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 everyday thing


A(I)nimism: Re-enchanting the World Through AI-Mediated Object Interaction

Mykhaylychenko, Diana, Thasin, Maisha, Baradari, Dunya, Mhungu, Charmelle

arXiv.org Artificial Intelligence

Animist worldviews treat beings, plants, landscapes, and even tools as persons endowed with spirit, an orientation that has long shaped human-nonhuman relations through ritual and moral practice. While modern industrial societies have often imagined technology as mute and mechanical, recent advances in artificial intelligence (AI), especially large language models (LLMs), invite people to anthropomorphize and attribute inner life to devices. This paper introduces A(I)nimism, an interactive installation exploring how large language objects (LLOs) can mediate animistic relationships with everyday things. Housed within a physical 'portal', the system uses GPT-4 Vision, voice input, and memory-based agents to create evolving object-personas. Encounters unfold through light, sound, and touch in a ritual-like process of request, conversation, and transformation that is designed to evoke empathy, wonder, and reflection. We situate the project within anthropological perspectives, speculative design, and spiritual HCI. AI's opacity, we argue, invites animistic interpretation, allowing LLOs to re-enchant the mundane and spark new questions of agency, responsibility, and design.


Do language models have coherent mental models of everyday things?

Gu, Yuling, Mishra, Bhavana Dalvi, Clark, Peter

arXiv.org Artificial Intelligence

When people think of everyday things like an egg, they typically have a mental image associated with it. This allows them to correctly judge, for example, that "the yolk surrounds the shell" is a false statement. Do language models similarly have a coherent picture of such everyday things? To investigate this, we propose a benchmark dataset consisting of 100 everyday things, their parts, and the relationships between these parts, expressed as 11,720 "X relation Y?" true/false questions. Using these questions as probes, we observe that state-ofthe-art pre-trained language models (LMs) like GPT-3 and Macaw have fragments of knowledge about these everyday things, but do not have fully coherent "parts mental models" (54-59% accurate, 19-43% conditional constraint violation). We propose an extension where we add a constraint satisfaction layer on top of the LM's raw predictions to apply commonsense constraints. As well as removing inconsistencies, we find that this also significantly improves accuracy (by 16-20%), suggesting how the incoherence of the LM's pictures of Figure 1: While humans appear to have coherent mental everyday things can be significantly reduced.


17 Everyday Things You Didn't Know Could Be Hacked

#artificialintelligence

Even news of millions of so-called smart speakers being hacked right before 2017's holiday season didn't seem to slow down sales of Amazon's Echo and Google Home. But if you have either, you should definitely take precautions against hackers. If these speakers are hacked, they could divulge sensitive information such as when you'll be out of town or any upcoming doctor's appointments, along with your credit card and bank account info, shares NBC.com. Equally alarming, if it's connected to your home security system, a hacker could simply turn it off and walk right through your front door. To keep yourself and your home safe, limit how much info you connect through these types of speakers and unplug it when you go on vacation. Don't worry--there's no risk with these 15 hilarious things you can ask Alexa to do.