Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained

Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained

Unlock the secrets of GPTs and Large Language Models (LLMs) in our comprehensive guide!

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Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained
Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained

🤖🚀 Dive deep into the world of AI as we explore ‘GPTs and LLMs: Pre-Training, Fine-Tuning, Memory, and More!’ Understand the intricacies of how these AI models learn through pre-training and fine-tuning, their operational scope within a context window, and the intriguing aspect of their lack of long-term memory.

🧠 In this article, we demystify:

  • Pre-Training & Fine-Tuning Methods: Learn how GPTs and LLMs are trained on vast datasets to grasp language patterns and how fine-tuning tailors them for specific tasks.
  • Context Window in AI: Explore the concept of the context window, which acts as a short-term memory for LLMs, influencing how they process and respond to information.
  • Lack of Long-Term Memory: Understand the limitations of GPTs and LLMs in retaining information over extended periods and how this impacts their functionality.
  • Database-Querying Architectures: Discover how some advanced AI models interact with external databases to enhance information retrieval and processing.
  • PDF Apps & Real-Time Fine-Tuning

Drop your questions and thoughts in the comments below and let’s discuss the future of AI! #GPTsExplained #LLMs #AITraining #MachineLearning #AIContextWindow #AILongTermMemory #AIDatabases #PDFAppsAI”

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Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover GPTs and LLMs, their pre-training and fine-tuning methods, their context window and lack of long-term memory, architectures that query databases, PDF app’s use of near-realtime fine-tuning, and the book “AI Unraveled” which answers FAQs about AI.

GPTs, or Generative Pre-trained Transformers, work by being trained on a large amount of text data and then using that training to generate output based on input. So, when you give a GPT a specific input, it will produce the best matching output based on its training.

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The way GPTs do this is by processing the input token by token, without actually understanding the entire output. It simply recognizes that certain tokens are often followed by certain other tokens based on its training. This knowledge is gained during the training process, where the language model (LLM) is fed a large number of embeddings, which can be thought of as its “knowledge.”

After the training stage, a LLM can be fine-tuned to improve its accuracy for a particular domain. This is done by providing it with domain-specific labeled data and modifying its parameters to match the desired accuracy on that data.

Now, let’s talk about “memory” in these models. LLMs do not have a long-term memory in the same way humans do. If you were to tell an LLM that you have a 6-year-old son, it wouldn’t retain that information like a human would. However, these models can still answer related follow-up questions in a conversation.

For example, if you ask the model to tell you a story and then ask it to make the story shorter, it can generate a shorter version of the story. This is possible because the previous Q&A is passed along in the context window of the conversation. The context window keeps track of the conversation history, allowing the model to maintain some context and generate appropriate responses.

As the conversation continues, the context window and the number of tokens required will keep growing. This can become a challenge, as there are limitations on the maximum length of input that the model can handle. If a conversation becomes too long, the model may start truncating or forgetting earlier parts of the conversation.

Regarding architectures and databases, there are some models that may query a database before providing an answer. For example, a model could be designed to run a database query like “select * from user_history” to retrieve relevant information before generating a response. This is one way vector databases can be used in the context of these models.

There are also architectures where the model undergoes near-realtime fine-tuning when a chat begins. This means that the model is fine-tuned on specific data related to the chat session itself, which helps it generate more context-aware responses. This is similar to how “speak with your PDF” apps work, where the model is trained on specific PDF content to provide relevant responses.

In summary, GPTs and LLMs work by being pre-trained on a large amount of text data and then using that training to generate output based on input. They do this token by token, without truly understanding the complete output. LLMs can be fine-tuned to improve accuracy for specific domains by providing them with domain-specific labeled data. While LLMs don’t have long-term memory like humans, they can still generate responses in a conversation by using the context window to keep track of the conversation history. Some architectures may query databases before generating responses, and others may undergo near-realtime fine-tuning to provide more context-aware answers.

GPTs and Large Language Models (LLMs) are fascinating tools that have revolutionized natural language processing. It seems like you have a good grasp of how these models function, but I’ll take a moment to provide some clarification and expand on a few points for a more comprehensive understanding.

When it comes to GPTs and LLMs, pre-training and token prediction play a crucial role. During the pre-training phase, these models are exposed to massive amounts of text data. This helps them learn to predict the next token (word or part of a word) in a sequence based on the statistical likelihood of that token following the given context. It’s important to note that while the model can recognize patterns in language use, it doesn’t truly “understand” the text in a human sense.

During the training process, the model becomes familiar with these large datasets and learns embeddings. Embeddings are representations of tokens in a high-dimensional space, and they capture relationships and context around each token. These embeddings allow the model to generate coherent and contextually appropriate responses.

However, pre-training is just the beginning. Fine-tuning is a subsequent step that tailors the model to specific domains or tasks. It involves training the model further on a smaller, domain-specific dataset. This process adjusts the model’s parameters, enabling it to generate responses that are more relevant to the specialized domain.

Now, let’s discuss memory and the context window. LLMs like GPT do not possess long-term memory in the same way humans do. Instead, they operate within what we call a context window. The context window determines the amount of text (measured in tokens) that the model can consider when making predictions. It provides the model with a form of “short-term memory.”

For follow-up questions, the model relies on this context window. So, when you ask a follow-up question, the model factors in the previous interaction (the original story and the request to shorten it) within its context window. It then generates a response based on that context. However, it’s crucial to note that the context window has a fixed size, which means it can only hold a certain number of tokens. If the conversation exceeds this limit, the oldest tokens are discarded, and the model loses track of that part of the dialogue.

It’s also worth mentioning that there is no real-time fine-tuning happening with each interaction. The model responds based on its pre-training and any fine-tuning that occurred prior to its deployment. This means that the model does not learn or adapt during real-time conversation but rather relies on the knowledge it has gained from pre-training and fine-tuning.

While standard LLMs like GPT do not typically utilize external memory systems or databases, some advanced models and applications may incorporate these features. External memory systems can store information beyond the limits of the context window. However, it’s important to understand that these features are not inherent to the base LLM architecture like GPT. In some systems, vector databases might be used to enhance the retrieval of relevant information based on queries, but this is separate from the internal processing of the LLM.

In relation to the “speak with your PDF” applications you mentioned, they generally employ a combination of text extraction and LLMs. The purpose is to interpret and respond to queries about the content of a PDF. These applications do not engage in real-time fine-tuning, but instead use the existing capabilities of the model to interpret and interact with the newly extracted text.

To summarize, LLMs like GPT operate within a context window and utilize patterns learned during pre-training and fine-tuning to generate responses. They do not possess long-term memory or real-time learning capabilities during interactions, but they can handle follow-up questions within the confines of their context window. It’s important to remember that while some advanced implementations might leverage external memory or databases, these features are not inherently built into the foundational architecture of the standard LLM.

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So, if you’re ready to embark on this incredible journey of unraveling the mysteries of AI, go ahead and grab your copy of “AI Unraveled” today. Trust me, you won’t regret it!

On today’s episode, we explored the power of GPTs and LLMs, discussing their ability to generate outputs, be fine-tuned for specific domains, and utilize a context window for related follow-up questions. We also learned about their limitations in terms of long-term memory and real-time updates. Lastly, we shared information about the book “AI Unraveled,” which provides valuable insights into the world of artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!

Mastering GPT-4: Simplified Guide for Everyday Users

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AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, AI Podcast)
AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, AI Podcast)

The Future of Generative AI: From Art to Reality Shaping

  • Is there an AI our there that can help?
    by /u/Brutallyhonest289 (Artificial Intelligence) on February 28, 2024 at 7:57 pm

    I currently make and run our production schedule in TEAMS excel form for our company. We are looking into going to a different system down the road but for now i feel like i need help like some kind of ai add in that can recognize patterns and optimize a better schedule based previous data points we already have. submitted by /u/Brutallyhonest289 [link] [comments]

  • Help us test Mobile Tethering – easy to use AI chat on mobile, powered by your PC
    by /u/PacmanIncarnate (Artificial Intelligence Gateway) on February 28, 2024 at 7:52 pm

    Hey everyone, We’re looking for people to help test Desktop-to-Mobile Tethering, a new feature we just released on Faraday. It’s an easy way to use locally-running AI models on mobile. You can spin up local LLMs on your Mac or PC, and seamlessly use them to chat with AI from your phone. Since all data is generated on your computer’s hardware (instead of on an expensive cloud server), it’s 100% free to use and you never have to worry about anyone else controlling your data. Here is a step-by-step guide on how to start using Tethering with Faraday. Please send us your feedback by joining our Discord community and SubReddit! Thank you! __ PS: For those of you who’ve never used Faraday – it’s a zero-config desktop app for AI chat and roleplay powered by locally-running LLMs. The full local chat experience “just works” out of the box; you never need to open a terminal. Faraday can run on CPU with only 8GB of RAM, and the app will automatically use your GPU to speed things up. We have our own Character Hub, as well as many power user features like lorebooks, author’s note, text-to-speech, and more! submitted by /u/PacmanIncarnate [link] [comments]

  • What will LLMs do about a finite amount of data to train on?
    by /u/Narrow_Meeting3126 (Artificial Intelligence Gateway) on February 28, 2024 at 7:47 pm

    As is common knowledge to anyone paying attention to the recent advancement in machine learning, these LLMs require huge amounts of data to train on to create a desired output. It’s assumed that this technology will get better as computational power increases, but isn’t there only a finite amount of data it can train on? If that’s true, won’t it be limited to only generating boilerplate stuff with the occasional minute error? Is there a fix to this problem? Can they train on their own generated data? Granted, I’m not an AI researcher so I’m not too familiar with a lot of this stuff. submitted by /u/Narrow_Meeting3126 [link] [comments]

  • Program to Interact with a Library of PDF's
    by /u/muckfustard (Artificial Intelligence) on February 28, 2024 at 7:44 pm

    Hello everyone, Are you aware of any programs that will find data that is stored in a library of PDF's? Basically, an advanced CTRL+F feature, where I could ask the program for X piece of information from X document, and it will source it for me (without having to open the source document). Thank you in advance! submitted by /u/muckfustard [link] [comments]

  • How can I use AI to improve revenue on a fish farm?
    by /u/Goldiegoodie (Artificial Intelligence Gateway) on February 28, 2024 at 7:16 pm

    Say I own a fish farm in Africa. I grow, sell and export fish, how can I use AI to improve my business, find new business opportunities and grow my revenue? submitted by /u/Goldiegoodie [link] [comments]

  • AI & Neuropsychology
    by /u/Xcuse_Me_Sir- (Artificial Intelligence Gateway) on February 28, 2024 at 6:41 pm

    Hello, I'm looking into doing an AI graduation project heavily involving neuroscience. I'm specifically interested in the field of psychiatry from a nuerobiological perspective. The main idea is to use brain images (from MRI, CT scans, possibly EEG videos to analyse the brains of people with mental disorders and attempt to use AI techniques to draw conclusions about certain brain networks and the roles they play in these disorders. Do you think this is a viable project? I'm looking into making it a research-based project mostly, and I know this is a very active area of research, but will I be able to find enough datasets of such brain images to make it possible? submitted by /u/Xcuse_Me_Sir- [link] [comments]

  • Possible scenarios in era of Superhuman Generative AI
    by /u/IntrepidRestaurant88 (Artificial Intelligence Gateway) on February 28, 2024 at 6:07 pm

    Token uniqueness and creativity tax Once trained, large language models use a type of tweaking called rlhf to write in a certain desired style. There are two methods here. orm and prm. orm rewards the model only on the output, while prm rewards the entire process. While prm maintains consistency and accuracy at the expense of creativity, orm is the opposite, which means a tax on creativity. In the future, a Gpt-5 level model might automatically restrict its creativity before outputting, i.e. token uniqueness, to avoid unintended consequences and give a generic, average answer without being boring enough. Economy of time, age of distinction and similarity of content When generative AI becomes superhuman in every aspect in the future, a number of scenarios may occur. You may have heard about the attention economy, your attention is analyzed according to various parameters about a product and how much you pay attention to it, and the algorithm uses your data to optimize it to make more profit from you or shares it with content producers. As of next year, artificial intelligence-generated content is expected to constitute the entire internet. Although some are right in saying that the content produced has already surpassed human consumption, what I mean is the scenario where the production of the content that attracts people's attention and demand is faster than its consumption per unit time. When this happens, we enter the era of discernment rather than attention. The difference is that, beyond automatic production, AI receives automatic feedback and distinguishes the content to best suit the demand. While attention is now the product, attention time spent on content becomes so valuable that it can finance itself, corresponding to a positive real return. While attention is the product, attention time will function as a kind of currency. Because the time when attention is devoted to content has become a scarce resource. Finally, content similarity is always examined in the context of copyright. one retired actor even sold the rights to his likeness. But once the superhuman diversity/quality curve is reached, it is plausible that the situation will reverse. That is, generative AI now sets the norm. Human content counterparts will increasingly try to emulate productive AI content in niches that suit their content profiles in order to license their content and make this license valuable, that is, to make money from it. Now algorithmic artificial intelligence creates preference profiles based on similarities in consumer preferences. but in this scenario it must now classify preference profiles according to how different they are from each other, because overall quality can now be scaled in favor of diversity rather than at its expense. submitted by /u/IntrepidRestaurant88 [link] [comments]

  • Outfit Anyone made by HumanAIGC Alibaba Research Group
    by /u/poopsmith38 (Artificial Intelligence) on February 28, 2024 at 5:43 pm

    submitted by /u/poopsmith38 [link] [comments]

  • Cat walking towards camera
    by /u/235iguy (Artificial Intelligence Gateway) on February 28, 2024 at 5:13 pm

    I seen a video, can't remember where, of a AI cat walking down toward the camera through some shrubbery. I hope you know the one, it looked quite real. How was this made? Is it a program I can download for PC? How long does the program take to render this? Is it instant or does it take hours/days. (total AI noob, sorry) Thanks. submitted by /u/235iguy [link] [comments]

  • Just to confirm. Every author (ones at Google) of the pivotal paper, "Attention is all you Need", have quit Google. That's kind of astonishing. Just trying to confirm.
    by /u/ejpusa (Artificial Intelligence Gateway) on February 28, 2024 at 5:10 pm

    Saw a post about Google and AI. People seem to be heading elsewhere. The MBAa came in and laid down the law. "We make our rent with Ads" not selling AI. Kind of a corporate mess. Stock is still crashing. submitted by /u/ejpusa [link] [comments]

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