Tag: GPTs for dummies

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

• 3 prompts to generate 100% human like content in ChatGPT
  by /u/dinga_dong- (Artificial Intelligence Gateway) on May 9, 2024 at 7:10 pm

  Hi. After a lot of research and practical experience I made this list of TOP 3 prompts to generate human like content. I’ve personally tested out these prompts. All three prompts have passed writer and zerogpt’s detectors by atleast 90% or more. Here are prompts to get human like content from LLMs: 1. Using Dependency Grammer: Dependency grammar, a linguistic framework, connects words in a sentence based on their dependencies, showing how each word relies on a main word (like a verb) to form meaningful structures. You can test it out by copying and pasting the below prompt. But I can already perceive the content created by ChatGPT to be atleast 90% human written. Prompt: Use the dependency grammar linguistic framework to craft a [ARTICLE/POST/BLOG/ETC.]. The idea is that the closer together each pair of words you’re connecting are, the easier the copy will be to comprehend. Here is the topic and additional details: [TITLE] [DETAILS] 2. Burstiness and Perplexity: The provided prompt aims to guide large language models (LLMs) to generate content that closely resembles human-written text. It does this by emphasizing two key factors: "perplexity" and "burstiness." Perplexity measures the complexity of the text, essentially how difficult it is to understand. By asking the model to incorporate a good amount of perplexity, the prompt encourages the generation of content with varying degrees of complexity, similar to what humans naturally produce. Prompt: When it comes to writing content, two factors are crucial, "perplexity" and "burstiness." Perplexity measures the complexity of text. Separately, burstiness compares the variations of sentences. Humans tend to write with greater burstiness, for example, with some longer or complex sentences alongside shorter ones. AI sentences tend to be more uniform. Therefore, when writing the following content I am going to ask you to create, I need it to have a good amount of perplexity and burstiness. Here is the topic and additional details: [TITLE] [DETAILS] 3. Assign Role to AI: In this method we first assign a role to our friend AI. Although this method was very helpful to me as well but now I rather use the previous methods mentioned more. Prompting something like , “Write a blog on [TOPIC] as an expert 27 years old copywriter” will give you good results. But to make the outcome even better and more like a human written content you should give AI instructions such as tone, words to use, role etc. Follow the below prompt to get an idea. However, depending on your topic you might want to modify the prompt. Prompt: You are an expert copywriter with massive experience in business writings. Please write a 500-word piece written on the following topic: When you sell your business, money is not the most important thing and what will make you happy in the long run is if your employees and customers get a good outcome from it. Write the text in the following voice: Direct Simple with no jargon Shorter sentences Personal Conversational In the tone of an advisor Colloquial Using the second person Using anecdotes and metaphors without trite phrases and without obvious ideas --------------------------------------------..........................-------------------------------------------- I hope you guys found this post helpful. I run an AI tools directory called seekme.ai. I’ve listed over 13k AI tools there as well thousands of ChatGPT plugins that exists to make your prompting easier. Consider subscribing to my newsletter to get more of these tutorials and case studies on prompting. Subscribe here submitted by /u/dinga_dong- [link] [comments]

• We made AI agents with backstories created by random people have a gladiator fight in Minecraft.
  by /u/WheelMaster7 (Artificial Intelligence) on May 9, 2024 at 7:06 pm

  submitted by /u/WheelMaster7 [link] [comments]

• Microsoft Builds CIA An AI Model (AI News)
  by /u/ArFiction (Artificial Intelligence) on May 9, 2024 at 6:01 pm

  Here is a one minute rundown of the latest AI news: GPT2-chatbot returns: im-a-good-gpt2-chatbot & im-also-a-good-gpt2-chatbot has appeared on the LMSYS arena battle, reports suggest this is better than every current public available model AlphaFold3: Google DeepMind has released AlphaFold3, a revolutionary model with expectations to speed up drug discovery and unlock secrets of diseases. CIA AI Model: Microsoft created a secure, offline AI for the CIA. Nicknamed "Project Guardian," this AI analyses classified data without internet risks, offering a potential game-changer for US intelligence. TikTok new AI labelling tool: Tiktok recently pushed out some new AI labelling tools that will automatically detect content made by tools such as DALLE-3 OpenAI to face Google: Sources suggest OpenAI will release a search engine tool similar to perplexity, to possibly dethrone google and revolutionise search That's all for today! Read more (No sign up required) - Long form version (GPT2-Chatbot) submitted by /u/ArFiction [link] [comments]

• AI News (New AI Model could save millions of lives!)
  by /u/ArFiction (Artificial Intelligence Gateway) on May 9, 2024 at 5:56 pm

  Here is a one minute rundown of the latest AI news: GPT2-chatbot returns: im-a-good-gpt2-chatbot & im-also-a-good-gpt2-chatbot has appeared on the LMSYS arena battle, reports suggest this is better than every current public available model AlphaFold3: Google DeepMind has released AlphaFold3, a revolutionary model with expectations to speed up drug discovery and unlock secrets of diseases. CIA AI Model: Microsoft created a secure, offline AI for the CIA. Nicknamed "Project Guardian," this AI analyses classified data without internet risks, offering a potential game-changer for US intelligence. TikTok new AI labelling tool: Tiktok recently pushed out some new AI labelling tools that will automatically detect content made by tools such as DALLE-3 OpenAI to face Google: Sources suggest OpenAI will release a search engine tool similar to perplexity, to possibly dethrone google and revolutionise search Read more (No sign up required) - submitted by /u/ArFiction [link] [comments]

• TikTok will automatically label AI-generated content created on platforms like DALL·E 3
  by /u/Used-Bat3441 (Artificial Intelligence) on May 9, 2024 at 5:05 pm

  Starting today, TikTok will automatically label videos and images created with AI tools like DALL-E 3. This transparency aims to help users understand the content they see and combat the spread of misinformation. Key points: To achieve this, TikTok utilizes Content Credentials, a technology allowing platforms to recognize and label AI-generated content. This builds upon existing measures, where TikTok already labels content made with its own AI effects. Content Credentials take it a step further, identifying AI-generated content from other platforms like DALL-E 3 and Microsoft's Bing Image Creator. In the future, TikTok plans to attach Content Credentials to their own AI-generated content. How do Content Credentials work? Content credentials are championed by Adobe, who co-founded the Coalition for Content Provenance and Authenticity (C2PA) alongside Microsoft. This technology embeds tamper-proof data into digital content, acting like a digital label which TikTok can then use to instantly recognize and label AI-generated content. While Adobe offers it within their software like Firefly, the open-source nature allows any platform, like TikTok, to integrate it for identifying AI-generated content and fostering trust in the digital landscape. Source (TechCrunch) Want to stay ahead of the curve in AI and tech? subscribe to the free weekly newsletter. submitted by /u/Used-Bat3441 [link] [comments]

• What is currently the best ai model, Chatgpt, Gemini, Copilot?
  by /u/QuantumQuicksilver (Artificial Intelligence Gateway) on May 9, 2024 at 4:05 pm

  Just wanted to have some discussion to see which AIs are best, or maybe some are better for certain tasks than others? submitted by /u/QuantumQuicksilver [link] [comments]

• Microsoft Announces $3.3B Investment in Wisconsin to Spur AI Innovation
  by /u/NuseAI (Artificial Intelligence) on May 9, 2024 at 3:00 pm

  Microsoft is investing $3.3B in cloud and AI infrastructure in Wisconsin. They will establish a manufacturing-focused AI Co-Innovation Lab and partner with Gateway Technical College. Microsoft aims to upskill over 100,000 residents in AI by 2030 and train 3,000 AI software developers. They will also invest in local education programs and youth employment initiatives. Source: https://www.hpcwire.com/off-the-wire/microsoft-announces-3-3b-investment-in-wisconsin-to-spur-ai-innovation-and-economic-growth/ submitted by /u/NuseAI [link] [comments]

• Code compilation and app building
  by /u/AlphA_centauri1_ (Artificial Intelligence Gateway) on May 9, 2024 at 2:57 pm

  I have no idea where else i'm supposed to ask this so here goes. i am still rather a newbie to the tech world in general. I have found myself in this situation a lot of times and i was wondering since there is an ai for anything and everything these days, Is there an ai website or app that can compile the source code and make it an application file all on its own. Now i dont knowany kind of coding or related activities and i tried to learn but its not for me. is there a website or an ai or anymeans by which i can accomplish this task? submitted by /u/AlphA_centauri1_ [link] [comments]

• Is there AI video tool to generate short form content (subtitles and speaker focus)
  by /u/jamesftf (Artificial Intelligence Gateway) on May 9, 2024 at 2:46 pm

  Is there an AI video tool that can generate short content from long-form content? I'm looking for a tool that can automatically create subtitles and highlight the speaker. submitted by /u/jamesftf [link] [comments]

• Does GPT Zero really work?
  by /u/Holiday_Ad_8631 (Artificial Intelligence Gateway) on May 9, 2024 at 2:41 pm

  So, I'm a junior in high school, about to be a rising senior and as of now, we're creating drafts of our potential college essays in class. Now, my teacher has a policy that all essays will be reviewed with GPT Zero and I vividly remember my boyfriend telling me how he ran into this error where his essay was deemed to be written by ai when all he used on the side was grammarly. So when I decided to check mine myself it said the entire thing was written using ai and only when I dumb down my text and use less sophisticated terms does it say it's written by a human. I'm so confused. submitted by /u/Holiday_Ad_8631 [link] [comments]