Transforming Language with Generative Pre-trained Transformers (GPT)

00:08:32
https://www.youtube.com/watch?v=bdICz_sBI34

Zusammenfassung

TLDRThis video provides an in-depth exploration of the technology behind GPT (Generative Pre-trained Transformer). GPT is a type of large language model that generates natural language text through deep learning, based on input sequences. Key components include generative capabilities, pre-training on vast datasets, and transformer architecture, which employs self-attention mechanisms for better understanding context and relationships within text. The video highlights the progressive development of GPT models from the original GPT-1 to the current models with trillions of parameters, emphasizing their application in practical scenarios such as correcting transcription errors in video captions using self-attention to understand and rectify context-driven errors. Additionally, it discusses the history and components of the Transformer's architecture, making it a cornerstone of modern AI language models.

Mitbringsel

  • 🤖 GPT stands for Generative Pre-trained Transformer, focusing on language generation.
  • 📚 It uses deep learning to process and generate natural language.
  • 🧠 GPT models work via self-attention mechanisms, enhancing context understanding.
  • 💡 Transformers revolutionized AI with mechanisms to focus on important text parts.
  • 📅 GPT's evolution has led to models like GPT-4 with 1.8 trillion parameters.
  • 🔄 Encoders and decoders in Transformers help map and predict language sequences.
  • 📜 Self-attention allows models to understand text in larger context.
  • 🛠 GPT improves video captioning by correcting transcription errors.
  • 🔍 Generative AI relies on training with vast unlabeled datasets.
  • ⚙️ Self-attention is key to modern natural language processing capabilities.

Zeitleiste

  • 00:00:00 - 00:08:32

    The video begins by introducing GPT, which stands for Generative Pre-trained Transformer, explaining it as a large language model using deep learning for natural language processing. It highlights the components of GPT: 'Generative' involves predicting text patterns using pre-training on unlabeled data (unsupervised learning), teaching the model to recognize patterns and apply them to new inputs. The 'Transformer' aspect is a neural network specialized in natural language processing, which processes data using self-attention mechanisms and distinguishes words through tokenization, utilizing encoders and decoders to maintain semantic relationships.

Mind Map

Video-Fragen und Antworten

  • What does GPT stand for?

    GPT stands for Generative Pre-trained Transformer.

  • What is the function of the generative aspect in GPT?

    The generative aspect refers to the model's ability to produce natural language text based on input.

  • What is the significance of the pre-trained component in GPT?

    Pre-training allows the model to learn patterns from large datasets without predefined labels, which it can apply to new inputs.

  • What is a Transformer in the context of GPT?

    In GPT, a Transformer is a type of neural network specialized in natural language processing that uses self-attention mechanisms.

  • How do self-attention mechanisms work?

    Self-attention mechanisms allow models to focus on important tokens within a sequence, considering the overall context to understand word relationships.

  • What is the history of the Transformer architecture?

    The Transformer architecture was introduced in 2017 by the Google Brain team in their paper "Attention is All You Need."

  • What are the roles of encoders and decoders in Transformers?

    Encoders map tokens into vector spaces and assign weights for semantic understanding, while decoders predict probable responses based on input embeddings.

  • What was the advancement from GPT-1 to GPT-2?

    GPT-2 built on GPT-1 by increasing the number of parameters to 1.5 billion, improving capability and reducing errors.

  • How has the development of GPT models progressed over time?

    GPT models have seen linear scaling with increasing parameters, making each version larger and more capable, like GPT-4 having 1.8 trillion parameters.

  • How are GPT models used in video captioning?

    GPT models improve captions by using self-attention to accurately interpret context and correct transcription errors.

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Untertitel
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Automatisches Blättern:
  • 00:00:00
    GPT stands for Generative Pre-trained  Transformer, the core technology behind ChatGPT,
  • 00:00:05
    but what is this technology, really?
  • 00:00:08
    Let's get into it.
  • 00:00:11
    So let's break this down into what a GPT is,
  • 00:00:15
    a little bit of history of GPT models,
  • 00:00:18
    and then an example of how we've put GPTs to work right here in the studio,
  • 00:00:24
    and let's start with what. What is a GPT?
  • 00:00:28
    Well, a GPT is a type of large language model that
  • 00:00:31
    uses deep learning to produce natural  language text based on a given inputs.
  • 00:00:37
    And GPT models work by analyzing an input  sequence and predicting the most likely outputs.
  • 00:00:42
    So let's break this down.
  • 00:00:45
    So we have generative is the G,
  • 00:00:50
    Pre-trained is the P,
  • 00:00:54
    and the T that is for Transformer.
  • 00:00:58
    So what does all of this actually mean?
  • 00:01:02
    Well, in generative pre-training, let's let's start with that.
  • 00:01:08
    So generative pre-training teaches the model  to detect patterns in data and then apply those patterns to new inputs.
  • 00:01:17
    It's actually a form of learning  called unsupervised learning,
  • 00:01:22
    where the model is given unlabeled data.
  • 00:01:25
    That means data that doesn't have  any predefined labels or categories.
  • 00:01:28
    And then it must interpret it independently.
  • 00:01:31
    And by learning to detect  patterns in those datasets.
  • 00:01:35
    The model can draw similar conclusions  when exposed to new unseen inputs.
  • 00:01:40
    Now, GPT models are trained with billions or even   trillions of parameters which are  refined over the training process.
  • 00:01:48
    Now, the T in GPT that stands for Transformer.
  • 00:01:55
    Transformers are a type of neural network  specialized in natural language processing.
  • 00:01:59
    Transformers don't understand language  in the same way that humans do.
  • 00:02:03
    Instead, they process words into discrete units.
  • 00:02:07
    Those units are called tokens, and  for those tokens, they're smaller
  • 00:02:13
    chunks of words or characters  that the model can understand
  • 00:02:17
    and transform all models of process data with  two modules known as encoders and decoders.
  • 00:02:22
    And they use something called  self attention mechanisms to
  • 00:02:25
    establish dependencies and relationships.
  • 00:02:28
    So let's define what those are and  let's start with self attention.
  • 00:02:34
    So what is a self attention mechanism?
  • 00:02:37
    Well, it's really the signature feature of  Transform is the secret sauce, if you like,
  • 00:02:43
    older models like recurrent neural  networks or convolutional neural networks.
  • 00:02:47
    They assess input data sequentially or  hierarchically, but transformers can self direct
  • 00:02:53
    to their attention to the most important tokens  in the input sequence, no matter where they are.
  • 00:03:00
    They allow the model to evaluate each word
  • 00:03:02
    significance within the context  of the complete input sequence,
  • 00:03:05
    making it possible for the model to understand  linkages and dependencies between words.
  • 00:03:11
    Okay, so that self attention.
  • 00:03:13
    What about the encoder?
  • 00:03:16
    Well, the encoder module maps tokens onto a  three dimensional vector space in a process
  • 00:03:22
    called embedding tokens encoded nearby in the  3D space or seem to be more similar in meaning.
  • 00:03:28
    The encoder blocks in the transformer  network assigns each embedding a weight
  • 00:03:33
    which determines its relative importance  and positioned encode as capture semantics,
  • 00:03:37
    which lets GPT models differentiate between  groupings of the same words in different orders.
  • 00:03:43
    So for example, the egg came before the chicken  as compared to the chicken came before the egg.
  • 00:03:49
    Same words in that sentence,  but different meanings.
  • 00:03:52
    There's also a decoder module as well.
  • 00:03:57
    And the decoder.
  • 00:03:58
    What that does is it predicts the most  statistically probable response to the
  • 00:04:02
    embeddings prepared by the encoders, by  identifying the most important portions
  • 00:04:07
    of the input sequence with self attention and then  determining the output most likely to be correct.
  • 00:04:13
    Now a quick word on the history of  generative Pre-trained Transformers.
  • 00:04:19
    The transformer architecture was first  introduced in 2017 in the Google brain paper,
  • 00:04:27
    "Attention is all you need."
  • 00:04:29
    Today there are a whole bunch of generative A.I. models built on this architecture, including  open source models like Llama from Meta
  • 00:04:37
    and Granite from IBM, and closed source frontier models  like Google Gemini and Claude from Anthropic,
  • 00:04:44
    but I think the GPT model that most comes to mind  for most people is ChatGPT from OpenAI.
  • 00:04:53
    Now ChatGPT is not a specific GPT model.
  • 00:04:56
    It's a chat interface that allows users to interact   with various generative pre-trained transformers.
  • 00:05:02
    You pick the model you want from a list and today xthere's likely to be a GPT4 model like GPT4o.
  • 00:05:09
    But the first GPT model from OpenAI was  GPT-1, and that came out back in 2018.
  • 00:05:18
    It was able to answer questions in a humanlike way to an extent, but it was
  • 00:05:23
    also highly prone to hallucinations and just general bouts of nonsense.
  • 00:05:28
    GPT2 That came out the following year as a much  larger model boasting 1.5 billion parameters.
  • 00:05:41
    Sounds like quite a lot.
  • 00:05:43
    Since then, linear scaling has resulted in each  subsequent model becoming larger and more capable.
  • 00:05:49
    So by the time we get to today's GPT4 models, well, those are estimated to contain something like 1.8 trillion parameters, which is a whole lot more.
  • 00:06:03
    So we talked about how a GPT is a  fundamentally different type of model,
  • 00:06:08
    one that uses self attention mechanisms to see  the big picture and evaluate the relationships
  • 00:06:12
    between words in a sequence, allowing it to  generate contextually relevant responses.
  • 00:06:17
    And I'd like to share a quick example of how
  • 00:06:21
    that's helped right here in  my role in video education.
  • 00:06:25
    We create close captions for every  video using a speech to text service.
  • 00:06:31
    Now here's a snippet from the  course I was working on this
  • 00:06:34
    week showing the transcript and the timestamps.
  • 00:06:38
    Now it's not bad, but there are some errors.
  • 00:06:41
    It's mis transcribed Cobal as CBL.
  • 00:06:44
    It's missed me saying a T in  HTTP and it had no idea that K.S. is actually a product called CICS,
  • 00:06:53
    which is pronounced kicks.
  • 00:06:54
    And that's all typical of air models built  on recurrent neural networks that process
  • 00:06:59
    data sequentially one word at a time.
  • 00:07:02
    So I gave this transcript to a GPT model,
  • 00:07:06
    along with the script that I based my talk on, which I called the ground truth.
  • 00:07:13
    So this was the actual script that I was reading from.
  • 00:07:18
    Then I told the GPT to fix the transcript,
  • 00:07:21
    and here's what it came up with.
  • 00:07:23
    It fixed all three errors.
  • 00:07:25
    CBL is Cobal, KS is CICS, and HTP is HTTP,
  • 00:07:31
    and in fact, I tried this again, but  this time removing the ground truth
  • 00:07:37
    entirely and instead just gave it a brief  synopsis that said This is a video about
  • 00:07:43
    a modern CISC application and it was  still able to fix those three errors.
  • 00:07:48
    And that's the self attention mechanism at work, processing the entire input   sequence and better understanding the context of what I was discussing.
  • 00:07:58
    Even without having the exact script in front of it.
  • 00:08:01
    The GPT model uses broader language and software knowledge  to correct technical terms and acronyms.
  • 00:08:08
    So that's generative Pre-trained, Transformers or  GPT as they form the foundation of generative A.I.
  • 00:08:14
    applications using transformer architecture and   undergoing supervised pre training on vast amounts of unlabeled data.
  • 00:08:24
    And if you happen to turn video captions on in this video and you spotted an error,
  • 00:08:30
    well now you know which model to blame.
Tags
  • GPT
  • Generative AI
  • Transformer
  • Deep Learning
  • Self-attention
  • Language Model
  • GPT-4
  • Neural Network
  • Video Captioning
  • OpenAI