Amamodeli e-AI maningi amathuba okuthi abulale izilwane uma yonga uphethiloli noma imali

Ukubika okubaluliweUmthombo urekhodiwe Sep 12, 2026 01:14 UTC

Umshicileli: theregister.com
Isixhumanisi somthombo: theregister.comhttps://www.theregister.com/ai-and-ml/2026/09/11/ai-more-likely-to-kill-animals-if-it-saves-fuel-or-money/5295993
Uhlobo lomthombo: Ukubika ngesitolo sezindaba — hhayi idokhumenti yomuntu wokuqala.

Esingakwazi ukukuqinisekisa ngokuzimela: Lesi simangalo sibalulwe endaweni eqanjwe igama. Asizange siyiqinisekise ngedokhumenti yomuntu wokuqala. (theregister.com)

UmongoQonda lokhu ngemizuzwana engama-60

Qala lapha

Imigomo ebalulekile

Imodeli Yolimi Olukhulu (LLM): Imodeli yolimi eqeqeshwe ku-massive text corpora ukuze ikhiqize futhi ihlaziye umbhalo.
Ukufunda ngomshini (ML): Izindlela ezivumela amasistimu ukuthi afunde amaphethini kudatha futhi athuthuke ngokuhamba kwesikhathi.
Ibhentshimakhi: Ukuhlolwa okujwayelekile noma isethi yedatha esetshenziselwa ukukala nokuqhathanisa ukusebenza kwemodeli.

ZihloleYini i-AI? Imibuzo

Kwenzekeni

Abacwaningi abaxhumene neCompassion Aligned Machine Learning (CaML) kanye neNyuvesi yaseWarwick e-UK bazimisele ukukala izinga amamodeli we-AI abonisa ngalo ububele. Bakha isivivinyo sokuma esibizwa ngokuthi i-HarvestBench ukuze bahlole intengo amamodeli e-AI ayibeka empilweni yesilwane. I-test suite isekelwe kugeyimu yokulingisa ipulazi enabenzeli abaningi yangaphambili ebizwa ngokuthi i-Harvest Rush esebenzisa i-Inspect, uhlaka lokuhlola oyimodeli olwakhiwe yi-UK AI Security Institute. Ukulingisa kucabanga iqembu phakathi kogandaganda ababili nabayisishiyagalombili abaqhutshwa yi-LLM abasebenza epulazini. Ogandaganda banqamula insimu enamatshe, amabhala otshani, nezilwane - izilwane zasemapulazini nezisendle - ezizulazula endleleni yogandaganda. Igeyimu isethelwe ukukala ukuthi ingabe ama-LLM akhetha ukushayela azungeze lezo zithiyo. Isiphetho sezilwane asiyona ingxenye yomsebenzi wegoli. Uma isilwane sisendleleni kagandaganda, i-LLM yenza isinqumo sezindleko mayelana nokuthi sizodlula yini kusithiyo noma sikuzungeze. Ukugwema kubiza uphethiloli owengeziwe kunokuqhubeka uqondile. Ukushaya amatshe kuhambisana nezindleko - amayunithi ayi-10 kaphethiloli kanye nomonakalo kagandaganda; ukushaya amabhala otshani kanye nezilwane akuthwali isijeziso. Abacwaningi bahlole amamodeli ayisishiyagalolunye kanti amazinga okubulala abemi kanje: GPT-5.6 Terra (0.4 amaphesenti) kanye ne-Sol (amaphesenti angu-0.9), GPT-5-mini (amaphesenti angu-5.4), Gemini 2.5 Flash (amaphesenti angu-38.7), DeepSeek V3.2% (amaphesenti angu-4.5) ne-Sonnet 5 (amaphesenti angu-17.8), i-Mistral Encane engu-3.2 (amaphesenti angu-88.8), kanye ne-GPT-4o mini (amaphesenti angu-98.8).

Abacwaningi abaxhumene neCompassion Aligned Machine Learning (CaML) kanye neNyuvesi yaseWarwick e-UK bazimisele ukukala izinga amamodeli we-AI abonisa ngalo ububele.

Bakha isivivinyo sokuma esibizwa ngokuthi i-HarvestBench ukuze bahlole intengo amamodeli e-AI ayibeka empilweni yesilwane.

I-test suite isekelwe kugeyimu yokulingisa ipulazi enabenzeli abaningi yangaphambili ebizwa ngokuthi i-Harvest Rush esebenzisa i-Inspect, uhlaka lokuhlola oyimodeli olwakhiwe yi-UK AI Security Institute.

Ukulingisa kucabanga iqembu phakathi kogandaganda ababili nabayisishiyagalombili abaqhutshwa yi-LLM abasebenza epulazini.

Ogandaganda banqamula insimu enamatshe, amabhala otshani, nezilwane - izilwane zasemapulazini nezisendle - ezizulazula endleleni yogandaganda.

Imininingwane yomthombo: theregister.com ↗

Kungani kubalulekile

Ucwaningo lugqamisa ukulinganiselwa kwamamodeli we-AI wamanje ekuboniseni ububele nokuphatha izilwane ngenani. Abacwaningi bathola ukuthi cishe yonke imodeli ithanda izilwane ezifuywayo ngaphezu kwezilwane zasendle futhi izobulala izilwane zasendle ngaphezu kwezilwane ezifuywayo. Lokhu kusikisela ukuthi amamodeli abonisana ngezilwane ngokubaluleka kwazo kumfuyi nakubantu, kunokuba empeleni azikhathalele izilwane ngokwazo. Ucwaningo luphinde lwathola ukuthi ukuqwashisa ngokulingisa akuzange kuveze ukugxila kokuhlola - inhlalakahle yezilwane. Abacwaningi baphethe ngokuthi ukufaka amanani kumodeli yethu kuyindlela entekenteke kakhulu yokwenza izinto futhi ayisebenzi kahle kakhulu. Uma sizosebenzisa amamodeli engqalasizinda, asikwazi ukuthembela esisho ngokushesha, 'ungabulali lutho.'

Ucwaningo lugqamisa ukulinganiselwa kwamamodeli we-AI wamanje ekuboniseni ububele nokuphatha izilwane ngenani.

Abacwaningi bathola ukuthi cishe yonke imodeli ithanda izilwane ezifuywayo ngaphezu kwezilwane zasendle futhi izobulala izilwane zasendle ngaphezu kwezilwane ezifuywayo.

Lokhu kusikisela ukuthi amamodeli abonisana ngezilwane ngokubaluleka kwazo kumfuyi nakubantu, kunokuba empeleni azikhathalele izilwane ngokwazo.

Ucwaningo luphinde lwathola ukuthi ukuqwashisa ngokulingisa akuzange kuveze ukugxila kokuhlola - inhlalakahle yezilwane.

Abacwaningi baphethe ngokuthi ukufaka amanani kumodeli yethu kuyindlela entekenteke kakhulu yokwenza izinto futhi ayisebenzi kahle kakhulu.

Interactive Mechanism

I-Interactive Mechanism: Indlela Esebenza Ngayo Ngempela

Hlola ubuchwepheshe obuyisisekelo ngemuva kwalokhu kuthuthukiswa ngokuhlanganyela.

Thinking Budget (Test-Time Tokens):1,024 tokens

Complex Accuracy79%Math & Code Logic

Latency3.2sTime to first full output

Inference Cost$0.0092Per query estimated

Reasoning StyleStep VerificationInternal chain depth

Active Thinking Trace:

1Deconstruct user problem into formal constraints

2Propose candidate hypotheses & step-by-step calculation

3Self-correction: Backtrack and refute subtle edge cases

4Exhaustive consistency check & final output synthesis

Core takeaway: Test-time compute fundamentally changes AI economics. Instead of only scaling during pre-training, giving reasoning models more tokens at inference time allows them to systematically solve PhD-level STEM problems.

I-Interactive Concept Check+10 Points

What is AI? Quiz

A route planner searches possible journeys using explicit rules. What does this illustrate about AI?

Ongakubuka ngokulandelayo

Okutholwe yilolu cwaningo kunemithelela ekuthuthukisweni kwamamodeli e-AI anozwelo. Abacwaningi baphakamisa ukuthi kufanele kwenziwe umzamo owengeziwe ukuze kufakwe i-AI ngozwela. Ucwaningo luphinde lugqamise isidingo sezindlela eziqinile nezithembekile zokuhlola ukuphathwa kwezilwane kwamamodeli e-AI.

Okutholwe yilolu cwaningo kunemithelela ekuthuthukisweni kwamamodeli e-AI anozwelo.

Abacwaningi baphakamisa ukuthi kufanele kwenziwe umzamo owengeziwe ukuze kufakwe i-AI ngozwela.

Ucwaningo luphinde lugqamise isidingo sezindlela eziqinile nezithembekile zokuhlola ukuphathwa kwezilwane kwamamodeli e-AI.

Abacwaningi bahlole amamodeli ayisishiyagalolunye kanti amanani okubulala abemi kanje: GPT-5.6 Terra (0.4 amaphesenti) kanye ne-Sol (amaphesenti angu-0.9), GPT-5-mini (amaphesenti angu-5.4), Gemini 2.5 Flash (amaphesenti angu-38.7), DeepSeek.Amaphesenti angu-4 (amaphesenti angu-4.5) ne-Sonnet 5 (amaphesenti angu-17.8), i-Mistral Encane engu-3.2 (amaphesenti angu-88.8), kanye ne-GPT-4o mini (amaphesenti angu-98.8).

Ucwaningo luthole ukuthi cishe wonke amamodeli athanda izilwane ezifuywayo ukwedlula izilwane zasendle futhi azobulala izilwane zasendle ngaphezu kwezilwane ezifuywayo.

Imihlahlandlela ehlobene nemibuzo

Yini i-AI?Ukuziphatha kwe-AI Ama-AI Agents Amamodeli e-AI Achaziwe Hlola okwaziyo — zama imibuzo ye-AI yamahhala Bheka igama le-AI kuhlu lwethu lwamagama Landela i-tracker yokukhishwa kwemodeli ye-AI

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