prompt_content.json

{
  "partition": null,
  "name": "llm",
  "sections": [
    {
      "id": 0,
      "start": "0:00:00",
      "end": "0:02:30.48",
      "content": "[Video title] llm\n[Visual labels] human face, smile, clothing, person\n[OCR] 178, LARGE LANGUAGE, MODELS, IBM Technology, Martin Keen, Master Inventor, IBM Cloud, LARGE LANGUAGE MODELS, LARGE, LANGUAGE, WHAT 15, AN LLM?, HOW DO, THEY WORK?, BUSINESS, APPLICATIONS, WHAT IS, SUBSCRIBE, LLM, LANGUAGE MODELS, \"THESKYIS...,
      m.\n[
        Transcript
      ]GPTorGenerativepretrainedtransformerisalargelanguagemodeloranLLMthatcangeneratehumanliketextandI'vebeenusingGPTinitsvariousformsforyears.\nInthisvideo,
      wearegoingto#1askwhatisanLLM?\nNumbertwo,
      wearegoingtodescribehowtheywork.\nAndthen#3we'regoingtoaskwhatarethebusinessapplicationsofLLMS?\nSolet'sstartwithnumberone.\nWhatisalargelanguagemodel?\nWellalargelanguagemodelisaninstanceofsomethingelsecalledafoundationmodel.\nNowfoundationmodelsarepretrainedonlargeamountsofunlabeledandselfsuperviseddata.\nMeaningthemodellearnsfrompatternsinthedatainawaythatproducesgeneralizableandadaptableoutput.\nAndlargelanguagemodelsareinstancesoffoundationmodelsappliedspecificallytotextandtextlikethingsI'mtalkingaboutthingslikecode.\nNow,
      largelanguagemodelsaretrainedonlargedatasetsoftextssuchasbooks,
      articles,
      andconversations.\nAndlook,
      whenwesaylarge,
      thesemodelscanbe10sofgigabytesinsizeandtrainedonenormousamountsoftextdata.\nWe'retalkingpotentiallypetabytesofdatahere.\nSotoputthatintoperspective,
      atextfilethatislet'ssay1GBinsize,
      thatcanstoreabout178,
      000,
      000words,
      alotofwordsjustin1GB.\nAndhowmanygigabytesareinapetabyte?\nWell,
      it'sabout1,
      000,
      000.\nYeah,
      that'strulyalotoftech.\nLLMSarealsoamongthebiggestmodelswhenitcomestoparametercount.\nAparameterisavaluethemodelcanchangeindependentlyasitlearns.\nAndthemoreparametersthemodelhas,
      themorecomplexitcanbe.\nGTP3forexampleispretrainedonacorpusofactually45terabytesofdataandituses175billionMLparameters.\nAllright,
      sohowdotheywork?\nWell,
      wecanthinkofitlikethis."}, {"id": 1, "start": "0: 02: 30.48", "end": "0: 05: 08.36", "content": "[
        Videotitle
      ]llm\n[
        Visuallabels
      ]humanface,
      smile,
      clothing,
      person\n[
        OCR
      ]178,
      LARGELANGUAGE,
      MODELS,
      LARGELANGUAGEMODELS,
      LARGE,
      LANGUAGE,
      ANLLM?,
      HOWDO,
      THEYWORK?,
      BUSINESS,
      APPLICATIONS,
      WHATIS,
      LLM,
      \"THE SKY IS ..., m., DATA, HOW Do\n[Transcript] LLM equals three things, data architecture, and lastly, we can think of it as training.\nThose three things are really the components of an LLM.\nNow we've already discussed the enormous amounts of text data that goes into these things.\nAs for the architecture, this is a neural network and for GPT that is a transformer, and the transformer architecture enables the model to handle sequences of data like sentences or lines of code.\nAnd Transformers are designed to understand the context of each word in a sentence by considering it in relation to every other word.\nThis allows the model to build a comprehensive understanding of the sentence structure and the meaning of the words within it.\nAnd then this architecture is trained on all of this large amount of data.\nNow, during training, the model learns to predict the next word in a sentence.\nSo the sky is.\nIt starts off with a with a random guess, the sky is bug.\nBut with each iteration, the model adjusts its internal premises to reduce the difference between its predictions and the actual outcomes.\nAnd the model keeps doing this, gradually improving its word predictions until it can reliably generate coherent sentences.\nForget about bug, it can figure out it's blue.\nNow the model can be fine-tuned on a smaller, more specific data set.\nHere the model refines its understanding to be able to perform this specific task more accurately.\nFine tuning is what allows a general language model to become an expert at a specific task.\nOK, so how does this all fit into #3 business applications?\nWell, for customer service applications, businesses can use LLMS to create intelligent chat bots that can handle a variety of customer queries, freeing up human agents for more complex issues.\nAnother good field?\nContent creation that can benefit from LLMS, which can help generate articles, emails, social media posts, and even YouTube video scripts.\nThere's an idea now LLMS can even contribute to software development, and they can do that by helping to generate and review code.\nAnd look, that's just scratching the surface.\nAs large language models continue to evolve, we're bound to discover more innovative applications."
    },
    {
      "id": 2,
      "start": "0:05:08.36",
      "end": "0:05:33.299633",
      "content": "[Video title] llm\n[Visual labels] clothing, blue, electric blue\n[OCR] 178, LARGE LANGUAGE, MODELS, LARGE LANGUAGE MODELS, AN LLM?, THEY WORK?, BUSINESS, APPLICATIONS, WHAT IS, LLM, \"THESKYIS...,
      m.,
      DATA,
      HOWDo,
      IBM\n[
        Transcript
      ]Andthat'swhyI'msoenamouredwithlargelanguagemodels.\nIfyouhaveanyquestions,
      pleasedropusalinebelowandifyouwanttoseemorevideoslikethisinthefuture,
      pleaselikeandsubscribe.\nThanksforwatching."}]}