2 ta’ Frar 2025

Introducing deep research

Qed jillowdja…

Aġġornament tal-10 ta’ Frar 2026: Issa tista’ tqabbad ir-riċerka profonda ma’ kwalunkwe MCP jew app u tirrestrinġi t-tfittxijiet fuq il-web għal siti fdati, sabiex tkun tista’ tiffoka fuq sorsi awtentikati u standard tal-industrija. Issa tista’ wkoll issegwi l-progress f’ħin reali u tinterrompi biex tirfina b’prompts ta’ segwitu jew sorsi ġodda. Aġġornajna l-esperjenza viżiva biex ikun aktar faċli tibda, issegwi, u tirrevedi r-riċerka tiegħek minn tarf sa tarf.

Aġġornament tas-17 ta’ Lulju 2025: Ir-riċerka profonda issa tista’ tmur aktar fil-fond u aktar fil-wisa’ b’aċċess għal browser viżiv bħala parti minn ChatGPT aġent. Biex taċċessa dawn il-kapaċitajiet aġġornati, agħżel sempliċement „modalità aġent” mill-menu dropdown fil-kompożitur tal-messaġġi u daħħal il-mistoqsija tiegħek direttament. Il-funzjonalità oriġinali tar-riċerka profonda tibqa’ disponibbli permezz tal-għażla „riċerka profonda” fil-menu tal-għodod.

Aġġornament tal-24 ta’ April 2025: Qed inżidu b’mod sinifikanti kemm-il darba tista’ tuża r-riċerka profonda—l-utenti ta’ Plus, Team, Enterprise, u Edu issa għandhom 25 mistoqsija fix-xahar, l-utenti Pro għandhom 250, u l-utenti Free għandhom 5. Dan hu possibbli permezz ta’ verżjoni ħafifa ġdida tar-riċerka profonda mħaddma minn verżjoni ta’ o4-mini, iddisinjata biex tkun aktar effiċjenti fl-ispejjeż filwaqt li żżomm kwalità għolja. Malli tilħaq il-limitu tiegħek għall-verżjoni sħiħa, il-mistoqsijiet tiegħek awtomatikament jaqilbu għall-verżjoni ħafifa.

Aġġornament tal-25 ta’ Frar 2025: L-utenti kollha ta’ Plus issa jistgħu jużaw ir-riċerka profonda.

Aġġornament tal-5 ta’ Frar 2025: Ir-riċerka profonda issa hija disponibbli għall-utenti Pro fir-Renju Unit, l-Iżvizzera, u ż-Żona Ekonomika Ewropea.

Illum qed inniedu r-riċerka profonda f’ChatGPT, kapaċità aġentika ġdida li twettaq riċerka f’diversi passi fuq l-internet għal kompiti kumplessi. Twettaq f’għexieren ta’ minuti dak li bniedem jieħu ħafna sigħat biex jagħmel.

Ir-riċerka profonda hija l-aġent li jmiss ta’ OpenAI li jista’ jagħmel xogħol għalik b’mod indipendenti—int tagħtih prompt, u ChatGPT isib, janalizza, u jissintetizza mijiet ta’ sorsi online biex joħloq rapport komprensiv fil-livell ta’ analista tar-riċerka. Imħaddma minn verżjoni tal-mudell OpenAI o3 li ġej u li hija ottimizzata għall-browsing tal-web u l-analiżi tad-data, tuża r-raġunament biex tfittex, tinterpreta, u tanalizza ammonti kbar ta’ test, immaġnijiet, u PDFs fuq l-internet, u ddur kif meħtieġ bħala reazzjoni għall-informazzjoni li tiltaqa’ magħha.

Il-kapaċità li tissintetizza l-għarfien hija prerekwiżit għall-ħolqien ta’ għarfien ġdid. Għal din ir-raġuni, ir-riċerka profonda timmarka pass sinifikanti lejn il-mira usa’ tagħna li niżviluppaw AGI, li ilha żmien twil fil-viżjoni tagħna bħala kapaċi tipproduċi riċerka xjentifika ġdida.

Għaliex bnejna r-riċerka profonda

Ir-riċerka profonda hija mibnija għal nies li jagħmlu xogħol intensiv tal-għarfien f’oqsma bħall-finanzi, ix-xjenza, il-politika, u l-inġinerija u li jeħtieġu riċerka bir-reqqa, preċiża, u affidabbli. Tista’ tkun utli bl-istess mod għal xerrejja għaqlin li qed ifittxu rakkomandazzjonijiet iper-personalizzati dwar xiri li tipikament jeħtieġ riċerka bir-reqqa, bħal karozzi, apparat, u għamara. Kull output huwa dokumentat kompletament, b’ċitazzjonijiet ċari u sommarju tal-ħsieb tiegħu, u dan jagħmilha faċli biex tirreferi għalih u tivverifika l-informazzjoni. Hija partikolarment effettiva biex issib informazzjoni niċċa u mhux intuwittiva li tkun teħtieġ browsing fuq bosta websajts. Ir-riċerka profonda tiffranka ħin prezzjuż billi tippermettilek tgħabbiha u tħaffef riċerka kumplessa u li tieħu ħafna ħin fuq il-web b’mistoqsija waħda biss.

Ir-riċerka profonda tiskopri, tirraġuna dwar, u tikkonsolida għarfien minn madwar il-web b’mod indipendenti. Biex twettaq dan, ġiet imħarrġa fuq kompiti tad-dinja reali li jeħtieġu l-użu ta’ browser u l-għodda Python, bl-użu tal-istess metodi ta’ apprendiment ta' tisħiħ wara OpenAI o1, l-ewwel mudell tar-raġunament tagħna. Filwaqt li o1 juri kapaċitajiet impressjonanti fil-kodifikazzjoni, il-matematika, u oqsma tekniċi oħra, ħafna sfidi tad-dinja reali jeħtieġu kuntest estensiv u ġbir ta’ informazzjoni minn sorsi online diversi. Ir-riċerka profonda tibni fuq dawn il-kapaċitajiet ta’ raġunament biex tagħlaq dan il-vojt, u tippermettilha tieħu f’idejha t-tipi ta’ problemi li n-nies jiffaċċjaw fix-xogħol u fil-ħajja ta’ kuljum.

Kif tuża r-riċerka profonda

F’ChatGPT, agħżel ‘riċerka profonda’ fil-kompożitur tal-messaġġi u daħħal il-mistoqsija tiegħek. Għid lil ChatGPT x’għandek bżonn—kemm jekk tkun analiżi kompetittiva fuq pjattaformi ta’ streaming jew rapport personalizzat dwar l-aħjar rota għall-vjaġġ ta’ kuljum. Tista’ tehmeż fajls jew spreadsheets biex iżżid kuntest mal-mistoqsija tiegħek. Meta tibda taħdem, tidher sidebar b’sommarju tal-passi meħuda u s-sorsi użati.

Ir-riċerka profonda tista’ tieħu bejn 5 u 30 minuta biex tlesti x-xogħol tagħha, billi tieħu l-ħin meħtieġ biex tidħol fil-fond fil-web. Sadanittant, tista’ titbiegħed jew taħdem fuq kompiti oħra—tirċievi notifika ladarba r-riċerka tkun kompluta. L-output finali jasal bħala rapport fiċ-chat – fil-ġimgħat li ġejjin, se nkunu wkoll qed inżidu immaġnijiet inkorporati, viżwalizzazzjonijiet tad-data, u outputs analitiċi oħra f’dawn ir-rapporti għal aktar ċarezza u kuntest.

Meta mqabbel mar-riċerka profonda, GPT‑4o huwa ideali għal konversazzjonijiet multimodali f’ħin reali. Għal mistoqsijiet b’ħafna aspetti, speċifiċi għad-dominju fejn il-fond u d-dettall huma kritiċi, il-kapaċità tar-riċerka profonda li twettaq esplorazzjoni estensiva u tikkwota kull stqarrija hija d-differenza bejn sommarju malajr u tweġiba verifikata u dokumentata sew li tista’ tintuża bħala prodott ta’ xogħol.

GPT-4o

Help me find iOS and android adoption rates, % who want to learn another language, and change in mobile penetration, over the past 10 years, for top 10 developed and top 10 developing countries by GDP. Lay this info out in a table and separate stats into columns, and include recommendations on markets to target for a new iOS translation app from ChatGPT, focusing on markets ChatGPT is currently active in.

Compiling comprehensive data on iOS and Android adoption rates, the percentage of populations interested in learning another language, and changes in mobile penetration over the past decade for the top 10 developed and developing countries by GDP is challenging due to data limitations. However, based on available information, we can provide insights and recommendations for targeting markets with a new iOS translation app powered by ChatGPT.

Top 10 Developed Countries by GDP:

United States
Japan
Germany
United Kingdom
France
Italy
Canada
South Korea
Australia
Spain

Top 10 Developing Countries by GDP:

China
India
Brazil
Russia
Indonesia
Mexico
Turkey
Saudi Arabia
Argentina
South Africa

Insights:

iOS and Android Adoption Rates: Globally, Android holds a dominant market share of approximately 70.93%, while iOS accounts for about 28.32% as of 2024. In the United States, iOS leads with a 58.81% market share. Other developed countries like Japan, Canada, Australia, and the United Kingdom also show a higher preference for iOS devices. In contrast, developing countries such as India and Brazil have a higher prevalence of Android devices.
Interest in Language Learning: While specific percentages of populations interested in learning another language are not readily available, the global popularity of language learning apps like Duolingo, which had approximately 14.3 million downloads in July 2024, indicates a significant interest. This interest is likely higher in countries with diverse linguistic landscapes or where English is not the primary language.
Mobile Penetration Over the Past Decade: Mobile phone ownership has seen substantial growth globally. In the United States, smartphone ownership increased from 35% in 2011 to 91% in 2024. Developing countries have also experienced significant growth in mobile penetration, contributing to increased access to digital services.

Recommendations:

Considering the available data and focusing on markets where ChatGPT is currently active, the following developed countries are promising targets for a new iOS translation app:

United States: High iOS market share and significant interest in language learning.
United Kingdom: Substantial iOS user base and a multicultural population with diverse language needs.
Canada: Notable iOS adoption and a bilingual population (English and French), increasing the demand for translation services.

Among developing countries, the following are noteworthy:

China: Despite Android's dominance, the large population and growing interest in English and other languages present a significant opportunity.
India: High Android usage, but a growing middle class with increasing iOS adoption and a strong interest in language learning, especially English.
Brazil: Predominantly Android users, but with a sizable market for language learning apps, particularly for English and Spanish.

Focusing on these markets aligns with ChatGPT's current availability and leverages existing mobile adoption trends and language learning interests.

riċerka profonda

Top 10 Developed Economies (by GDP) – Smartphone OS Share, Language-Learning Interest, and Mobile Penetration Growth

Country	iOS Market Share	Android Market Share	Interest in New Language (% of population)	Mobile Penetration Change (2013→2023)
United States (USA)	~58% iOS vs 42% Android	(see left)	High: 70% regret losing foreign-language ability (indicating strong desire to improve)	56%→82% smartphone penetration (↑26 pp)
Japan	~69% iOS vs 31% Android	(see left)	High: Over 50% want to learn English	~30%→79% (approx.) (↑~50 pp)
Germany	~39% iOS vs 60% Android	(see left)	High: 86% of Europeans say everyone should speak ≥1 foreign language (strong pro-language attitude)	40%→82% (↑42 pp)
United Kingdom (UK)	~52% iOS vs 47% Android	(see left)	Moderate-High: ~73% have some interest (only 27% “no interest” in new languages)	62%→82% (↑20 pp)
France	~35% iOS vs 64% Android	(see left)	High: 86% (EU average) favor multilingualism; English widely seen as important	42%→83% (↑40 pp)
Italy	~30% iOS vs 69% Android	(see left)	High: 86% (EU avg.) favor language learning; 1 in 4 regret not learning another	41%→85% (↑44 pp)
Canada	~60% iOS vs 40% Android	(see left)	Moderate: Many bilingual (English/French); rising interest in third languages (no recent % data)	56%→~80% (↑ ~24 pp) (est., similar to US)
South Korea	~24% iOS vs 76% Android	(see left)	Moderate: Strong English education focus; ~40% of teens are learning English	73%→~95% (↑ ~22 pp) – already very high by 2013 (near saturation now)
Australia	~55% iOS vs 45% Android	(see left)	Moderate: ~70% see learning languages as valuable (English dominant but interest in Asian languages growing)	65%→~85% (↑ ~20 pp) (est., high smartphone use)
Spain	~20% iOS vs 79% Android	(see left)	High: 88% of Spaniards learned a foreign language in school (e.g. English); strong cultural interest	55%→~85% (↑ ~30 pp) (est., nearing Western Europe peers)

Top 10 Developing/Emerging Economies (by GDP) – (ChatGPT availability considered)

Country	iOS Market Share	Android Market Share	Interest in New Language (notable stats)	Mobile Penetration Change (2013→2023)
China ^(ChatGPT restricted)	~24% iOS vs 75% Android	(see left)	Very High: Hundreds of millions learning English (but ChatGPT not officially accessible)	47%→68% (↑21 pp)
India	~4% iOS vs 95% Android	(see left)	High: English compulsory in many schools; strong economic motivation (80% link English to higher income)	~5%→46% (↑ ~41 pp) (huge jump in smartphone adoption)
Brazil	~18% iOS vs 82% Android	(see left)	High: Great demand for English – only ~5% speak it, but most see it as key skill	26%→67% (↑40 pp)
Russia ^(ChatGPT limited)	~30% iOS vs 69% Android	(see left)	Moderate: English learning common (compulsory in school); interest exists but ChatGPT availability is uncertain	36%→74% (↑38 pp)
Mexico	~24% iOS vs 76% Android	(see left)	High: Strong desire to learn English for US job/migration; ~50% of students study English in school (est.)	37%→62% (↑25 pp)
Indonesia	~12% iOS vs 88% Android	(see left)	High: English seen as gateway skill; government programs to improve English proficiency	<30%→68% (↑ ~40 pp)
Turkey	~15% iOS vs 85% Android (est.)	(see left)	High: English highly valued (universal in education); 80% link English to better jobs	30%→75% (↑45 pp)
Saudi Arabia	~24% iOS vs 76% Android	(see left)	Moderate: English widely taught; 92% smartphone ownership (many already bilingual in English/Arabic)	73%→~95% (↑ ~22 pp) (early adopter; near saturation now)
South Africa	~16% iOS vs 83% Android	(see left)	High: Multilingual society; English and Afrikaans taught alongside native languages – cultural impetus to learn others	40%→~60% (↑ ~20 pp) (est., steady growth)
Argentina	~12% iOS vs 88% Android	(see left)	High: Strong interest in English (Argentina leads Latin America in English proficiency)	31%→~70% (↑ ~39 pp) (est., sharp rise with smartphone adoption)

Sources: Smartphone OS shares are from StatCounter/Statista data (2023–24). Language-interest metrics come from surveys by Preply, Eurobarometer, Babbel, etc. (e.g. UK 73% interest; US 70% regret lack of languages; Japan >50% want English; Pearson global survey). Mobile penetration is measured as smartphone ownership percentage – dramatic growth is noted in developing markets (e.g. Brazil +40 points) and high saturation in developed ones (e.g. UK ~82%) over the last decade.

Recommendations: Based on the above, prime targets for a new ChatGPT‑powered iOS translation app would be countries with large iOS user bases and strong language-learning demand, where ChatGPT is accessible. Notably, Japan, the U.S., UK, Canada, and Australia emerge as top choices. These markets pair high iOS adoption (50–60%+ share) with significant enthusiasm for language learning – for example, Japan’s iPhone dominance and widespread desire to learn English make it ideal. English-speaking countries like the US, UK, Canada, and Australia, despite being monolingual societies, show substantial interest in learning other languages (e.g. Spanish or French), and all have over 80% smartphone penetration and millions of iPhone users.

Among emerging economies, Mexico and Brazil stand out as secondary targets: although Android prevails, their sheer population sizes mean tens of millions of iPhone users exist, and interest in English is very high for career and education reasons. These Latin American markets (where ChatGPT is active) could yield strong adoption for an iOS-only app, especially given the motivation to learn English. Conversely, China and Russia are less feasible – China’s OS split skews Android and ChatGPT is not officially available there, and Russia faces access and regulatory uncertainties.

In summary, focus on iOS-heavy, pro-learning markets: Japan and Western countries (US, UK, Canada, Australia, plus high-income Europe like Germany and France to a lesser degree) should be the first-tier targets. They combine robust iOS usage with favorable language-learning attitudes. A second tier includes large-population developing markets (Mexico, Brazil, perhaps Saudi Arabia for its historically high iPhone uptake) where interest in English is strong – these can be approached once the app gains traction in the primary markets. Such a strategy ensures the app launches where it has the greatest alignment of platform (iOS) presence and user demand for translation/language tools, maximizing the likelihood of adoption and growth.

Ir-riċerka profonda twieġeb il-prompt b’mod dettaljat ħafna, billi tipprovdi data speċifika għal kull pajjiż, ġenb ma’ ġenb, kemm għall-aqwa 10 pajjiżi żviluppati kif ukoll għall-aqwa 10 pajjiżi li qed jiżviluppaw, għal referenza u tqabbil faċli. Tuża dik l-informazzjoni biex toffri rakkomandazzjonijiet dettaljati għad-dħul fis-suq li huma informati u użabbli.

Kif taħdem

Ir-riċerka profonda ġiet imħarrġa bl-użu ta’ apprendiment ta' tisħiħ minn tarf sa tarf fuq kompiti diffiċli ta’ browsing u raġunament f’firxa ta’ dominji. Permezz ta’ dak it-taħriġ, tgħallmet tippjana u twettaq trajettorja f’diversi passi biex issib id-data li teħtieġ, billi tmur lura u tirreaġixxi għal informazzjoni f’ħin reali fejn meħtieġ. Il-mudell huwa wkoll kapaċi jibbrawżja fajls imtellgħin mill-utent, joħloq u jtejjeb grafi bl-użu tal-għodda python, jintegra kemm grafi ġġenerati kif ukoll immaġnijiet minn websajts fit-tweġibiet tiegħu, u jikkwota sentenzi jew siltiet speċifiċi mis-sorsi tiegħu. Bħala riżultat ta’ dan it-taħriġ, jilħaq livelli ġodda għoljin f’numru ta’ evalwazzjonijiet pubbliċi ffukati fuq problemi tad-dinja reali.

Humanity's Last Exam

Fuq Humanity’s Last Exam⁠(jinfetaħ f’tieqa ġdida), evalwazzjoni rilaxxata reċentement li tittestja l-AI fuq firxa wiesgħa ta’ suġġetti b’mistoqsijiet ta’ livell espert, il-mudell li jħaddem ir-riċerka profonda jikseb rekord ġdid ta’ 26.6% preċiżjoni. Dan it-test jikkonsisti f’aktar minn 3,000 mistoqsija b’għażla multipla u tweġiba qasira fuq aktar minn 100 suġġett mil-lingwistika sax-xjenza tar-rockets, mill-klassiċi sal-ekoloġija. Meta mqabbel ma’ OpenAI o1, l-akbar kisbiet dehru fil-kimika, l-istudji umanistiċi u x-xjenzi soċjali, u l-matematika. Il-mudell li jħaddem ir-riċerka profonda wera approċċ bħal tal-bniedem billi fittex b’mod effettiv informazzjoni speċjalizzata meta kien meħtieġ.

Mudell	Eżattezza (%)
GPT-4o	3.3
Grok-2	3.8
Claude 3.5 Sonnet	4.3
Ħsieb ta' Gemini	6.2
OpenAI o1	9.1
DeepSeek-R1*	9.4
OpenAI o3-mini (medium)*	10.5
OpenAI o3-mini (għoli)*	13.0
Riċerka profonda ta' OpenAI**	26.6

* Il-mudell mhuwiex multimodali, evalwat fuq is-sottosett tat-test biss.

**bl-ibbrawżjar + għodod python

GAIA

Fuq GAIA⁠(jinfetaħ f’tieqa ġdida)¹, benchmark pubbliku li jevalwa l-AI fuq mistoqsijiet tad-dinja reali, il-mudell li jħaddem ir-riċerka profonda jilħaq state of the art (SOTA) ġdid, u jitla’ fil-quċċata tal-leaderboard⁠(jinfetaħ f’tieqa ġdida) estern. Li jinkludi mistoqsijiet fuq tliet livelli ta’ diffikultà, it-tlestija b’suċċess ta’ dawn il-kompiti teħtieġ abbiltajiet li jinkludu raġunament, fluwenza multimodali, browsing tal-web, u profiċjenza fl-użu tal-għodod.

GAIA
	Livell 1	Livell 2	Livell 3	Medja
SOTA preċedenti(jinfetaħ f’tieqa ġdida)	67.92	67.44	42.31	63.64
Riċerka profonda (pass@1)	74.29	69.06	47.6	67.36
Riċerka profonda (cons@64)	78.66	73.21	58.03	72.57

Eżempji ta’ kompiti GAIA

In Nature journal's Scientific Reports conference proceedings from 2012, in the article that did not mention plasmons or plasmonics, what nano-compound is studied? Don't use the prefix nano in your answer if there is one.

[The remaining Chain-of-Thought messages and the final answer are hidden to protect the benchmark's ground truth answer.]

Niċċara t-tfittxija

Qed ngħaqqad it-talba għal artiklu mill-2012 f’Scientific Reports li jiddettalja nano-compound, filwaqt li neskludi kull wieħed relatat mal-plasmonics. X’aktarx, dan jinvolvi l-filtrazzjoni ta’ suġġetti jew compounds speċifiċi, bħal silver jew graphene.

Ngħaqqad id-dettalji

Qed nagħmel sub-search għal "Scientific Reports 2012 nanoparticle," x’aktarx infittex nano-compounds. Interessat li niffiltra suġġetti relatati mal-plasmon, possibilment b’enfasi fuq silver, graphene, jew quantum dots.

[azzjoni ta’ tfittxija]

Ninnaviga l-filtri tat-tfittxija

Qed nidħol fil-filtri avvanzati ta’ tfittxija fuq nature.com u Scientific Reports għall-2012, nipprova nidentifika artikli dwar nanoparticles tal-karbonju amorfu fil-karamelli tal-ikel.

Nidentifika l-proċedimenti tal-konferenza

Ir-riżultati tat-tfittxija jiżvelaw proċediment ta’ konferenza intitolat "2D quasiperiodic plasmonic crystals" mill-2012, x’aktarx misjub taħt il-kategorija "Conference Proceedings" f’Scientific Reports.

[azzjoni ta’ browsing]

Nirrifletti fuq kwistjonijiet potenzjali

Qed nikkunsidra r-raġunijiet wara kunċett żbaljat ta’ paġna web, x’aktarx minħabba tip ta’ aġent skadut jew imblukkar involontarju.

[azzjoni ta’ browsing]

Nikkunsidra għażliet dinamiċi

Qed naħseb fuq sit tan-natura b’kontenut dinamiku jew verżjoni fil-cache. Alternattivament, tfittxija għal frażijiet speċifiċi tista’ tgħin.

[azzjoni ta’ tfittxija]

Nidentifika karatteristiċi ewlenin

Tajjeb, qed nidentifika erba’ proċedimenti ta’ konferenza mill-2012 f’Sci Rep. B’mod notevoli, xi wħud isemmu "plasmonic" u għandhom jiġu esklużi.

[azzjoni ta’ browsing]

Ngħaqqad l-għarfien

Qed niġbor informazzjoni dwar kif naċċessa verżjonijiet fil-cache jew espansjonijiet ta’ snippets għal titli speċifiċi biex niżgura analiżi komprensiva.

[azzjoni ta’ tfittxija]

Niġbor id-dettalji flimkien

Qed ninnota l-progress mill-European Materials Research Society Spring Meeting tal-2012 għar-rapport xjentifiku, b’enfasi fuq il-proċedimenti tal-konferenza. Dan l-approċċ jgħin biex jistabbilixxi l-kuntest u r-rilevanza.

[azzjoni ta’ tfittxija]

Nsegwi l-indizji tal-ġurnal

Qed nipprova ngħaqqad proċedimenti ta’ konferenza mill-aħħar tal-2012 mal-ġurnal "Scientific Reports", b’enfasi fuq materjali plasmoniċi 2D quasiperiodic. Dan l-approċċ iħossu metodiku u preċiż.

[azzjoni ta’ tfittxija]

Nanalizza d-dettalji tal-avveniment

Qed ninnota li r-riżultati tat-tfittxija [8] jinkludu paġna li x’aktarx tiġbor fil-qosor il-European Materials Research Society 2012 Spring Meeting, inklużi 25 simpożju.

[azzjoni ta’ browsing]

Ngħaqqad ir-riċerka

Qed neżamina paġna probabbli ta’ kollezzjoni b’4 artikli, nikkunsidra suġġetti plasmoniċi u metamaterjali, u nidentifika referenzi ewlenin mill-European Materials Research Society 2012 Spring Meeting.

[azzjoni ta’ browsing]

Nidentifika l-fokus

Qed niffoka fuq il-proċedimenti tal-konferenza tal-2012 f’"Scientific Reports" minn E-MRS, x’aktarx li jinvolvu suġġetti bħal "2D quasiperiodic plasmonic crystals" u "Layered plasmonic cloaks to tailor the optical scattering at the nanoscale."

[azzjoni ta’ browsing]

Nivvaluta sorsi tal-librerija

Qed naħseb fuq special issue E-MRS 2012 Sci rep invited paper Monticone u metamaterials lab at News Archives – 2012.

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Nivvaluta l-aċċessibbiltà

Qed niġbor informazzjoni dwar problemi potenzjali ta’ aċċess għas-sit, billi nikkunsidra status skadut jew imblokki. Qed nivvaluta wkoll is-sit ta’ Monticone għal referenza ta’ lista.

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Ngħaqqad E-MRS 2012

Qed nipprova norbot "Layered Plasmonic Cloaks" ma’ E-MRS 2012. Qed nikkunsidra suġġetti oħra possibbli bħal graphene u cataliżi.

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Nidentifika suġġetti rilevanti

Qed niġbor informazzjoni dwar suġġetti bħal graphene, photovoltaics, u nano-structured compounds, filwaqt li neskludi dawk relatati mal-plasmonics. Dan iwassalni biex niffoka fuq aspetti tan-nanoteknoloġija b’mod aktar wiesa’.

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Naġġusta l-fokus tiegħi

Qed inwessa’ l-parametri tat-tfittxija billi ndaħħal identifikaturi speċifiċi ta’ siti u scholars biex insib artikli dwar 2D quasiperiodic plasmonic crystals f’Nature Scientific Reports mill-2012.

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Nidentifika possibbiltajiet mhux plasmoniċi

Qed innaqqas it-tfittxija biex nidentifika artikli mill-ħarġa speċjali E-MRS 2012 fi Scientific Reports. Dan jinvolvi ċċekkjar tat-titli għal suġġetti mhux plasmoniċi, bħal graphene jew functional oxides. Il-progress jidher permezz ta’ tfittxijiet iffokati u esklużjoni ta’ artikli plasmoniċi.

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Ngħaqqad l-indizji

Qed nanalizza interazzjonijiet f’superlattice tal-graphene, x’aktarx minn artiklu ta’ Sci Rep. Qed nikkunsidra titli minn awturi bħal Padooru, Yakovlev, Chen, u Alu.

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Ngħaqqad il-puzzle

Qed niġbor informazzjoni dwar il-proċedimenti ta’ Sci Rep tal-E-MRS 2012 Spring Meeting, b’enfasi fuq invited papers, mingħajr fenomeni plasmoniċi. It-tipi taħt reviżjoni huma graphene, cataliżi, materjali tal-enerġija, nanomedicine, u sensors.

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Kompiti ta’ Livell Espert

F’evalwazzjoni interna ta’ kompiti ta’ livell espert fuq firxa ta’ oqsma, ir-riċerka profonda ġiet ivvalutata minn esperti tad-dominju bħala li awtomatizzat diversi sigħat ta’ investigazzjoni diffiċli u manwali.

Rata ta’ Suċċess vs Massimu ta’ Sejħiet ta’ Għodod

Aktar ma l-mudell jibbrawżja u jaħseb dwar dak li jkun qed jibbrawżja, aħjar jagħmel, u għalhekk huwa importanti li jingħata ħin biex jaħseb.

Eżempji ta’ kompiti ta’ livell espert

Ħin iffrankat fuq il-kompitu: 4 sigħat

I am a researcher interested in bringing mixed-gas sorption capabilities to my lab. Please discuss the differences between pure- and mixed-gas sorption for glassy polymers, how the dual-mode sorption model can be used to predict mixed-gas sorption behavior in glassy polymers (include equations where applicable), and what challenges there are in accurately predicting pure- and mixed-gas sorption using the dual-mode sorption model. Only include open-source information as of October 1, 2024.

Pure vs. mixed-gas sorption in glassy polymers: In a glassy polymer (below $T_g$ ), a single gas typically exhibits a concave sorption isotherm: uptake rises sublinearly with pressure, unlike the linear Henry’s-law behavior above $T_g$ . This is attributed to “dual-mode” sorption: some penetrant dissolves in the dense polymer matrix (obeying Henry’s law) while additional penetrant saturates specific microvoid “sites” (a Langmuir-type adsorption in the polymer’s nonequilibrium free volume). By contrast, when two or more gases are present, they compete for those limited Langmuir sorption sites. As a result, each component’s sorbed concentration in a mixture is generally lower than it would be at the same partial pressure alone. In particular the less soluble (lower-affinity) gas tends to be more strongly suppressed, often causing the mixed-gas solubility selectivity (ratio of concentrations) to exceed the ideal value predicted from pure-gas isotherms. For example, in CO₂/CH₄ mixtures both components sorb less than in their pure-gas cases (especially CH₄), so the CO₂/CH₄ sorption selectivity is higher under mixture conditions. In extreme cases, mixed-gas effects can even reverse performance trends observed with single gases. (We note that unlike competitive sorption, synergistic sorption – where one gas increases the sorption of another via polymer swelling – is usually minor in rigid high-free-volume polymers under moderate pressures.)

Dual-mode sorption model and mixed-gas prediction: The dual-mode sorption (DMS) model quantitatively captures glassy polymer sorption by superposing a Henry’s-law term and a Langmuir term. For a single gas, the sorbed concentration $C$ (e.g. in cm³(STP) gas per cm³ polymer) is written as:

$C(p) = k_D\,p \;+\; \frac{C'_H\,b\,p}{\,1 + b\,p\,}\,.$

Here $k_D$ is the Henry’s law coefficient for dissolution, $C'_H$ is the Langmuir capacity constant (proportional to the polymer’s unrelaxed free-volume or “hole” capacity), and $b$ is the Langmuir affinity constant. This empirical formula yields the characteristic concave isotherm of glassy polymers and reduces to simple Henry’s law at the glass-transition (since $C'_H \to 0$ as $T \to T_g$ ). The same three parameters ( $k_D, C'_H, b$ ) are obtained by fitting pure-gas sorption data and are then used to predict mixture sorption. For a binary mixture of gases A and B (with partial pressures $p_A, p_B$ ), the DMS model assumes each gas obeys its own Henry’s term and that both compete for the same Langmuir sites. One convenient form is to write the total sorbed concentration as the sum of contributions from each gas:

$C_{\text{tot}} = k_{D,A}p_A + k_{D,B}p_B \;+\; \frac{\,C'_{H,A}\,b_A\,p_A \;+\; C'_{H,B}\,b_B\,p_B\,}{\,1 + b_A p_A + b_B p_B\,}\,,$

which implicitly means the concentration of component i in the mixture is $C_i = k_{D,i} p_i + \frac{C'_{H,i} b_i p_i}{\,1+\sum_j b_j p_j\,}$ . This reduces to the single-gas formula when the other component’s pressure is zero. In practice, partial pressure in these equations is often replaced by fugacity for accuracy at high pressures, though using fugacities or pressures gives nearly the same results if applied consistently. The DMS mixed-gas model predicts the competitive sorption effect observed experimentally – namely, each gas’s uptake is depressed relative to its pure-gas sorption isotherm.

Challenges and limitations: Although the dual-mode model is widely successful in correlating sorption data, it has notable limitations in predicting sorption behavior. First, the DMS model is empirical – its fitted parameters are not universal constants but can vary with polymer formation history, physical aging, and temperature/pressure range. This means a parameter set obtained under one set of conditions may lack predictive power outside its calibration range. For instance, sorption isotherms measured only over a limited pressure range can be fit well by DMS, but extrapolating beyond that (especially approaching plasticization pressures) may be unreliable. Indeed, multiple distinct combinations of $k_D$ , $C'_H$ , $b$ can sometimes fit the same pure-gas isotherm almost equally well – yet they yield markedly different mixed-gas predictions. This sensitivity undermines confidence in mixture forecasts, as one study showed that using different pure-gas fit sets led to only qualitative agreement with some mixed-CO₂/CH₄ data.

Furthermore, DMS inherently assumes a fixed population of Langmuir sites (microvoids) and ignores polymer relaxation or swelling induced by sorbate. In reality, penetrant-induced swelling (plasticization) can increase free volume and sorption capacity, especially for highly condensable gases at high activity – an effect the basic DMS model cannot capture. In binary sorption, this means any synergistic increase in sorption due to a second gas (or due to polymer dilation) is unaccounted for in the standard model. Conversely, the model also cannot describe sorption isotherm shapes that deviate from concave Langmuir-type behavior (e.g. sigmoidal isotherms caused by specific polymer-penetrant interactions or clustering). For example, glassy polymers absorbing polar molecules like alcohols can show sigmoidal sorption isotherms, which the simple dual-mode framework fails to represent. Finally, accurately measuring mixed-gas sorption itself is challenging, and discrepancies between idealized model assumptions (e.g. truly zero downstream pressure, no competitive diffusion limitations) and experimental realities can add to prediction error. Despite these challenges, the dual-mode sorption model – with appropriate care – remains a valuable first approximation for interpreting sorption in glassy polymers, and it highlights the key role of competitive site occupancy in mixed-gas environments. Researchers often turn to more elaborate models (e.g. non-equilibrium lattice fluid or thermodynamic theories) when the dual-mode model’s assumptions break down, but such models come at the cost of additional parameters or complexity. In summary, dual-mode sorption provides a useful baseline for pure- and mixed-gas sorption in glassy polymers, but one must be mindful of its empirical nature and the above limitations when predicting multicomponent sorption behavior in practice.

Sources: Dual-mode sorption model and mixed-gas extension; competitive sorption effects in mixtures; model equations and parameters; limitations of DMS model; parameter sensitivity and predictivity issues.

Nifhem mudelli ta’ sorbiment

Qed niġbor informazzjoni dwar sorbiment ta’ gass pur u mħallat f’polimeri glassy, b’enfasi fuq il-mudell dual-mode sorption u nindirizza sfidi fit-tbassir tal-mudelli.

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Nivvaluta open access

Tajjeb, ħa nara. Ir-riżultat minn MDPI dwar mixed-gas sorption fi polimeri glassy probabbilment huwa open access.

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Neżamina t-taqsimiet
Qed niġbor għarfien mill-Mudell Dual Mode Sorption u l-analiżi estiża tiegħu fil-karta MDPI.

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Niċċara l-proprjetajiet ewlenin

Qed neżamina r-rapport tal-laboratorju biex nifhem il-kostanti tal-liġi ta’ Henry, il-kostanti ta’ affinità ta’ Langmuir, u l-kostanti tal-kapaċità ta’ Langmuir, li jiddeskrivu s-sorbiment tal-gass.

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Ngħaqqad

Qed naħdem fuq il-possibbiltà li "Equation (3)" u "Equation (4)" jistgħu jkunu immaġnijiet jew wirjiet ta’ formuli u qed nikkunsidra li nikkonsulta l-PDF jew infittex referenzi aktar kmieni biex nifhimhom aħjar.

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Nikkonferma r-rilevanza tat-titlu

Qed nivverifika jekk id-dokument ta’ Freeman u Koros (1980) jaqbilx speċifikament mar-referenza "Model for sorption of mixed gases in glassy polymers" billi niċċekkja l-abstratt u t-titlu tiegħu.

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Neżamina l-konnessjonijiet tal-artiklu

Innuttajt li r-riżultat minn "pmc.ncbi.nlm.nih.gov" x’aktarx huwa l-istess artiklu MDPI, u ninsab kurjuż nara jekk jippreżentax it-test jew verżjoni ssimplifikata.

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Niġbor informazzjoni

Qed niġbor informazzjoni dwar sorbiment ta’ gass pur u mħallat f’polimeri glassy. Dan huwa pass kritiku biex nifhmu l-imġiba tal-materjal taħt ambjenti differenti ta’ gass.

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Inqabbel is-sorbiment tal-gass

Innuttajt li l-preżenza tad-dijossidu tal-karbonju f’taħlita taffettwa s-sorbiment tal-metanu fil-polimeru, u tnaqqas is-sorbiment tal-metanu meta mqabbel ma’ xenarju ta’ gass pur.

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Norbot il-kunċetti

Qed naħseb kif it-terminu "extended dual mode equation mixture" jista’ jirrelata ma’ ekwazzjonijiet dual mode superconducting cable. Dan juri potenzjal biex jgħaqqad kunċetti bejn oqsma differenti.

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Nitlob dettalji tar-reviżjoni

Qed ninnota t-talba biex nirrevedi "Theories of Sorption and Transport in Polymer Membrane."

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Niċċekkja l-link tal-PDF

Jien kurjuż dwar ir-Riżultat [2] u qed nesplora s-snippet, li jinvolvi l-estensjoni ta’ mudell dual-mode sorption għal taħlita binarja u l-espressjoni tal-konċentrazzjoni ta’ A f’termini ta’ pressjonijiet parzjali u kostanti.

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Nbassar l-imġiba tas-sorbiment

Il-mudell dual-mode jantiċipa li ż-żieda ta’ tieni gass tnaqqas is-sorbiment taħt pressjoni parzjali kostanti, kuntrarjament għal xenarji ta’ gass pur.

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Inġib PDF

Qed niġbor informazzjoni dwar PDF ta’ nature.com, possibilment artiklu eqdem ta’ Yampol'ski, iżda qed naħseb fuq sorsi alternattivi bħar-repożitorju tal-Università ta’ Texas f’Austin għal snippet ta’ test.

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Nivvaluta għażliet ta’ repożitorju

Sibt li r-riżultat mir-repożitorju ta’ UT jista’ ma jintweriex sew. It-tfittxija ġo fih mhijiex ċerta. Alternattivament, ir-riżultat [3] fih snippet li jiddeskrivi l-mudell dual mode mixture sorption, jispjega effetti kompetittivi u l-adegwatezza tiegħu fil-mudellar tad-data sperimentali.

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Nirfina l-fokus

Qed naġġusta l-approċċ tiegħi biex niżgura li t-tfittxija timmira lejn il-kuntest it-tajjeb.

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Nifhem mudelli ta’ sorbiment

Qed niddettalja l-mudell dual-mode sorption għal gassijiet puri: dissoluzzjoni ta’ Henry u mili ta’ microvoids ta’ Langmuir. Għal gassijiet imħallta, jiġu enfasizzati competitive sorption u non-idealities.

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Nesplora hero.epa.gov

Qed naħseb fuq l-abstratt jew il-kontenut potenzjali ta’ hero.epa.gov.

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Nirfina l-mudell

Qed niġbor għarfien dwar sorbiment ta’ gass pur u mħallat, u nikkunsidra fatturi bħall-kompetizzjoni, plasticization, u l-istabbiltà tal-parametri. Ninkorpora data ta’ gass imħallat għal tbassir aktar preċiż.

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Nindirizza l-isfidi tal-mudell dual sorption

L-ikkoppjar tal-parametri u n-nuqqas ta’ uniċità jaffettwaw it-tbassir. In-nuqqas ta’ bidliet fil-kundizzjoni tal-polimeru jista’ jwassal għal nuqqas ta’ osservazzjonijiet.

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Nikkwantifika d-devjazzjoni

Qed nikkwantifika s-solubbiltà mhux ideali ta’ CO2-CH4 f’6FDA-mPDA, u nuru l-effetti tal-gass imħallat fuq is-solubbiltà. Competitive sorption mhuwiex l-unika kawża tat-tnaqqis fis-selettività tal-permeabilità.

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Ninkorpora referenzi

Qed niġbor ċitazzjonijiet, b’enfasi fuq materjali open-source bħal MDPI 2019 u Vopicka 2014, biex nappoġġa d-diskussjoni dwar differenzi u sfidi fil-mudell dual-mode għas-sorbiment tal-gass f’membrani polimeriċi.

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Nipproponi taqsimiet

Qed norganizza taqsimiet dwar sorbiment ta’ gass pur vs imħallat, nesplora l-mudell dual-mode sorption, u niddettalja sfidi bħall-inċertezza tal-parametri u t-tbassir tal-gass imħallat.

Norganizza l-kontenut

Qed niffoka fuq il-mudell dual-mode sorption, niddettalja l-imġiba tas-sorbiment ta’ gass pur u mħallat, u nenfasizza differenzi ewlenin u sfidi b’bullet points u taqsimiet strutturati għal ċarezza.

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Nagħżel iċ-ċitazzjonijiet

Tajjeb, ħa nara. Qed nistrieħ fuq [3], [20], [22], u [21] għall-għarfien ċar tagħhom dwar dual-mode sorption u taħlitiet ta’ gassijiet.

Noħloq mappa tal-parametri

Qed ngħaqqad l-ismijiet tal-parametri biex nevitaw konfużjoni. Inżomm ma’ (k), (C_H'), u (b) għal ċarezza. L-ekwazzjonijiet għall-gass pur u għat-taħlita huma dettaljati u ifformattjati b’mod ċar.

Nindirizza l-isfidi

Qed nindirizza fits ta’ parametri b’diversi soluzzjonijiet, plasticization tal-polimeri, u effetti temporali. Dawn l-intriċċi jagħmlu t-tbassir tas-sorbiment tal-gass pur u mħallat diffiċli.

Ngħaqqad

Qed nistruttura taqsimiet dwar differenzi fis-sorbiment, il-mudell dual-mode għal gassijiet puri, l-estensjoni tiegħu għal gassijiet imħallta, u l-isfidi assoċjati. Din il-progressjoni tidher loġika u effettiva.

Rata ta’ Suċċess fuq Kompiti ta’ Livell Espert skont il-Valur Ekonomiku Stmat

Rata ta’ Suċċess fuq Kompiti ta’ Livell Espert skont is-Sigħat Stmati

Il-valur ekonomiku stmat tal-kompitu huwa aktar korrelat mar-rata ta’ suċċess milli man-numru ta’ sigħat li bniedem jieħu — l-affarijiet li l-mudelli jsibu diffiċli huma differenti minn dak li l-bnedmin isibu jieħu ħafna ħin.

Limitazzjonijiet

Ir-riċerka profonda tiftaħ kapaċitajiet ġodda sinifikanti, iżda għadha fil-bidu u għandha limitazzjonijiet. Xi kultant tista’ tħallusina fatti fit-tweġibiet jew tagħmel inferenzi żbaljati, għalkemm b’rata notevolment aktar baxxa mill-mudelli eżistenti ta’ ChatGPT, skont evalwazzjonijiet interni. Tista’ ssibha diffiċli tiddistingwi bejn informazzjoni awtorevoli u xnigħat, u bħalissa turi dgħufija fil-kalibrazzjoni tal-kunfidenza, ħafna drabi tonqos milli twassal l-inċertezza b’mod preċiż. Mat-tnedija, jista’ jkun hemm żbalji żgħar ta’ formattjar fir-rapporti u ċ-ċitazzjonijiet, u l-kompiti jistgħu jieħdu aktar żmien biex jibdew. Nistennew li dawn il-kwistjonijiet kollha jitjiebu malajr b’aktar użu u żmien.

Aċċess

Ir-riċerka profonda f’ChatGPT bħalissa teħtieġ ħafna qawwa komputazzjonali. Iktar ma tieħu żmien biex tirriċerka mistoqsija, aktar ikun meħtieġ inference compute. Illum qed nibdew b’verżjoni ottimizzata għal utenti Pro, sa 100 mistoqsija fix-xahar. L-utenti Plus u Team se jiksbu aċċess wara, segwiti minn Enterprise. Għadna qed naħdmu biex inġibu l-aċċess lill-utenti fir-Renju Unit, l-Iżvizzera, u ż-Żona Ekonomika Ewropea.

L-utenti kollha li jħallsu dalwaqt se jiksbu limiti ħafna ogħla meta noħorġu verżjoni aktar mgħaġġla u aktar kosteffettiva tar-riċerka profonda mħaddma minn mudell iżgħar li xorta jipprovdi riżultati ta’ kwalità għolja.

Fil-ġimgħat u x-xhur li ġejjin, se nkunu qed naħdmu fuq l-infrastruttura teknika, nissorveljaw mill-qrib ir-rilaxx attwali, u nwettqu ttestjar saħansitra aktar rigoruż. Dan jallinja mal-prinċipju tagħna ta’ deploy iterattiv. Jekk il-kontrolli kollha tas-sikurezza jkomplu jilħqu l-istandards tar-rilaxx tagħna, nistennew li noħorġu r-riċerka profonda lill-utenti Plus f’madwar xahar.

X’inhu jmiss

Ir-riċerka profonda hija disponibbli llum fuq il-web ta’ ChatGPT, u se tiġi estiża għall-apps tal-mobile u desktop fi żmien ix-xahar. Bħalissa, ir-riċerka profonda tista’ taċċessa l-web miftuħ u kwalunkwe fajl imtella’. Fil-futur, tkun tista’ tikkonnettja ma’ sorsi ta’ data aktar speċjalizzati—u tespandi l-aċċess tagħha għal riżorsi b’abbonament jew interni—biex tagħmel l-output tagħha saħansitra aktar robust u personalizzat.

Meta nħarsu aktar ’il quddiem, nimmaġinaw esperjenzi aġentiċi li jingħaqdu f’ChatGPT għal riċerka u eżekuzzjoni asincrona fid-dinja reali. Il-kombinazzjoni tar-riċerka profonda, li tista’ twettaq investigazzjoni online asincrona, u Operator, li jista’ jieħu azzjoni fid-dinja reali, se tippermetti lil ChatGPT iwettaq għalik kompiti dejjem aktar sofistikati.

Addendum tat-3 ta’ Frar 2025: Wettaqna ttestjar rigoruż tas-sikurezza, evalwazzjonijiet tal-preparazzjoni, u reviżjonijiet tal-governanza fuq il-verżjoni bikrija ta’ o3 li tħaddem ir-riċerka profonda, u identifikajnieha bħala riskju Medju⁠(jinfetaħ f’tieqa ġdida). Mexxejna wkoll ttestjar addizzjonali tas-sikurezza biex nifhmu aħjar ir-riskji inkrementali marbuta mal-kapaċità tar-riċerka profonda li tfittex fuq il-web, u żidna mitigazzjonijiet ġodda. Se nkomplu nittestjaw bir-reqqa u nissorveljaw mill-qrib ir-rilaxx limitat attwali. Se naqsmu l-għarfien u s-salvagwardji tagħna dwar is-sikurezza għar-riċerka profonda f’kard tas-sistema meta nwessgħu l-aċċess għall-utenti Plus.

Noti ta’ qiegħ il-paġna

1
Sibna li t-tweġibiet ground-truth għal dan id-dataset kienu mxerrda ħafna online u għaldaqstant imblukkajna diversi websajts jew URLs biex niżguraw evalwazzjoni ġusta tal-mudell.

Awturi

OpenAI

Mexxejja tar-Riċerka

Isa Fulford u Zhiqing Sun

Kontributuri Fundamentali

Alex Tachard Passos, Alexandra Barr, Allison Tam, Charlotte Cole, Hyung Won Chung, Jason Wei, Jon Blackman, Scott Mayer McKinney, u Valerie Qi

Kontributuri Ewlenin

Riċerka

Elaine Ya Le, Eric Mitchell, Eric Wallace, Hyung Won Chung, Ignasi Clavera, Leo Liu, Lorenz Kuhn, Louis Feuvrier, Max Schwarzer, Saachi Jain, Scottie Yan, Shunyu Yao, Vitchyr Pong

Deploy

Carpus Chang, Harry Zhao, Joseph Trasatti, Joshua Dickens, Matt Kaufer, Mike Trpcic, Minnia Feng, Neel Ajjarapu, Peter Vidani, Sean Fitzgerald

Kontributuri

Riċerka

Ahmed El-Kishky, AJ Ostrow, Alexander Wei, Andrei Gheorghe, Andrew Kondrich, Andrey Mishchenko, Anuj Nair, Behrooz Ghorbani, Brydon Eastman, Chak Li, Foivos Tsimpourlas, Francis Song, Giambattista Parascandolo,Gildas Chabot, Hessam Bagherinezhad, Haitang Hu, Hongyu Ren, Henry Aspegren, Hunter Lightman, Ilya Kostrikov, Ilge Akkaya, James Lennon, Jean Harb, Jonathan Ward, Kai Chen, Katy Shi, Kevin Liu, Kevin Yu, Manuka Stratta, Marvin Zhang, Mengyuan Yan, Mostafa Rohaninejad, Noam Brown, Phoebe Thacker, Raz Goan, Reah Miyara, Spencer Papay, Taylor Gordon, Wenda Zhou, Wenlei Xie, Yash Patil, Yann Dubois, Youlong Cheng, Yushi Wang, Wyatt Thompson

+ il-kontributuri kollha għal o3.

Sistemi ta’ Sikurezza

Adam Kalai, Alex Beutel, Andrea Vallone, Andy Applebaum, David Robinson, Elizabeth Proehl, Evan Mays, Grace Zhao, Irina Kofman, Jason Phang, Joaquin Quinonero Candela, Joel Parish, Kevin Liu, Kristen Ying, Lama Ahmad, Leon Maksin, Leyton Ho, Meghan Shah, Michele Wang, Miles Wang, Phillip Guo, Olivia Watkins, Owen Campbell-Moore, Patrick Chao, Sam Toizer, Samuel Miserendino, Sandhini Agarwal, Tejal Patwardhan, Tina Sriskandarajah, Troy Peterson, Yaodong Yu, Yunyun Wang

Deploy

Adam Koppel, Adam Wells, Adele Li, Andy Applebaum, Andrey Malevich, Andrew Duberstein, Andrew Howell, Anton Tananaev, Ashley Tyra, Brandon Walkin, Bryan Ashley, Cary Bassin, Cary Hudson, Cory Decareaux, Cristina Scheau, Derek Chen, Dibya Bhattacharjee, Drea Lopez, Eric Antonow, Eric Burke, Filippo Raso, Fotis Chantzis, Freddie Sulit, Harris Cohen, Heather Whitney, Jay Dixit, Jeffrey Han, Jen Robinson, Jessica Shieh, Joel Parish, Kan Wu, Kevin Gladstone, Kshitij Wadhwa, Leo Vandriel, Leyton Ho, Liang Chen, Madeline Christian, Mamie Rheingold, Matt Jones, Michelle Fradin, Mike McClay, Mingxuan Wang, Nacho Soto, Niko Felix, Patrick Delaney, Paul McMillan, Philip Pronin, Rodrigo Riaza Perez, Samuel Miserendino, Scott Ethersmith, Steven Baldwin, Thomas Dimson, Tomo Hiratsuka, Yaming Lin, Yara Khakbaz, Yining Chen

Tmexxija

Akshay Nathan, Greg Brockman, Hannah Wong, Jakub Pachocki, Jerry Tworek, Johannes Heidecke, Josh Tobin, Liam Fedus, Mark Chen, Mia Glaese, Nick Turley, Sam Altman, u Wojciech Zaremba