Tootfinder

Tesla quietly launches its robotaxi service in Austin, with ~10 vehicles and a human safety driver; Elon Musk says customers will pay "a $4.20 flat fee" (Financial Times)
https://www.ft.com/content/5aefc191-d3de-4e6b-a2eb-08defd0678ee

Tesla launches robotaxi service in Austin
Self-driving technology on which Elon Musk has staked future of his company debuts in Texas

Crowdsourcing eHMI Designs: A Participatory Approach to Autonomous Vehicle-Pedestrian Communication
Ronald Cumbal, Didem Gurdur Broo, Ginevra Castellano
https://arxiv.org/abs/2506.18605

Crowdsourcing eHMI Designs: A Participatory Approach to Autonomous Vehicle-Pedestrian Communication
As autonomous vehicles become more integrated into shared human environments, effective communication with road users is essential for ensuring safety. While previous research has focused on developing external Human-Machine Interfaces (eHMIs) to facilitate these interactions, we argue that involving users in the early creative stages can help address key challenges in the development of this technology. To explore this, our study adopts a participatory, crowd-sourced approach to gather user-ge…

Wi-Fi Sensing Tool Release: Gathering 802.11ax Channel State Information from a Commercial Wi-Fi Access Point
Zisheng Wang, Feng Li, Hangbin Zhao, Zihuan Mao, Yaodong Zhang, Qisheng Huang, Bo Cao, Mingming Cao, Baolin He, Qilin Hou
https://arxiv.org/abs/2506.16957

Wi-Fi Sensing Tool Release: Gathering 802.11ax Channel State Information from a Commercial Wi-Fi Access Point
Wi-Fi sensing has emerged as a powerful technology, leveraging channel state information (CSI) extracted from wireless data packets to enable diverse applications, ranging from human presence detection to gesture recognition and health monitoring. However, CSI extraction from commercial Wi-Fi access point lacks and out of date. This paper introduces ZTECSITool,a toolkit designed to capture high-resolution CSI measurements from commercial Wi-Fi 6 (802.11ax) access points, supporting bandwidths u…

“Apocalypse forces us to radically change. But by facing the future with optimism instead of doom, we can transform ourselves into the kinds of people-the kinds of communities—who can survive.”
https://slate.com/technology/2025/05/h

Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and this more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of manservant caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.

“Toyama’s research reminds us that there are few one-size-fits-all solutions. If technology is going to improve the lives of the world’s poorest, it must be grounded in a deep understanding of human behavior and an appreciation for cultural differences.”
— Bill Gates
https://

Q&A with Hugging Face Chief Ethics Scientist Margaret Mitchell on aligning AI development with human needs, the "illusion of consensus" around AGI, and more (Melissa Heikkilä/Financial Times)
https://www.ft.com/content/7089bff2-25fc-4a25-98bf-8828ab24…

Margaret Mitchell: artificial general intelligence is ‘just vibes and snake oil’
One of the pioneers of AI ethics explains why human needs should be the central driver in the development of the technology

At Cannes, YouTube CEO Neal Mohan says YouTube plans to integrate Google's most advanced AI video generation tool, Veo 3, into YouTube Shorts later this summer (Alex Weprin/The Hollywood Reporter)
https://www.hollywoodreporter.com/business

YouTube to Add Google’s Veo 3 to Shorts in Move That Could Turbocharge AI on the Video Platform
The Veo 3 video generator is capable of creating both videos and sound based on text prompts, with YouTube CEO Neal Mohan saying the "AI technology will push the limits of human creativity."

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps

Vaseline. Vaseline (or, indeed, lip balm) on the lenses works a treat. Causes no lasting damage but places the vehicle out of use for some time. https://www.theguardian.com/technology/2025/jun/10/uber-uk-trial-of-self…

Meta is preparing to unveil a new artificial intelligence research lab
dedicated to pursuing “superintelligence,”
a hypothetical A.I. system that exceeds the powers of the human brain,
as the tech giant jockeys to stay competitive in the technology race,
according to four people with the knowledge of the company’s plans.
Meta has tapped Alexandr Wang, 28,
the founder and chief executive of the A.I. start-up Scale AI,
to join the new lab, the people s…

Meta Is Creating a New A.I. Lab to Pursue ‘Superintelligence’
The new lab, set to include the Scale AI founder Alexandr Wang, is part of a reorganization of Meta’s artificial intelligence efforts under Mark Zuckerberg.

The problem of course is that our problems are complex.
“Advanced AI suffers ‘complete accuracy collapse’ in face of complex problems, study finds” - https://www.theguardian.com/technology/2025/jun/09/apple-artificial-intelligence…

Understanding Human-AI Trust in Education
Griffin Pitts, Sanaz Motamedi
https://arxiv.org/abs/2506.09160 https://arxiv.org/pdf/2506.0…

Understanding Human-AI Trust in Education
As AI chatbots become increasingly integrated in education, students are turning to these systems for guidance, feedback, and information. However, the anthropomorphic characteristics of these chatbots create ambiguity regarding whether students develop trust toward them as they would a human peer or instructor, based in interpersonal trust, or as they would any other piece of technology, based in technology trust. This ambiguity presents theoretical challenges, as interpersonal trust models ma…

Questions about the meaning of #artificialintelligence and its implications for human dignity and well-being highlighted a major conference at YDS last month, titled, “A.I. and the Ends of Humanity: Thinking Theologically After ChatGPT.” Full story by Timothy Cahill '16 M.A.R.

Another of my forays into AI ethics is just out! This time the focus is on the ethics (or lack thereof) of Reinforcement Learning Feedback (RLF) techniques aimed at increasing the 'alignment' of LLMs.
The paper is fruit of the joint work of a great team of collaborators, among whom @… and @…

Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback - Ethics and Information Technology
This paper critically evaluates the attempts to align Artificial Intelligence (AI) systems, especially Large Language Models (LLMs), with human values and intentions through Reinforcement Learning from Feedback methods, involving either human feedback (RLHF) or AI feedback (RLAIF). Specifically, we show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness. Through a multidisciplinary sociotechnical critique, we examine both the theoretical underpinni…

Quantum Adiabatic Generation of Human-Like Passwords
Sascha M\"ucke, Raoul Heese, Thore Gerlach, David Biesner, Loong Kuan Lee, Nico Piatkowski
https://arxiv.org/abs/2506.08917

Quantum Adiabatic Generation of Human-Like Passwords
Generative Artificial Intelligence (GenAI) for Natural Language Processing (NLP) is the predominant AI technology to date. An important perspective for Quantum Computing (QC) is the question whether QC has the potential to reduce the vast resource requirements for training and operating GenAI models. While large-scale generative NLP tasks are currently out of reach for practical quantum computers, the generation of short semantic structures such as passwords is not. Generating passwords that mi…

At Cannes, YouTube CEO Neal Mohan says YouTube plans to integrate Google's most advanced AI video generation tool, Veo 3, into YouTube Shorts later this summer (Alex Weprin/The Hollywood Reporter)
https://www.hollywoodreporter.com/business

YouTube to Add Google’s Veo 3 to Shorts in Move That Could Turbocharge AI on the Video Platform
The Veo 3 video generator is capable of creating both videos and sound based on text prompts, with YouTube CEO Neal Mohan saying the "AI technology will push the limits of human creativity."

The more time goes on, the more conflicted I get about #AI. (or, more specifically, generative AI. I like causal AI a fair bit)
On one hand, I hate so much about it:
the needless environmental and electronic parts waste, the impact on labor, the monopolistic nature of main organizations driving it, the endless conversations about AGI and other absurdly utopian (or dystopian) futures, the widespread theft of IP and human work, the devaluation of labor and craft… so much of it is not okay at all.
On the other hand, I kinda get it?
I like to test software for myself, so I've been dipping my toes into some popular AI tools over the past couple of years. I have a paid subscription to ChatGPT (which I don’t feel great about, I know), and I have found genuine utility in it.
More so, I feel so conflicted when I talk to people I respect and who I think are very smart and creative and they tell me how in love they are with all of these AI tools, about the complex workflows they build, about their experiments with agentic AI and integrations... These people seem so excited, so alive, so joyous about the things that this technology allows them to do. They often wouldn't have had the skills / knowledge / financial capital to do some of those things with human efforts alone. And now I see them coding their own tools, doing complex data analysis, trying creative experiments with graphics / text / video...
And I feel like a total jerk going "BUT ACTUALLY THIS IS UNETHICAL AND INEFFICIENT AND YOU SHOULD STOP IT BECAUSE AI SUCKS".
The technology behind all these gen AI models does have real utility, and it has kicked off a lot of creativity from people who wouldn't have dabbled in those kinds of projects otherwise.
So I don't know how to feel. Because I can't let go of the guilt and the real problems and the awareness of how much empty hype there is.
#technology #artificialintelligence #genAI #ChatGPT

This https://arxiv.org/abs/2407.18950 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Unexplainability of Artificial Intelligence Judgments in Kant's Perspective
Kant's Critique of Pure Reason, a major contribution to the history of epistemology, proposes a table of categories to elucidate the structure of the a priori principles underlying human judgment. Artificial intelligence (AI) technology, grounded in functionalism, claims to simulate or replicate human judgment. To evaluate this claim, it is necessary to examine whether AI judgments exhibit the essential characteristics of human judgment. This paper investigates the unexplainability of AI judgme…

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps

«AI systems are an attack on workers, climate goals, our information environment, and civil liberties. Rather than enhancing our human qualities, these systems degrade our social relations, and undermine our capacity for empathy and care.»
Nice writing by @…!
https://thedabbler.patatas.ca/pages/ai-is-dehumanization-technology.html

This https://arxiv.org/abs/2505.16512 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Beyond Face Swapping: A Diffusion-Based Digital Human Benchmark for Multimodal Deepfake Detection
In recent years, the rapid development of deepfake technology has given rise to an emerging and serious threat to public security: diffusion model-based digital human generation. Unlike traditional face manipulation methods, such models can generate highly realistic videos with consistency through multimodal control signals. Their flexibility and covertness pose severe challenges to existing detection strategies. To bridge this gap, we introduce DigiFakeAV, the first large-scale multimodal digi…

Want super-thorough AI responses by applying deep thought & reasoning AI technology?
Any Microsoft 365 Copilot (Commercial) licensed user can now use the Researcher & Analyst agents at no additional cost! These new agents provide very thorough responses to help you collect FACTS on informational topics & review DATA to derive insights... with human language.
✅ #Researcher

Researcher and Analyst are now generally available | Microsoft 365 Blog
We’re excited to announce the general availability of Researcher and Analyst, two first-of-their-kind reasoning agents designed for work. Learn more.

"Jim Coplien ends his talk with a simple call to action: at every conference where new technology is introduced, software engineers should strive to be skeptical; to ask whether it improves the quality of life of society as a whole, and to take it home only to increase the human value of our products and services.
Focus on the people. Again and again. Because that is what Agile and OOP were all about, to begin with."
https://deprogrammaticaipsum.com/james-coplien/

And by “prevent crime,” what they mean is AI will be able to automatically yell at homeless people to move along! Innovation!
https://mastodon.social/@eff/114626708912715793

I just ran across this on LinkedIn: yet another study showing that LLMs embed and amplify human biases in the training data—this time in resume screening.
Yet this is the technology Trump, Musk, and DOGE want to use to rewrite things like HUD regulations and to replace human support agents.

Gender, race, and intersectional bias in AI resume screening via language model retrieval
Kyra Wilson and Aylin Caliskan examine gender, race, and intersectional bias in AI resume screening and suggest protective policies.

FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines
Athena Vakali, Ilias Dimitriadis
https://arxiv.org/abs/2506.09107 https:…

FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines
AI models have become active decision makers, often acting without human supervision. The rapid advancement of AI technology has already caused harmful incidents that have hurt individuals and societies and AI unfairness in heavily criticized. It is urgent to disrupt AI pipelines which largely neglect human principles and focus on computational biases exploration at the data (pre), model(in), and deployment (post) processing stages. We claim that by exploiting the advances of agents technology,…

Energy-Oriented Computing Architecture Simulator for SNN Training
Yunhao Ma (Pengcheng Laboratory, Southern University of Science and Technology), Wanyi Jia (Pengcheng Laboratory, Southern University of Science and Technology), Yanyu Lin (Pengcheng Laboratory), Wenjie Lin (Pengcheng Laboratory), Xueke Zhu (Pengcheng Laboratory), Huihui Zhou (Pengcheng Laboratory), Fengwei An (Southern University of Science and Technology)

Energy-Oriented Computing Architecture Simulator for SNN Training
With the growing demand for intelligent computing, neuromorphic computing, a paradigm that mimics the structure and functionality of the human brain, offers a promising approach to developing new high-efficiency intelligent computing systems. Spiking Neural Networks (SNNs), the foundation of neuromorphic computing, have garnered significant attention due to their unique potential in energy efficiency and biomimetic neural processing. However, current hardware development for efficient SNN train…

Behold :) My new #cybersecurity talk is ready and you can see it in the best events around you.
Title: The archetypes of the attackers.
Summary: This talk will lead you on a journey to discover the archetypes of attackers, the tools they use, their motivations for targeting what you've built, and how a geopolitical shift can alter their interest in your resources.

Bogomil Shopov - Бого's Speaker Profile
Human. Artist. Hacker. I care about free and open-source software(F/LOSS), cybersecurity, ethical design, privacy, and technology. 20+ experience in t...

Pureformer-VC: Non-parallel Voice Conversion with Pure Stylized Transformer Blocks and Triplet Discriminative Training
Wenhan Yao, Fen Xiao, Xiarun Chen, Jia Liu, YongQiang He, Weiping Wen
https://arxiv.org/abs/2506.08348

Pureformer-VC: Non-parallel Voice Conversion with Pure Stylized Transformer Blocks and Triplet Discriminative Training
As a foundational technology for intelligent human-computer interaction, voice conversion (VC) seeks to transform speech from any source timbre into any target timbre. Traditional voice conversion methods based on Generative Adversarial Networks (GANs) encounter significant challenges in precisely encoding diverse speech elements and effectively synthesising these elements into natural-sounding converted speech. To overcome these limitations, we introduce Pureformer-VC, an encoder-decoder frame…

Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration
Matthew Russell, Aman Shah, Giles Blaney, Judith Amores, Mary Czerwinski, Robert J. K. Jacob
https://arxiv.org/abs/2506.04167

Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration
AI-based interactive assistants are advancing human-augmenting technology, yet their effects on users' mental and physiological states remain under-explored. We address this gap by analyzing how Copilot for Microsoft Word, a LLM-based assistant, impacts users. Using tasks ranging from objective (SAT reading comprehension) to subjective (personal reflection), and with measurements including fNIRS, Empatica E4, NASA-TLX, and questionnaires, we measure Copilot's effects on users. We also evaluate …

The tape recorder (or any recording and playback technology) remains one of the most important inventions in its philosophical implications in human history, imo. For the first time, we gained new access to sound as raw material: almost as pure phenomena, potentially unfiltered by description, notation, or the cultural identifiers that usually shape musical/sonic experience. (I don’t mean that tape recording gave us direct, unmediated access - whether such a thing is ever possible with any p…

«AI systems are an attack on workers, climate goals, our information environment, and civil liberties. Rather than enhancing our human qualities, these systems degrade our social relations, and undermine our capacity for empathy and care.»
Nice writing by @…!
https://thedabbler.patatas.ca/pages/ai-is-dehumanization-technology.html

David Cope, a composer and algorithmic composition pioneer who created a program in the 1980s to write music in the style of Bach and others, died at age 83 (Miguel Salazar/New York Times)
https://www.nytimes.com/2025/06/02/technology/david-cope-dead-ai…

David Cope, Godfather of A.I. Music, Is Dead at 83
His EMI algorithm, an early form of artificial intelligence that he developed in the 1980s, prompted searching questions about the limits of human creativity.

I just ran across this on LinkedIn: yet another study showing that LLMs embed and amplify human biases in the training data—this time in resume screening.
Yet this is the technology Trump, Musk, and DOGE want to use to rewrite things like HUD regulations and to replace human support agents.

Gender, race, and intersectional bias in AI resume screening via language model retrieval
Kyra Wilson and Aylin Caliskan examine gender, race, and intersectional bias in AI resume screening and suggest protective policies.

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps

This https://arxiv.org/abs/2201.03074 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Survey of Passive Sensing for Workplace Wellbeing and Productivity
The modern workplace is undergoing a radical transformation, driven by technological advances that blur the boundaries between human capability and digital augmentation. At the forefront of this evolution is passive sensing technology - a suite of tools that quietly monitor and interpret human behavior without active user engagement. This paper examines how these technologies are reshaping our understanding of workplace dynamics, with a particular focus on employee wellbeing and productivity. T…

This https://arxiv.org/abs/2505.18422 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

A Task-Driven Human-AI Collaboration: When to Automate, When to Collaborate, When to Challenge
According to several empirical investigations, despite en-hancing human capabilities, human-AI cooperation fre-quently falls short of expectations and fails to reach true synergy. We propose a task-driven framework that reverses prevalent approaches by assigning AI roles according to how the task's requirements align with the capabilities of AI technology. Three major AI roles are identified through task analysis across risk and complexity dimensions: au-tonomous, assistive/collaborative, and a…

Exploring the Convergence of HCI and Evolving Technologies in Information Systems
Rajan Das Gupta, Ashikur Rahman, Md Imrul Hasan Showmick, Md. Yeasin Rahat, Md. Jakir Hossen
https://arxiv.org/abs/2506.08549

Exploring the Convergence of HCI and Evolving Technologies in Information Systems
Modern technology driven information systems are part of our daily lives. However, this deep integration poses new challenges to the human computer interaction (HCI) professionals. With the rapid growth of mobile and cloud computing and the Internet of Things (IoT), the demand for HCI specialists to design user-friendly and adaptable interfaces has never been more pressing. Especially for diverse user groups such as children, the elderly and people with disabilities who need interfaces tailored…