As large language models (LLMs) rapidly evolve into large multimodal models (LMMs), the integration of these technologies into building information modeling (BIM) tasks to enhance work performance is signiLicantly increasing. The use of generative artiLicial intelligence (AI) during the conceptual design phase is particularly becoming a norm in industry and academia. A recent survey by the Royal Institute of British Architects (RIBA) reported that 68% of the responding architects are already using generative AI, such as text-to-image models, for early design visualization While the application of LLMs in BIM tasks beyond the early design phase is still in an early stage, it is foreseeable that BIM systems with natural language interfaces supported by LLMs will supplant BIM tools with traditional user interfaces in the near future. In this paper, we use the term "LLM-augmented BIM" as a general expression to indicate a task or a process of querying, generating, and managing BIM data and/or models via speech or text in natural language. We refer to the former as "speech-to-BIM" and the latter as "text-to-BIM" tasks.

Characters are integral to long-form narratives, but are poorly understood by existing story analysis and generation systems. While prior work has simplified characters via graph-based methods and brief character descriptions, we aim to better tackle the problem of representing complex characters by taking inspiration from advice given to professional writers. We propose CHIRON, a new `character sheet' based representation that organizes and filters textual information about characters. We construct CHIRON sheets in two steps: a Generation Module that prompts an LLM for character information via question-answering and a Validation Module that uses automated reasoning and a domain-specific entailment model to eliminate false facts about a character. We validate CHIRON via the downstream task of masked-character prediction, where our experiments show CHIRON is better and more flexible than comparable summary-based baselines. We also show that metrics derived from CHIRON can be used to automatically infer character-centricity in stories, and that these metrics align with human judgments.

From a four-legged robot goalkeeper to a Google AI ping-pong champion, a number of robots are already taking the world of sports by storm. Now, it seems that droids are preparing to take on the pros at boxing. An alarming video has revealed a huge humanoid robot boxing with a human - and throwing some impressive punches. The robot, dubbed Nadia, is described as a'next generation humanoid for the next generation of robotics.' The footage has raised concerns from many viewers, with one commenting: 'Wait till Mr robot get the mind of its own and punches you out for real.' Nadia is the brainchild of researchers at IHMC Robotics and Boardwalk Robotics.

Nadia Ady and Faun Rice are working on a research project exploring where artificial intelligence (AI) researchers find inspiration and ideas about human intelligence and what approaches they use to translate ideas from the disciplines that study human intelligence (e.g. We spoke to Nadia and Faun about the project, what they've learnt so far, and how they plan to further develop the work. Faun: We are doing a multidisciplinary project – I'm from the social sciences, while Nadia works on artificial intelligence. We're interviewing other artificial intelligence researchers who work with direct analogs from human psychology and try to translate them for machines in some way. For one, we talk to them about how they find the definitions that they're working with.

Abstract-- Complex multibody legged robots can have complex rotational control challenges. In this paper, we propose a concise way to understand and formulate a whole-body orientation that (i) depends on system configuration only and not a history of motion, (ii) can be representative of the orientation of the entire system while not being attached to any specific link, and (iii) has a rate of change that approximates total system angular momentum. We relate this orientation coordinate to past work, and discuss and demonstrate, including on hardware, several different uses for it. Many legged robots are best represented by nontrivial multibody dynamic models. The total system center of mass (CoM) is likely the most well-known of these model-based coordinates.

Artificial intelligence has put the future of technology on a high pedestal. In the digital world, artificial intelligence is already capable of doing several things that humans can do. The technology can help robots draw your picture amazingly, write a poem for you, click pictures, do house chores, etc. In a nutshell, artificial intelligence is turning to be humans in the 21st century. However, all is not working out in its favor. If we look closely at AI technology, we can see there are some things that artificial intelligence can't do.

Machine learning is an important part of working in R. Packages like mlr3 simplify the whole process. Its no need to manually split data into training and test set, no need to manually fit linear models. Even more, packages like mlr3pipelines let you crate complex pipelines and include feature engineering and transformation in your models. R is also used by statisticians, from statisticians we have advanced methods of imputing missing data like mice or Amelia. What happens when we want to connect machine learning with a statistical approach.

This blog post was guest-written by Annie O'Rourke, CEO of Digital Workforce Australia and 89 Degrees East. She will be guest speaking about'Addressing real world problems with Artificial Intelligence' session of the Women Rock-IT series on 17 October. To sign up for this or other webinars in the series, click here. Don't tell my husband, but I've recently started an affair. No need to be too shocked though, because I'm pretty sure I can package it as so-called ethical polygamy.

The robotics group at the Institute for Human & Machine Cognition (IHMC) in Pensacola, Fla., has an enormous amount of experience with walking robots. They came in second at the DARPA Robotics Challenge with their Running Man Atlas, one of just three teams to score a perfect 8 out of 8, and they've continued to advance bipedal locomotion using both Atlas and NASA's Valkyrie. We write about their research all the time--just a few months ago, they taught Atlas to walk with straight legs, much like a human does. Humans set a very high standard for bipedal mobility. We're well designed for it in both hardware and software, and we can do some absolutely amazing things.

Artificial intelligence (AI) and emotional intelligence (EI) and how they can optimise the customer experience (CX) is the focus of an investment by Daimler Financial Services into New Zealand start-up, Soul Machines. The undisclosed strategic investment in Soul Machines aims to further develop artificial and emotional intelligence for a multi-channel, customer-service pilot using an avatar'Sarah' with facial recognition. Soul Machines is a leader in the field of emotional Intelligence for use in machines and digital avatars. Soul Machines has created a number of avatars including Nadia, a virtual human voiced by Cate Blanchett that's being trialled by the National Disability Insurance Agency (NDIA). Nadia was designed to operate across multiple channels from the outset to create a consistent interaction for clients, with the face being just one representation.