Natural language processing has made progress in incorporating human context into its models, but whether it is more effective to use group-wise attributes (e.g., over-45-year-olds) or model individuals remains open. Group attributes are technically easier but coarse: not all 45-year-olds write the same way. In contrast, modeling individuals captures the complexity of each person's identity. It allows for a more personalized representation, but we may have to model an infinite number of users and require data that may be impossible to get. We compare modeling human context via group attributes, individual users, and combined approaches. Combining group and individual features significantly benefits user-level regression tasks like age estimation or personality assessment from a user's documents. Modeling individual users significantly improves the performance of single document-level classification tasks like stance and topic detection. We also find that individual-user modeling does well even without user's historical data.

As research in human-centered NLP advances, there is a growing recognition of the importance of incorporating human and social factors into NLP models. At the same time, our NLP systems have become heavily reliant on LLMs, most of which do not model authors. To build NLP systems that can truly understand human language, we must better integrate human contexts into LLMs. This brings to the fore a range of design considerations and challenges in terms of what human aspects to capture, how to represent them, and what modeling strategies to pursue. To address these, we advocate for three positions toward creating large human language models (LHLMs) using concepts from psychological and behavioral sciences: First, LM training should include the human context. Second, LHLMs should recognize that people are more than their group(s). Third, LHLMs should be able to account for the dynamic and temporally-dependent nature of the human context. We refer to relevant advances and present open challenges that need to be addressed and their possible solutions in realizing these goals.

We are living the generative AI hype, with everyone getting crazy about ChatGPT. And every day I read articles about how AI can disrupt the metaverse, how ChatGPT and metaverse are the perfect match, and especially about how the fact that if your XR company doesn't have an AI strategy, it is already old and ready to die a terrible death. I have my point of view on all of this, and I would like to tell to you. I know that now you only read ChatGPT's opinions on everything, but for once, also listen to the meaty Tony. Generative AI is incredibly cool. The moment I tried ChatGPT and saw its potential, I was literally stocked. For the first time, I saw an AI that had an almost believable way of speaking, and that was able to substitute many tools that I'm still using now.

For cybersecurity experts, artificial intelligence (AI) can both respond to and predict threats. But because AI security is everywhere, attackers are using it to launch more refined attacks. Each side is seemingly playing catch-up, with no clear winner in sight. How can defenders stay ahead? To gain context about AI that goes beyond prediction, detection and response, our industry will need to'humanize' the process.

Call it artificial intelligence with a human touch. This week, two California universities separately announced new centers devoted to studying the ways in which AI can help humanity. The University of California's Viterbi School of Engineering and its School of Social Work said Wednesday that they had joined forces to launch the Center on Artificial Intelligence for Social Solutions. A day earlier, the University of California, Berkeley, unveiled its newly minted Center for Human-Compatible Artificial Intelligence. Even as science and technology pundits, including Stephen Hawking, Bill Gates and Elon Musk, warn of the overthrow of humanity by advanced artificial intelligence -- a prospect that appears nowhere on the horizon, experts say -- scientists are increasingly looking to the ways in which AI might aid humans.

Call it artificial intelligence with a human touch. This week, two California universities separately announced new centers devoted to studying the ways in which AI can help humanity. USC's Viterbi School of Engineering and its School of Social Work said Wednesday that they had joined forces to launch the Center on Artificial Intelligence for Social Solutions. A day earlier, the University of California, Berkeley unveiled its newly minted Center for Human-Compatible Artificial Intelligence. Even as science and technology pundits (including Stephen Hawking, Bill Gates and Elon Musk) warn of the overthrow of humanity by advanced artificial intelligence - a prospect that appears nowhere on the horizon, experts say - scientists are increasingly looking ahead to the ways in which AI might actually aid human lives.

We consider the problem of learning object arrangements in a 3D scene. The key idea here is to learn how objects relate to human poses based on their affordances, ease of use and reachability. In contrast to modeling object-object relationships, modeling human-object relationships scales linearly in the number of objects. We design appropriate density functions based on 3D spatial features to capture this. We learn the distribution of human poses in a scene using a variant of the Dirichlet process mixture model that allows sharing of the density function parameters across the same object types. Then we can reason about arrangements of the objects in the room based on these meaningful human poses. In our extensive experiments on 20 different rooms with a total of 47 objects, our algorithm predicted correct placements with an average error of 1.6 meters from ground truth. In arranging five real scenes, it received a score of 4.3/5 compared to 3.7 for the best baseline method.

The first annual workshop on the role of AI in ambient intelligence was held in Riva de Garda, Italy, on August 29, 2006. The workshop was colocated with the European Conference on Artificial Intelligence (ECAI 2006). It provided an opportunity for researchers in a variety of AI subfields together with representatives of commercial interests to explore ambient intelligence technology and applications.