Unlike in language or vision, one of the fundamental challenges in robot learning is the lack of access to vast data resources. We can further break down the problem into (1) data sparsity from the angle of data representation and (2) data scarcity from the angle of data quantity. In this thesis, I will discuss selected works on two domains: (1) tactile sensing and (2) rehabilitation robots, which are exemplars of data sparsity and scarcity, respectively. Tactile sensing is an essential modality for robotics, but tactile data are often sparse, and for each interaction with the physical world, tactile sensors can only obtain information about the local area of contact. I will discuss my work on learning vision-free tactile-only exploration and manipulation policies through model-free reinforcement learning to make efficient use of sparse tactile information. On the other hand, rehabilitation robots are an example of data scarcity to the extreme due to the significant challenge of collecting biosignals from disabled-bodied subjects at scale for training. I will discuss my work in collaboration with the medical school and clinicians on intent inferral for stroke survivors, where a hand orthosis developed in our lab collects a set of biosignals from the patient and uses them to infer the activity that the patient intends to perform, so the orthosis can provide the right type of physical assistance at the right moment. My work develops machine learning algorithms that enable intent inferral with minimal data, including semi-supervised, meta-learning, and generative AI methods.

The exponential development of generative artificial intelligence (GenAI) technologies like ChatGPT has raised increasing curiosity about their use in higher education, specifically with respect to how students view them, make use of them, and the implications for learning outcomes. This paper employs a hybrid methodological approach involving a systematic literature review and simulation-based modeling to explore student perceptions of GenAI use in the context of higher education. A total of nineteen empirical articles from 2023 through 2025 were selected from the PRISMA-based search targeting the Scopus database. Synthesis of emerging patterns from the literature was achieved by thematic categorization. Six of these had enough quantitative information, i.e., item-level means and standard deviations, to permit probabilistic modeling. One dataset, from the resulting subset, was itself selected as a representative case with which to illustrate inverse-variance weighting by Monte Carlo simulation, by virtue of its well-designed Likert scale format and thematic alignment with the use of computing systems by the researcher. The simulation provided a composite "Success Score" forecasting the strength of the relationship between student perceptions and learning achievements. Findings reveal that attitude factors concerned with usability and real-world usefulness are significantly better predictors of positive learning achievement than affective or trust-based factors. Such an interdisciplinary perspective provides a unique means of linking thematic results with predictive modelling, resonating with longstanding controversies about the proper use of GenAI tools within the university.

Generative AI, especially through large language models (LLMs), is transforming how technical knowledge can be accessed, reused, and extended. PETSc, a widely used numerical library for high-performance scientific computing, has accumulated a rich but fragmented knowledge base over its three decades of development, spanning source code, documentation, mailing lists, GitLab issues, Discord conversations, technical papers, and more. Much of this knowledge remains informal and inaccessible to users and new developers. To activate and utilize this knowledge base more effectively, the PETSc team has begun building an LLM-powered system that combines PETSc content with custom LLM tools -- including retrieval-augmented generation (RAG), reranking algorithms, and chatbots -- to assist users, support developers, and propose updates to formal documentation. This paper presents initial experiences designing and evaluating these tools, focusing on system architecture, using RAG and reranking for PETSc-specific information, evaluation methodologies for various LLMs and embedding models, and user interface design. Leveraging the Argonne Leadership Computing Facility resources, we analyze how LLM responses can enhance the development and use of numerical software, with an initial focus on scalable Krylov solvers. Our goal is to establish an extensible framework for knowledge-centered AI in scientific software, enabling scalable support, enriched documentation, and enhanced workflows for research and development. We conclude by outlining directions for expanding this system into a robust, evolving platform that advances software ecosystems to accelerate scientific discovery.

There is an increasing imperative to integrate programming platforms within AI frameworks to enhance educational tasks for both teachers and students. However, commonly used platforms such as Code.org, Scratch, and Snap fall short of providing the desired AI features and lack adaptability for interdisciplinary applications. This study explores how educational platforms can be improved by incorporating AI and analytics features to create more effective learning environments across various subjects and domains. We interviewed 8 K-12 teachers and asked their practices and needs while using any block-based programming (BBP) platform in their classes. We asked for their approaches in assessment, course development and expansion of resources, and student monitoring in their classes. Thematic analysis of the interview transcripts revealed both commonalities and differences in the AI tools needed between the STEM and non-STEM groups. Our results indicated advanced AI features that could promote BBP platforms. Both groups stressed the need for integrity and plagiarism checks, AI adaptability, customized rubrics, and detailed feedback in assessments. Non-STEM teachers also emphasized the importance of creative assignments and qualitative assessments. Regarding resource development, both AI tools desired for updating curricula, tutoring libraries, and generative AI features. Non-STEM teachers were particularly interested in supporting creative endeavors, such as art simulations. For student monitoring, both groups prioritized desktop control, daily tracking, behavior monitoring, and distraction prevention tools. Our findings identify specific AI-enhanced features needed by K-12 teachers across various disciplines and lay the foundation for creating more efficient, personalized, and engaging educational experiences.

Nvidia's $100 billion investment is meant to help OpenAI build data centers with a capacity of at least 10 gigawatts of power -- equipped with Nvidia's advanced chips to train and deploy AI models. Nvidia will invest as much as $100 billion in OpenAI to support new data centers and other artificial intelligence infrastructure, a blockbuster deal that underscores booming demand for AI tools like ChatGPT and the computing power needed to make them run. The companies announced the agreement Monday, saying they'd signed a letter of intent for a strategic deal. The investment is meant to help OpenAI build data centers with a capacity of at least 10 gigawatts of power -- equipped with Nvidia's advanced chips to train and deploy AI models. The money will be provided in stages, with the first $10 billion coming when the deal is signed, according to people familiar with the matter. Nvidia is making the investment in cash and will receive OpenAI equity as part of the deal, said the people, who asked not to be identified because the talks were private.

US tech giant Nvidia will invest up to $100bn (£73bn) in OpenAI, the firm behind ChatGPT, the companies announced. Nvidia said it will supply high-performance chips needed for the processing power required by artificial intelligence (AI), of which OpenAI is a specialist. Described as a strategic partnership by Nvidia, it is the latest move by two high profile tech firms in the global AI race, where China is an emerging rival. The announcement comes after a series of high-profile investments by Nvidia, including a $5bn investment in Intel and a £2bn investment in the UK's AI sector. Nvidia said its latest investment will go towards growing data centres for OpenAI's next-generation AI infrastructure.

What is the H-1B visa programme? The White House Peace Vigil is dismantled - why? Who said what at Charlie Kirk's memorial? Chipmaker Nvidia will invest up to $100bn in OpenAI and provide it with data center chips, a tie-up between two of the highest-profile leaders in the global artificial intelligence (AI) race. The deal, announced on Monday, will see Nvidia start delivering chips as soon as late 2026 and will involve two separate but intertwined transactions, according to a person close to OpenAI. The first $10bn of Nvidia's investment in OpenAI, which was most recently valued at $500bn, will begin when the two companies reach a definitive agreement for OpenAI to purchase Nvidia chips. Nvidia did not respond to immediate requests for clarification about the deal.

Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. With only a 50-50 chance of detecting synthetic media online, users are more vulnerable than ever to being duped. Advances in generative AI technology have made it easier than ever for anyone to manufacture increasingly realistic synthetic media (colloquially known as deepfakes) at faster speeds, larger scales, and with more customization than ever. This in turn has led to synthetic media increasingly being used for harmful purposes, including disinformation campaigns, nonconsensual pornography, financial fraud, child sexual abuse and exploitation, and espionage. As of today, the principal defense to combat deceptive synthetic media depends in large part on the human observer's perceptual detection capabilities--their ability to visually or auditorily identify AI-generated content when they encounter it. Yet the growing realism of synthetic media impedes this ability, heightening people's vulnerability to weaponized synthetic content. Moreover, people overestimate how capable they are at identifying synthetic media, further exacerbating the problem. As synthetic media continues to advance in sophistication, so too does the threat posed by its growing weaponization, from financial fraud to the production of nonconsensual intimate materials of adults and children.

On this episode of, we discuss OpenAI's new teen safety features, the right's retaliation against critics of the late Charlie Kirk, and more of the week's biggest stories. Charlie Kirk (R) shaking hands with US President Donald Trump as he speaks on stage at America Fest 2024 in Phoenix, Arizona. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. In today's episode, our host Zöe Schiffer is joined by WIRED's senior culture editor Manisha Krishnan to run through five of the best stories we published this week--from OpenAI implementing teen safety features to how human design is the new astrology. Zöe and Manisha also discuss the reverberating reactions to Charlie Kirk's death and why the work of many creators, from comic book artists to late night show hosts, is getting cancelled.

Generative Artificial Intelligence (GenAI) has made significant advancements in fields such as computer vision (CV) and natural language processing (NLP), demonstrating its capability to synthesize high-fidelity data and improve generalization. Recently, there has been growing interest in integrating GenAI into wireless sensing systems. By leveraging generative techniques such as data augmentation, domain adaptation, and denoising, wireless sensing applications, including device localization, human activity recognition, and environmental monitoring, can be significantly improved. This survey investigates the convergence of GenAI and wireless sensing from two complementary perspectives. First, we explore how GenAI can be integrated into wireless sensing pipelines, focusing on two modes of integration: as a plugin to augment task-specific models and as a solver to directly address sensing tasks. Second, we analyze the characteristics of mainstream generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, and discuss their applicability and unique advantages across various wireless sensing tasks. We further identify key challenges in applying GenAI to wireless sensing and outline a future direction toward a wireless foundation model: a unified, pre-trained design capable of scalable, adaptable, and efficient signal understanding across diverse sensing tasks.