Thousands of plateaus of science and technology
Anirudh VK
In the process of making GPT-4 better than its predecessor, OpenAI may have chewed too much
In March, OpenAI launched GPT-4 with great fanfare, but dark clouds hung over the horizon. Scientists and AI enthusiasts alike have criticized the company for not releasing any details about the model, such as parameter sizes or architecture. However, a top AI researcher speculated about the inner workings of GPT-4, revealing why OpenAI chose to hide this information – which is disappointing.
OpenAI CEO Sam Altman famously said on GPT-4 about the model's potential scale: "People beg for disappointment, and they will be disappointed." Rumors before the launch of the model were that it would have trillions of parameters and be the best thing the world has ever seen. However, the reality is different. In the process of making GPT-4 better than GPT-3.5, OpenAI may be greedy and chewy.
There are 8 GPTs in the trench coat
World-renowned hacker and software engineer George Hotz recently appeared on a podcast speculating about the architectural nature of GPT-4. Hotz says the model could be a set of eight different models, each with 220 billion parameters. This speculation was later confirmed by PyTorch co-founder Soumith Chintala.
While this brings the number of GPT-4 parameters to 1.76 trillion, it's worth noting that all of these models don't work simultaneously. Instead, they are deployed in a mix of expert architectures. This architecture separates each model into different components, also known as expert models. Each of these models is fine-tuned for a specific purpose or domain and is able to provide a better response for that domain. All expert models then leverage the collective intelligence of the expert models to work with the complete model.
There are many benefits to this approach. One is that because the model is fine-tuned for various topics, it gets a more accurate response. The MoE architecture is also easy to update because the maintainers of the model can improve it in a modular way instead of updating the overall model. Hotz also speculates that the model may rely on an iterative inference process to get better outputs. Through this process, the output or inference results of the model are refined through multiple iterations.
This approach may also allow GPT-4 to take input from each expert model, which can reduce hallucinations in the model. Hotz said the process could take 16 times, which would significantly increase the model's running costs. This approach is similar to the ancient metaphor of three children wearing trench coats disguised as adults. Many liken the GPT-4 to 8 GPT-3s in a trench coat, trying to blind the world.
cut corners
While GPT-4 has excelled in benchmarks where GPT-3 has struggled, the MoE architecture seems to have become a pain point for OpenAI. In the now-deleted interview, Altman acknowledged the scaling issues OpenAI faces, particularly when it comes to GPU shortages.
Running inference 16 times on a model with a MoE architecture would certainly increase cloud costs at a similar scale. When scaling to ChatGPT's millions of users, it's no surprise that even Azure's supercomputers run out of power. This appears to be one of the biggest problems OpenAI faces right now, with Altman saying the cheaper and faster GPT-4 is the company's top priority right now.
This has also reportedly led to a decrease in ChatGPT output quality. Across the internet, users have reported that even ChatGPT Plus's response quality has deteriorated. We found that ChatGPT's release notes seem to confirm this, which states: "We have updated the performance of the ChatGPT model in our free plan to serve more users". In the same note, OpenAI also informs users that Plus users will default to using the "Turbo" variant of the model, which is optimized for inference speed.
API users, on the other hand, seem to avoid this problem altogether. Reddit users have noticed that other products that use the OpenAI API provide better answers to their queries than ChatGPT Plus. This may be because the OpenAI API has fewer users compared to ChatGPT users, causing OpenAI to cut costs on ChatGPT and ignore the API.
In the process of frantically bringing GPT-4 to market, OpenAI seems to have taken a shortcut. While the so-called MoE model is a big step forward in improving the performance of the GPT series, the scaling issues it faces suggest that the company may just be greedy for more.
https://analyticsindiamag.com/the-truth-behind-openais-silence-on-gpt-4/
ChatGPT glossary
If you're interested in learning more about ChatGPT, use similar terms that are most relevant to what you hear in everyday conversations when reading articles online. This ChatGPT glossary will provide more knowledge about the 50 most relevant terms, accompanied by a detailed description of each.
Artificial intelligence (AI) is becoming ubiquitous, permeating almost every aspect of our lives. Among AI technologies, one of the most notable is ChatGPT, developed by OpenAI. Let's dive into this fascinating technology with a comprehensive glossary of the most basic ChatGPT terms.
1. Artificial Intelligence (AI): This is an umbrella term for any system that mimics human intelligence. This can include anything from speech recognition and decision-making to visual perception and language translation.
2. Natural Language Processing (NLP): This term refers to the field of artificial intelligence that focuses on the interaction between computers and humans through natural language. The ultimate goal of NLP is to read, decipher, understand, and understand human language in a valuable way.
3. Machine learning (ML): This is an artificial intelligence that enables a system to automatically learn and improve from experience without explicit programming. Machine learning focuses on developing computer programs that can access data and use it for self-learning.
4. Deep learning: This is a subset of machine learning based on artificial neural networks with representation learning. Deep learning models can achieve state-of-the-art accuracy, often outperforming humans in certain tasks.
5. Generative Pre-Training Transformer (GPT): This is an autoregressive language prediction model that uses deep learning to generate human-like text. GPT is the model on which ChatGPT is based.
6) ChatGPT: An AI program developed by OpenAI. It uses the GPT model to generate human-like text based on the prompts given.
7) Transformer: This is a model architecture introduced in Attention is All You Need, which uses a self-attention mechanism and has been used in models such as GPT.
8. Autoregressive model: This term refers to a statistical analysis model that uses time skew values as input variables. ChatGPT uses this method to predict the next word in a sentence.
9. Hint: In the context of ChatGPT, a prompt is input to the model, and the model responds to that input.
10. Token: A part of the whole, so the word is the Token in the sentence, and the sentence is the Token in the paragraph. Tokens are the building blocks of natural language processing.
11. Fine-tuning: This is a process after the initial training phase where the model is tuned or adapted to a specific task, such as answering questions or language translation.
12. Context window: In ChatGPT, this is the amount of recent conversation history that the model can use to generate a response.
13. Zero-shot learning: This refers to the ability of the model to understand the task and generate an appropriate response without seeing such examples during training.
14. One-time learning: This is the ability of the model to understand the task with only a single example during training.
15. Less-shot learning: This is the ability of the model to understand the task after providing a small number of examples during training.
16. Attention mechanism: This is a technique used in deep learning models that assign different weights or "attention" to different words or features when processing data.
17. Human Feedback Reinforcement Learning (RLHF): This is a fine-tuning method used in ChatGPT where models learn from feedback provided by humans.
18. Supervised fine-tuning: This is the first step in fine-tuning, where the human AI trainer provides the model with a conversation with the user and the AI persona.
19. Reward models: These models are used to rank different responses
20. API (Application Programming Interface): This allows interaction between different software programs. OpenAI provides APIs for developers to integrate ChatGPT into their applications or services.
21.AI Trainer: A person who guides an AI model during fine-tuning by providing feedback, ranking responses, and writing sample dialogs.
22. Security measures: These measures are measures taken to ensure that AI operates in a safe, ethical and respectful manner that respects user privacy.
OpenAI: AI lab for developing GPT-3 and ChatGPT. OpenAI aims to ensure that artificial general intelligence (AGI) benefits all of humanity.
24. The Law of Scaling: In the context of AI, this refers to the observed trend that AI models tend to improve performance when they get more data, more computation, and at a larger scale.
25. Bias in AI: This refers to situations where an AI system may exhibit bias in its response due to bias present in the training data. OpenAI aims to reduce obvious and subtle biases in the way ChatGPT responds to different inputs.
26. Audit tools: These tools are for developers to control the behavior of models in their applications and services.
27. User Interface (UI): This is the point of human-computer interaction and communication in a device, application, or website.
28. Model Cards: Documents that provide detailed information about the performance, limitations, and ideal use cases of machine learning models.
29. Language model: A model that uses mathematical and probabilistic frameworks to predict the next word or sequence of words in a sentence.
30. Decoding rules: These rules control the text generation process of the language model.
31. Overuse penalty: A factor used in the ChatGPT decoding process to penalize the model for its tendency to repeat the same phrase.
32. System message: This is the initial message that is displayed to the user when they start a conversation with ChatGPT.
33. Data Privacy: This is to ensure that conversations with ChatGPT are private and not stored for more than 30 days.
34. Maximum Response Length: A limit on the length of text that ChatGPT can generate in a single response.
35. Turing test: A test proposed by Alan Turing to measure the ability of machines to exhibit intelligent behavior that is identical or indistinguishable from human behavior.
36. InstructGPT: An extension of ChatGPT designed to follow the instructions given in the tips and provide detailed explanations.
37. Multi-turn dialogue: A conversation involving the back-and-forth exchange between two participants, such as users and artificial intelligence.
38. Dialogue system: A system designed to talk to humans in a human-like way.
39. Response quality: Measures the degree to which AI responds to user prompts, including the relevance, coherence, and authenticity of responses.
40. Data augmentation: Techniques used to increase the amount of training data, such as introducing variations of existing data or creating synthetic data.
41. Semantic search: A type of search that aims to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms.
42. Policies: Rules that govern how AI responds to different types of input.
43. Offline reinforcement learning (RL): A method of training AI models using fixed datasets without real-time interaction with the environment.
44. Proximal Policy Optimization (PPO): An optimization algorithm used to improve model training in reinforcement learning.
45. Sandbox Environment: A controlled setup where developers can safely experiment and test new code without impacting the actual product.
46.Distributed training: This is the practice of training AI models on multiple machines. This allows the training process to process more data and complete faster.
47.Bandit Optimization: A method in machine learning to make decisions in real time based on limited information. It's about balancing exploration (trying new things) and leveraging (sticking to what works).
48. Upstream sampling: A technique used in the ChatGPT fine-tuning process to generate multiple responses and then sort them to select the best response.
49.Transformer Decoder: Part of the Transformer model used to predict the next mark in a sequence.
50. Backpropagation: This is a method of training a neural network by calculating the gradient of the loss function. This is essential for fine-tuning network weights.
Obviously, the technology behind ChatGPT is extensive and complex. However, its implications are more far-reaching, with the potential to redefine human-computer interaction and our relationship with artificial intelligence. Whether you're a developer planning to integrate the technology into a project or a curiosity trying to understand the building blocks of this impressive AI model, it's important to be familiar with basic terminology and concepts.
Understanding these terms will not only give you a better understanding of how ChatGPT works, but also appreciate the complex process of developing such a complex piece of artificial intelligence. We hope this glossary will be a handy reference guide for you exploring ChatGPT and the broader field of AI.
If you’re interested in learning more about ChatGPT in similar terms most associated with it that you will hear in everyday conversation when reading articles online. This ChatGPT glossary will provide a little more insight into the 50 most relevant terms with equipped description about each.
Artificial Intelligence (AI) is becoming ubiquitous, permeating nearly every facet of our lives. Among AI technologies, one that stands out is ChatGPT, developed by OpenAI. Let’s dive deep into this fascinating technology with our comprehensive glossary of the most essential ChatGPT terms.
1. Artificial Intelligence (AI): This is the overarching term for any system that mimics human intelligence. This can include anything from speech recognition and decision-making to visual perception and language translation.
2. Natural Language Processing (NLP): This term refers to the field of AI that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human language in a valuable way.
3. Machine Learning (ML): This is a type of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
4. Deep Learning: This is a subset of machine learning that’s based on artificial neural networks with representation learning. Deep learning models can achieve state-of-the-art accuracy, often exceeding human-level performance in certain tasks.
5. Generative Pre-training Transformer (GPT): This is an autoregressive language prediction model that uses deep learning to produce human-like text. GPT is the model upon which ChatGPT is based.
6. ChatGPT: An AI program developed by OpenAI. It uses the GPT model to generate human-like text based on the prompts it’s given.
7. Transformer: This is a model architecture introduced in “Attention is All You Need” that uses self-attention mechanisms and has been used in models like GPT.
8. Autoregressive Model: This term refers to a statistical analysis model that uses time-lagged values as input variables. ChatGPT uses this approach to predict the next word in a sentence.
9. Prompt: In the context of ChatGPT, a prompt is an input given to the model, to which it responds.
10. Token: A piece of a whole, so a word is a token in a sentence, and a sentence is a token in a paragraph. Tokens are the building blocks of Natural Language Processing.
11. Fine-Tuning: This is a process that follows the initial training phase, where the model is tuned or adapted to specific tasks, such as question answering or language translation.
12. Context Window: In ChatGPT, this is the amount of recent conversation history that the model can utilize to generate a response.
13. Zero-Shot Learning: This refers to the model’s ability to understand a task and generate appropriate responses without having seen such examples during training.
14. One-Shot Learning: This is the model’s ability to comprehend a task from just a single example during training.
15. Few-Shot Learning: This is the model’s ability to understand a task after being provided a small number of examples during training.
16. Attention Mechanism: This is a technique used in deep learning models, where the model assigns different weights or “attention” to different words or features when processing data.
17. Reinforcement Learning from Human Feedback (RLHF):This is a fine-tuning method used in ChatGPT, where models learn from feedback provided by humans.
18. Supervised Fine-Tuning: This is the first step in fine-tuning, where human AI trainers provide conversations with both the user and AI role to the model.
19. Reward Models: These are models used to rank different responses from the
20. API (Application Programming Interface): This allows for the interaction between different software programs. OpenAI provides an API for developers to integrate ChatGPT into their applications or services.
21. AI Trainer: Humans who guide the AI model during the fine-tuning process by providing it with feedback, ranking responses, and writing example dialogues.
22. Safety Measures: These are steps taken to ensure that the AI behaves in a way that is safe, ethical, and respects user privacy.
23. OpenAI: The artificial intelligence lab that developed GPT-3 and ChatGPT. OpenAI aims to ensure that artificial general intelligence (AGI) benefits all of humanity.
24. Scaling Laws: In the context of AI, this refers to the observed trend that AI models tend to improve in performance as they’re given more data, more computation, and are made larger in size.
25. Bias in AI: This refers to situations when AI systems may demonstrate bias in their responses due to biases present in their training data. OpenAI is committed to reducing both glaring and subtle biases in how ChatGPT responds to different inputs.
26. Moderation Tools: These are tools provided to developers to control the behavior of the model in their applications and services.
27. User Interface (UI): This is the point of human-computer interaction and communication in a device, application, or website.
28. Model Card: Documentation that provides detailed information about a machine learning model’s performance, limitations, and ideal use cases.
29. Language Model: A type of model that uses mathematical and probabilistic framework to predict the next word or sequence of words in a sentence.
30. Decoding Rules: These are rules that control the text generation process from a language model.
31. Overuse Penalty: A factor used in ChatGPT’s decoding process that penalizes the model’s tendency to repeat the same phrase.
32. System Message: This is the initial message displayed to users when they start a conversation with ChatGPT.
33. Data Privacy: This is about ensuring that conversations with ChatGPT are private and not stored beyond 30 days.
34. Maximum Response Length: The limit on the length of text that ChatGPT can generate in a single response.
35. Turing Test: A test proposed by Alan Turing to measure a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, human behavior.
36. InstructGPT: An extension of ChatGPT designed to follow instructions given in a prompt and provide detailed explanations.
37. Multi-turn Dialogue: A conversation involving back-and-forth exchanges between two participants, such as a user and an AI.
38. Dialogue System: A system designed to converse with humans in a human-like manner.
39. Response Quality: The measure of how well the AI responds to user prompts, including relevance, coherence, and factuality of the response.
40. Data Augmentation: Techniques used to increase the amount of training data, such as introducing variations of existing data or creating synthetic data.
41. Semantic Search: A type of search that seeks to improve search accuracy by understanding the searcher’s intent and the contextual meaning of terms.
42. Policy: The rules that govern how the AI responds to different types of input.
43. Offline Reinforcement Learning (RL): A method of training AI models using a fixed dataset without real-time interaction with the environment.
44. Proximal Policy Optimization (PPO): An optimization algorithm used in reinforcement learning to improve model training.
45. Sandbox Environment: A controlled setting where developers can safely experiment and test new code without affecting the live product.
46. Distributed Training: This is the practice of training AI models on multiple machines. This allows the training process to handle more data and complete faster.
47. Bandit Optimization: An approach in machine learning that makes decisions based on limited information in real-time. It’s about balancing exploration (trying new things) with exploitation (sticking with what works).
48. Upstream Sampling: A technique used in the fine-tuning process of ChatGPT, where multiple responses are generated and then ranked to select the best one.
49. Transformer Decoder: A part of the transformer model that predicts the next token in the sequence.
50. Backpropagation: This is a method used to train neural networks by calculating the gradient of the loss function. This is vital for fine-tuning the weights of the network.
It’s clear that the technology behind ChatGPT is expansive and complex. Yet, its implications are even more profound, having the potential to redefine human-computer interaction and our relationship with AI. Whether you’re a developer aiming to integrate this technology into your project or a curious mind trying to understand the building blocks of this impressive AI model, it’s important to familiarize yourself with the fundamental terminologies and concepts.
Understanding these terms will not only allow you to better comprehend how ChatGPT works but also appreciate the intricate process that goes into developing such a sophisticated AI. We hope this glossary will serve as a handy reference guide in your exploration of ChatGPT and the broader field of artificial intelligence.