AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,682 papers
It is suggested that generative AI may not necessarily alter final ethical judgments but may be associated with broader exploration of perspectives prior to reaching those judgments.
As organizations accelerate the adoption and use of artificial intelligence, a common misconception arises that equates automation with progress and strategic necessity. This paper argues that not all that can be automated should be automated. It introduces Strategic Minimalism, a disciplined and ethically grounded app...
The rapid integration of artificial intelligence (AI) into high-stakes decision-making has outpaced established mechanisms for human oversight and accountability, leaving organisations with limited guidance on the responsible delegation of decision authority. This study examines four widely documented AI deployments: t...
The mouse cursor has remained visually mute for half a century: it shows where we point, but says nothing about how we move. We present the Onomatopoeia Cursor, a shipped macOS overlay that classifies cursor kinematics in real time and renders Japanese mimetic words (onomatopoeia) as animated comic-style lettering abov...
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Large Language Models (LLMs) are everywhere, yet many learners lack a concrete mental model of how they generate text. This paper presents LLMs Unplugged, an unplugged set of activities that teaches the training-to-generation loop (and beyond) using hand-built n-gram models and simple weighted sampling. Workshops based...
Open peer review is advocated as a way to make review more dialogic, yet evidence on whether it changes participant behavior remains scarce. We compare two transparent review designs: Nature Human Behaviour, an editor-mediated model, and International Conference on Learning Representations 2023, a discussion-based mode...
A review of adaptive human-AI collaboration literature through a closed-loop lens and integrates three taxonomies into MCAL, a dual-timescale Multimodal Co-Adaptation Loop reference model.
Narrative visualization embeds data in visual stories to make medical information more relatable for non-experts. Despite the growing use of character elements in health communication, evidence on whether individual characters support affective responses remains inconclusive. Physiological evidence independent of verba...
Dark commercial patterns and deceptive user interface (UI) designs (or DPs) trick consumers into actions that benefit the shareholders. The legal and ethical implications of DPs are shaped by the sociocultural context. Special types of DPs exist in Japan, but the impact of these DPs on consumer attitudes and behaviour...
Impaired driving, including distraction, fatigue, and intoxication, leads to thousands of fatalities annually. Impairment detection and related assistive technologies are rapidly advancing, but their full potential remains unrealized. The inconsistent maturity of impairment research and anomalies within individual doma...
Multimodal chat analysis of social media stickers (MCAS) benefits from jointly modeling text and sticker semantics, yet it is inherently challenged by the interference between sentiment and intent recognition. Although existing multi-task approaches achieve competitive performance, they largely ignore this inter-task i...
(un)stable equilibrium is an ongoing series of works that is based on a practice of training generative neural networks without data. This paper introduces the second series of (un)stable equilibrium works, in which the process of training without data is visualised in a series of video pieces. These works show a gener...
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
A Blog post by H company on Hugging Face
The PKG Center for Social Impact expands Code.Tulsa experiential learning program.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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