unobstructed surveillance or communication in complex environments. On one hand, one wishes to use a minimal number of sensors to cover the environment. On the other hand, it is often important to consider solutions that are robust against sensor failure or adversarial attacks. This paper addresses these challenges of designing minimal sensor sets that achieve multi-coverage constraints -- every point in the environment is covered by a prescribed number of sensors. We propose a greedy algorithm to achieve the objective. Further, we explore deep learning techniques to accelerate the evaluation of the objective function formulated in the greedy algorithm. The training of the neural network reveals that the geometric properties of the data significantly impact the network's performance, particularly at the end stage. By taking into account these properties, we discuss the differences in using greedy and $\epsilon$-greedy algorithms to generate data and their impact on the robustness of the network.

For cancer patients, getting a swift and accurate diagnosis is critical for their prognosis--and peace of mind. But if the screening is done by an endoscopy, the process is more complex. Typically, doctors look for lesions with specialized cameras, but limitations leave the door open to oversight and errors. In fact, about 25% of all colorectal neoplasms, or cancerous tumors, are missed by experts using this standard process. Today, those same cameras are being enhanced with AI and machine learning technology, helping improve patient outcomes.

In an office park in Hawthorne, a robot built by rocket scientists is making pizza. On a conveyor belt, a nozzle spits out sauce, dispensers shake cheese and toppings on top, then a robotic lift carries the raw pie to one of four 900-degree deck ovens. Cameras and sensors track the progress from step to step, making tiny adjustments along the way. In 45 seconds, a finished pizza pops out. It costs just $7 to order (or as much as $10, depending on toppings).

Emerging technology spend will give way to cloud and productivity software, according to Spiceworks Ziff Davis 2021 State of IT report. Enterprise technology felt the jolt of COVID-19, bringing priority tasks to the top and accelerating trends underway prior to disruption. The pivot to remote work and the financial pressures in the pandemic made value the keyword for enterprise technology, altering how executives gauge return on investment. After crisis mode subsided, shrinking budgets needed to stretch further. Tech spend is "so important for business continuity and business operations that they've become a mission-critical part of the budget" even in the context of future financial downturns, said Robin Peto, director of research strategy at Spiceworks Ziff Davis.