Tag: AI Fine-Tuning

AI Jobs and Career

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• Full Stack Engineer [$150K-$220K]
• Software Engineer, Tooling & AI Workflow, Contract [$90/hour]
• DevOps Engineer, India, Contract [$90/hour]
• More AI Jobs Opportunitieshere

Job Title	Status	Pay
Full-Stack Engineer	Strong match, Full-time	$150K - $220K / year
Developer Experience and Productivity Engineer	Pre-qualified, Full-time	$160K - $300K / year
Software Engineer - Tooling & AI Workflows (Contract)	Contract	$90 / hour
DevOps Engineer (India)	Full-time	$20K - $50K / year
Senior Full-Stack Engineer	Full-time	$2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India	Contract	$20 - $30 / hour
Senior Software Engineer	Contract	$100 - $200 / hour
Senior Software Engineer	Pre-qualified, Full-time	$150K - $300K / year
Senior Full-Stack Engineer: Latin America	Full-time	$1.6K - $2.1K / week
Software Engineering Expert	Contract	$50 - $150 / hour
Generalist Video Annotators	Contract	$45 / hour
Generalist Writing Expert	Contract	$45 / hour
Editors, Fact Checkers, & Data Quality Reviewers	Contract	$50 - $60 / hour
Multilingual Expert	Contract	$54 / hour
Mathematics Expert (PhD)	Contract	$60 - $80 / hour
Software Engineer - India	Contract	$20 - $45 / hour
Physics Expert (PhD)	Contract	$60 - $80 / hour
Finance Expert	Contract	$150 / hour
Designers	Contract	$50 - $70 / hour
Chemistry Expert (PhD)	Contract	$60 - $80 / hour

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.

Are you ready to dive into the fascinating world of artificial intelligence? Well, I’ve got just the thing for you! It’s an incredible book called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” Trust me, this book is an absolute gem!

Now, you might be wondering where you can get your hands on this treasure trove of knowledge. Look no further, my friend. You can find “AI Unraveled” at popular online platforms like Etsy, Shopify, Apple, Google, and of course, our old faithful, Amazon.

This book is a must-have for anyone eager to expand their understanding of AI. It takes those complicated concepts and breaks them down into easily digestible chunks. No more scratching your head in confusion or getting lost in a sea of technical terms. With “AI Unraveled,” you’ll gain a clear and concise understanding of artificial intelligence.

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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Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” available at Etsy, Shopify, Apple, Google, or Amazon

The Future of Generative AI: From Art to Reality Shaping

• 
  Misconceptions about LLMs & the real AI revolution
  by /u/Suspicious_Pain7866 (Artificial Intelligence) on November 9, 2025 at 9:11 am

  DISCLAIMER: Since AI is such a hot topic theses days, I urge you not to take any direct or indirect financial advice from me, whatsoever. Before everything has been AI, things were "smart" and before that "digital". With smart things like smart phones I never really felt like they were smart. They often merely had a couple of algorithms to make things more accessible, often poorly executed to slap the next buzzword on a product. Since then, it seems the tech industry is ahead of itself with this framing. The same goes for AI. Now bear with me, it's going to get philosophical. After ChatGPT-4o, I have to admit it caught me off guard for a moment thinking big changes are ahead. They very well are, just not with the current approach. And this is the problem with the here and now. A lot of funding, private and tax payer money is impacting our lives in many ways and lead into - what I believe - is a dead end. Although the current quote on quote "AI" is solving real problems and it is nice to quickly generate an image for a blog article, it is not the AI revolution people expect. Here is why not. Imagine a network of probabilities - an arbitrary system of causally connected nodes - is able to develop a consciousness. This would in turn mean, that any system of causally connected nodes can be a conscious entity. That means, any superset of system of causally connected nodes can be a conscious entity. And that means inside of you countless conscious entities exist at the same time, each believing they are alone in there having original thoughts. The same would go for any material thing, really, because everything is full of connected nodes in different scales. It can be molecules, atoms, quarks, but also star systems and ecosystem each being a conscious entity. I do not know about you, but for me this is breaking reality. And just imagine what you are doing to your are doing to your toilet brush everyday! Let's take it further. If LLMs and other material things can not become conscious by being a complex enough system, that means our consciousness is not material. Do not take it as god-proof, though (looking in your direction, religious fundamentalists). What I am saying is, that the current state of the AI industry will change again and the software stacks as well as the hardware around it will be in far less demand. The real AI revolution will not be consciousness, I think. My belief is, that the revolution lies ahead with insanely efficient memristor chips so that everybody gets to have his own little assistant. I am not so sure about general purpose robots. The complexity of the outside world has not really been managed to deal with without even a glimpse of light in there, which even goes for plants, and ants. I want to end this with some food for thought. If we some day can definitely confirm to have created a consciousness, we may suddenly have cracked understanding of ourselves in such a profound way, that we turn away from hype, misery and infancy of our species. One more thing though: upload you into a machine can never keep you alive. You would vanish as the wonderful conscious entity you are. Stay optimistic and don't get caught in the noise of hype and echo chambers. Cheers submitted by /u/Suspicious_Pain7866 [link] [comments]

• 
  Its not even a joke anymore we only have 25 years till it becomes reality with AI
  by /u/Dull_Constant1399 (Artificial Intelligence) on November 9, 2025 at 8:47 am

  Its not even a joke anymore we only have 25 years till it becomes reality with AI taking over the world. They are straight just letting use have it and we have been using it and wen have to accepted it. Everything has just been a warm up of showing us slowly so that everyone is just "yeah they have been saying that for years". submitted by /u/Dull_Constant1399 [link] [comments]

• 
  Imagine Ai companies start charging you to delete your chat history
  by /u/Upper-Smile5938 (Artificial Intelligence) on November 9, 2025 at 8:44 am

  While many people fear AI taking their jobs, a valid concern, the bigger issue is how much money and energy are being wasted on it. AI has real potential to advance humanity, from developing new technologies and medicines to improving our methods of doing things. But the way generative AI is being used right now isn’t leading us in that direction. It’s overhyped, overfunded, and diverting resources that would be better spent on building real infrastructure and long-term projects. Worse, most AI companies still have no clear path to profitability, which makes them likely to turn on their users. In that scenario, people will pay not with money, but with their data, privacy will become a myth, if it isn’t already. I wouldn’t be surprised if one day these companies start charging users just to delete their own AI chat histories. submitted by /u/Upper-Smile5938 [link] [comments]

• 
  Evaluating Generative AI as an Educational Tool for Radiology Resident Report Drafting
  by /u/ThePromptIndex (Artificial Intelligence) on November 9, 2025 at 8:32 am

  Evaluating Generative AI as an Educational Tool for Radiology Resident Report Drafting I'm finding and summarising interesting AI research papers every day so you don't have to trawl through them all. Today's paper is titled "Evaluating Generative AI as an Educational Tool for Radiology Resident Report Drafting" by Antonio Verdone, Aidan Cardall, Fardeen Siddiqui, Motaz Nashawaty, Danielle Rigau, Youngjoon Kwon, Mira Yousef, Shalin Patel, Alex Kieturakis, Eric Kim, Laura Heacock, Beatriu Reig, and Yiqiu Shen. This study investigates the potential of a generative AI model, specifically GPT-4o, as a pedagogical tool to enhance the report drafting skills of radiology residents. The authors aimed to tackle the challenge presented by increased clinical workloads that limit the availability of attending physicians to provide personalized feedback to trainees. Key findings from the paper include: Error Identification and Feedback: Three prevalent error types in resident reports were identified: omission or addition of key findings, incorrect use of technical descriptors, and inconsistencies between final assessments and the findings noted. GPT-4o demonstrated strong agreement with attending consensus in identifying these errors, achieving agreement rates between 90.5% to 92.0%. Reliability of GPT-4o: The inter-reader agreement demonstrated moderate to substantial reliability. Replacing a human reader with GPT-4o had minimal impact on inter-reader agreement, with no statistically significant changes observed across all error types. Perceived Helpfulness: The feedback mechanism provided by GPT-4o was rated as helpful by the majority of readers, with approximately 86.8% of evaluations indicating that the AI's suggestions were beneficial, especially among radiology residents who rated it even more favorably. Educational Applications: The integration of GPT-4o offers significant potential in radiology education by facilitating personalized, prompt feedback that can complement traditional supervision, thereby addressing the educational gap caused by clinical demands. Scalability of AI Tools: The study posits that LLMs like GPT-4o can be effectively utilized in various capacities, including daily feedback on reports, identification of common errors for teaching moments, and tracking a resident's progress over time—thus enhancing medical education in radiology. The insights gained from this study highlight the evolving role of AI in medical education and suggest a future wherein AI can significantly improve the training experience for radiology residents by offering real-time, tailored feedback within their clinical workflows. You can catch the full breakdown here: Here You can catch the full and original research paper here: Original Paper submitted by /u/ThePromptIndex [link] [comments]

• 
  Alibaba’s AI aces top global maths contests, challenging OpenAI’s dominance
  by /u/esporx (Artificial Intelligence (AI)) on November 9, 2025 at 7:38 am

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

• 
  Qubic’s Neuraxon, a Bio-Inspired Breakthrough in AI Neural Networks
  by /u/Defiant-Industry-626 (Artificial Intelligence) on November 9, 2025 at 7:07 am

  Hey guys, Qubic researchers just released Neuraxon. Bio-inspired AI blueprint with trinary neurons (+1/0/-1) for brain-like computation. Aims to let AI evolve itself on decentralized Aigarth (Qubics Ai system).Currently training their own AI “Anna” using computational power from miners under this system. Open-source; can anyone confirm it’s legit? • Paper: researchgate.net/publication/397331336_Neuraxon • Code: github.com/DavidVivancos/Neuraxon • Demo: huggingface.co/spaces/DavidVivancos/Neuraxon • X post: x.com/VivancosDavid/status/1986370549556105336 Could be worth discussing for its potential implications on neuromorphic computing and AGI paths. (Not affiliated with Qubic, just sharing something intriguing I found.) submitted by /u/Defiant-Industry-626 [link] [comments]

• 
  Any good AI Discord / Telegram / WhatsApp groups?
  by /u/thehalfbloodprince_8 (Artificial Intelligence) on November 9, 2025 at 6:17 am

  I've been getting deeper into AI and automation lately and I'd love to join some good, active communities. Looking specifically for places where people actually share tools, discuss agents, and help each other build things, not just promo or spam. If you know any Discord, Telegram, or WhatsApp groups, please share. Thanks in advance! submitted by /u/thehalfbloodprince_8 [link] [comments]

• 
  One-Minute Daily AI News 11/8/2025
  by /u/Excellent-Target-847 (Artificial Intelligence) on November 9, 2025 at 5:50 am

  What parents need to know about Sora, the generative AI video app blurring the line between real and fake.[1] Pope Leo XIV urges Catholic technologists to spread the Gospel with AI.[2] OpenAI asked Trump administration to expand Chips Act tax credit to cover data centers.[3] How to Build an Agentic Voice AI Assistant that Understands, Reasons, Plans, and Responds through Autonomous Multi-Step Intelligence.[4] Sources included at: https://bushaicave.com/2025/11/08/one-minute-daily-ai-news-11-8-2025/ submitted by /u/Excellent-Target-847 [link] [comments]

• 
  One-Minute Daily AI News 11/8/2025
  by /u/Excellent-Target-847 (Artificial Intelligence (AI)) on November 9, 2025 at 5:49 am

  What parents need to know about Sora, the generative AI video app blurring the line between real and fake.[1] Pope Leo XIV urges Catholic technologists to spread the Gospel with AI.[2] OpenAI asked Trump administration to expand Chips Act tax credit to cover data centers.[3] How to Build an Agentic Voice AI Assistant that Understands, Reasons, Plans, and Responds through Autonomous Multi-Step Intelligence.[4] Sources: [1] https://abcnews.go.com/GMA/Family/what-is-sora/story?id=127188940 [2] https://www.usccb.org/news/2025/pope-leo-xiv-urges-catholic-technologists-spread-gospel-ai [3] https://techcrunch.com/2025/11/08/openai-asked-trump-administration-to-expand-chips-act-tax-credit-to-cover-data-centers/ [4] https://www.marktechpost.com/2025/11/08/how-to-build-an-agentic-voice-ai-assistant-that-understands-reasons-plans-and-responds-through-autonomous-multi-step-intelligence/ submitted by /u/Excellent-Target-847 [link] [comments]

• 
  Is DSA Really Needed to Get Into AI Companies Like Anthropic?
  by /u/Puzzle_Age555 (Artificial Intelligence) on November 9, 2025 at 4:43 am

  Straight to the point! Is DSA necessary to get into AI companies, especially Anthropic? I have a decent CS background, recently graduated, and have already secured a job, but I’m not satisfied. I’m just starting to brush up on my old DSA skills, and I also have solid knowledge of AI and a strong interest in the field. The problem is the environment it feels like screaming into an empty void. Joining a company or a research lab would be better for my AI growth. I need real world experience, not just theory. Lastly, please don’t suggest those ChatGPT-like roadmaps. I’ve tried them many times and they didn’t work. There are countless videos on how to crack FAANG/MAANG by practising DSA and following a strict roadmap, but almost none about how to get into OpenAI, Anthropic, xAI, DeepMind, etc. My target is Anthropic. I like the company and its creativity. How should I approach this, and how important is DSA in that journey? How can I engage with opensource labs? Please help me figure this out I don’t know what to do right now. I just want to join that company. submitted by /u/Puzzle_Age555 [link] [comments]