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Demystifying Gen AI: A Beginners Guide

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Artificial intelligence that can produce text, graphics, and other kinds of material is known as generative AI. It’s an amazing technology since it democratizes AI so that anyone can utilize it with just a natural language sentence or text command. To achieve anything valuable, you don’t need to learn a language like Html, Java, or SQL. You just need to provide the requirement in natural language, and an AI model will provide a proposal. This has enormous applications and an influence because it allows you to generate or comprehend reports, write applications, and much more in seconds.

Machine Learning is an AI Approach Based on Statistics.

A pivotal moment occurred in the 1990s when text analysis adopted a statistical methodology. As a result, new algorithms that are referred to as machine learning were created. These algorithms learn patterns from data without the need for explicit programming. With this method, a statistical model trained on text-label pairings can replicate human language comprehension by classifying unknown incoming text with a pre-defined label that indicates the message’s intention.

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Deep Learning and Contemporary Virtual Assistants

More recently, as hardware has evolved to handle larger volumes of data and more complex computations, research in AI domains has been stimulated, creating sophisticated machine learning algorithms that involve neural networks or deep learning algorithms.

Neural networks, especially Recurrent Neural Networks (RNNs), have greatly improved natural language processing. These networks value a word’s context in a sentence and allow for a more meaningful representation of the text’s meaning.


Generative AI

Following decades of AI research, a new model architecture known as Transformer overcomes the limitations of RNNs by being able to accept considerably longer text sequences as input. Transformers, which are based on the attention mechanism, allow the model to assign varying weights to the inputs it receives, regardless of their order in the text sequence. This allows the model to “pay more attention” to where the most significant information is concentrated.

Since most of the most recent generative AI models handle textual inputs and outputs, they are commonly referred to as LLMs or Large Language Models. This design is actually the basis for most of these models. These models are interesting because, having been trained on a large amount of unlabeled data from numerous sources, such as books, papers, and websites, they may be customized to a wide range of circumstances.


Working of LLM's

Tokenizer, text to numbers: Generate text as output from text input using large language models. However, as they are statistical models, they perform far better with numerical data than with textual sequences. For this reason, before the main model uses any input, it is tokenized and processed. The main job of the tokenizer is to break the input into an array of tokens, each token being a fragment of text with a variable number of characters. Next, an integer encoding of the original text chunk is assigned to each token, and this mapping is called a token index.

Token prediction: The model can predict one token as an output given n input tokens (the maximum number of tokens varies among models). After that, this token is added to the input of the following iteration in an expanding window pattern, improving the user’s ability to receive one (or more) sentence responses. This clarifies why, if you’ve ever used ChatGPT, you may have seen that, sometimes, it appears to halt in the middle of a phrase.

Probability distribution and selection process: The model selects the output token based on how likely it is to occur after the current text sequence. This is because the model, having been trained, forecasts a probability distribution across all potential “next tokens.” However, based on the resultant distribution, the token with the highest probability is not always selected. This decision is made with a certain amount of randomness, which causes the model to behave non-deterministically—that is, we don’t always obtain the same result for the same input. To mimic the process of creative thought, this level of unpredictability is added, and it may be adjusted using a model parameter called temperature.

A large language model’s prompt is its input, and its completion, a word referring to the model technique of generating the next token to finish the current input, is its output. Prompts are the instructions provided to models to generate a particular output. The model can be given a comment requesting the creation of a piece of code that accomplishes a certain task or a piece of code together with the request to explain and document it, as shown below.


Generative AI models cannot be considered 100% accurate. Furthermore, generative AI models do not always produce flawless results; occasionally, the model’s creativity can work against it, producing a combination of words that the human user may find insulting or a mystification of reality. At least when it comes to the broader definition of intelligence, which encompasses critical and creative reasoning as well as emotional intelligence, generative AI is not intelligent. It is also not deterministic and untrustworthy because false statements, references, and content can be combined with accurate information and presented convincingly and self-assuredly.

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