What is Gen AI vs AI: Exploring the Differences

Artificial Intelligence (AI) has been around for decades, but General AI (Gen AI) is a relatively new concept. Gen AI is designed to perform any intellectual task that a human can.

The key difference between AI and Gen AI lies in their capabilities. AI systems are typically narrow and specialized, meaning they're designed to excel in a specific area, whereas Gen AI aims to be a general-purpose intelligence that can adapt to various tasks and domains.

While AI can process vast amounts of data, Gen AI has the potential to learn and improve on its own, making it a more dynamic and versatile technology.

GANs, or Generative Adversarial Networks, are a type of AI that falls under the generative modeling category, which tries to understand the dataset structure and generate similar examples.

Discriminative modeling, on the other hand, is used in AI for tasks like image classification, where existing data points are classified into respective categories, like cats and guinea pigs.

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Generative modeling is mostly used for unsupervised and semi-supervised machine learning tasks, whereas discriminative modeling is used for supervised machine learning tasks.

The more neural networks intrude on our lives, the more the discriminative and generative modeling areas grow.

GANs specifically use a competition between two neural networks to generate new, realistic data points, which is a key characteristic of generative modeling.

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Generative AI models are a type of AI that combine various algorithms to represent and process content. They can generate text, images, and even human faces by transforming raw data into vectors using techniques like natural language processing and encoding.

One technique used in generative AI is GANs, which stands for Generative Adversarial Networks. GANs are a type of neural network that can generate realistic human faces by pitting a generator against a discriminator.

Transformer-based models are highly effective for natural language processing tasks, such as translation and text generation. They learn to find patterns in sequential data like written text or spoken language.

The first transformer architecture was described in a 2017 Google paper. Some well-known examples of transformer-based models include GPT-4 by OpenAI and Claude by Anthropic.

Tokenization is the first step in a transformer-based model, where input text is broken down into tokens, such as words or subwords. For example, the word "unbelievable" might be broken down into the token "unbeliev".

Embedding converts input tokens into numerical vectors called embeddings, where each token is represented by a unique vector. These vectors represent the semantic characteristics of a word, with similar words having vectors that are close in value.

Positional encoding adds information about the position of each token within a sequence to the input embedding. This is important because the order of words in a sentence is crucial for understanding the text.

The transformer neural network consists of two blocks: the self-attention mechanism and the feedforward network. The self-attention mechanism computes contextual relationships between tokens by weighing the importance of each element in a series.

The self-attention mechanism can detect subtle relationships between words in a phrase, such as the difference in meaning between "full" and "empty" in the sentence "I poured water from the pitcher into the cup until it was full" and "I poured water from the pitcher into the cup until it was empty".

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The feedforward network refines token representations using knowledge about the word it learned from training data. However, it's challenging even for scientists to explain exactly what it does.

The self-attention and feedforward stages are repeated multiple times through stacked layers, allowing the model to capture increasingly complex patterns before generating the final output.

Deep models are a class of machine learning models that have revolutionized the field of artificial intelligence. They are particularly useful for generative tasks such as image generation and text translation.

Variational autoencoders (VAEs), introduced in 2013, were the first deep-learning models to be widely used for generating realistic images and speech. They opened the floodgates to deep generative modeling by making models easier to scale.

Deep models are often used in combination with other techniques such as GANs and transformers to achieve state-of-the-art results in various tasks. For example, GANs have been used to generate realistic human faces, synthetic data for AI training, and even facsimiles of particular humans.

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The transformer architecture, first described in a 2017 Google paper, is a machine learning framework that is highly effective for NLP tasks. It learns to find patterns in sequential data like written text or spoken language, and is perfect for translation and text generation.

Some well-known examples of transformer-based models are GPT-4 by OpenAI and Claude by Anthropic. These models have achieved impressive results in various tasks, including language translation and text generation.

Here are some key characteristics of deep models:

DALL-E is a multimodal AI application that connects the meaning of words to visual elements, trained on a large data set of images and their associated text descriptions.

It was built using OpenAI's GPT implementation in 2021 and a second, more capable version, DALL-E 2, was released in 2022, enabling users to generate imagery in multiple styles driven by user prompts.

ChatGPT is an AI-powered chatbot that took the world by storm in November 2022, built on OpenAI's GPT-3.5 implementation, and allows users to interact and fine-tune text responses via a chat interface with interactive feedback.

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ChatGPT incorporates the history of its conversation with a user into its results, simulating a real conversation, and after its incredible popularity, Microsoft announced a significant new investment into OpenAI and integrated a version of GPT into its Bing search engine.

Gemini is a public-facing chatbot built on a lightweight version of Google's LaMDA family of large language models, but it suffered a significant loss in stock price following its rushed debut due to inaccurate results and erratic behavior.

Google has since unveiled a new version of Gemini built on its most advanced LLM, PaLM 2, which allows Gemini to be more efficient and visual in its response to user queries.

Generative AI has a wide range of applications, including computer vision where it can enhance the data augmentation technique.

Generative AI can be applied in various use cases to generate virtually any kind of content. The technology is becoming more accessible to users of all kinds thanks to cutting-edge breakthroughs like GPT that can be tuned for different applications.

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Some of the use cases for generative AI include implementing chatbots for customer service and technical support. This can help businesses provide 24/7 support to their customers.

Generative AI can also be used to deploy deepfakes for mimicking people or even specific individuals. This can be used in movie dubbing and educational content in different languages.

Writing email responses, dating profiles, resumes, and term papers are also use cases for generative AI. This can help individuals save time and improve their writing skills.

Creating photorealistic art in a particular style is another use case for generative AI. This can be used in various industries such as advertising and entertainment.

Here are some examples of generative AI use cases:

Large Language Models are a type of AI that can understand and generate human-like language. They're based on the transformer architecture, which was first described in a 2017 Google paper and has since become highly effective for NLP tasks.

These models learn to find patterns in sequential data like text and can predict the next element in a series, such as the next word in a sentence. They're perfect for tasks like translation and text generation.

Some well-known examples of transformer-based models are GPT-4 by OpenAI and Claude by Anthropic. These models have been widely used for a range of text-based tasks, including language translation, content generation, and content personalization.

Large Language Models can also power customer service chatbots that respond to inquiries from humans and are commonly used with copilots that act as virtual assistants. They can even be used to generate code and answers from critical business documents.

The transformer architecture works by breaking down input text into tokens, which are then converted into numerical vectors called embeddings. These vectors represent the semantic characteristics of a word, with similar words having vectors that are close in value.

The order of words in a sentence is also important, so the model adds information about the position of each token within a sequence, known as positional encoding. This allows the model to understand the context of the text and generate more accurate results.

Large Language Models have been widely adopted in various industries, including business and customer service. They can help offload repetitive tasks from workers to AI, freeing them up for higher-value work and increasing work efficiency.

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Generative AI models and algorithms are the backbone of AI-generated content, using algorithms and models to create new, realistic content from existing data. These models, like GANs, have distinct mechanisms and capabilities, and some, like the softmax function, are used to calculate the likelihood of different outputs and choose the most probable option.

The softmax function is used at the end to calculate the likelihood of different outputs and choose the most probable option. This helps to ensure that the generated output is accurate and relevant.

Neural networks, which form the basis of much of the AI and machine learning applications today, are designed to mimic how the human brain works. They "learn" the rules from finding patterns in existing data sets, making them a powerful tool for generating content.

Machine learning models are used to make predictions, and most of them are used for classification. Discriminative modeling is a type of algorithm that tries to classify input data given some set of features and predict a label or a class to which a certain data example belongs.

Discriminative algorithms compare each prediction to the actual label during training and learn the relationships between features and classes based on the difference between the two values. This process helps the model to gradually learn and improve its predictions.

A discriminative model can be thought of as compressing information about the differences between classes, without trying to understand what each class is. For example, a model trained to distinguish between cats and guinea pigs might learn to recognize the presence of a tail and the shape of the ears as key features.

Generative modeling, on the other hand, tries to understand the structure of the dataset and generate similar examples. It focuses on learning features and their relations to get an idea of what makes cats look like cats and guinea pigs look like guinea pigs.

Generative algorithms do the opposite of discriminative algorithms, predicting features given a certain label instead of predicting a label given some features. This allows generative models to capture the probability of x and y occurring together and recreate or generate images of cats and guinea pigs.

Discriminative models are easier to monitor and more explainable than generative models, making them a popular choice for many applications. They excel in tasks such as image recognition, document classification, and fraud detection, where the goal is to detect a category or label.

Supervised Learning is a type of machine learning where the algorithm is trained on labeled data, meaning the data is already categorized or classified.

This approach is useful because it allows the algorithm to learn from the correct answers and make predictions on new, unseen data.

In Supervised Learning, the algorithm is given a set of input data and corresponding output labels, which it uses to adjust its internal parameters.

The goal is to minimize the difference between the predicted output and the actual output, resulting in an accurate model.

The type of Supervised Learning is determined by the type of output variable, which can be either continuous or categorical.

For example, predicting the exact price of a house is a continuous output variable, while classifying an image as a cat or dog is a categorical output variable.

In the example of predicting house prices, the algorithm would try to find the best relationship between the input features and the output price.

By analyzing the labeled data, the algorithm can learn to recognize patterns and make accurate predictions, which is useful in applications such as credit risk assessment or medical diagnosis.

The choice of algorithm and model architecture depends on the specific problem and dataset, but some popular Supervised Learning algorithms include Linear Regression and Decision Trees.

These algorithms can be used to solve a wide range of problems, from predicting stock prices to classifying customer churn.

Neural networks have revolutionized the field of machine learning by enabling computers to learn from data and make predictions or decisions without being explicitly programmed.

The first neural networks were developed in the 1950s and 1960s, but they were limited by a lack of computational power and small data sets. It wasn't until the advent of big data in the mid-2000s and improvements in computer hardware that neural networks became practical for generating content.

Neural networks are designed to mimic how the human brain works, learning the rules from finding patterns in existing data sets. This approach has led to significant advances in AI-generated content, including text, images, and speech.

Generative adversarial networks (GANs) and variational autoencoders (VAEs) are two types of neural networks that have been particularly effective in generating realistic human faces and synthetic data for AI training.

The ability of neural networks to process complex data types, such as images and speech, has been made possible by the rise of deep learning. This has led to the development of models like BERT, GPT, and Google AlphaFold, which can not only encode language, images, and proteins but also generate new content.

The use of GPUs to run neural networks in parallel has also been a key factor in the recent advances in AI-generated content. This has enabled researchers to train large neural networks on massive datasets, leading to significant improvements in performance and accuracy.

Generative AI tools have come a long way since the 1960s, when the Eliza chatbot was created by Joseph Weizenbaum. This early example of generative AI used a rules-based approach that had several limitations.

Some popular generative AI tools include text generators like GPT, Jasper, AI-Writer, and Lex, image generators like Dall-E 2, Midjourney, and Stable Diffusion, and music generators like Amper, Dadabots, and MuseNet.

Generative AI history is marked by a resurgence in the 2010s, thanks to advances in neural networks and deep learning. This enabled the technology to automatically learn to parse existing text, classify image elements, and transcribe audio.

Generative AI capabilities have expanded significantly since 2014, when Ian Goodfellow introduced GANs, which provided a novel approach for organizing competing neural networks to generate and then rate content variations.

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Generative AI tools exist for various modalities, such as text, imagery, music, code, and voices. Some popular AI content generators to explore include the following:

These tools can be used for a wide range of applications, from creating art and music to generating code and designing chips.

The history of generative AI is a fascinating story that spans several decades. Generative AI has come a long way since the 1960s when Joseph Weizenbaum created the Eliza chatbot, one of the earliest examples of generative AI.

Early chatbots were limited by their rules-based approach, which broke easily due to a limited vocabulary, lack of context, and overreliance on patterns. This made them difficult to customize and extend.

The field saw a resurgence in the wake of advances in neural networks and deep learning in 2010, which enabled the technology to automatically learn to parse existing text, classify image elements, and transcribe audio.

Ian Goodfellow introduced GANs in 2014, a deep learning technique that provided a novel approach for organizing competing neural networks to generate and then rate content variations. This led to the creation of realistic people, voices, music, and text.

Since then, progress in other neural network techniques and architectures has helped expand generative AI capabilities. Techniques such as VAEs, long short-term memory, transformers, diffusion models, and neural radiance fields have further advanced the field.

Generative AI is evolving rapidly, and its future is exciting. Enhancing Large Language Models (LLMs) with retrieval-augmented generation is one area of focus.

Research is also underway to develop tools that make generative AI more transparent. This is a crucial step in ensuring the safe and responsible adoption of this technology.

The popularity of generative AI tools has sparked an interest in training courses, with many aimed at helping developers create AI applications. Others focus on business users looking to apply the technology across the enterprise.

Generative AI will continue to advance in areas like translation, drug discovery, and anomaly detection. It will also generate new content, from text and video to fashion design and music.

Here are some examples of how generative AI will change our workflows:

The impact of generative AI will be significant, and we will need to reevaluate the nature and value of human expertise as a result.