Tag: AI Database Queries

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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🤖🚀 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.

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.

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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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The Future of Generative AI: From Art to Reality Shaping

• Do you think ChatGPT will have grandfathered pricing?
  by /u/ticketbroken (Artificial Intelligence Gateway) on December 6, 2024 at 2:21 pm

  I just made the tough decision to go pro because I want to utilize AI to the fullest. Will I possibly receive any type of discount in the future for signing up to this? Has OpenAI ever done anything like give long-term customers discounts? submitted by /u/ticketbroken [link] [comments]

• Did Rings of Power use Ai to write their script or story?
  by /u/RivRobesPierre (Artificial Intelligence Gateway) on December 6, 2024 at 1:27 pm

  I think they did. And I think they used it from what they have in their KDP library. Of course the show is considered terrible. So how can Ai be worth anyone’s time? Let people write stories. Let idiots use Ai to copy them. And please don’t think I’m just hating on Ai, it is wonderful in sake ways. And obviously here to stay. I just don’t see the use in creating if anyone can take it and use it for their own. submitted by /u/RivRobesPierre [link] [comments]

• What I learnt coding up 7 LLM-powered games in a week
  by /u/cokeandcode (Artificial Intelligence Gateway) on December 6, 2024 at 1:19 pm

  I spent a week dogfooding my own SDK by writing a series of AI/LLM games, trying out some generative AI gameplay concepts that have been burning in my brain for a while now. I tried to write down all the things that I saw and learnt through the process. Be really interesting to see if what I found lines up with others experiences. https://developers.rune.ai/blog/7-ai-games-in-7-days submitted by /u/cokeandcode [link] [comments]

• How is Gemini?
  by /u/relapse_rif (Artificial Intelligence Gateway) on December 6, 2024 at 11:43 am

  I updated my phone. After update i saw GEMINI app installed automatically. I want to know how is google Gemini? I saw after second or third attempt, Chatgpt gives almost accurate answer, is gemini works like Chatgpt? submitted by /u/relapse_rif [link] [comments]

• ChatGPT pro mode - concerned?
  by /u/AppropriateRespect91 (Artificial Intelligence Gateway) on December 6, 2024 at 11:19 am

  I guess most of the Plus users concerns are if they will get compromised upgrades from now on, with all the cream going to Pro users. I’m a Plus user, and I would say it’s unlikely. The competition is going to get very interesting with Grok 3 and anything else that the others release. Plus you can get close to or pretty much equal reasoning to o1 with Google AI studio, which is free, so yeah competition. Lastly, most of the subscribers will be Plus users, so it’s in Open AI’s interest to keep them happy. So overall, nothing to be pissed about, or have I missed something? submitted by /u/AppropriateRespect91 [link] [comments]

• AI to create PowerPoint presentations
  by /u/etiquetricity (Artificial Intelligence Gateway) on December 6, 2024 at 10:50 am

  Can anyone recommend AI that can create a PowerPoint presentation for me based on the content I upload? submitted by /u/etiquetricity [link] [comments]

• Upskilling and reskilling in the age of AI
  by /u/AlwaysNever22 (Artificial Intelligence Gateway) on December 6, 2024 at 10:45 am

  Guess not everyone can (or want to) become a plumber or electrician in the future. What is the “smartest” thing to do as a white-collar worker? Embrace continuous learning is obvious but not specific enough for me. submitted by /u/AlwaysNever22 [link] [comments]

• I'm looking for a way to age me in a photo.
  by /u/Elephant789 (Artificial Intelligence Gateway) on December 6, 2024 at 10:26 am

  I have a nice photo of me but it's around 10 years old. I would like to use it for my resume but I have aged too much. Is this possible? I'm looking for something free. submitted by /u/Elephant789 [link] [comments]

• The Impact of AI On Business Travel
  by /u/Amandacerni (Artificial Intelligence Gateway) on December 6, 2024 at 9:09 am

  AI is transforming business travel, from smarter booking and personalized experiences to real-time assistance and enhanced security. It’s making trips more efficient, cost-effective, and stress-free. The future of business travel is here! ✈️ submitted by /u/Amandacerni [link] [comments]

• Even with AI tools (Runway for video, ElevenLabs for sound Effects, and more voices than Runway has) I’m slacking!
  by /u/justahuman555 (Artificial Intelligence Gateway) on December 6, 2024 at 8:36 am

  My first point being that using AI doesn’t mean “one button press and nothing else”. If you want even somewhat decent content, it still takes a lot of human input, tweaking, editing, sometimes it takes many many prompts until the AI/LLM/ML MAYBE does what you want it to do. I still have OCD, ADD, laziness, frustration, and all the other human emotions and dysfunctions I’ve had over the years. (Maybe AI in my brain would fix that, that’d be an extreme measure probably though! And I can’t help but be wary of “The Mark of the Beast” at that point, because I have real life reasons to believe that Christianity is likely true, I definitely can’t NOT believe in some type of supernatural/paranormal, but I’ve gotten immediate help a few times after praying to God, I don’t mean to offend anyone’s belief or religion, just my own personal experiences). I’ll be honest, I do wish I could just directly beam my thoughts and ideas from my head onto the screen, because that’s what it’s mostly about for me, getting my ideas in my head out in the real world. I don’t particularly look forward to the physical and technical creation process, even when it’s EASY, and much of it is automated. I don’t know what’s wrong with me, why I’m so depressed, have emotional highs and lows, possibly psychosis, why I seem to have different people or beings in me, why I have vivid nightmares and voices or some type of mind control attempts on me sometimes (demons trying to possess me?). AI would definitely make a lot of life easier, I’m not talking about content creation tools even, but like self driving cars, automated other daily tasks…… Some people say AI is from the AntiChrist, but seems a bit extreme, the AntiChrist could or would just use whatever would reach the most people (political power, social media, doesn’t mean he actually created it, any enemy can weaponize existing tools and platforms). I can’t just grind consistently, especially when it’s not a paid job. I mean it could turn into that, and I enjoy it, even though I’M PAYING to create, I just don’t feel like doing it every single day. Then a few months gone by and I realize I probably could have finished my video sooner. And I’ve been paying $95 a month and probably could have cancelled (and renewed later on) and saved a few months (a few hundred dollars) but I just put on my credit card because I decided it doesn’t really make much difference if I have even a few hundred dollars of debt each month, it won’t ruin my life anymore than it already is! Debt isn’t what ruined my life, I’ve barely even had any real debt. Too many people OVERCREDIT AI’s capabilities, I mean it WILL PROBABLY get there, but not for a bit more time…… Other people UNDERCREDIT it, because they’re naive and don’t realize real possible innovation (and/or threats). I could be in an AI simulation right now. Or a coma, reality is off a lot……. submitted by /u/justahuman555 [link] [comments]