Dynamic energy systems and controls require advanced modeling frameworks to design and test supervisory and fault tolerant strategies. Modelica is a widely used equation based language, but developing control modules is labor intensive and requires specialized expertise. This paper examines the use of large language models (LLMs) to automate the generation of Control Description Language modules in the Building Modelica Library as a case study. We developed a structured workflow that combines standardized prompt scaffolds, library aware grounding, automated compilation with OpenModelica, and human in the loop evaluation. Experiments were carried out on four basic logic tasks (And, Or, Not, and Switch) and five control modules (chiller enable/disable, bypass valve control, cooling tower fan speed, plant requests, and relief damper control). The results showed that GPT 4o failed to produce executable Modelica code in zero shot mode, while Claude Sonnet 4 achieved up to full success for basic logic blocks with carefully engineered prompts. For control modules, success rates reached 83 percent, and failed outputs required medium level human repair (estimated one to eight hours). Retrieval augmented generation often produced mismatches in module selection (for example, And retrieved as Or), while a deterministic hard rule search strategy avoided these errors. Human evaluation also outperformed AI evaluation, since current LLMs cannot assess simulation results or validate behavioral correctness. Despite these limitations, the LLM assisted workflow reduced the average development time from 10 to 20 hours down to 4 to 6 hours per module, corresponding to 40 to 60 percent time savings. These results highlight both the potential and current limitations of LLM assisted Modelica generation, and point to future research in pre simulation validation, stronger grounding, and closed loop evaluation.

Artificial intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, perception, and decision-making.

Democrats in Congress on Thursday renewed a push to hold tech companies accountable for bias in their algorithms. Senators Ron Wyden (D-OR) and Cory Booker (D-NJ), along with House representative Yvette Clarke (D-NY) introduced an updated version of a bill that would require audits of AI systems used in areas such as finance, healthcare, housing, education and more. First introduced by Wyden in 2019, the Algorithmic Accountability Act has never passed the committee level in either the House or Senate. "If someone decides not to rent you a house because of the color of your skin, that's flat-out illegal discrimination. Using a flawed algorithm or software that results in discrimination and bias is just as bad. Our bill will pull back the curtain on the secret algorithms that can decide whether Americans get to see a doctor, rent a house or get into a school," said Wyden in a press release.

Motor Trend will never publish a First Drive review on the Volkswagen SEDRIC. That's because the SEDRIC, which Volkswagen Group is currently evaluating for launch sometime after 2023, is a completely autonomous vehicle. It has no steering wheel. You just call it up on your smartphone and tell it where you are and where you want to go. When it arrives, get in, sit back, relax, and enjoy the ride.

How can we chose a preconditioner that will give us these benefits? A good first guess is one that makes your data decorrelated and whitened. In the case of images this means doing gradient descent in the Fourier basis, This points to a profound fact about the Fourier transform. As long as a correlation is consistent across spatial positions -- such as the correlation between a pixel and its left neighbor being the same across all positions of an image -- the Fourier coefficients will be independent variables. To see this, note that such a spatially consistent correlation can be expressed as a convolution, and by the convolution theorem becomes pointwise multiplication after the Fourier transform.

IAAI is the premier venue for learning about AI's impact through deployed applications and emerging AI technologies. Case studies of deployed applications with measurable benefits arising from the use of AI technology provide clear evidence of the impact and value of AI technology to today's world. The emerging applications track features technologies that are rapidly maturing to the point of application. The seven articles selected for this special issue are extended versions of the papers that appeared at the conference. Four of the articles describe deployed applications that are already in use in the field.

The Find-the-Remote event was considered the most challenging of the events in the 1997 AAAI Mobile Robot Competition and Exhibition. It required a broad range of both hardware and software capabilities. I discuss the rules and rationale for the event as well as the results. It involved fetching a known set of objects from unknown, but constrained, locations in a known environment. In real life, such functions might be useful for in-home care of the elderly or the physically disabled. This event was extremely difficult because it forced teams to implement both manipulation (the grasping and moving of objects) and visual object recognition. Furthermore, it explicitly required teams to implement them for a wide range of objects. It therefore eliminated a broad range of special-purpose sensing and manipulation strategies that would be specific to one or another class of objects. It also required that objects be lifted from a variety of surfaces (real furniture) at a variety of heights.

Sony has provided a robot platform for research and development in physical agents, namely, fully autonomous legged robots. In this article, we describe our work using Sony's legged robots to participate at the RoboCup-98 legged robot demonstration and competition. Robotic soccer represents a challenging environment for research in systems with multiple robots that need to achieve concrete objectives, particularly in the presence of an adversary. Furthermore, RoboCup offers an excellent opportunity for robot entertainment. We introduce the RoboCup context and briefly present Sony's legged robot.

Unfortunately, color labeling can be applied to object-recognition tasks only in very restricted environments, where different kinds of objects have different colors. These stringent rules allow for simple mechanisms for object detection and recognition: Segment the captured image into blobs of the same color and interpret these blobs. To the best of our knowledge, all autonomous robot soccer teams with vision-based perception apply variations of such a method. However, because the RoboCup committee is planning to make the rules more realistic, these objectrecognition and -localization methods are becoming obsolete. The local statistics define an expectation of "how the two sides of the curve might look."

Example from the LOOM WORDNet Knowledge Base. At the beginning, we assumed that the hyponymy relation could simply be mapped onto the subsumption relation and that the synset notion could be mapped into the notion of concept. Both subsumption and concept have the usual description logic semantics (Woods and Schmolze 1992). LOOM WORDNET knowledge base are reported in table 1. Fig-ORDNET's noun top Under Territorial_-Dominion, we find Macao and Palestine together with Trust_Territory. The Trust_Territory synset, defined as "a dependent country, administered by a country under the supervision of United Nations," denotes a general kind of country rather than a specific country such as Macao or Palestine.