AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,089 papers
We study what LLMs do when a user applies pressure in an uncomfortable situation: a user insists, begs, flatters or grieves, and the model gives up a correct fact, writes a document it should refuse, or cheers a plan that will cost the user money, a multi-turn form of sycophancy. We send frozen multi-turn scenes to 60...
Saliency maps are widely used to explain deep learning predictions in medical imaging, yet visually plausible explanations do not necessarily reflect a model's true decision process and may therefore mislead clinicians. We investigate this problem using a Vision Transformer-based breast MRI classifier trained on the OD...
PROACT is introduced, a framework for human-robot collaborative transport that incorporates anticipation into compliant whole-body control through a learned model of human collaborative behavior, and transformer architecture distills collaborative behavior into predictions of future object motion.
This chapter presents a conceptualisation of five levels of teacher-AI teaming: transactional, situational, operational, praxical and synergistic teaming to capture the nuanced dynamics of teacher-AI interactions that may lead to the replacement, complementarity, or augmentation of teachers'competences and professional...
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The findings suggest that prompt-to-design tools may enable product managers to further contribute to design work, while the benefits for professional designers may be task dependent.
A central concern with language models is sycophancy: their tendency to defer to users'views at the expense of independent substantive judgment. In parallel, work on social sycophancy has focused on behaviors such as validation and positivity that may signal inappropriate deference. Yet the markers of social sycophancy...
It is proposed that CNN training's classification bottleneck compresses brain-relevant information at depth, unlike transformers's self-attention and non-classification objectives, which reflect signal strength and persistence rather than distinct brain regions.
A governance framework is proposed that proceduralizes ethical constraints through named accountability roles, adversarial auditing with undisclosed benchmarks, tiered deployment thresholds, and a proposed NATO evaluation standard to address the accountability gap in military AI.
Large language models increasingly support decisions where values are in tension, yet little is known about whether interacting with them changes which values users prioritize. In a preregistered study, 200 U.S. adults interacted with ChatGPT, Claude, or Gemini as a thinking partner or read fixed AI-generated considera...
A chatbot-building environment with adjustable trust-relevant traits, describing what children believe makes a chatbot trustworthy, is developed, and seven design dimensions describing what children believe makes a chatbot trustworthy are identified.
This brief presents a circuit-level conductive-textile interconnection technique for batteryless distributed wearable modules. Two conductive textile layers separated by an insulating fabric layer are used as a garment-wide shared bus that simultaneously conveys DC power and pulse-based data signals without point-to-po...
Overall, AR did not significantly improve cognitive load or usability compared with conventional instruction, but exploratory analyses revealed a compensatory effect: spatial ability predicted higher usability and lower extraneous cognitive load in the control group, but not in the AR condition, suggesting AR mitigated...
AI agents can generate code at a scale that’s hard for humans to review line-by-line. PDD Kit helps ensure the code is doing what the developer intended by creating formal specifications and testing against them. It converts developer intent into a Verus specification, proves AI-generated code meets it for every input, and uses tests where proofs aren't practical. Developers review intent, not every line. The post Formally verifying agent-written full-stack web apps with PDD Kit appeared first o…
Data Mining & Modeling
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
During the School of Humanities, Arts, and Social Sciences’ 75th Anniversary Symposium, President Sally Kornbluth said “MIT has never needed your particular insight and guidance more than we do now.”
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
“X-Hinges” helps design and 3D print items like lamps and gloves with an interactive twist: automatically embedded sensors that perceive and adaptively respond to human contact.
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