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Monday, July 17, 2023

“ChatGPT — The Era of Generative Conversational AI Has Begun” (Week #4 - article series)

AI Colleagues, our Week 4 article on “ChatGPT — The Era of Generative Conversational AI Has Begun” addresses the “Applications of ChatGPT in Natural Language Processing and Generation” of AI and specifically  the ChatGPT LLM. (Audible) (Kindle)

Natural Language Processing (NLP) is a field of Artificial Intelligence that deals with the interaction between computers and human languages. It uses computational techniques to process, analyze, and generate human language. The goal of NLP is to enable computers to understand, interpret, and generate human language in a way that is as natural as possible. The field of computer science known as natural language processing (NLP) focuses on teaching machines to mimic human language comprehension to translate written text and spoken language. Combining statistical, machine learning, and deep learning models with computational linguistics (rule-based modeling of human language) is what natural language processing (NLP) is all about. As a whole, these innovations equip computers with the ability to 'understand' the full meaning of human language, including the speaker's or writer's intent and sentiment, in the form of text or audio data.


Machines using natural language processing can now translate between languages, follow verbal instructions, and quickly summarize vast amounts of text, sometimes in real-time. In the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences, NLP is something you've already engaged with. However, natural language processing (NLP) is now playing an increasingly important role in enterprise solutions that aim to improve the efficiency of businesses, boost employee productivity, and simplify crucial business processes.


Natural Language Generation (NLG) is a subfield of NLP that deals with the automatic generation of natural language text or speech from structured data. The goal of NLG is to produce text or speech that is similar to human-written text or speech. This can be used for tasks such as text summarization, machine translation, and automatic writing is a field of AI that deals with the interaction between computers and human languages, and it involves using computational techniques to process, analyze and generate human language. NLG is a subfield of NLP that deals with the automatic generation of natural language text or speech from structured data.


It is extremely challenging to develop software that correctly identifies the intended meaning of text or voice data due to the ambiguities inherent in human language. Programmers must educate natural language-driven systems to identify and interpret effectively from the outset the peculiarities of human languages, such as homonyms, homophones, sarcasm, idioms, metaphors, grammatical and usage exceptions, and differences in sentence structure.


Various NLP tasks dissect the human text and voice data into smaller pieces that are easier for the computer to process. The following are examples of some of these responsibilities:


  • Speech recognition, often known as a speech-to-text conversion, is the process of accurately transcribing audio recordings into text. Any program that listens for spoken instructions or inquiries and responds verbally must have speech recognition. How people speak—fast, jumbled up, with shifting emphasis and intonation, in diverse accents, and often with faulty grammar—makes speech identification particularly difficult.

  • The technique of assigning a label to a word or passage of text based on how it is typically used is known as part of speech tagging or grammatical tagging. "I can make a paper plane" is an example of the verb form, while "what make of automobile do you own" is the noun form.

  • Word sense disambiguation is the process of using semantic analysis to identify the most appropriate meaning of a word that can have many meanings. One use of word sense disambiguation is to tell the difference between the two meanings of the verb "make," as in "make the grade" (achieve) and "make a bet" (put a wager) (place).

  • The NEM technique recognizes key concepts embedded in the text. NEM recognizes "Kentucky" as a state in the United States and "Fred" as a male-given name.

  • Determining whether or not two words refer to the same thing is known as coreference resolution. Common examples include replacing pronouns with their correct names (such as "she" for "Mary") and spotting instances of metaphor and idiom (such as "bear" for "big hairy person").

  • Sentiment analysis aims to identify and understand the underlying attitudes, emotions, sarcasm, bewilderment, and mistrust expressed in written communication.

  • Putting structured information into human language is the goal of natural language generation, commonly contrasted with speech recognition or speech-to-text.


To use ChatGPT, you can input a prompt or question, and the model will generate a response based on its training. You can use ChatGPT for various tasks, such as language translation, writing, and conversation. You can interact with the model through a user interface or by making API calls. You can also fine-tune the model for specific tasks by training it on a dataset that is relevant to the task. ChatGPT can be applied in natural language processing and generation tasks in a few ways:


  • Fine-tuning: ChatGPT is a pre-trained language model that uses a technique called fine-tuning to adapt to specific natural language tasks. Fine-tuning involves training the model on a small amount of task-specific data while keeping the pre-trained weights fixed. This allows the model to learn task-specific representations from the new data while leveraging the general knowledge it already acquired during pre-training. Fine-tuning can be used for a wide range of natural language tasks, including language translation, question answering, and text generation. One of the most common ways to apply ChatGPT in natural language processing is by fine-tuning the pre-trained model on a specific task or dataset. For example, the task is to build a chatbot. In that case, the model can be fine-tuned on a dataset of conversational data, such as dialogue transcripts, to improve its ability to generate appropriate and coherent responses to user input.


  • Text generation: ChatGPT can generate human-like text, such as writing essays, articles, and stories. It can also generate automated responses to customer inquiries, chatbot conversations, and more. ChatGPT generates text using autoregression, which involves predicting the next word in a sequence of text based on the preceding words. A neural network architecture powers a “transformer” designed to handle sequential data. The model is trained on a large text dataset, allowing it to learn patterns and relationships between words, phrases, and sentences. During the training process, the model learns to recognize patterns in the input data, such as the relationship between certain words and phrases, which enables it to generate new text similar to the input data. When generating text, the model starts with a prompt, a set of input text that provides context for the text being generated. The model then uses its trained parameters to predict the next word in the sequence based on the input prompt. The process is repeated, and each time the model predicts a new word, the prompt is updated to include the new word, and so on.


The model can be fine-tuned to generate text for specific applications by training it on a smaller, relevant dataset. The fine-tuning process allows the model to adapt to the specific characteristics of the dataset and improve its performance on the task. In summary, ChatGPT generates text using a neural network architecture called a transformer and autoregressive process. It starts with a prompt and predicts the next word in the sequence by using the context of the input prompt. The model can be fine-tuned to generate text for specific applications by training it on a smaller, relevant dataset.


  • Language understanding: ChatGPT can extract meaning and understand the context of user input. This allows the model to generate more accurate and relevant responses. ChatGPT uses a type of neural network called a transformer to understand and generate natural language. The transformer architecture allows the model to attend to different parts of the input and output sequences, which allows it to focus selectively on the most relevant information when processing or generating text. The model is pre-trained on a large corpus of text data, which enables it to learn general knowledge about the structure and meaning of natural language. During this pre-training, the model learns to encode the input text into a representation that captures its meaning and to decode this representation into a coherent output text. During fine-tuning, the model is further trained on a task-specific dataset, which allows it to learn task-specific representations and generate text that is relevant to the specific task. For example, when fine-tuned on a Q&A dataset, the model can use the context of a question to generate an answer that is relevant and informative. In summary, ChatGPT uses a transformer-based neural network to learn general and task-specific representations of natural language, enabling it to understand and generate text in various natural language processing tasks.


  • Language generation: One of the most obvious applications of ChatGPT is in language generation. The model can be used to generate text that is similar to human-written text. This can be used for tasks such as text summarization, machine translation, and automatic writing. ChatGPT uses language generation to produce coherent and natural-sounding text in various natural language processing tasks. The model is trained to predict the next word in a sequence of words, given the previous words, which enables it to generate coherent and fluent text.


During pre-training, the model learns to generate text similar to the text in the training corpus, enabling it to learn about the structure and style of natural language. During fine-tuning, the model is further trained on a task-specific dataset, which allows it to learn task-specific representations and generate text that is relevant to the specific task.


For example, when fine-tuned on a question-answering task, the model can use the context of a question to generate a coherent and informative answer. When fine-tuned on a language translation task, the model can use the source text to generate a coherent and natural-sounding translation in the target language. When fine-tuned on a text completion task, the model can use the given context to generate readable and natural-sounding text to complete the given prompt.


In summary, ChatGPT uses language generation to produce coherent and natural-sounding text in various natural language processing tasks. The model is trained to predict the next word in a sequence of words, which enables it to generate fluent and coherent text relevant to the specific task it's fine-tuned on.


  • Question-Answering: ChatGPT can be fine-tuned for question-answering tasks. It can be trained on a dataset of question-answer pairs to generate answers to questions it has not seen before. ChatGPT uses question answering (QA) to understand and generate text in a natural language processing task. QA is a task in which the model receives a question and must generate an answer based on the given context. During pre-training, the model learns to encode the input text into a representation that captures its meaning and to decode this representation into a coherent output text. This enables the model to learn about the structure and meaning of natural language and to understand the context of a given question.


During fine-tuning, the model is further trained on a QA dataset, which allows it to learn task-specific representations and generate text that is relevant to the specific task. The fine-tuning process enables the model to learn to extract and reason over the information in the context to generate a coherent and informative answer. For example, when given a question like "What is the capital of France?", the model can extract the information from the context that "France" is a country and that capital is the city that serves as the administrative center of a country. It then generates an answer, "Paris," which is France's correct capital. In summary, ChatGPT uses question-answering to understand and generate text in a natural language processing task. The model is fine-tuned on a QA dataset to learn task-specific representations and generate coherent and informative answers based on the context of the question.


  • Multi-Turn Dialogue: ChatGPT can be fine-tuned to handle multi-turn dialogue, where the model can keep track of the context and the conversation flow. This allows the model to generate more natural and human-like responses. ChatGPT uses multi-turn dialogue for natural language processing and generation. Multi-turn dialogue is the process of conducting a conversation between two or more participants, where each participant takes turns speaking and responding.


During pre-training, the model learns to encode the input text into a representation that captures its meaning and to decode this representation into a coherent output text. This enables the model to learn about the structure and meaning of natural language and to understand the context of a given conversation. During fine-tuning, the model is further trained on a dialogue dataset, which allows it to learn task-specific representations and generate text that is relevant to the specific task. The fine-tuning process enables the model to learn to maintain the context of the conversation, understand the conversation flow, and generate coherent and natural-sounding responses.


For example, when fine-tuned on a dialogue dataset, the model can use the context of previous turns in the conversation to generate coherent and natural-sounding responses. It can also understand the conversation's intent and generate responses relevant to the conversion goal. ChatGPT uses multi-turn dialogue for natural language processing and generation. The model is fine-tuned on a dialogue dataset to learn task-specific representations and generate coherent and natural-sounding responses based on the context of the conversation and the conversation flow.


  • Language Translation: ChatGPT can translate text from one language to another, such as English to Spanish. ChatGPT can be used for language translation by fine-tuning the model on a parallel corpus, a dataset containing sentences in two languages (e.g., English and Spanish) that have been translated from one language to the other. The fine-tuning process can be done by training the model on a smaller dataset relevant to the translation task. The model learns to recognize patterns and relationships between words, phrases, and sentences in the two languages, enabling it to generate translations similar to the input data.


Once fine-tuned, the model can translate new sentences by providing a sentence in one language and asking it to generate a translation in another. The model uses its trained parameters to generate a new sentence in the target language that is similar to the input sentence. One way to do this is to use the Encoder-Decoder architecture, where the encoder encodes the input sentence into a fixed-length representation, and the decoder generates the translation from this representation. It's worth noting that machine translation is a challenging task, and the quality of the translation depends on the quality of the dataset used during the fine-tuning process. Also, it's important to note that the model can be fine-tuned for a specific pair of languages, for example, English-Spanish, but it would only be able to translate between other language pairs with additional fine-tuning. ChatGPT can be used for language translation by fine-tuning the model on a parallel corpus dataset. Once fine-tuned, the model can translate new sentences by providing a sentence in one language and asking it to generate a translation in another. The quality of the translation depends on the quality of the dataset used during the fine-tuning process, and it's important to note that the model can be fine-tuned for a specific pair of languages.


  • Text summarization: ChatGPT can automatically summarize text by extracting the most important information from a document or article. ChatGPT can be used for text summarization by fine-tuning the model on a dataset of text that has been manually summarized. The fine-tuning process can be done by training the model on a smaller dataset relevant to the summarization task. The model learns to recognize patterns and relationships between words, phrases, and sentences in the text, which enables it to generate summaries similar to the input data. Once fine-tuned, the model can be used to summarize new text by providing it with a longer piece of text and asking it to generate a shorter summary containing the most important information. One way to do this is to use the Extractive summarization method, which involves selecting the most important sentences or phrases from the text and concatenating them to form the summary. Extractive summarization methods can be based on keyword frequency, sentence importance, or other criteria.


Another approach is the Abstractive summarization method, which involves generating new phrases and sentences that convey the most important information from the input text. This approach is more challenging than extractive summarization but provides more human-like summaries. It's worth noting that text summarization is a challenging task, and the quality of the summary depends on the quality of the dataset used during the fine-tuning process. Also, the model can be fine-tuned for a specific type of text, such as news articles, but it would only be able to summarize other types of text with additional fine-tuning. In summary, ChatGPT can be used for text summarization by fine-tuning the model on a manually summarized dataset. Once fine-tuned, the model can be used to summarize new text by providing it with a longer piece of text and asking it to generate a shorter summary containing the most important information. Text summarization is a challenging task, and the summary quality depends on the quality of the dataset used during the fine-tuning process. The model can be fine-tuned for a specific type of text, such as news articles.


  • Sentiment analysis: ChatGPT can determine text sentiment, such as whether a sentence expresses a positive or negative emotion. ChatGPT can be used for sentiment analysis by fine-tuning the model on a dataset of text that has been manually labeled with sentiment scores. The fine-tuning process can be done by training the model on a smaller dataset relevant to the sentiment analysis task. The model learns to recognize patterns and relationships between words, phrases, and sentences in the text, which enables it to predict the sentiment of new text. Once fine-tuned, the model can be used to predict the sentiment of a new text by providing it with a piece of text and asking it to generate a sentiment score or label (e.g., positive, negative, or neutral).

There are different ways to represent the sentiment of text, but one of the most common is to use a binary classification approach, where the model is trained to predict whether a piece of text is positive or negative. Another approach is a multi-class classification approach, where the model is trained to predict text sentiment from a set of predefined labels (e.g., positive, negative, neutral). It's worth noting that sentiment analysis is a challenging task, and the model's accuracy depends on the quality of the dataset used during the fine-tuning process. Also, the model can be fine-tuned for a specific type of text, such as tweets, but it would only be able to analyze the sentiment of other types of text with additional fine-tuning. ChatGPT can be used for sentiment analysis by fine-tuning the model on a dataset of text that has been manually labeled with sentiment scores. Once fine-tuned, the model can be used to predict the sentiment of a new text by providing it with a piece of text and asking it to generate a sentiment score or label. Sentiment analysis is a challenging task, and the model's accuracy depends on the quality of the dataset used during the fine-tuning process. The model can be fine-tuned for a specific type of text, such as tweets.


  • Text classification: ChatGPT can classify text into different categories, such as spam or non-spam emails or positive or negative reviews. ChatGPT can be used for text classification by fine-tuning the model on a dataset of text manually labeled with predefined categories. The fine-tuning process can be done by training the model on a smaller dataset relevant to the text classification task. The model learns to recognize patterns and relationships between words, phrases, and sentences in the text, enabling it to predict the new text category. Once fine-tuned, the model can be used to predict the category of a new text by providing it with a piece of text and asking it to generate a label or classification (e.g., spam or not spam, positive or negative)

There are different types of text classification, such as:


  • Binary classification: Binary classification is a text classification with the goal of predicting one of two possible outcomes or classes. The two classes are typically labeled as "positive" and "negative." In NLP, binary classification can be used for various applications, including sentiment analysis, spam detection, and topic categorization. For example, a binary classifier can be trained in sentiment analysis to predict whether a given text represents positive or negative sentiment. In spam detection, the binary classifier is trained to predict whether an email is spam.

ChatGPT, it can be used for binary classification by fine-tuning the pre-trained language model on a large corpus of labeled text data for a specific task. During the fine-tuning process, the model learns to associate certain words and phrases with either the positive or negative class. Once the model is trained, it can then be used to make predictions on new, unseen text data. Binary classification is a fundamental task in NLP and is a building block for more complex tasks, such as multi-class classification or sequence labeling.


  • Multi-class classification: Multi-class classification is a text classification problem in which the goal is to assign a text document or sequence of words to one of multiple classes or categories. It is commonly used in natural language processing applications to categorize text data into predefined categories, such as sentiment analysis (positive, negative, neutral), news articles (sports, politics, entertainment), or product reviews (electronics, books, clothing). ChatGPT, a language model developed by OpenAI, can be used for multi-class classification in NLP. For example, it can be trained on a large dataset of text documents to recognize the sentiment of a given text by assigning it to the positive, negative, or neutral class. During the training process, the model learns the patterns and features in the text data indicative of each class.

Once the model is trained, it can then be used to predict the sentiment of new text data by analyzing the features of the text and assigning it to the class with the highest predicted probability. This enables ChatGPT to perform sentiment analysis on new text data in real time, making it a useful tool for businesses and organizations to monitor and analyze customer sentiment. Overall, multi-class classification is a powerful tool for text classification in NLP, and ChatGPT is a highly capable model for performing this type of task.


  • Multi-label classification: Multi-label classification is a type of text classification where each document or text can belong to multiple classes or categories simultaneously rather than just one class, as in traditional binary or multiclass text classification. For example, in a movie classification task, a movie can belong to multiple genres, such as action, adventure, and sci-fi, at the same time. Multi-label classification can be applied to various NLP tasks such as sentiment analysis, topic classification, and intent classification. ChatGPT can perform multi-label classification tasks by fine-tuning its pre-trained transformer-based architecture on a labeled text dataset. The fine-tuned model can then predict the probabilities of a given text belonging to multiple classes. Applications of multi-label classification with ChatGPT in NLP include:


  • Sentiment analysis: where a document can be classified as having multiple sentiments, such as positive, negative, and neutral, at the same time.

  • Topic classification: a document can be classified into topics such as politics, sports, and entertainment.

  • Intent classification: where a text can belong to multiple intents, such as providing information, making a recommendation, and answering a question.

Multi-label classification with ChatGPT can be useful in several NLP and text generation tasks as it allows for a more nuanced understanding of the text and can help better capture the multiple facets of a text.


It's worth noting that text classification is a challenging task, and the model's accuracy depends on the quality of the dataset used during the fine-tuning process. Also, the model can be fine-tuned for a specific type of text, such as news articles, but it would only be able to classify other text types with additional fine-tuning.


In summary, ChatGPT can be used for text classification by fine-tuning the model on a dataset of text that has been manually labeled with predefined categories. Once fine-tuned, the model can be used to predict the category of a new text by providing it with a piece of text and asking it to generate a label or classification. Text classification is a challenging task, and the model's accuracy depends on the quality of the dataset used during the fine-tuning process. The model can be fine-tuned for a specific type of text, such as news articles.



High-level Chatbot Architecture

Source: Biswas, D. CHATGPT, and its implications for enterprise AI, LinkedIn. 



In summary, ChatGPT can be applied in natural language processing and generation tasks by fine-tuning the pre-trained model on specific tasks or datasets, using it for language understanding, language generation, question answering, and handling multi-turn dialogues. This means that this sentence says that ChatGPT is a powerful tool that can be used for various tasks related to natural language processing and generation. These tasks include but are not limited to language understanding, language generation, question answering, and handling multi-turn dialogues.


"By fine-tuning the pre-trained model on specific tasks or datasets" - To perform these tasks, the model needs to be fine-tuned on specific datasets or tasks. Fine-tuning is adapting the pre-trained model to a new task or dataset by continuing the training process on this new dataset. This allows the model to learn task-specific representations and generate text that is relevant to the specific task.


"Using it for language understanding" - One of the tasks ChatGPT can use is language understanding. This refers to the ability of the model to understand the meaning of the input text. During fine-tuning, the model learns to encode the input text into a representation that captures its meaning, which enables it to understand the context and intent of the text.


"Language generation" - Another task ChatGPT can be used for is language generation. This refers to the model's ability to produce coherent and natural-sounding text. During fine-tuning, the model learns to predict the next word in a sequence of words, enabling it to generate fluent and coherent text relevant to the specific task it's fine-tuned on.


"Question answering" - One of the NLP tasks ChatGPT can use is question answering. This refers to the ability of the model to understand and generate text in a natural language processing task. The model is fine-tuned on a QA dataset to learn task-specific representations and generate coherent and informative answers based on the context of the question.


"Handling multi-turn dialogues" - Another task ChatGPT can be used for is handling multi-turn dialogues. This refers to the ability of the model to maintain the context of the conversation, understand the conversation flow, and generate coherent and natural-sounding responses. The model is fine-tuned on a dialogue dataset to learn task-specific representations and generate coherent and natural-sounding responses based on the context of the conversation and the conversation flow.


ChatGPT is a versatile and powerful tool with many uses in natural language processing (NLP) and text generation. It can automate tasks related to understanding and generating human language, making it useful for various industries and groups, including businesses, researchers, and developers. ChatGPT has a wide range of applications within NLP and text generation, making it a valuable tool for many users and use cases.


Resources:

 

  1. The workings of ChatGPT, the latest natural language processing tool. Varahasimhan;

  2. Franciscu, Shehan. (2023). ChatGPT: A Natural Language Generation Model for Chatbots. 

  3. Chen; How ChatGPT is revolutionizing natural language processing. 


The Transformative Innovation series is for your listening-reading pleasure. Order your copies today!



Regards, Genesys Digital (Amazon Author Page) https://tinyurl.com/hh7bf4m9 


Thursday, July 13, 2023

AI Jobs Pay An Average of $146,000 - 69,045 Openings in the US

Colleagues, the CNBC article “U.S. companies are on a hiring spree for A.I. jobs—and they pay an average of $146,000” (July 13, 2023). This article cited recent data from research firm Adzuna that goes on to state there are “69,045 jobs in the U.S. cited AI needs, and 3,575 called for generative AI work in particular … the average job using the skill pays $146,244.” 

The new ebook “AI Software Engineer - ChatGPT, Bard … and Beyond” aims to help Software Engineers and Developers capture their ideal job offer and manage their medium-to-long-term career growth in the global Artificial Intelligence (AI) arena. First, we will look at the global market overview for AI Globally. The artificial intelligence (AI) market size was valued at around $136.55 billion in 2022 (Grand View Research, 2022). Also, it was projected to have a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030. if you're interested in AI, you should be familiar with and have expertise with at least one of the following programming languages: Python, C/C++, MATLAB. According to Indeed, salaries for artificial intelligence professionals often range from $99,568 for a full-stack developer to $142,318 for a data scientist. Glassdoor reports the annual average base salary for artificial intelligence professionals in the US is $120,048. Based on a Talent.com report, the average artificial intelligence salary is $143,054 annually. Entry positions start at $115,000, and experienced employees can earn up to $200,000 yearly. 

Access this new Amazon book today:


And download your free AI-ML-DL - Career Transformation Guide.

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team) 

Monday, July 10, 2023

“ChatGPT — The Era of Generative Conversational AI Has Begun” (Week #3 - article series)

AI Colleagues, our Week 3 article on “ChatGPT — The Era of Generative Conversational AI Has Begun” addresses the “The Technology Underlying ChatGPT” of AI and specifically  the ChatGPT LLM. (Audible) (Kindle)

III - The Technology Underlying ChatGPT

Training and Fine-Tuning ChatGPT models

After its introduction in December 2022, ChatGPT was hailed as

 "The best artificial intelligence chatbot ever released to the general public" by The New York Times.


A writer for The Guardian named Samantha Lock praised its ability to produce "impressively detailed" and "human-like" writing. 


After using ChatGPT to complete a student assignment, technology journalist Dan Gillmor concluded that "academia has some very significant difficulties to tackle" because the generated content was on par with what a decent student would deliver.


Among "the generative-AI eruption" that "may transform our perspective about how we work, think, and what human creativity truly is," Derek Thompson placed ChatGPT in The Atlantic's "Breakthroughs of the Year" for 2022.


According to Vox contributor Kelsey Piper, "ChatGPT is the general public's first hands-on introduction to how powerful modern AI has gotten, and as a result, many of us are [stunned]" and "clever enough to be useful despite its flaws."


ChatGPT, short for "generative pre-training transformer," is an innovative AI technique created by OpenAI that improves the accuracy and fluency with which chatbots can understand and generate natural language. With 175 billion parameters and the ability to comprehend billions of words in a second, it is the most advanced and comprehensive language model ever built. To accomplish its goals, ChatGPT-3 pre-trains a deep neural network on a large body of text and then fine-tunes it for individual tasks like question answering and content generation. The network consists of layers, or "transformer blocks," which work together to analyze the input text and predict the desired output. ChatGPT’s ability to grasp the flow of a discussion and provide pertinent replies is one of its most impressive characteristics. This is made feasible by self-attention processes that let the network prioritize certain words and phrases in the input text based on their significance to the task.


Now we know that ChatGPT is based on the GPT model's third iteration. But just what is GPT? Let's get started with a non-technical explanation of the acronyms.


  • GPT's "Generative" part refers to its capacity to produce text in a human-sounding, natural language.

  • The model has been trained on a limited dataset, as shown by the "pre-trained." Like taking a test after reading a book (or numerous books) on the subject.

  • The "Transformer" alludes to the machine-learning framework that provides the muscle for GPT.

  • To summarize, Generative Pre-trained Transformer (GPT) is an internet-trained language model designed to produce human-language text responding to requests. As such, we have repeatedly stated that GPT was trained, but how exactly was it trained?

First, as mystical as ChatGPT may appear, it was created by human brilliance, just like every other significant software technology. OpenAI developed ChatGPT, a revolutionary AI research and development business responsible for groundbreaking AI tools like DALL-E, InstructGPT, and Codex. ChatGPT’s ability to generate coherent and consistent text from a small set of input words is another strong suit. Transformers are used because they simulate long-range dependencies in the input text and produce logical string outputs. A deep learning model known as a Transformer serves as the basis for ChatGPT's underlying technology. Researchers from Google published a study in 2017 in which they described a neural network design that they called "The Transformer." The attention mechanism, which gives the model the ability to determine how much weight to give various aspects of the input while making predictions, is the most important new feature introduced by the Transformer. This makes it possible for the model to handle sequential data such as text in a more efficient manner than was possible with earlier architectural approaches. ChatGPT is based on large language models (LLMs). LLMs are deep learning models trained on large amounts of text data to generate human-like language. These models are trained using unsupervised learning techniques and are capable of generating highly coherent and semantically meaningful text.


The Transformer-based model is trained on massive amounts of text data, typically in the order of billions of words, and capable of generating highly coherent, coherent, and semantically meaningful text. The ChatGPT model is designed to process and analyze user input in real time and generate a text response that is semantically meaningful, coherent, and relevant to the user's request or question. This is achieved by using the LLM to analyze the user's input and generate a text response that is semantically meaningful, coherent, and relevant to the user's request or question.


The ChatGPT architecture is a subtype of the Transformer framework that was developed specially to carry out natural language processing jobs. It does this by analyzing a substantial amount of text data to discover the patterns and connections between words and sentences in human language. Because of this, the model can generate material comparable to human language in terms of grammatical structure, vocabulary, and writing style. Unsupervised learning, a type of pre-training in which the model is trained on a huge amount of text input without any labels or a specific task in mind, is utilized as well. This helps the model generalize for usage in various tasks performed further down the pipeline.


The ChatGPT language model is a large-scale language model built on transformer architecture. It was trained using unsupervised learning on a large corpus of text data, enabling it to generate human-like prose. On top of GPT-3.5, ChatGPT was modified using supervised learning and reinforcement learning for optimal performance. Human trainers were utilized in each of these methods to increase the performance of the model. During the process of supervised learning, the model was exposed to dialogues in which the trainers took on the role of both the user and the AI assistant. These interactions were used to teach the model. During the reinforcement step, human trainers began by ranking the model's previous responses during another conversation. These rankings were utilized in creating reward models,' which were then fine-tuned using numerous iterations of proximal policy optimization to improve upon (PPO). The use of proximal policy optimization algorithms offers a cost-effective benefit compared to the use of trust region policy optimization algorithms; these algorithms eliminate many computationally expensive procedures while also improving performance. The training of the models took place using Microsoft's Azure supercomputing infrastructure in conjunction with Microsoft.


In addition, OpenAI is continuously collecting data from users of ChatGPT, which may be used in the future to train further and improve the accuracy of ChatGPT. ChatGPT uses a process called autoregression to produce answers. Autoregression is a method where the model generates text one token (word or punctuation mark) at a time based on the previous tokens it has generated. Users have the option to either upvote or downvote the responses they receive from ChatGPT. In addition, when users upvote or downvote a response, they are presented with a text box in which they can provide additional feedback. It does this by learning patterns and correlations between words and phrases in human language by looking over a vast corpus of text data and making connections between the words and phrases it finds.


It is important to note that ChatGPT was not originally trained to do what it does. Instead, it's an improved version of GPT-3.5, developed from GPT-3 with some tweaks. During its training phase, the GPT-3 model used a humongous quantity of information gathered from the web. Those curious about how GPT training works know that GPT-3 was trained using a hybrid of supervised learning and Reinforcement Learning via Human Feedback (RLHF). In the first, "supervised," phase, the model is taught using a massive collection of web-scraped text. In the reinforcement learning phase, it is taught to make decisions that align with what people would consider being made and correct.

Large Language Models (LLMs): A Technology Underlying ChatGPT

Large Language Models (LLMs) are a crucial technology underlying ChatGPT. LLMs are advanced artificial intelligence models that use deep learning techniques to analyze and process natural language data. These models are trained on massive amounts of data, typically in the order of billions of words, enabling them to generate highly coherent, coherent, and semantically meaningful text.


LLMs are trained using a technique known as unsupervised learning, where the model is exposed to a large corpus of text and encouraged to generate language patterns and relationships on its own. The objective is to enable the model to capture language use patterns and generate new text that resembles human-generated text. Once trained, LLMs can be used for various tasks, including text generation, classification, question answering, and conversation modeling. In the case of ChatGPT, LLMs are used to generate text responses to user input in real time. The model analyzes the user's input and generates a semantically meaningful response, coherent and relevant to the user's question or request.


LLMs have several advantages over traditional language models. Firstly, they can process and analyze vast amounts of data, which enables them to generate more coherent and semantically meaningful text than traditional models. Secondly, they can adapt and improve over time as they are trained on new data and exposed to new language patterns. Finally, LLMs can be fine-tuned for specific use cases, allowing for highly-specific language models that are capable of generating text for specific industries or domains.


In conclusion, Large Language Models (LLMs) are a critical technology that enables ChatGPT to generate text responses that are semantically meaningful, coherent, and relevant to user input. Their ability to process and analyze vast amounts of data, adapt and improve over time, and be fine-tuned for specific use cases makes them a powerful tool for enabling advanced language-based AI applications.


The following is an explanation of ChatGPT's functionality in broad strokes:


  • Unsupervised learning is utilized for training the model using a large corpus of text data, which typically consists of billions of words. During this phase of the training process, the model obtains the knowledge necessary to accurately represent the structures and connections that exist between the words and phrases that make up the language

  • After it has been trained, the model can be used for a wide variety of natural language processing activities, including the production of text, the translation of languages, the answering of questions, and many more.

  • When the model is given a specific task, such as generating a response to a given prompt, it uses the patterns it learned while it was being trained to generate text that is comparable to human-written text in terms of grammar, vocabulary, and style. For example, when the model is given the task of generating a response to the given prompt, it generates the response.

  • This is accomplished by the model digesting the input prompt, parsing it into smaller components such as individual words or phrases, and then using its internal representations of these parts to construct a response that makes sense.

  • When making predictions, the model uses attention to determine the relative relevance of various input components. As a result, the model can handle sequential material, such as text, more effectively than possible with earlier designs. After that, the text that was generated is what is returned as the output.


It is essential to keep in mind that ChatGPT, like any other AI model, cannot comprehend the text; rather, it merely generates text according to the patterns it has observed throughout its training process. Here is a general overview of the process ChatGPT uses to produce answers:


  • The model receives an input prompt, a piece of text to which the model is supposed to respond.

  • The model encodes the input prompt into a fixed-length vector representation called a "context vector." This context vector contains information about the meaning and structure of the input prompt.

  • The model then generates the first token of the output by sampling from a probability distribution over all possible tokens based on the context vector.

  • The model then generates the next token by sampling from a probability distribution over all possible tokens based on the context vector and the previously generated token.

  • This process is repeated until the model generates a stop token, indicating the end of the output, or a maximum output length is reached.

  • The final output is a sequence of tokens generated by the model, which is then decoded back into human-readable text.

  • ChatGPT uses a large amount of data and computational resources during this process, which allows it to generate text similar to human-written text in terms of grammar, vocabulary, and style.


It's important to note that while the model generates coherent and fluent text, it needs to understand its meaning. It simply generates text based on patterns and relationships learned during training.



How ChatGPT works (Source: OpenAI


In conclusion, the underlying technology of ChatGPT is based on large language models (LLMs), specifically Transformer-based models, which are trained on vast amounts of text data to generate human-like language. These models can process and analyze user input in real time, generating a text response that is semantically meaningful, coherent, and relevant to the user's request or question. ChatGPT's functionality could shift when new developments in the field are studied. But its basic operating principles will remain unchanged until a game-changing new technology appears.


To better grasp the idea of response prediction, think of ChatGPT as a detective trying to solve a murder. The evidence is delivered to the investigator, but they still need to find out who did it or how. The investigator may not be able to "predict" with 100% certainty who committed the murder or how it was committed, but with enough evidence, they can make a strong case against the suspect(s). ChatGPT discards the original data it received from the internet and keeps the neural connections or patterns it learned. ChatGPT treats these associations or patterns as evidence when formulating a response to a question.

ChatGPT can also be compared to a very competent investigator. It cannot anticipate the specific facts of an answer, but it does an amazing job of anticipating the most likely sequence of human language text that would provide the best answer. This is how inquiries are answered. Technically speaking, ChatGPT is quite intricate. However, in its most basic form, it functions in the same way that humans do: by picking up new information and applying it when given a chance.

References:

  1. The Technology Behind ChatGPT 

  2. (2023). ChatGPT for (Finance) research: The Bananarama Conjecture. Finance Research Letters, 103662.

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