CSS3 and SVG with Claude 3 E-Book

Oswald Campesato

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 315

Veröffentlichungsjahr: 2024

Beschreibung

This book is an essential guide for mastering advanced CSS3 and SVG techniques, ideal for web developers aiming to create visually striking and interactive web content. It starts with an in-depth look at CSS3, covering shadow effects, gradients, 2D and 3D transforms, and sophisticated animations. You'll learn to implement complex styling, use media queries for responsive design, and create dynamic keyframe animations. Security practices and effective use of CSS3 are also addressed.
Moving to SVG, the book explores creating scalable vector graphics for advanced data visualizations and animations. You'll learn to use SVG elements like shapes, gradients, and Bezier curves, and how to seamlessly integrate them with CSS3 for dynamic, interactive graphics. Detailed tutorials on SVG filters, transforms, and animations enable you to build intricate visual effects and engaging content. Practical examples show how to combine CSS3 and SVG for responsive, high-performance web projects.
By the end, you’ll have the skills to craft compelling web applications using CSS3 and SVG, whether you're a beginner or an experienced developer looking to elevate your web design expertise.

Leseprobe

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O. Campesato. CSS3 and SVG with Claude 3.

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I’d like to dedicate this book to my parents – may this bring joy and happiness into their lives.

CONTENTS

Preface

Chapter 1: The Generative AI Landscape

What Is Generative AI?

Key Features of Generative AI

Popular Techniques in Generative AI

What Makes Generative AI Different

Conversational AI Versus Generative AI

Primary Objective

Applications

Technologies Used

Training and Interaction

Evaluation

Data Requirements

Is Claude 3 Part of Generative AI?

Is DALL-E Part of Generative AI?

DALLE-3 Sample Images

What Are LLMs?

What Are SLMs?

Popular SLMs

DeepMind

DeepMind and Games

Player of Games (PoG)

OpenAI

Cohere

Hugging Face

Hugging Face Libraries

Hugging Face Model Hub

AI21

Introduction to Claude 3

Advantages of Claude 3

Limitations of Claude 3

Interacting with Claude 3

Claude 3 Custom Instructions

Claude 3 and Prompts

Claude 3 Competitors

Google Gemini

YouChat

CoPilot (OpenAI/Microsoft)

Codex (OpenAI)

Apple GPT

PaLM-2

Llama 3

Summary

Chapter 2: Prompt Engineering

What Is Prompt Engineering?

Prompts and Completions

Types of Prompts

Instruction Prompts

Reverse Prompts

System Prompts Versus Agent Prompts

Prompt Templates

Prompts for Different LLMs

Poorly Worded Prompts

Inference Parameters

Temperature Parameter

Temperature and the softmax() Function

LLMs Try to Be Helpful

Meta AI

Gemini

GPT-4

Claude 3

Perplexity

Aspects of LLM Development

LLM Size Versus Performance

Emergent Abilities of LLMs

Kaplan and Undertrained Models

Summary

Chapter 3: Introduction to CSS3

HTML and <DOCTYPE>

CSS3 Features and Browser Support

Browser-Specific Prefixes for CSS3

A Quick Overview of CSS3 Features

CSS3 Shadow Effects and Rounded Corners

CSS3 and Text Shadow Effects

CSS3 and Box Shadow Effects

CSS3 and Rounded Corners

CSS3 Gradients

Linear Gradients

Radial Gradients

CSS3 2D Transforms

Zoom Effects with Scale Transforms

Rotate Transforms

Skew Transforms

Translate Transforms

Summary

Chapter 4: CSS3 3D Animation

A CSS3-Based Cube

CSS3 Transitions

Simple CSS3 Animation Effects

Glowing Effects

Image Fading and Rotating Effects with CSS3

Bouncing Effects

CSS3 Effects for Text

Rendering Multicolumn Text

CSS3 Media Queries

CSS3 3D Animation Effects

Summary

Chapter 5: CSS3 and Claude 3

Working with HTML

Popular Use Cases for HTML

HTML and Accessibility

Security Issues with HTML

An HTML Web Page with a DropDown List

Working with CSS

Use Cases for CSS3

Security Issues with CSS3

Text with a CSS3 Linear Gradient

Text with a CSS3 Radial Gradient

A Rounded Rectangle with a CSS3 Radial Gradient

Diagonal Gradients and Multigradients

Media Queries

Keyframe Animation Effects

CSS3 Animation Effects

A Carousel Effect with CSS3

CSS3 and 2D Transforms

CSS3 Quadratic Bezier Curves

CSS3 Cubic Bezier Curves

Artistic CSS3 Cubic Bezier Curves

CSS3 Filters

CSS3 Blur Filter

CSS3 Drop Shadow

Summary

Chapter 6: Introduction to SVG

Overview of SVG

Basic 2D Shapes in SVG

SVG Gradients

SVG <polygon> Element

Bezier Curves

SVG Filters, Shadow Effects, and Text Paths

SVG Transforms

SVG Animation

SVG and JavaScript

CSS3 and SVG Bar Charts

Summary

Chapter 7: SVG and Claude 3

Working with SVG

Use Cases for SVG

Accessibility and SVG

Security Issues with SVG

SVG Linear Gradients

SVG Radial Gradients

A Triangle with a Radial Gradient

SVG 2D Shapes and Gradients

A Bar Chart in SVG

SVG Quadratic Bezier Curves

SVG Cubic Bezier Curves

SVG and 2D Transforms

Animated SVG Cubic Bezier Curves

Hover Effects

Hover Animation Effects

SVG Versus CSS3: A Comparison

SVG Versus PNG: A Comparison

SVG Filters

SVG Blur Filter

SVG Turbulence Filter

SVG and CSS3 in HTML Web Pages

SVG and JavaScript in HTML Web Pages

Elliptic Arcs with a Radial Gradient

An SVG Checkerboard Pattern

An SVG Checkerboard Pattern with Filter Effects

A Master-Detail HTML Web Page

Summary

Index

PREFACE

WHAT IS THE PRIMARY VALUE PROPOSITION FOR THIS BOOK?

This book explores the synergy between Claude 3, a state-of-the-art AI model, and Web technologies such as HTML, CSS3, and SVG, offering both novices and seasoned developers the tools to create stunning web graphics and animations.

KEY TOPICS

Generative AI and Claude 3: Explore the fundamental concepts of Generative AI, key players in the field, and the unique features of Claude 3.Prompt Engineering: Master the art of crafting effective prompts to interact seamlessly with AI models like Claude 3.CSS3 3D Animation: Learn how to create dynamic 3D animations using CSS3, including effects like glowing, image fading, and rotating.SVG and Claude 3: Discover how to generate sophisticated SVG graphics and animations using Claude 3, enhancing your Web designs with advanced visual effects.

THE TARGET AUDIENCE

This book is ideal for both beginners and experienced developers, offering in-depth knowledge about AI, Web development, and programming. Moreover, this book is structured to provide both theoretical knowledge and practical insights, making it a valuable resource for those looking to deepen their understanding of these rapidly evolving fields. As such, it is useful as a go-to resource for modern developers looking to stay ahead in an AI-focused world.

CHAPTER OVERVIEW

Chapter 1 explores Generative AI, discussing its key features, the differences between Conversational AI and Generative AI, and its various applications. It also examines the roles of prominent AI players like DeepMind, OpenAI, Cohere, Hugging Face, AI21, and others in this field. A significant portion of this chapter is dedicated to understanding Claude 3, such as its key features and a comparison of Claude 3 with GPT-4.

Chapter 2 shifts focus to Prompt Engineering, providing a comprehensive overview, including the types and importance of prompts, and guidelines for effective prompt design. This part of the book is crucial for understanding how to interact effectively with AI models such as Claude 3. Chapter 3 introduces CSS3, along with manually created HTML Web pages that contain CSS3 code for linear gradients, radial gradients, and other CSS3-based effects. Chapter 4 contains Claude 3-generated HTML Web pages with CSS3 that show you how to create 3D animation effects. Chapter 5 contains an assortment of Claude 3-generated Web pages that contain CSS3. Chapter 6 introduces SVG, along with manually created HTML Web pages that contain SVG code for linear gradients, radial gradients, and other CSS3-based effects. Chapter 7 contains examples of Claude 3-generated HTML Web pages that contain SVG code.

GETTING THE MOST FROM THIS BOOK

Some Web developers learn well from prose, others learn well from sample code (and lots of it), which means that there is no single style that can be used for everyone.

Moreover, some Web developers want to run the code first, see what it does, and then return to the code to delve into the details (and others use the opposite approach).

Consequently, there are several types of code samples in this book in order to illustrate some aspects of CSS3 and SVG, as well as how to supply prompts to Claude 3 in order to generate CSS3 code and SVG code.

HOW WAS THE CODE FOR THIS BOOK TESTED?

The code samples in this book have been tested in a recent version of Firefox on a MacBook Pro Sonoma 14.2.1 (earlier versions of OS X support the code samples in this book).

WHAT DO I NEED TO KNOW FOR THIS BOOK?

The most useful prerequisite is familiarity with HTML, CSS3, and SVG, which will enable you to understand the code samples more quickly. The less technical knowledge that you have, the more diligence will be required in order to understand the diverse topics that are covered.

If you want to be sure that you can grasp the material in this book, glance through some of the code samples to get an idea of how much is familiar to you and how much is new for you.

DOES THIS BOOK CONTAIN PRODUCTION-LEVEL CODE SAMPLES?

Clarity has a higher priority than writing compact code that is difficult to understand (and possibly prone to bugs). If you decide to use any of the code in this book on a production website, you ought to subject that code to the same rigorous analysis as the other parts of your HTML Web pages.

HOW DO I SET UP A COMMAND SHELL?

If you are a Mac user, there are three ways to do so. The first method is to use Finder to navigate to Applications > Utilities and then double click on the Utilities application. Next, if you already have a command shell available, you can launch a new command shell by typing the following command:

open/ Applications/Utilities/Terminal.app

A second method for Mac users is to open a new command shell on a MacBook from a command shell that is already visible simply by clicking command+n in that command shell, and your Mac will launch another command shell.

If you are a PC user, you can install Cygwin (open source https://cygwin.com/) that simulates bash commands or use another toolkit such as MKS (a commercial product). Please read the online documentation that describes the download and installation process. Note that custom aliases are not automatically set if they are defined in a file other than the main start-up file (such as .bash_login).

COMPANION FILES

All the code samples and figures in this book may be obtained by writing to the publisher at [email protected].

O. Campesato

August 2024

CHAPTER1

THE GENERATIVE AI LANDSCAPE

This chapter contains an introduction to generative AI and some of the companies that have a significant presence in generative AI. Some of the main features of Claude 3 will also be discussed, along with some of the competitors for Claude 3.

The first section of this chapter starts with information generated by Claude 3 regarding the nature of generative AI as well as conversational AI versus generative AI. According to Claude 3, it’s also true that ChatGPT, GPT-4, and DALL-E are included in generative AI. This section also discusses Claude 3 and some of its features, as well as alternatives to Claude 3, such as Llama 2 (Meta) and Gemini (formerly Google Bard).

The second section provides an overview of several important companies that are heavily involved in generative AI, such as OpenAI, Google, Meta, Cohere, and Hugging Face (as well as Anthropic, of course).

The third section discusses Claude 3, which includes a description of the three versions of Claude 3, called Opus, Sonnet, and Haiku. Claude 3 Opus is the most powerful, and available as a paid service ($20 per month), whereas Sonnet and Haiku are freely available to everyone. This section also briefly describes some of its competitors, such as Google Gemini and Meta AI (Meta).

As this book goes to print Anthropic released an updated version of Claude 3 Sonnet that outperforms GPT-4o from OpenAI.

WHAT IS GENERATIVE AI?

Generative AI refers to a subset of artificial intelligence models and techniques that are designed to generate new data samples that are similar to the input data. The goal is to produce content or data that wasn’t part of the original training set but is coherent, contextually relevant, and in the same style or structure.

Generative AI stands apart in its ability to create and innovate, as opposed to merely analyzing or classifying. The advancements in this field have led to breakthroughs in creative domains and practical applications, making it a cutting-edge area of AI research and development.

Key Features of Generative AI

The following bullet list contains key features of generative AI, followed by a brief description for each bullet item:

data generationsynthesislearning distributions

Data generation refers to the ability to create new data points that are not part of the training data but resemble it. This can include text, images, music, videos, or any other form of data.

Synthesis means that generative models can blend various inputs to generate outputs that incorporate features from each input, like merging the styles of two images.

Learning distributions means that generative AI models aim to learn the probability distribution of the training data so they can produce new samples from that distribution.

Popular Techniques in Generative AI

Generative adversarial networks (GANs): GANs consist of two networks, a generator and a discriminator, that are trained simultaneously. The generator tries to produce fake data, while the discriminator tries to distinguish between real data and fake data. Over time, the generator gets better at producing realistic data.

Variational autoencoders (VAEs): VAEs are probabilistic models that learn to encode and decode data in a manner in which the encoded representations can be used to generate new data samples.

Recurrent neural networks (RNNs): Used primarily for sequence generation, such as text or music.

What Makes Generative AI Different

Creation versus classification: While most traditional AI models aim to classify input data into predefined categories, generative models aim to create new data.

Unsupervised learning: Many generative models, especially GANs and VAEs, operate in an unsupervised manner, meaning they don’t require labeled data for training.

Diverse outputs: Generative models can produce a wide variety of outputs based on learned distributions, making them ideal for tasks like art generation, style transfer, and more.

Challenges: Generative AI poses unique challenges, such as mode collapse in GANs or ensuring the coherence of generated content.

Furthermore, there are numerous areas that involve generative AI applications, some of which are in the following bullet list:

art and music creationdata augmentationstyle transfertext generationimage synthesisdrug discovery

Art and music creation includes generating paintings, music, or other forms of art.

Data augmentation involves creating additional data for training models, especially when the original dataset is limited.

Style transfer refers to applying the style of one image to the content of another.

Text generation is a very popular application of generative AI, which involves creating coherent and contextually relevant text.

Image synthesis is another popular area of generative AI, which involves generating realistic images, faces, or even creating scenes for video games.

Drug discovery is a very important facet of generative AI that pertains to generating molecular structures for new potential drugs.

CONVERSATIONAL AI VERSUS GENERATIVE AI

Both conversational AI and generative AI are prominent subfields within the broader domain of artificial intelligence. However, these subfields have a different focus regarding their primary objective, the technologies that they use, and applications.

The primary differences between the two subfields are in the following sequence of bullet points:

primary objectiveapplicationstechnologies usedtraining and interactionevaluationdata requirements

Primary Objective

The main goal of conversational AI is to facilitate human-like interactions between machines and humans. This includes chatbots, virtual assistants, and other systems that engage in dialogue with users.

The primary objective of generative AI is to create new content or data that wasn’t in the training set but is similar in structure and style. This can range from generating images, music, and text to more complex tasks like video synthesis.

Applications

Common applications for conversational AI include customer support chatbots, voice-operated virtual assistants (like Siri or Alexa), and interactive voice response (IVR) systems.

Generative AI has a broad spectrum of common applications such as creating art or music, generating realistic video game environments, synthesizing voices, and producing realistic images or even deepfakes.

Technologies Used

Conversational AI often relies on natural language processing (NLP) techniques to understand and generate human language. This includes intent recognition, entity extraction, and dialogue management.

Generative AI commonly utilizes generative adversarial networks (GANs), variational autoencoders (VAEs), and other generative models to produce new content.

Training and Interaction

While training can be supervised, semisupervised, or unsupervised, the primary interaction mode for conversational AI is through back-and-forth dialogue or conversation.

The training process for generative AI, especially with models like GANs, involves iterative processes where the model learns to generate data by trying to fool a discriminator into believing the generated data is real.

Evaluation

Conversational AI evaluation metrics often revolve around understanding and response accuracy, user satisfaction, and the fluency of generated responses.

Generative AI evaluation metrics for models like GANs can be challenging and might involve using a combination of quantitative metrics and human judgment to assess the quality of generated content.

Data Requirements

Data requirements for conversational AI typically involve dialogue data, with conversations between humans or between humans and bots.

Data requirements for generative AI involve large datasets of the kind of content it is supposed to generate, be it images, text, music, and so on.

Although both conversational AI and generative AI deal with generating outputs, their primary objectives, applications, and methodologies can differ significantly. Conversational AI is geared toward interactive communication with users, while generative AI focuses on producing new, original content.

IS CLAUDE 3 PART OF GENERATIVE AI?

According to Claude 3 itself, Claude 3 is not part of generative AI (which might be surprising). The following prompt was given to Claude 3 to ask if it’s part of generative AI: Are you considered part of generative AI?

Claude 3 provided the following response to the preceding question:

No, I am not part of what is typically considered "Generative AI." I am a large language model trained by Anthropic to have natural conversations and provide informative responses, but I do not have generative capabilities to create new content like images, videos, audio, or computer programs from scratch.

My training allows me to understand and process existing data and information in order to summarize, analyze, explain, or translate it into natural language responses. However, I do not have the ability to autonomously generate truly new content in domains like visual art, music, software code, etc.

I rely on my training data provided by Anthropic to form my responses, rather than generating wholly original content through machine learning techniques like generative adversarial networks (GANs) or diffusion models that are used in Generative AI applications.

So in summary, while I use advanced natural language processing capabilities, I am not considered part of the specific field of Generative AI that aims to create new artificial data across different modalities like images, video, and audio. I am a very capable language model, but not one with generative AI capabilities.

IS DALL-E PART OF GENERATIVE AI?

DALL-E and similar tools that generate graphics from text are indeed examples of generative AI. In fact, DALL-E is one of the most prominent examples of generative AI in the realm of image synthesis.

Here’s a bullet list of generative characteristics of DALL-E, followed by brief descriptions of each bullet item:

image generationlearning distributionsinnovative combinationsbroad applicationstransformer architecture

Image generation is a key feature of DALL-E, which was designed to generate images based on textual descriptions. Given a prompt like “a two-headed flamingo,” DALL-E can produce a novel image that matches the description, even if it’s never seen such an image in its training data.

Learning distributions: Like other generative models, DALL-E learns the probability distribution of its training data. When it generates an image, it samples from this learned distribution to produce visuals that are plausible based on its training.

Innovative combinations: DALL-E can generate images that represent entirely novel or abstract concepts, showcasing its ability to combine and recombine learned elements in innovative ways.

In addition to image synthesis, DALL-E has provided broad application support in areas like art generation, style blending, and creating images with specific attributes or themes, highlighting its versatility as a generative tool.

DALL-E leverages a variant of the transformer architecture, similar to models like GPT-3, but adapted for image generation tasks.

Other tools that generate graphics, art, or any form of visual content based on input data (whether it’s text, another image, or any other form of data) and can produce outputs not explicitly present in their training data are also considered generative AI. They showcase the capability of AI models to not just analyze and classify but to create and innovate.

DALLE-3 SAMPLE IMAGES

The following prompt was supplied to Meta AI (currently Claude 3 cannot generate images via text prompts) in order to generate an image of a pirate steering a cigar boat.

Please generate a clown steering a cigar boat.

The image in Figure 1.1 displays a clown steering a cigar boat.

The following prompt was also supplied to Meta AI in order to generate an image of a movie star steering a cigar boat.

Please generate a movie star steering a cigar boat engulfed in swirling ocean waters.

The image in Figure 1.2 displays a movie star steering a cigar boat.

FIGURE 1.1 A clown steering a cigar boat.

FIGURE 1.2A movie star steering a cigar boat.

As can be seen from the two markedly different preceding images, the text in a prompt can significantly affect the type of image that Meta AI will generate.

Presumably these features will spur OpenAI to provide the same set of features (some are implemented in Meta AI, but they are not publicly available).

WHAT ARE LLMS?

The following prompt was supplied to Meta AI regarding a description of LLMs, along with their advantages and disadvantages:

Please describe LLMs and their advantages as well as their disadvantages.

Meta AI

Large Language Models (LLMs) are complex algorithms trained on vast datasets to understand and generate human-like text, offering automation, creativity, and information processing opportunities. Here are some of the advantages and disadvantages of LLMs:

Advantages:

Comprehensibility: LLMs tend to produce translations that are grammatically correct and easy to comprehend.Adaptability: Owing to their training on diverse corpora, LLMs are well-equipped to handle a broad range of topics and language styles.Improvement Over Time: As LLMs continue to learn from user interactions and a broadening corpus of text, their performance can improve over time.

Disadvantages:

Domain-Specific Translation: LLMs may struggle due to the lack of specialized training data.Limitations on the Availability of Language Pairs: LLMs are often trained on a large number of language pairs, which can dilute their proficiency in any specific pair.Contextual Understanding: While LLMs have a decent understanding of immediate context, they often falter when it comes to understanding larger or implicit context within a text.

Meta AI also provided the following sources that are referenced in the preceding description of LLMs:

https://projectmanagers.net/top-10-disadvantages-of-large-language-models-llm/

https://blog.pangeanic.com/advantages-and-disadvantages-of-llm-machine-translation-versus-neural-machine-translation

WHAT ARE SLMS?

The following prompt was supplied to Meta AI regarding a description of SLMs, along with their advantages and disadvantages:

Please describe Small Language Models (SLMs) and their advantages as well as their disadvantages.

Meta AI

Small Language Models (SLMs) are a type of neural network that generate natural language content and are designed to be more lightweight and resource-efficient. SLMs are ideal for applications that need to operate within limited computational environments, offering advantages such as:

Advantages:

Efficiency: SLMs require less computational power and energy.Speed: They are easier and quicker to deploy.Robustness: SLMs are more manageable and controllable.Security: They present a lower risk of data leakage.

Disadvantages:

Niche focus: SLMs may not perform well outside their specific domain of training.Limited generalization: They lack the broad knowledge base of LLMs.Rapid evolution: The field of Language Models is rapidly evolving, making it challenging to keep up with the latest advancements.Evaluation and selection difficulties: Choosing the appropriate SLM for a specific application can be daunting due to the influx of various models.

Meta AI also provided the following sources that are referenced in the preceding description of SLMs:

https://winder.ai/exploring-small-language-models/

https://www.harrisonclarke.com/blog/large-language-models-vs-small-language-models

https://aisera.com/blog/small-language-models/

Popular SLMs

The following prompt was supplied to Meta AI regarding popular SLMs, along with their features:

Please provide a list of popular Small Language Models (SLMs), along with their features.

Meta AI

Here are some popular Small Language Models (SLMs):

DistilBERT: This is a smaller version of the BERT model, with around half of the parameters. It is a good balance of speed, efficiency, and cost, making it suitable for use in resource-constrained environments.GPT-Neo: This is an open-source alternative to GPT-3, with similar architecture and capabilities. It has 2.7 billion parameters and is designed to provide high-quality results for a variety of language tasks without the need for fine-tuning.GPT-J: This is similar to GPT-3 in design, with 6 billion parameters. It includes Rotary Position Embeddings and attention mechanisms, and is effective for tasks such as translating from English to French.Domain-Specific LLMs: These models are designed for the healthcare sector and are specialized to process and generate information related to medical terminologies, procedures, and patient care.Micro Language Models: These models are designed for AI customer support and are fine-tuned to understand the nuances of customer interactions, product details, and company policies.

Meta AI also provided the following sources that are referenced in the preceding list of SLMs:

https://aisera.com/blog/small-language-models/

https://winder.ai/exploring-small-language-models/

The next several sections briefly introduce some of the companies that have a strong presence in the AI world.

DEEPMIND

DeepMind has made significant contributions to artificial intelligence, which includes the creation of various AI systems. DeepMind was established in 2010 and became a subsidiary of Google 2014, and its home page is here: https://deepmind.com/

DeepMind created the 280GB language model Gopher that significantly outperforms its competitors, including GPT-3, J1-Jumbo, and MT-NLG. DeepMind also developed AlphaFold that solved a protein folding task in literally thirty minutes that had eluded researchers for ten years. Moreover, DeepMind made AlphaFold available for free for everyone in July 2021. DeepMind has made significant contributions in the development of world-caliber AI game systems, some of which are discussed in the next section.

DeepMind and Games

DeepMind is the force behind the AI systems StarCraft and AlphaGo that defeated the best human players in Go (which is considerably more difficult than chess). These games provide “perfect information,” whereas games with “imperfect information” (such as poker) have posed a challenge for ML models.

AlphaGo Zero (the successor of AlphaGo) mastered the game through self-play in less time and with less computing power. AlphaGo Zero exhibited extraordinary performance by defeating AlphaGo 100–0. Another powerful system is AlphaZero that also used a self-play technique and learned to play Go, chess, and shogi, and also achieved SOTA (state of the art) performance results.

By way of comparison, ML models that use tree search are well-suited for games with perfect information. By contrast, games with imperfect information (such as poker) involve hidden information that can be leveraged to devise counter strategies to counteract the strategies of opponents. In particular, AlphaStar is capable of playing against the best players of StarCraft II, and also became the first AI to achieve SOTA results in a game that requires “strategic capability in an imperfect information world.”

Player of Games (PoG)

The DeepMind team at Google devised the general-purpose PoG (Player of Games) algorithm that is based on the following techniques:

CVPN (counterfactual value-and-policy network)GT-CFT (growing tree CFR)

The counterfactual value-and-policy network (CVPN) is a neural network that calculates the counterfactuals for each state belief in the game. This is key to evaluating the different variants of the game at any given time.

Growing tree CFR (GT-CFR) is a variation of CFR that is optimized for game trees that grow over time. GT-CFR is based on two fundamental phases, which is discussed in more detail here:

https://medium.com/syncedreview/deepminds-pog-excels-in-perfect-and-imperfect-information-games-advancing-research-on-general-9dbad5c04221

OPENAI

OpenAI is an AI research company that has made significant contributions to AI, including DALL-E, ChatGPT, and GPT-4o, and its home page is here: https://openai.com/api/

OpenAI was founded in San Francisco by Elon Musk and Sam Altman (as well as others), and one of its stated goals is to develop AI that benefits humanity. Given Microsoft’s massive investments in and deep alliance with the organization, OpenAI might be viewed as an arm of Microsoft. OpenAI is the creator of the GPT-x series of LLMs (large language models) as well as Claude 3 that was made available on November 30, 2022.

OpenAI made GPT-3 commercially available via API for use across applications, charging on a per-word basis. GPT-3 was announced in July 2020 and was available through a beta program. Then in November 2021 OpenAI made GPT-3 open to everyone, and more details are accessible here:

https://openai.com/blog/api-no-waitlist/

In addition, OpenAI developed DALL-E that generates images from text. OpenAI initially did not permit users to upload images that contained realistic faces. Later OpenAI changed its policy to allow users to upload faces into its online system. Check the OpenAI Web page for more details. Incidentally, diffusion models have superseded the benchmarks of DALL-E.

OpenAI has also released a public beta of Embeddings, which is a data format that is suitable for various types of tasks with machine learning, as described here:

https://beta.openai.com/docs/guides/embeddings

OpenAI is the creator of Codex that provides a set of models that were trained on NLP. The initial release of Codex was in private beta, and more information is accessible here: https://beta.openai.com/docs/engines/instruct-series-beta

OpenAI provides four models that are collectively called their Instruct models, which support the ability of GPT-3 to generate natural language. To learn more about the features and services that OpenAI offers, navigate to the following link: https://platform.openai.com/overview

COHERE

Cohere is a start-up and a competitor of OpenAI, and its home page is here: https://cohere.ai/

Cohere develops cutting-edge NLP technology that is commercially available for multiple industries. Cohere is focused on models that perform textual analysis instead of models for text generation (such as GPT-based models). The founding team of Cohere is impressive: CEO Aidan Gomez is one of the co-inventors of the transformer architecture, and CTO Nick Frosst is a protege of Geoff Hinton, who is a co-winner of the Turing award.

HUGGING FACE

Hugging Face is a popular community-based repository for open-source NLP technology, and its home page is here: https://github.com/huggingface

Unlike OpenAI or Cohere, Hugging Face does not build its own NLP models. Instead, Hugging Face is a platform that manages a plethora of open-source NLP models that customers can fine-tune and then deploy those fine-tuned models. Indeed, Hugging Face has become the eminent location for people to collaborate on NLP models and is sometimes described as “GitHub for machine learning and NLP.”

Hugging Face Libraries

Hugging Face provides three important libraries: datasets, tokenizers, and transformers. The Accelerate library supports PyTorch models. The datasets library provides an assortment of libraries for NLP. The tokenizers library enables you to convert text data to numeric values.

Perhaps the most impressive library is the transformers library that provides an enormous set of pretrained BERT-based models in order to perform a wide variety of NLP tasks. The Github repository is here: https://github.com/huggingface/transformers

Hugging Face Model Hub

Hugging Face provides a model hub that provides a plethora of models that are accessible online. Moreover, the website supports online testing of its models, which includes the following tasks:

masked word completion with BERTname entity recognition with Electranatural language inference with RoBERTaquestion answering with DistilBERTsummarization with BARTtext generation with GPT-2translation with T5

Navigate to the following link to see the text generation capabilities of “write with transformer”: https://transformer.huggingface.co

AI21

AI21 is a company that provides proprietary large language models via API to support the applications of its customers. The current SOTA model of AI21 is called Jurassic-1 (roughly the same size as GPT-3), and AI21 also creates its own applications on top of Jurassic-1 and other models. The current application suite of AI21 involves tools that can augment reading and writing.

Primer is an older competitor in this space, founded two years before the invention of the transformer. The company primarily serves clients in government and defense.

INTRODUCTION TO CLAUDE 3

Anthropic was created in 2021 by former employees of OpenAI and its home page is here: https://www.anthropic.com/

Anthropic has significant financial support from an assortment of companies, including Google and Salesforce.

In March 2024 Anthropic released Claude 3 as a competitor to ChatGPT and GPT-4. Claude 3 is available in three versions: Opus, Sonnet, and Haiku (Opus is the most powerful version).

Claude 3 Opus has a high degree of comprehension and expert level knowledge in fields such as mathematics. Opus is currently available in many countries for a monthly subscription of $20.

Claude 3 Sonnet is twice as fast as earlier versions of Claude (i.e., Claude 2 and Claude 2.1), along with improved reasoning capabilities, and it’s well-suited for fast answers to queries. In addition, Sonnet is freely available.

Claude 3 Haiku is the most cost effective as well as the fastest version of Claude 3, and it’s available on a per-token basis.

There are other aspects of Claude 3 to keep in mind when comparing Claude 3 with other LLMs. For example, Claude 3 is more likely to respond faster to prompts than earlier versions. Second, Claude 3 is less likely to respond with incorrect results and more likely to indicate that it lacks information regarding a prompt. In addition, Claude 3 currently provides a 200K context window, and is likely to support one million tokens in future iterations of Claude 3.

Incidentally, Claude 3 is not “self-aware” in the sense that it does not provide information about itself submitting prompts to Claude 3. For instance, submit the following prompt to Claude 3:

Which 3 versions of Claude 3 are available?

You will see the following response:

I do not actually have different numbered versions like "claude 3". I am an AI assistant created by Anthropic, but I do not have detailed information about product versions or naming conventions. I am simply known as Claude.

If you provide Claude Sonnet with the following prompt:

Who can provide me with information regarding Claude Opus, Sonnet, and Haiku?

You will see the following response:

Unfortunately, I do not have any specific information about models or versions named Claude Opus, Claude Sonnet, or Claude Haiku. As an AI assistant created by Anthropic, I do not have access to details about the company's internal AI development efforts, code names, or different model variants they may be working on.

Since you are asking about these apparently unannounced or confidential model names, the best source of accurate information would be from Anthropic employees directly involved with the development of these systems. As an external AI, I do not have any insider knowledge about unreleased or unannounced projects the company may be pursuing.

Unless Anthropic has publicly released details about things like Claude Opus, Sonnet, and Haiku, which I'm not aware of, I cannot provide any authoritative information about what those refer to or represent. My role is limited to being a general conversational AI assistant without access to Anthropic's proprietary research or confidential product roadmap details. I cannot be more helpful regarding those specific model names you mentioned.

Advantages of Claude 3