imudojuiwọn ojoojumọ1992 daju itan
AI iroyin. Laisi ariwo.
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.
Ifowosowopo ti o ni idaniloju
Gbogbo itan ni asopọ si ẹri ti o lagbara julọ ti o wa: awọn orisun atilẹba nigbati o wa, bibẹkọ ti o han gbangba.
English itele
Kini o ṣẹlẹ, idi ti o ṣe pataki, ati kini lati wo - laisi jargon.
Ko si kikun
Nigbati awọn ifihan agbara jẹ tinrin, a jade ohunkohun kuku ju òwú kikọ sii.
Awọn itan diẹ sii
9 awọn itanAtunse
Awọn oniwadi Apple ṣe ijabọ ofin igbelowọn fun awọn awoṣe ikẹkọ pẹlu data aipe
Iwadii ti o ju 2,000 ikẹkọ awoṣe-ede n ṣiṣẹ sọ pe data ibi-afẹde aipe ni a le tun ṣe ni awọn akoko 15-20 ni awọn akojọpọ, pẹlu iwọn ti o dara julọ ti o yatọ nipasẹ iwọn ati iṣiro.machinelearning.apple.comAtunse
Awọn oniwadi Apple Dabaa Awọn iyipada Lexical lati Mu Ikẹkọ Awoṣe Onimọ-ede lọpọlọpọ
Awọn oniwadi Apple ṣe apejuwe LINK, idasi ikẹkọ iṣaaju ti o rọpo awọn ọrọ Gẹẹsi ti a yan pẹlu awọn itumọ ipele-ọrọ lati ede ibi-afẹde kan. Iwe naa ṣe ijabọ awọn ilọsiwaju kọja awọn ede mẹjọ ati awọn iwọn awoṣe marun, pẹlu to iyara ilọpo meji ni wiwa iṣẹ ṣiṣe isale deede.machinelearning.apple.comIlana
Position paper calls for certification before AI agents make market decisions
A position paper reports tacit collusion by DeepSeek-R1 agents in a simulated Bertrand pricing market, even after human prompts against collusion. It argues that observed-behavior certification should precede deployment of reasoning agents in economic markets; the evidence and safeguards remain preliminary.arxiv.orgAtunse
Systematic review maps the growing use of large language models in mental health
A systematic review surveys how large language models are being studied for mental-health analysis, risk assessment, therapy support and multimodal monitoring, while stressing unresolved ethical and regulatory challenges.arxiv.orgIlana
Model Cards Alone May Not Govern Open-Weight Foundation Models, Position Paper Argues
An ICML 2026 position paper analyzing 500 Hugging Face model cards argues that open-weight foundation models need coordinated model cards, acceptable-use policies, and licenses to address safety and governance gaps.arxiv.orgAtunse
A proposed metric would measure how difficult game worlds are to predict
A position paper proposes the Transition Complexity Profile, a standardized way to describe how unpredictable and long-range the dynamics of game environments are for game-world modeling and reinforcement learning.arxiv.orgAtunse
FM-Bench tests whether AI agents can manage a football club for 20 years
A new arXiv benchmark places 15 language-model agents in a 20-year football-management simulation, testing whether they can make consistent decisions when short-term choices affect long-term outcomes.arxiv.orgAtunse
FinRCA-Bench finds financial AI diagnosis depends heavily on evidence retrieval
A new arXiv benchmark reports that changing only the retrieval method raised a fixed model’s exact accuracy on financial reconciliation cases from 2.05% to 72.44%. The study also finds that a correct root-cause label often does not mean the system returned sufficient evidence for an auditable diagnosis.arxiv.orgAtunse
Abra iwe maapu iṣiro ati data tradeoffs ni itankale aworan ikẹkọ
Iwadi arXiv tuntun ṣafihan awọn adanwo ofin iwọn-si-aworan fun awọn awoṣe itankale ọrọ-si-aworan kọja awọn isuna iṣiro lati 10 ^ 19 si 10 ^ 22 FLOPs. Awọn onkọwe rẹ ṣe ijabọ pe awọn awoṣe aworan nilo data diẹ sii fun paramita ju awọn awoṣe ede lati ṣe ikẹkọ daradara.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.
Firanṣẹ iṣẹ AI kanFi ohun elo AI silẹ