{"id":5454,"date":"2024-01-23T15:33:26","date_gmt":"2024-01-23T15:33:26","guid":{"rendered":"https:\/\/processtalks.com\/?p=5454"},"modified":"2024-01-23T15:33:29","modified_gmt":"2024-01-23T15:33:29","slug":"revoluciona-la-teva-estrategia-digital-incorpora-models-dia-generativa-amb-process-talks","status":"publish","type":"post","link":"https:\/\/processtalks.com\/ca\/revoluciona-la-teva-estrategia-digital-incorpora-models-dia-generativa-amb-process-talks\/","title":{"rendered":"Revoluciona la Teva Estrat\u00e8gia Digital: Incorpora Models d\u2019IA Generativa amb Process Talks"},"content":{"rendered":"\n

En una era en qu\u00e8 la interacci\u00f3 digital \u00e9s essencial, les organitzacions que encara no han adoptat la IA generativa en els seus programes o productes SaaS es troben en una cru\u00eflla vital. La tend\u00e8ncia cap a interf\u00edcies d’usuari m\u00e9s conversacionals i intel\u00b7ligents<\/a> no \u00e9s una moda passatgera sin\u00f3 un canvi transformador en la forma com interactuem amb la tecnologia. Imagineu un programari que no nom\u00e9s respon, sin\u00f3 que conversa, ent\u00e9n i assisteix a l\u2019usuari d\u2019una manera sense precedents. Aix\u00f2 \u00e9s possible amb la integraci\u00f3 de Models Massius de Llenguatge (MML) en les interf\u00edcies d\u2019acc\u00e9s a programari de diferent tipus, un moviment que que permet de redefinir l’experi\u00e8ncia d’usuari, l’efici\u00e8ncia operativa i, en definitiva, el nivell d\u2019implicaci\u00f3 del client,.<\/p>\n\n\n\n

<\/a>Ascens mete\u00f2ric dels models massius de llenguatge (MML)<\/h2>\n\n\n\n

Durant l’\u00faltim any el paisatge de la intel\u00b7lig\u00e8ncia artificial (IA) s\u2019ha vist modificat completament per l\u2019increment mete\u00f2ric en l\u2019\u00fas d\u2019MML. Aquests models sofisticats d’IA han evolucionat de manera molt r\u00e0pida, passant de ser curiositats experimentals a esdevenir components essencials en una gran quantitat de programes i productes SaaS. Models com la s\u00e8rie GPT d’OpenAI han establert nous est\u00e0ndards per comprendre i generar text a un nivell equiparable a l\u2019hum\u00e0, fent que les interaccions amb el programari siguin m\u00e9s intu\u00eftives, naturals i conversacionals que mai.<\/p>\n\n\n\n

Aquesta evoluci\u00f3 remarcable ha estat marcada per fites significatives: els models han crescut en mida i complexitat, han millorat en grau de subtilesa i capacitat per entendre el context, i a resultes d\u2019aix\u00f2 s\u2019han comen\u00e7at a fer servir en camps diversos com ara el servei al client, la creaci\u00f3 de contingut o fins i tot la soluci\u00f3 de problemes complexos. L’aven\u00e7 r\u00e0pid en el camp dels MML significa un canvi de paradigma que inaugura una nova era on la IA conversacional no \u00e9s nom\u00e9s un luxe sin\u00f3 una necessitat per a les empreses que volen estar al capdavant de la innovaci\u00f3 pel que fa al servei i la interacci\u00f3 amb el client. Ara b\u00e9, \u00e9s cr\u00edtic que les organitzacions que es plantegen d\u2019integrar aquestes tecnologies a la seva infraestructura digital n\u2019entenguin les implicacions i subtileses de la seva implementaci\u00f3, i aix\u00ed poder-ne aprofitar al m\u00e0xim el seu potencial transformador.<\/p>\n\n\n\n

<\/a>Com adaptar la IA a les vostres necessitats espec\u00edfiques<\/h2>\n\n\n\n

Quan les organitzacions entren en el m\u00f3n dels MML es troben amb dos conceptes clau: el re-afinament<\/strong> (en angl\u00e8s, fine-tuning<\/em>)<\/strong> i la t\u00e8cnica de generaci\u00f3 augmentada a partir d\u2019extracci\u00f3<\/strong> (o Retrieval-Augmented Generation<\/em><\/strong>, que aqu\u00ed referirem fent servir l\u2019acr\u00f2nim angl\u00e8s de RAG). Aquestes dues t\u00e8cniques s\u00f3n fonamentals per transformar un MML gen\u00e8ric en un actiu especialitzat, adaptat a les necessitats i contextos de cada negoci. Vegem doncs en qu\u00e8 consisteixen per entendre qu\u00e8 poden aportar a les empreses a l\u2019hora de modelar les interaccions amb els seus usuaris:<\/p>\n\n\n\n

Fine-Tuning<\/em><\/strong> (o re-afinament)<\/strong>: Consisteix en entrenar de nou un model que ha estat pr\u00e8viament entrenat per a una tasca gen\u00e8rica, fent servir un conjunt de dades, t\u00edpicament m\u00e9s petit, que l\u2019especialitzen per a les tasques o dominis espec\u00edfics que necessita l\u2019empresa. \u00c9s com donar al model una ‘educaci\u00f3 enfocada’ en un camp particular.<\/p>\n\n\n\n

Retrieval-Augmented Generation<\/em><\/strong> (RAG)<\/strong>: RAG combina el poder generatiu de models com els GPTs amb un pas addicional de recuperaci\u00f3 de coneixement extern. Aix\u00f2 permet al model treure informaci\u00f3 rellevant d’una base de dades o conjunt de dades de tipus divers, com ara fitxers en pdf, fulls de c\u00e0lcul, etc. D\u2019aquesta manera, s\u2019aconsegueix que les respostes del model siguin m\u00e9s informades, actualitzades i adaptades a un cos espec\u00edfic de coneixement.<\/p>\n\n\n\n

Mentre que el fine-tuning<\/em> modifica la comprensi\u00f3 b\u00e0sica d’un model, fent-lo m\u00e9s espec\u00edfic per a una tasca, RAG<\/em> en millora la capacitat d’interactuar amb fonts d’informaci\u00f3 externes, ampliant el seu abast de coneixement i rellev\u00e0ncia. L\u2019elecci\u00f3 entre una o altra t\u00e8cnica dep\u00e8n de les necessitats particulars de cada organitzaci\u00f3: si es necessita un alt nivell d\u2019especialitzaci\u00f3 per a una tasca molt concreta, o b\u00e9 una adaptabilitat i capacitat de resposta \u00e0mplies a conjunts de dades de fonts variades i en evoluci\u00f3 permanent. No cal dir que tamb\u00e9 hi ha escenaris on aquestes dues t\u00e8cniques poden combinar-se per obtenir el millor dels dos m\u00f3ns.<\/p>\n\n\n\n

En aquest article ens centrem en el re-afinament (fine-tuning<\/em>). De totes dues, \u00e9s la t\u00e8cnica m\u00e9s dif\u00edcil de personalitzar a les necessitats d\u2019una empresa i comporta una fase carregosa i intensa de generaci\u00f3 de dades per tal d\u2019especialitzar els MML als contextos o tasques espec\u00edfiques que es necessiten. La creaci\u00f3 d\u2019aquests conjunts de dades \u00e9s un proc\u00e9s labori\u00f3s i complicat que requereix un equip de treball amb expertesa en ling\u00fc\u00edstica i en sistemes de processament del llenguatge natural. A Process Talks tenim tant les eines com l’expertesa per dur a terme aquesta tasca, i volem compartir amb vosaltres les lli\u00e7ons apreses fins ara.<\/p>\n\n\n\n

<\/a>Principals solucions en l\u2019adopci\u00f3 d\u2019MML fent servir fine-tuning<\/em><\/h2>\n\n\n\n

Les empreses que opten per iniciar un projecte de fine-tuning<\/em> d\u2019MML han de d\u2019analitzar b\u00e9 les diferents possibilitats de desplegar aquests models avan\u00e7ats d’IA, que van des de l\u2019opci\u00f3 de fer servir models privats de grans empreses com OpenAI fins a models disponibles al m\u00f3n emergent dels MML d’acc\u00e9s obert. Cada cam\u00ed ofereix avantatges \u00fanics i exigeix consideracions acurades de factors com ara el cost, l’escalabilitat i l’expertesa t\u00e8cnica de qu\u00e8 es disposa a l\u2019empresa. A continuaci\u00f3 aprofundim en els detalls de cada una d\u2019aquestes opcions.<\/p>\n\n\n\n

<\/a>Opci\u00f3 1: Personalitzaci\u00f3 de models comercials<\/h3>\n\n\n\n

Models comercials com el GPT d\u2019OpenAI ofereixen un cam\u00ed directe per ser adaptats a les necessitats espec\u00edfiques de cada empresa a trav\u00e9s d\u2019APIs proporcionades per part de la mateixa organitzaci\u00f3 propiet\u00e0ria del model. A trav\u00e9s d\u2019aquest sistema d\u2019APIs una empresa pot fer re-afinament (fine-tuning<\/em>) per personalitzar el model base als requeriments del seu negoci, per exemple amb dades d\u2019un tipus o domini determinat, o amb estils de resposta concrets. Les APIs proporcionades faciliten aquest proc\u00e9s, permetent una adaptaci\u00f3 f\u00e0cil de les capacitats avan\u00e7ades de la IA per a aplicacions diverses.<\/p>\n\n\n\n

Per facilitar-ne l’an\u00e0lisi, ens centrarem ara en OpenAI, ja que actualment domina el mercat pel que fa a aquesta alternativa. Una an\u00e0lisi similar s’aplicaria tamb\u00e9 a competidors com Google respecte del seu nou model Gemini.<\/p>\n\n\n\n

<\/a>Avantatges de personalitzar els models d’OpenAI:<\/h4>\n\n\n\n

Models de base molt robustos<\/strong>: Els models d’OpenAI s\u00f3n dels millors en termes de rendiment i capacitat.<\/p>\n\n\n\n

APIs amigables per a l’usuari<\/strong>: Aix\u00f2 simplifica tant el fine-tuning<\/em> (\u00e9s a dir, el re-afinament del model) com el proc\u00e9s posterior d\u2019infer\u00e8ncia (aix\u00f2 \u00e9s, l’\u00fas del model un cop re-afinat, per tal de processar noves dades). El sistema d\u2019APIs \u00e9s de fet accessible fins i tot per a usuaris amb una expertesa t\u00e8cnica limitada.<\/p>\n\n\n\n

Altament personalitzable<\/strong>: Els models es poden re-afinar per satisfer requisits espec\u00edfics, cosa que els fa ideals per a aplicacions a mida.<\/p>\n\n\n\n

<\/a>Desavantatges de personalitzar els models d’OpenAI:<\/h4>\n\n\n\n

Cost<\/strong>: L’\u00fas dels models i APIs d’OpenAI no \u00e9s gratu\u00eft, i el cost pot ser significatiu, especialment amb l’\u00fas extensiu de models avan\u00e7ats, o b\u00e9 aplicant capes de  fine-tuning<\/em> intensiu.<\/p>\n\n\n\n

Depend\u00e8ncia d\u2019un servei extern<\/strong>: Confiar en la infraestructura d’OpenAI pot comportar problemes amb la privacitat de les dades, i tamb\u00e9 el control i la disponibilitat a llarg termini dels models refinats a les necessitats de la pr\u00f2pia empresa.<\/p>\n\n\n\n

Personalitzaci\u00f3 limitada del model de base<\/strong>: Tot i que el fine-tuning<\/em> \u00e9s possible, l’arquitectura subjacent del model no es pot alterar, la qual cosa pot limitar-ne el grau de personalitzaci\u00f3 i re-afinament.<\/p>\n\n\n\n

<\/a>Opci\u00f3 2: Aprofitar MML d’Acc\u00e9s Obert<\/h3>\n\n\n\n

Models de codi obert com Falcon, Llama i Mistral ofereixen una alternativa efectiva i flexible en termes de cost. S\u00f3n models de diverses mides i capacitats, adequats per a aplicacions de diferent escala. Hi ha plataformes de codi obert com ara el HuggingFace que en faciliten l’acc\u00e9s i ofereixen recursos per personalitzar-los i desplegar-los convenientment.<\/p>\n\n\n\n

<\/a>Avantatges de fer servir MML d’acc\u00e9s obert:<\/h4>\n\n\n\n

Costos baixos<\/strong>: No tenen quotes de llic\u00e8ncia. El seu cost es deriva principalment del maquinari necessari per executar-los.<\/p>\n\n\n\n

Varietat de models<\/strong>: Es t\u00e9 acc\u00e9s a una gamma de models adequats a diferents necessitats.<\/p>\n\n\n\n

Comunitat i suport<\/strong>: Plataformes com HuggingFace no nom\u00e9s ofereixen un acc\u00e9s estandarditzat als models, sin\u00f3 tamb\u00e9 un servei de suport comunitari, documentaci\u00f3 i tutorials.<\/p>\n\n\n\n

<\/a>Desavantatges de fer servir MML d’acc\u00e9s obert:<\/h4>\n\n\n\n

Exig\u00e8ncies de maquinari per a models m\u00e9s grossos<\/strong>: L\u2019execuci\u00f3 de models voluminosos demana inversions significatives en maquinari.<\/p>\n\n\n\n

Necessitat d\u2019expertesa i coneixement t\u00e8cnic<\/strong>: Cal expertesa en les tasques de fine-tuning<\/em>, optimitzaci\u00f3 i desplegament de models, cosa que pot ser un obstacle per a algunes organitzacions.<\/p>\n\n\n\n

Evoluci\u00f3 r\u00e0pida del camp<\/strong>: El r\u00e0pid desenvolupament de la IA pot fer que sigui dif\u00edcil estar al dia i gestionar de manera efectiva les conseq\u00fc\u00e8ncies que els canvis i la innovaci\u00f3 tenen sobre els models.<\/p>\n\n\n\n

En resum, totes dues vies de desplegament de Models Massius de Llenguatge \u2013sigui fent servir models comercials o b\u00e9 d’acc\u00e9s obert– tenen els seus avantatges i inconvenients. Sovint, la dificultat per escollir-ne una o l\u2019altra radica en el fet que no tots els factors que influeixen en la decisi\u00f3 s\u00f3n clars a l’inici del projecte.<\/strong><\/p>\n\n\n\n

<\/a>Implementaci\u00f3 d\u2019MML amb Process Talks<\/h2>\n\n\n\n

Aquest paisatge tan complex i r\u00e0pidament canviant dels MML pot ser intimidatori o fins i tot dissuasiu per a qualsevol organitzaci\u00f3. Aqu\u00ed \u00e9s on Process Talks entra com a aliat vital. La nostra expertesa i recorregut previ en el camp ajuden a navegar a trav\u00e9s de les complexitats de la implementaci\u00f3 i el desplegament dels MML, i assegura que el teu negoci prengui decisions estrat\u00e8giques informades i adaptades a les seves necessitats particulars.<\/p>\n\n\n\n

Tant si es tracta de triar entre les t\u00e8cniques de fine-tuning<\/em> o RAG, entendre\u2019n les implicacions de cost o b\u00e9 gestionar-ne els aspectes t\u00e8cnics de la seva implementaci\u00f3, Process Talks proporciona el suport i el coneixement necessari per integrar sense problemes els MML que m\u00e9s convinguin al teu marc organitzatiu.<\/p>\n\n\n\n

Permet-nos d\u2019associar-nos amb tu per ajudar-te a aprofitar tot el potencial de la IA conversacional, transformant aix\u00ed la manera com interactues amb els clients i agilitzant les teves operacions.<\/p>\n\n\n\n

Vols comen\u00e7ar un projecte d’IA generativa? Reserva una cita<\/a> amb nosaltres.<\/p>\n","protected":false},"excerpt":{"rendered":"

En una era en qu\u00e8 la interacci\u00f3 digital \u00e9s essencial, les organitzacions que encara no han adoptat la IA generativa en els seus programes o productes SaaS es troben en una cru\u00eflla vital. 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