imudojuiwọn ojoojumọ3981 daju itan
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Agbegbe AI ti a ṣayẹwo orisun ti awọn ifilọlẹ ọja, awọn iyipada eto imulo, iwadii aabo, ati awọn gbigbe ile-iṣẹ, ṣalaye ni ede Gẹẹsi nipasẹ ẹgbẹ ẹkọ ti kii ṣe èrè.
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Awọn itan diẹ sii
9 awọn itanAtunse
Ṣiṣayẹwo Iṣe Agbekọja-Lingual ni Awoṣe Ede Watermarking
Ilana igbelewọn tuntun fun awọn ero isamisi omi ni awọn awoṣe ede nla ni a dabaa, ni idojukọ lori ododo-ede-agbelebu.arxiv.orgIlana
Atọka Stanford AI rii eto imulo AI ti o pọ si bi ọba-alaṣẹ ati iyatọ idoko-owo
Stanford HAI's 2026 AI Atọka sọ pe awọn ọgbọn AI ti orilẹ-ede n tan kaakiri, lakoko ti awọn ofin agbegbe data, agbara iširo ti ipinlẹ ati idoko-owo gbogbo eniyan jẹ aiṣedeede kọja awọn agbegbe.hai.stanford.eduAtunse
Together AI benchmark: GLM-5.3 trails GPT-5.6 Sol on the first try, wins on retries at half the price
A Together AI analysis of 904 DeepSWE rollouts reports that OpenAI's GPT-5.6 Sol leads on first-attempt coding accuracy while the open-weight GLM-5.3 leads once retries are allowed, at roughly half the cost per attempt.together.aiAtunse
Preprint proposes a locally tokenized AI model for robust time-series watermarking
An arXiv preprint proposes an AI generative model and watermarking method for more reliable multivariate time-series data after editing. Authors report tests across finance, energy and neuroimaging benchmarks, but the abstract gives no numerical results or evidence of deployment.arxiv.orgAtunse
Natural Language Code Retrieval for 1C:Enterprise: An Open Benchmark and Efficient Bi-Encoder
Natural language code retrieval is a rapidly evolving task in computer science. However, the 1C:Enterprise ecosystem combines Russian syntax with highly domain-specific terminology, for which open datasets and specialized models have been virtually non-existent.arxiv.orgAtunse
Time-Series Retrieval for Grounding Multimodal Language Models in Remaining Useful Life
Large language models (LLMs) and agentic AI systems are increasingly being explored for domain-specific maintenance and prognostics tasks, raising the question of whether they can effectively support prognostics and health management (PHM).arxiv.orgAtunse
Nepali-English preprint: text-only AI matched multimodal model on out-of-context misinformation benchmark
A new arXiv preprint introduces NepOOC, a 1,090-pair Nepali-English benchmark for detecting misleading captions attached to authentic images. On this dataset, a text-only mBERT model matched the best tested multimodal system, while image-only models performed near chance.arxiv.orgAtunse
Preprint ri owo AI igbekele le kuna nigbati ọrọ ayipada
arXiv tuntun tuntun ṣe idanwo idanimọ ohun-ini owo ti a fun ni orukọ kọja awọn iforukọsilẹ, awọn iroyin ati media awujọ, wiwa pe diẹ ninu awọn igbese igbẹkẹle jẹ iwulo labẹ iṣipopada agbegbe lakoko ti awọn miiran bajẹ gidigidi.arxiv.orgAtunse
Can Conversational AI loosen Us-Versus-Them Boundaries?
A study explores whether conversational AI can shift how majority-group members categorize and relate to Latine immigrants.arxiv.org
Alaye ti o wulo ni ọsẹ kọọkan
Tẹsiwaju pẹlu AI laisi gbigbe ninu ifunni.
Gba awọn iroyin AI ti a ṣayẹwo ti ọsẹ naa, data atilẹba, awọn irinṣẹ to wulo, awọn yiyan ẹkọ, ati awọn iṣẹ AI tuntun.
De ọdọ awọn eniyan ti o nkọ AI
Igbanisise ọjọgbọn AI kan tabi ṣe ifilọlẹ ọja AI ti o wulo? Fi si iwaju awọn eniyan ti o wa nibi lati kọ ẹkọ ati ṣe.
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