Systems of interacting objects often evolve under the influence of field effects that govern their dynamics, yet previous works have abstracted away from such effects, and assume that systems evolve in a vacuum. In this work, we focus on discovering these fields, and infer them from the observed dynamics alone, without directly observing them.

General-purpose spatial representations are essential for building transferable geospatial foundation models (GFMs). Among them, the AlphaEarth Foundation (AE) represents a major step toward a global, unified representation of the Earth's surface, learning 10-meter embeddings from multi-source Earth Observation (EO) data that capture rich physical and environmental patterns across diverse landscapes. However, such EO-driven representations remain limited in capturing the functional and socioeconomic dimensions of cities, as they primarily encode physical and spectral patterns rather than human activities or spatial functions. We propose AETHER (AlphaEarth-POI Enriched Representation Learning), a lightweight framework that adapts AlphaEarth to human-centered urban analysis through multimodal alignment guided by Points of Interest (POIs). AETHER aligns AE embeddings with textual representations of POIs, enriching physically grounded EO features with semantic cues about urban functions and socioeconomic contexts. In Greater London, AETHER achieves consistent gains over the AE baseline, with a 7.2% relative improvement in land-use classification F1 and a 23.6% relative reduction in Kullback-Leibler divergence for socioeconomic mapping. Built upon pretrained AE, AETHER leverages a lightweight multimodal alignment to enrich it with human-centered semantics while remaining computationally efficient and scalable for urban applications. By coupling EO with human-centered semantics, it advances geospatial foundation models toward general-purpose urban representations that integrate both physical form and functional meaning. Introduction Understanding the spatial organization and functional dynamics of cities remains a long-standing challenge in GIScience and urban computing. Addressing this challenge requires spatial representations that generalize across scales, modalities, and urban contexts.

Systems of interacting objects often evolve under the influence of field effects that govern their dynamics, yet previous works have abstracted away from such effects, and assume that systems evolve in a vacuum. In this work, we focus on discovering these fields, and infer them from the observed dynamics alone, without directly observing them. We theorize the presence of latent force fields, and propose neural fields to learn them. Since the observed dynamics constitute the net effect of local object interactions and global field effects, recently popularized equivariant networks are inapplicable, as they fail to capture global information. To address this, we propose to disentangle local object interactions -- which are $\mathrm{SE}(n)$ equivariant and depend on relative states -- from external global field effects -- which depend on absolute states. We model interactions with equivariant graph networks, and combine them with neural fields in a novel graph network that integrates field forces. Our experiments show that we can accurately discover the underlying fields in charged particles settings, traffic scenes, and gravitational n-body problems, and effectively use them to learn the system and forecast future trajectories.

Telling humans apart and following them as they move in their surrounding environment could be two highly valuable skills for service robots. In fact, when combined, these two capabilities would allow robots to follow specific people as they are interacting with them or offering their assistance. Researchers at Monash University, JDQ Systems and University of British Columbia recently developed a service robot designed to assist residents at elderly care homes or patients at other healthcare facilities. In a paper pre-published on arXiv, they presented a computational technique that allows their robot to identify and track people in its vicinity, following specific users as they are assisting them. "Our team has been developing a socially assistive robot platform, Aether, for providing daily routine assistance to staff and residents at elderly care and assisted living facilities," Wesley P. Chan, one of the researchers who carried out the study, told TechXplore.

Machine-learning models are trained on human behavior and excel at highlighting predictable or "normal" behaviors and patterns. However, the sudden onset of a global pandemic caused a massive change in human behavior that by some accounts has caused automation to go into a "tailspin," exposing fragilities in integrated systems we have come to rely upon. The realization of the scale and scope of these vulnerabilities -- which affect operations ranging from inventory management to global supply chain logistics -- comes at a time when we need artificial intelligence (AI) more than ever. For example, AI technologies are enabling contact tracing applications that may help mitigate the spread of the coronavirus. And amidst widespread testing shortages, hospitals have started to use AI technologies to help diagnose COVID-19 patients.

Microsoft's AI ethics committee helped craft internal Department of Defense contract policy, and G20 member nations wouldn't have passed AI ethics principles if it weren't for Japanese leadership. Published Tuesday, the UC Berkeley Center for Long-Term Cybersecurity (CLTC) case study examines how organizations are putting AI ethics principles into practice. Ethics principles are often vaguely phrased rules that can be challenging to translate into the daily practices of an engineer or other frontline worker. CLTC research fellow Jessica Cussins Newman told VentureBeat that many AI ethics and governance debates have focused more on what is needed, but less on the practices and policies necessary to implement goals enshrined in principles. The study focuses on OpenAI's rollout of GPT-2; the adoption of AI principles by OECD and G20; and the creation of the AI, Ethics, and Effects in Engineering and Research (AETHER) committee at Microsoft.

Eric Horvitz is a technical fellow and director at Microsoft Research Labs. A recipient of the Feigenbaum and the Allen Newell Prizes for contributions to artificial intelligence (AI), he is also on the US President's Council of Advisors on Science and Technology, Defense Advanced Research Projects Agency, and the Allen Institute for Artificial Intelligence. He is also part of the standing committee of Stanford University's One Hundred Year Study on Artificial Intelligence. Horvitz, who comes at least once a year to the country to interact with the India labs team, spoke about his work at Microsoft Research. He also shared his thoughts on the benefits and fear of AI, and attempts to address the bias in algorithms.

Microsoft's executive leadership is being reorganized, reflecting the Redmond, Wash., technology giant's continual evolution from a maker of packaged software products to a provider of artificial intelligence technologies and cloud computing services. In an email to employees, Microsoft CEO Satya Nadella outlined how the company is being arranged into three distinct engineering groups in the months ahead, namely Experiences & Devices, Cloud AI Platform and AI Research. "This reorganization clearly outlines Microsoft's priorities--cloud, coupled with AI, and an improved experience for users of Microsoft's applications," Ed Anderson, research vice president and distinguished analyst at Gartner, told eWEEK. "In the past, Windows was always the focus. This move reiterates the cloud-first priority that Microsoft has been stating for some time."

Earlier this year, I had the pleasure of addressing a large audience at a conference, presenting to a group of mixed professionals (including C-suite, Manager, Mid-level and executives, across a broad range of industry including IT, services govt, utilities, education, non-profit, hospital, health care, Financial services, insurance, automotive, Pharmaceuticals, aviation, aerospace, medical devices). The topic was, embracing and humanizing customer self-service channels; creating a personalized user experience in self-service channels. I have provided an overview of the presentation here for your convenience. At the conference I had the pleasure of meeting Mr. Rohit Mandana and we had a very interesting discussion about life outside of our professional engagements and then as these things do, our passion for our professions jumped back in and we were back and hot on the trail of bots, chatbots, AI and the role that they are playing in reshaping life as we know it. Sure there's all the latest stuff like Google Home and Uber Driverless (although now suspended I believe this tech will proceed) but we're beyond that now.