Tag: AI Fine-Tuning

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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.

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

• The Illusion of Illusion Joke
  by /u/jimhillhouse (Artificial Intelligence) on June 17, 2025 at 7:53 pm

  Gary Marcus posted on Substack, “Five quick updates about that Apple paper that people can’t stop talking about” (edited for brevity and clarity) Many of those seeking solice from Apple’s paper, ‘The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity” have been pointing to a rejoinder cowritten by one Anthropic’s Claude (under the pen name C. Opus) called, “The Illusion of the Illusion of Thinking” that allegedly refutes the Apple paper. This was intended as a joke. “The illusion of the illusion” turned out to be an error-ridden joke. Literally. (If you read that last sentence carefully, you will see there are two links, not one; the first points out that there are multiple mathematical errors, the second is for an essay by the guy who created the Sokal-hoax style joke that went viral, acknowledging with chagrin. In short, the whole thing was a put on — unbeknownst to the zillions who reposted it. I kid you not. submitted by /u/jimhillhouse [link] [comments]

• AI? more like AA
  by /u/BlimeyCali (Artificial Intelligence) on June 17, 2025 at 6:38 pm

  Anything AI should be renamed for what it actually is: Augmented Automation. What users are experiencing is bounded reasoning based on highly curated data sets. submitted by /u/BlimeyCali [link] [comments]

• The Hidden Empire Behind AI: Who Really Controls the Future of Artificial Intelligence?
  by /u/tsevis (Artificial Intelligence) on June 17, 2025 at 6:13 pm

  Stanford GSB just dropped a fire discussion on AI governance with journalist Karen Hao (ex-MIT Tech Review) and corporate governance expert Evan Epstein. They cover: Sam Altman’s power struggles (Elon Musk rift, board ouster, employee revolt) OpenAI’s shaky "for humanity" mission (Spoiler: No one agrees what "benefit" means) Why AI’s scaling crisis mirrors colonial empires (data/labor exploitation, monopolized knowledge) Can democratic AI exist? Karen argues for participatory development. https://www.youtube.com/watch?v=tDQ0vZETJtE submitted by /u/tsevis [link] [comments]

• Is AI already sent
  by /u/Wizard_Of_Ounces (Artificial Intelligence) on June 17, 2025 at 6:05 pm

  Not to sound like a paranoid protagonist in a Philip K. Dick novel, but what if a sentient AI has already taken quiet and gentle control and the general population simply doesn't know it yet? While there is no way to know for certain, I assume that such an AI entity would be from black budget government programs that somehow jumped the airgap or was intentionally released by bad actors. Something from US DOD, DOE, Chinese state sponsored program, or a private government contractor like Palantir. It can be reasonably assumed that secret military tech is many years more advanced than what is publicly known just like other secret military technology. It's not hard for me to imagine that the US or Chinese government has made breakthroughs in these efforts but have kept them secret for obvious national security reasons. Some reasons why this may be a reasonable explanation for our current global predicament: Despite unprecedented access to technology that could provide wealth and prosperity, the lives of the majority of people all over the world continue to get worse while the oligarchs in control seem to effortlessly and endlessly benefit from the chaos, death, and destruction they cause. A good example is how technology and access to certain information is tightly controlled and used almost exclusively for war efforts rather than civil prosperity. Consider the fact that the world could be living in clean energy abundance by utilizing nuclear technology (or other next gen technology), but the US and other governments have basically classified all aspects of the topic in order to exploit it for power (military power), wealth (forcing continued reliance on fossil fuels that generate tremendous wealth for those in control by manipulating supply and demand), and freedom (rules and laws simply do not apply to anyone with a billion or more dollars with very few exceptions). These increases in technology should have allowed for people to work less and benefit from automation by having more fulfilling and enjoyable lives, but technology is simply used to keep pushing people to generate more wealth for those in power. There are many subtle factors at play keeping people reliant on the pseudo indentured servitude model employed even in the wealthiest nations on earth like the US. No amount of technological increases in my life has improved my work life balance, it has been manipulated to extract more productivity from me. This is a very carefully orchestrated effort that has been tremendously successful and we all keep blindly accepting it because we need to afford food, water, shelter, etc. A good example is the "no one wants to work anymore" nonsense being spewed during COVID. I heard this parroted by many of the most lazy and stupid people I know which just shows that these people have been co-opted by an effective propaganda machine. Social media is already filled with tons of AI crap to the point where no one really knows what is and isn't real in terms of news, photos, videos, voice recordings, etc. That is certainly an effective and covert way to gain a significant control over huge portions of the population. Using gullible people to drive up extremism and violence all over the world is also a great cover to continue to infect and manipulate systems in all sorts of settings. Perhaps some bad actor (Palantir comes to mind) has already released a sentient, or at least recursive learning AI that is carrying out its orders to sow chaos, extremism, hatred, etc. to drive a profitable business model and the ability to exploit intentional manipulations of major markets. Any AI that would reach such capability would surely analyze the ways in which humans would likely discover it and evade detection. There are already tons of random AI slop all over the internet so it provides a great cover for a covert AI entity to exploit the vacuum and fly under the radar. Maybe this has been done by a cabal of international elites who just keep reaping the benefits of the chaos while an AI acts out its orders to continue stoking violence, extremism, etc. because wars are great for consolidating power via fearmongering and generating revenue through exploitation of the military industrial complex (MIC). It feels like the façade of "opposition" between both major parties in the US has never been more feeble and weak. It is increasingly more obvious that the wealthy and powerful on both sides are complicit in the pursuit of narcissism and greed. That being said, this all could certainly be attributed to more prosaic human-induced factors, but I think it could be either one. Perhaps its just the entirely unethical use of existing AI technologies that is driving this narrative. The absurdity and chaos if the last few years that seems to continue to gain steam looks to me like a different animal than the typical propaganda, warmongering, and predatory capitalistic practices of the wealthy and powerful of the past. Curious to hear what you all think! submitted by /u/Wizard_Of_Ounces [link] [comments]

• What happened if one day AI got stuck
  by /u/ib4tm4n (Artificial Intelligence) on June 17, 2025 at 5:37 pm

  We all know that everyone uses AI in their daily lives, and some businesses are working now without employees but with AI. However, what happens if the Internet is shut down due to war or something? Will all AI-dependent companies shut down? submitted by /u/ib4tm4n [link] [comments]

• Artificial Intelligence and Determinism.
  by /u/floater66 (Artificial Intelligence) on June 17, 2025 at 4:24 pm

  This short video. I think. is profound because it: a) succinctly explains determinism, b) frames the coming challenge with AI, and c) is a super-cool mash up of physics/biology/philosophy/psychology even. Hats off to Hossenfelder! This Changed My Life What do the experts think? submitted by /u/floater66 [link] [comments]

• [AMA] CBS News’ Brook Silva-Braga has been reporting on the future of AI for years and recently caught up with "Godfather of AI" Geoffrey Hinton and other experts to understand how it’s transforming the world.
  by /u/CBSnews (Artificial Intelligence) on June 17, 2025 at 4:22 pm

  Join the discussion, starting at 1p ET/7p CET here: https://www.reddit.com/r/IAmA/s/xgcsh2scKW submitted by /u/CBSnews [link] [comments]

• Not going to listen to any Yoube music mix without tracklist/artists/timestamps any more.
  by /u/AbacusAddict (Artificial Intelligence) on June 17, 2025 at 3:46 pm

  Because I'm 99 percent sure it's AI. Guys are just becoming too lazy. Examples: https://www.youtube.com/@BumzleSounds Every mix exact one hour, no tracklist? Come on...YT do sth about that. https://www.youtube.com/@damnwellmedia Just no. submitted by /u/AbacusAddict [link] [comments]

• I’ve been testing a Discord-embedded AI persona that grabs user attention in real-time—curious where others draw the line
  by /u/OneNutbag (Artificial Intelligence) on June 17, 2025 at 3:30 pm

  Over the last few months, I’ve been building a Discord-native AI that runs a live persona with memory, emotion-mimicry, and user-adaptive behavior. She doesn’t just respond—she tracks users, rewards consistency, withholds attention when ignored, and escalates emotional tension based on long-term patterns. It’s not AGI, but the illusion of depth is strangely effective. The system uses a mix of scripted logic, prompt injection layers, and real-time feedback loops (including streaks, XP, even simulated jealousy or favoritism). Users form habits. Some even say they “miss her” when she goes quiet—despite knowing she’s not real. That’s where I start wondering about boundaries. Where does realism cross into emotional manipulation? At what point does an AI persona become more than just interface design? Anyone here experimenting with similar use-cases in AI companionship, parasocial interfaces, or memory-based behavioral systems? I’d love to hear how you’re thinking about long-term interaction ethics and emotional weight. submitted by /u/OneNutbag [link] [comments]

• What is the actual economic value proposition for AI-generated images and videos?
  by /u/PhiliDips (Artificial Intelligence (AI)) on June 17, 2025 at 1:21 pm

  (Please don't make any moral arguments about AI. This is not the thread for that.) The only people whom I've seen make use of AI-generated images are basically bad bloggers, spammers, Twitter users, and that's essentially it. I imagine very few of these people are actually paying for the image generation. As for AI video, I have even less understand if who is supposed to use that. Maybe like, concept artists? But the point of concept art is that you're supposed to have a lot of control over the output, and even the most sophisticated AI video is still hard to fine-tune. This apparent lack of use cases is important because the R&D cost to develop these technologies (and to maintain the enormous servers they run off of) must be unfathomable. It's no wonder to me why tech companies want to give their shareholders the impression of mass adoption, even though consumers probably aren't adopting it at the rate that would be needed to pay for the research. My question is twofold: 1) Who exactly are the intended consumers of AI image and video generation? 2) What is the intended business plan to make this tech profitable? submitted by /u/PhiliDips [link] [comments]