Tag: GPTs

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.

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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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The Future of Generative AI: From Art to Reality Shaping

• Which field is more promising?
  by /u/xenocya (Artificial Intelligence Gateway) on May 8, 2024 at 11:51 am

  Hi guys, I'm an data automation engineer, 26 at a start up company.. This is my second job after data analyst.. I'm more interested towards AI, so badly wanted to become an expert in AI as AI Engineer either in NLP or DL. My current job involves data collection, web scrapping, embedding collected data and RAG..since it's a small company, most of the work I do by myself and I'm learning a lot.. I'm having interest in both NLP and Deep Learning. I know both the subsections are more prominent.. But I'm skeptic on my decision to choose, because I love both.. What are your suggestions please.. submitted by /u/xenocya [link] [comments]

• Conversational Mastery (GPT4-Turbo) 👾✨
  by /u/No-Transition3372 (Artificial Intelligence Gateway) on May 8, 2024 at 11:27 am

  submitted by /u/No-Transition3372 [link] [comments]

• Why Live Awareness is one of the biggest AI categories no one is talking about (yet)
  by /u/tridium32 (Artificial Intelligence Gateway) on May 8, 2024 at 11:07 am

  Guys, read this from Subhash Challa, early expert of Object Tracking. The title is his (originally appeared in ITWire) What do you think about the concept? If there were any doubt as to the strength of the AI market in the post-ChatGPT era, one only need to look at the market capitalisation of the software/hardware giant Nvidia. Forming the foundation architecture upon which many of AI’s LLMs sit, Nvidia has recently been declared the third-most valuable company in the world at an assessment of US$2.06 trillion. The valuation confirms the adage of the picks-and-shovels manufacturers profiting most handsomely during a gold rush. But it simultaneously obscures not only the serious conflicts brewing in the current system that could derail this progress, but how they are already being resolved, in cities around the world, without the analysts and short-sellers being any the wiser. First, we must acknowledge that despite the apocalyptic fear-mongering about deepfakes subverting global democracy, generative AI is for the most part a brand-new tool that businesses and consumers are only just now figuring out how to use. Microsoft may have rolled out its Copilot, but businesses are only beginning to learn how to fly with it. Thankfully, the progress towards AI’s full realisation is already underfoot –and it’s transforming our cities. In the latter years of the 20th century, I became intrigued in the growing ubiquity of sensor data. Whether it be temperature sensors, motion detectors, Lidar, Radar sensors, thermal sensors or CCTV cameras, such technologies have reached like tendrils of a vine to encompass much of the modern world. Similar to the development of any emergent sense perception, however, organisations struggled at first in how to make use of the hidden powers such sensor capacity gave them. We all knew there was value in the vast amounts of data sensors were creating, but how to unlock? This was especially true in the emerging smart city movement where sensors and cameras could deliver terabytes of information on crowd densities and street parking habits. Incredible data, but how could we find the signal hidden in so much seeming noise? For me, as a trained scientist focused on the complicated challenge of object detection and tracking, the answer lay in Live Awareness, an innate but little-examined quality that underpins intelligence at both the human and now the digital realm. It is not enough for animals to have the Pax6 gene that will encode for the development of an eye, or even environmental inputs, such as light, which serve as the organ’s input data –we need a brain to make sense of all these inputs. An entire sensemaking apparatus must develop over the course of millennia to transform this sensory data into the objects that populate our reality — and it must do so in real time. This ability to decipher and make sense of our sense-data allows us to drive automobiles, and one day, properly evolved, it will enable such automobiles to drive themselves better than humans ever could. It is the API, even the brain, if you will, that connects AI to the Big Data it feasts upon, and detects new possibilities that would otherwise be missed. Behind the scenes, this critical component of the AI revolution has already begun paying substantial dividends to its early adopters, primarily in the Smart Cities space. Consider the curb, for instance. Monetised as it often is in busy urban areas, until recently its inherent value remained underutilised, and enforcement of the laws which govern them have traditionally been spotty. Left to fester, such concerns can lead to diminished business as well as child endangerment, particularly in school zones such as those that exist in Australia’s Top-Five Councils. With the integration of Live Awareness into its curb management procedures, one council was able to increase its school zone reporting by 900%, allowing its enforcement personnel to visit all the sites in a geographical cluster in one afternoon. Using a real-time virtual model of each street and curb environment, personnel can also determine from the software who is illegally utilising the curb. Such technologies are mapping out curb environments in Toronto, Chicago, and Las Vegas, helping these cities gain new value and revenue from these previously ignored assets. Welcome to the age of the digitised curb. And it won’t stop there. As cities continue to examine AI technologies which implement Live Awareness, they will also determine new applications for it in areas such as water/power utilities, traffic management and greater law enforcement capabilities. Such developments are arguably much further along than in generative AI, which means that Live Awareness is probably the biggest AI story no one is talking about. That’s to be expected; after all, evolution takes time, and only those focused on the gradual developments underpinning these phenomena could really see it coming. But now it is the time for more of us to take notice –especially those managing the cities of today and planning the cities of the future. Our cities have possessed the ability to see for decades, but only now are they learning HOW to see. Once they do, the real transformation of our societies from AI will be here. Original here: https://itwire.com/guest-articles/guest-opinion/why-live-awareness-is-one-of-the-biggest-ai-categories-no-one-is-talking-about-yet.html submitted by /u/tridium32 [link] [comments]

• How to properly fine tune tortoise TTS model? Weird sentence endings
  by /u/Elwii04 (Artificial Intelligence Gateway) on May 8, 2024 at 11:05 am

  Hey dear Reddit-Community! I am trying to fine tune tortoise TTS on my own dataset. I have now tried different Datasets, all pretty high quality but I could not achieve good results. The audio is weird in some way all the time. Most commonly repeating sentence endings or the audio track going on for a few secounds and saying nothing after saying the prompted text. I especially focused on removing clips from my training files with bad/cut off endings, but this did not help anything. I also have been watching Jarods Journey on YT for many months now, also his tricks did not help. (I am also using his voice-cloner for inference and training) I want to use tortoise with Jarods audio-book-maker so just cutting all endings manually in post is not an option for me. Maybe you guys can share your experiences with finetuning 😀 submitted by /u/Elwii04 [link] [comments]

• Want an ai where I can load multiple pdfs that are thousands of pages long for analysis.
  by /u/kraken_enrager (Artificial Intelligence Gateway) on May 8, 2024 at 10:12 am

  Is there any AI where I can load many pdfs or thousands of pages worth if material and ask it questions like I would chatgpt? The AI must answer only from the info given to it. Consumer grade solutions preferred but enterprise solutions would be fine too. Thank you guys. submitted by /u/kraken_enrager [link] [comments]

• Please help me find a way into this all.
  by /u/throwthere10 (Artificial Intelligence Gateway) on May 8, 2024 at 10:03 am

  Hi, I am seeking a career shift and find the burgeoning field of AI and all it entails absolutely fascinating, so I'd love to get into it. The problem is I don't know where to start. So please, explain to me like I'm 10, the steps one should take to embark on a career in the field? Is it a bootcamp? Is it a Google course? Is self teaching the way to go? If I go the self teaching route, what tools do I need? How can I get started TODAY? Knowing what you know now, how would you advise a neophyte like me on how to proceed? I'm sorry for sounding silly, but I'm eager to jump in, but haven't the foggiest on where to begin. And even if you do not know, could you please point me to an article or a subreddit I should follow / read that will help answer my questions? I don't know what I don't know. Please help. Tl;Dr: I'm eager to get into AI and get my feet wet, but don't known where or how to begin. Please help. submitted by /u/throwthere10 [link] [comments]

• Free AI summariser that allows you to summarise any text, whatever the subject (as long as it is not criminal)?
  by /u/thisisifix1 (Artificial Intelligence Gateway) on May 8, 2024 at 9:39 am

  I use a chat gpt summariser add-on in my web browser to summarise articles and news that I read on different websites. However, chat gpt does not allow summaries of articles that are about the conflict in Israel/Palestine, for example. I keep getting meaningless answers (like 'My knowledge is up to date only until January 2022, and I can't provide real-time information...'). So I used the Perplexity.ai addon to summarise texts that chat gpt could not. However, as I now realise, Perplexity is no longer able to summarise such articles. I don't want to start a discussion about why this is so (I have my very clear suspicions about that...), but just ask here: does anyone know of a free ai service that allows quick and easy summarising with a browser addon, and that also works when summarising such topics as mentioned above? submitted by /u/thisisifix1 [link] [comments]

• OpenAI is currently testing two new tools for AI detection, focusing on images and audio
  by /u/Technicallysane02 (Artificial Intelligence Gateway) on May 8, 2024 at 8:48 am

  submitted by /u/Technicallysane02 [link] [comments]

• AI Fashion Design
  by /u/SquizFlavourTing (Artificial Intelligence Gateway) on May 8, 2024 at 8:29 am

  Hi, I really want to get into AI for fashion design and I've seen a massive rise in people doing highly specific designs/model looks and backgrounds with AI on Instagram - it looks a lot more elaborate and specific than just typing in a normal AI prompt and it being generated. I'd like to do some designs on it - does anyone know what program they are using to create these beautiful, intricate designs and would they be hard for a beginner to master? (I asked some of the pages directly but no response so seeing if anyone knows on here) submitted by /u/SquizFlavourTing [link] [comments]

• Ai reddit radio/summary/podcast
  by /u/inteblio (Artificial Intelligence Gateway) on May 8, 2024 at 8:22 am

  I want to listen to a digest of all my favorite subs posts and comments. I want an AI voice/ radio show / podcast... i can lisren to (and ideally ask questions of). It will not take me very long to make this but I've not got round to it. One of you has! Thanks for info/ ideas. If it doesn't yet exist... lets teamwork it into reality please... submitted by /u/inteblio [link] [comments]