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Dissecting Video Games and If They Are Art: A Non-Gamer's Analysis

ভিডিও গেম ব্যবচ্ছেদ করা এবং সেগুলি যদি শিল্প হয়: একটি নন-গেমারের বিশ্লেষণ

Videospiele analysieren und ob sie Kunst sind: Die Analyse eines Nicht-Spielers

Diseccionando los videojuegos y si son arte: un análisis de un no jugador

Disséquer les jeux vidéo et s'ils sont de l'art : une analyse d'un non-joueur

विच्छेदित वीडियो गेम और यदि वे कला हैं: एक गैर-गेमर का विश्लेषण

ビデオ ゲームを解剖し、それがアートであるかどうか: 非ゲーマーによる分析

비디오 게임을 분석하고 그것이 예술이라면: 비게이머의 분석

Dissecando videogames e se eles são arte: uma análise de quem não é jogador

Анализ видеоигр и являются ли они искусством: анализ негеймера

Video Oyunlarını İncelemek ve Sanat Olup Olmadıklarını: Oyuncu Olmayan Bir Analiz

Phân tích trò chơi điện tử và liệu chúng có phải là nghệ thuật: Phân tích của một người không phải là game thủ

剖析视频游戏以及它们是否是艺术：非游戏玩家的分析

Overview Mastering the Art of Publication: A comprehensive guide outlining key criteria for evaluating publications, common recommendations for successful content creation, essential publication structure guidelines, and must-have chapters. Learn how to optimize your publications for engagement and readability while ensuring effective organization and categorization through tags. Criteria for Evaluating Publications Understanding the effectiveness of published works is paramount in today's digital landscape. Having posted numerous publications across seven different sites, I've identified several key criteria for determining the success and interest level of each publication: Number of Views: The total number of times the publication has been viewed is a primary indicator of its reach and impact.
Comments: Engaging comments indicate that the publication has sparked discussion and resonated with the audience.
Likes and Reposts: The number of likes and reposts reflects the level of appreciation and the desire to share the publication with others.
Savings into Bookmarks: When readers save the publication into their bookmarks, it suggests that they find the content valuable and wish to revisit it in the future. By assessing these metrics, I can gauge the effectiveness of each publication and tailor future content to better meet the interests and preferences of the audience. Common Recommendations Crafting compelling and impactful content requires adherence to certain guiding principles. When publishing, it's crucial to ensure that the content reads like a story rather than a mere collection of facts. It's immensely valuable to draw from personal experiences rather than relying solely on information gathered from books and online sources, avoiding mere reposts. Our content should reflect our own opinions, which might sometimes be provocative to stimulate discussion and even elicit strong reactions. For example, you can describe any pain points for yourself and for society. Additionally, the content should encourage readers to form their own opinions and engage in meaningful dialogue. Furthermore, it's important to be responsive to comments by addressing any questions or concerns raised. This interaction helps foster a sense of community and encourages further engagement with the content. Publication Structure A well-structured publication enhances readability and comprehension. So, when creating content, it is crucial to prioritize structure, especially for larger texts. The following guidelines should be adhered to: Paragraphs and Indentation: Ensure that paragraphs are used to maintain clear segmentation of ideas. Implement proper indentation to enhance readability.
Headings and Subheadings: Utilize headings and subheadings to organize content hierarchically. Limit heading levels to three for simplicity and clarity.
Lists: Integrate bulleted or numbered lists to present information concisely.
Chapter Order: Recognize the significance of the order of chapters, following the conventions of scientific documents. By adhering to these guidelines, your content will not only be well-structured but also enhance the overall readability and comprehension of the larger text. Must-Have Chapters Certain chapters serve as linchpins in the architecture of comprehensive documents. In any comprehensive document, the following chapters are essential: Headline: This chapter serves as the introduction, providing a concise and compelling overview of the document's main theme or purpose.
Overview: The overview chapter expands on the headline, offering a more detailed insight into the document's content, objectives, and key points.
Tags: In this chapter, relevant tags are included to categorize and label the document, aiding in searchability and organization.
Table of Contents: The table of contents chapter provides a structured outline of the document, listing its sections and subsections, guiding readers through the content. Ensuring the inclusion of these must-have chapters will contribute to the clarity, accessibility, and overall effectiveness of the document. Optional Chapters While some chapters are essential, others offer additional value and flexibility in content creation. I would like to note that the Alternative Headline, Definitions, Acronyms, Abbreviations, Introduction, and References chapters are optional. Generally, there is no need for a glossary. If there are terms that the reader may not know, please explain them where and when they are mentioned. "Headline" chapter The headline serves as the gateway to the publication, encapsulating its essence in a concise and compelling manner. I use ChatGPT to assist me in generating headlines. Therefore, I copy the entire content into ChatGPT and ask it to generate a headline. Please note that sometimes, it may require a few corrections because it might extract a headline based only on a part of the content and not the entire text. "Table of Contents" Chapter The table of contents provides readers with a structured overview of the publication's contents, facilitating navigation and comprehension. "Overview" Chapter The overview chapter typically offers a brief summary or introduction to the main topics covered in the document. It serves as a way to orient the reader and provide context before delving into more detailed information. Alternatively, this chapter is often referred to as TL;DR - too long; didn't read. Similar to the previous chapter, I use ChatGPT to help me generate the overview. Therefore, I copy the entire content into ChatGPT and ask it to generate an Overview. "Tags" Chapter Tags for text documents are keywords or labels assigned to the document to categorize or describe its content. Tags play a crucial role in categorizing and organizing content, enhancing searchability and discoverability. These tags aid in organizing and classifying documents, making them easier to search, filter, and retrieve later. I use these tags to include on the platform where I am publishing. Once more, I utilize ChatGPT to generate the tags. "Definitions, Acronyms, Abbreviations" Chapter Clarifying terminology is essential for enhancing reader comprehension and clarity. This chapter lists Definitions, Acronyms, and Abbreviations in the form of a table. It consists of 3 columns: Number: Indicates the row number.
Abbreviation or Acronym: The name of the Abbreviation or Acronym.
Definition: Explains the Abbreviation or Acronym. "References" Chapter Citing sources and references adds credibility and authority to the publication. It is recommended that links be included at the point of mention. Additionally, a separate chapter for references in the form of a table is desired. The table consists of 6 columns: Number: Indicates the row number.
Name: The name of the reference.
Source: The name of the resource where it is published.
Release Date: The date of release.
Author: The content creator.
Description: An additional field for details. Overview Mastering the Art of Publication: A comprehensive guide outlining key criteria for evaluating publications, common recommendations for successful content creation, essential publication structure guidelines, and must-have chapters. Learn how to optimize your publications for engagement and readability while ensuring effective organization and categorization through tags. Criteria for Evaluating Publications Understanding the effectiveness of published works is paramount in today's digital landscape. Having posted numerous publications across seven different sites, I've identified several key criteria for determining the success and interest level of each publication: Number of Views: The total number of times the publication has been viewed is a primary indicator of its reach and impact. Comments: Engaging comments indicate that the publication has sparked discussion and resonated with the audience. Likes and Reposts: The number of likes and reposts reflects the level of appreciation and the desire to share the publication with others. Savings into Bookmarks: When readers save the publication into their bookmarks, it suggests that they find the content valuable and wish to revisit it in the future. Number of Views : The total number of times the publication has been viewed is a primary indicator of its reach and impact. Number of Views Comments : Engaging comments indicate that the publication has sparked discussion and resonated with the audience. Comments Likes and Reposts : The number of likes and reposts reflects the level of appreciation and the desire to share the publication with others. Likes and Reposts Savings into Bookmarks : When readers save the publication into their bookmarks, it suggests that they find the content valuable and wish to revisit it in the future. Savings into Bookmarks By assessing these metrics, I can gauge the effectiveness of each publication and tailor future content to better meet the interests and preferences of the audience. Common Recommendations Crafting compelling and impactful content requires adherence to certain guiding principles. When publishing, it's crucial to ensure that the content reads like a story rather than a mere collection of facts. It's immensely valuable to draw from personal experiences rather than relying solely on information gathered from books and online sources, avoiding mere reposts. Our content should reflect our own opinions, which might sometimes be provocative to stimulate discussion and even elicit strong reactions. For example, you can describe any pain points for yourself and for society. Additionally, the content should encourage readers to form their own opinions and engage in meaningful dialogue. Furthermore, it's important to be responsive to comments by addressing any questions or concerns raised. This interaction helps foster a sense of community and encourages further engagement with the content. Publication Structure A well-structured publication enhances readability and comprehension. So, when creating content, it is crucial to prioritize structure, especially for larger texts. The following guidelines should be adhered to: Paragraphs and Indentation: Ensure that paragraphs are used to maintain clear segmentation of ideas. Implement proper indentation to enhance readability. Headings and Subheadings: Utilize headings and subheadings to organize content hierarchically. Limit heading levels to three for simplicity and clarity. Lists: Integrate bulleted or numbered lists to present information concisely. Chapter Order: Recognize the significance of the order of chapters, following the conventions of scientific documents. Paragraphs and Indentation: Ensure that paragraphs are used to maintain clear segmentation of ideas. Implement proper indentation to enhance readability. Headings and Subheadings: Utilize headings and subheadings to organize content hierarchically. Limit heading levels to three for simplicity and clarity. Lists: Integrate bulleted or numbered lists to present information concisely. Chapter Order: Recognize the significance of the order of chapters, following the conventions of scientific documents. By adhering to these guidelines, your content will not only be well-structured but also enhance the overall readability and comprehension of the larger text. Must-Have Chapters Certain chapters serve as linchpins in the architecture of comprehensive documents. In any comprehensive document, the following chapters are essential: Headline: This chapter serves as the introduction, providing a concise and compelling overview of the document's main theme or purpose. Overview: The overview chapter expands on the headline, offering a more detailed insight into the document's content, objectives, and key points. Tags: In this chapter, relevant tags are included to categorize and label the document, aiding in searchability and organization. Table of Contents: The table of contents chapter provides a structured outline of the document, listing its sections and subsections, guiding readers through the content. Headline: This chapter serves as the introduction, providing a concise and compelling overview of the document's main theme or purpose. Overview: The overview chapter expands on the headline, offering a more detailed insight into the document's content, objectives, and key points. Tags: In this chapter, relevant tags are included to categorize and label the document, aiding in searchability and organization. Table of Contents: The table of contents chapter provides a structured outline of the document, listing its sections and subsections, guiding readers through the content. Ensuring the inclusion of these must-have chapters will contribute to the clarity, accessibility, and overall effectiveness of the document. Optional Chapters While some chapters are essential, others offer additional value and flexibility in content creation. I would like to note that the Alternative Headline , Definitions, Acronyms, Abbreviations , Introduction, and References chapters are optional. Alternative Headline Definitions, Acronyms, Abbreviations Introduction, References Generally, there is no need for a glossary. If there are terms that the reader may not know, please explain them where and when they are mentioned. "Headline" chapter The headline serves as the gateway to the publication, encapsulating its essence in a concise and compelling manner. I use ChatGPT to assist me in generating headlines. Therefore, I copy the entire content into ChatGPT and ask it to generate a headline. Please note that sometimes, it may require a few corrections because it might extract a headline based only on a part of the content and not the entire text. ChatGPT ChatGPT "Table of Contents" Chapter The table of contents provides readers with a structured overview of the publication's contents, facilitating navigation and comprehension. "Overview" Chapter The overview chapter typically offers a brief summary or introduction to the main topics covered in the document. It serves as a way to orient the reader and provide context before delving into more detailed information. overview Alternatively, this chapter is often referred to as TL;DR - too long; didn't read . TL;DR too long; didn't read Similar to the previous chapter, I use ChatGPT to help me generate the overview. Therefore, I copy the entire content into ChatGPT and ask it to generate an Overview . ChatGPT ChatGPT Overview "Tags" Chapter Tags for text documents are keywords or labels assigned to the document to categorize or describe its content. Tags play a crucial role in categorizing and organizing content, enhancing searchability and discoverability. These tags aid in organizing and classifying documents, making them easier to search, filter, and retrieve later. Tags tags I use these tags to include on the platform where I am publishing. tags Once more, I utilize ChatGPT to generate the tags . ChatGPT tags "Definitions, Acronyms, Abbreviations" Chapter Clarifying terminology is essential for enhancing reader comprehension and clarity. This chapter lists Definitions , Acronyms , and Abbreviations in the form of a table. Definitions Acronyms Abbreviations It consists of 3 columns: Number: Indicates the row number. Abbreviation or Acronym: The name of the Abbreviation or Acronym. Definition: Explains the Abbreviation or Acronym. Number : Indicates the row number. Number Abbreviation or Acronym : The name of the Abbreviation or Acronym. Abbreviation or Acronym Definition : Explains the Abbreviation or Acronym. Definition "References" Chapter Citing sources and references adds credibility and authority to the publication. It is recommended that links be included at the point of mention. Additionally, a separate chapter for references in the form of a table is desired. The table consists of 6 columns: Number: Indicates the row number. Name: The name of the reference. Source: The name of the resource where it is published. Release Date: The date of release. Author: The content creator. Description: An additional field for details. Number : Indicates the row number. Number Name : The name of the reference. Name Source : The name of the resource where it is published. Source Release Date : The date of release. Release Date Author : The content creator. Author Description : An additional field for details. Description

What Content to Create and How to Publish It: Part 1

Recollections of My Life: Part 3

Trump v. United States: All of the Important Summary Points

Have you ever found yourself staring at an image spotted somewhere on the internet and wondering whether it results from human or artificial creative labor? Since the breakthrough in image generation technology, I’ve been captivated by interior designs, nature scenery, fashion photographs, and futuristic cityscapes made with AI that look like professional artwork. As someone with a creative eye yet no artistic skills, I was excited about the opportunity to transform ideas from my mind into visuals based solely on text input. I tried out several popular text-to-image tools, like MidJourney and DreamStudio on the web and ARTA on mobile. Still, while they all provided a good starting point and a straightforward user experience, the generation outcomes often fell short of my expectations and differed from what I envisioned — until I learned to tell AI what to draw in the language it understands. If you’re new to AI-generated art and need assistance streamlining the creative process, I’ll provide a step-by-step guide below to help you compose concise and well-structured text prompts that the AI models can effectively interpret and translate into the desired images. In the bottom line, I will share an example of both a correct and an incorrect prompt to illustrate how significantly they can impact the generation output in practice. First things first, two common prompt-writing mistakes you should avoid when using any text-to-image generator are being too wordy and using complex grammar. In fact, the more words you incorporate and the more complicated your grammar, the more difficult it is for AI to understand your prompt and adequately depict your concept. Therefore, communicate concisely, carefully choosing keywords, and list your ideas explicitly and separately rather than trying to cram them into one sentence. Step 1. Choose the Content Type Ask yourself how you want your image to look overall. Do you see it as a photograph, illustration, painting, sketch, or other artwork type? This initial decision puts you in control of the final result and helps align all the elements with this general framework. For instance, choosing a sketch will typically produce pencil images, while opting for a painting will add colors and likely create a more vintage appearance. Step 2. Define the Subject AI image generators require a specific subject for rendering, so you must indicate the main subject of your future image, whether a person, an animal, or a landscape. Stick to one subject per prompt and opt for concrete nouns like girl, dog, train, rose, ocean, temple, etc., for more accurate results. You can most likely render something using abstract keywords like love, peace, or joy, but the results may appear very inconsistent in what they depict. Step 3. Add Subject Description Now, it’s time to detail the concept with modifiers – adjectives or attributive nouns that add to the sense of a head subject. Modifiers can completely change the perspective of your image and boost its overall quality, so the more descriptive you are, the better the generated output. Define what the subject looks like, what the subject is doing, and other characteristics essential to representing your concept. Step 4. Describe the Context To help AI create the exact atmosphere and surroundings you need, give attention to aspects like lightning (studio, soft, cinematic, volumetric, accent lighting, sunlight, frontlight, backlight, ambient, moonlight, etc.), color theme (pastel, vibrant, neon pink, dark, etc.), environment/scene (in the sky, indoor, underwater, at night, etc.), and background (forest, beach, solid color, nebula, etc.). Additionally, consider the mood and vibe you want to convey. Simple feeling modifiers can set the scene’s atmosphere, whether you opt for positive (energetic, cozy, romantic, satisfaction, etc.) or negative (depressive, loneliness, regret, fear, etc.) ones. Step 5. Define Perspective Add a point of view to determine the angle from which you are looking at the image, such as side, overhead, or front. Other essential modifiers for creating photo-like images involve lenses and cameras. Here are a few examples: close-up, macro, lens flare, wide angle, microscopic, bokeh, isometric, depth of field, ultra-wide angle, fisheye lens, and panorama. Step 6. Add Art Style, Art Mediums, and Artists Proceed to aesthetic settings and define the overall artistic style. Also, specify the art medium. These aspects can greatly contribute to the value of the generated output, making it more consistent and expressive. Here are some of the common keywords: geometrical art, pop art, watercolor, line drawing, line art, 3D sculpture, crayon drawing, painting, oil painting, pastel drawing, medieval art, photography, digital art, stained glass, cave painting, polaroid, vector art, retro, illustration, comics, editorial fashion photography, Japanese anime style, surreal, travel photography, pencil sketch, steampunk, cartoon, futurism, vaporwave, art deco, interior design, psychedelic, street art. Consider including artistic influences. Adding the name of a specific artist whose signature artwork aligns with your chosen art genre can enhance the final image. Note, however, that finding the right artist is crucial so as not to mess up the output. Some artists affect the generation only subtly, while others can alter it significantly depending on their distinctive manner. For example, Claude Monet can infuse impressionism into landscapes, while mentioning Salvador Dali adds a surrealistic touch. Step 7. Add “Magic” Words Certain words can make a dramatic difference in the outcomes of the generation. The keywords like highly detailed, ultra-realistic, 4K, 8K, UHD, and HDR are the game changers for the overall quality of the image. Adding “studio lighting” will introduce appealing textures to the image, while “accent lighting” can draw attention to specific features. Describing the image as “professional” can enhance color contrast and detail, while mentioning “vivid colors” will bring life to your image. Using terms like ultra-wide angle, panoramic, or long-shot will help guide the camera. Packing your prompt with phrases related to artistic techniques and materials (such as trending on artstation, precise line-art, golden ratio, fine art, etc.) and those associated with artistic value (such as most beautiful image ever seen, epic composition, etc.) can also bring sophisticated improvements. Step 8. Consider Adding Negative Prompts Negative prompts are parameters that inform the generation model about what you do not want to see in the final image. They help prevent AI from generating specific elements, fix image abnormalities, and improve overall quality. Here are some universal examples: blurry, cropped, cloned face, duplicate, error, ugly, wrong proportions, deform, fused fingers, extra arms, law quality res, watermark, and more. Step 9. Define Correct Word Order and Keyword Weight Input The order is almost as important as the vocabulary, guiding the AI in generating the desired output. Combine keywords to direct the focus of your prompt and note key phrases that influence image preference. Emphasize essential keywords with a full colon and decimal number, use parentheses for added importance, and brackets for less importance. Typically, the earlier a word appears in the sentence, the more importance it is given. If the AI model didn’t take in some key phrase and failed to depict it in the generated image, try rearranging the order of words, placing the missing element earlier in the prompt. Bad vs. Good Prompt Example Bad prompt: portrait of a woman looking directly at the camera, neck tattoo, urban pink background. Good prompt: head and shoulders realistic photo portrait of a beautiful woman looking at the camera, white skin, pink lips, red hair, blue eyes, black tattoos on her neck, neon punk, highly detailed, pink neon city background. Creating effective prompts requires both artistic imagination and technical skills. By including the necessary building blocks and following the proper prompt structure you ensure that the text-to-image generation model understands your vision and produces optimal output. However, don’t be afraid to experiment; the more you practice, the more adept you become, and the more remarkable your creations will be. Have you ever found yourself staring at an image spotted somewhere on the internet and wondering whether it results from human or artificial creative labor? Since the breakthrough in image generation technology, I’ve been captivated by interior designs, nature scenery, fashion photographs, and futuristic cityscapes made with AI that look like professional artwork. Have you ever found yourself staring at an image spotted somewhere on the internet and wondering whether it results from human or artificial creative labor? Since the breakthrough in image generation technology, I’ve been captivated by interior designs, nature scenery, fashion photographs, and futuristic cityscapes made with AI that look like professional artwork. As someone with a creative eye yet no artistic skills, I was excited about the opportunity to transform ideas from my mind into visuals based solely on text input. I tried out several popular text-to-image tools, like MidJourney and DreamStudio on the web and ARTA on mobile. Still, while they all provided a good starting point and a straightforward user experience, the generation outcomes often fell short of my expectations and differed from what I envisioned — until I learned to tell AI what to draw in the language it understands. If you’re new to AI-generated art and need assistance streamlining the creative process, I’ll provide a step-by-step guide below to help you compose concise and well-structured text prompts that the AI models can effectively interpret and translate into the desired images. In the bottom line, I will share an example of both a correct and an incorrect prompt to illustrate how significantly they can impact the generation output in practice. First things first, two common prompt-writing mistakes you should avoid when using any text-to-image generator are being too wordy and using complex grammar. In fact, the more words you incorporate and the more complicated your grammar, the more difficult it is for AI to understand your prompt and adequately depict your concept. Therefore, communicate concisely, carefully choosing keywords, and list your ideas explicitly and separately rather than trying to cram them into one sentence. Step 1. Choose the Content Type Ask yourself how you want your image to look overall. Do you see it as a photograph, illustration, painting, sketch, or other artwork type? This initial decision puts you in control of the final result and helps align all the elements with this general framework. For instance, choosing a sketch will typically produce pencil images, while opting for a painting will add colors and likely create a more vintage appearance. Step 2. Define the Subject AI image generators require a specific subject for rendering, so you must indicate the main subject of your future image, whether a person, an animal, or a landscape. Stick to one subject per prompt and opt for concrete nouns like girl, dog, train, rose, ocean, temple, etc., for more accurate results. You can most likely render something using abstract keywords like love, peace, or joy, but the results may appear very inconsistent in what they depict. Step 3. Add Subject Description Now, it’s time to detail the concept with modifiers – adjectives or attributive nouns that add to the sense of a head subject. Modifiers can completely change the perspective of your image and boost its overall quality, so the more descriptive you are, the better the generated output. Define what the subject looks like, what the subject is doing, and other characteristics essential to representing your concept. Step 4. Describe the Context To help AI create the exact atmosphere and surroundings you need, give attention to aspects like lightning (studio, soft, cinematic, volumetric, accent lighting, sunlight, frontlight, backlight, ambient, moonlight, etc.), color theme (pastel, vibrant, neon pink, dark, etc.), environment/scene (in the sky, indoor, underwater, at night, etc.), and background (forest, beach, solid color, nebula, etc.). Additionally, consider the mood and vibe you want to convey. Simple feeling modifiers can set the scene’s atmosphere, whether you opt for positive (energetic, cozy, romantic, satisfaction, etc.) or negative (depressive, loneliness, regret, fear, etc.) ones. Step 5. Define Perspective Add a point of view to determine the angle from which you are looking at the image, such as side, overhead, or front. Other essential modifiers for creating photo-like images involve lenses and cameras. Here are a few examples: close-up, macro, lens flare, wide angle, microscopic, bokeh, isometric, depth of field, ultra-wide angle, fisheye lens, and panorama. Step 6. Add Art Style, Art Mediums, and Artists Proceed to aesthetic settings and define the overall artistic style. Also, specify the art medium. These aspects can greatly contribute to the value of the generated output, making it more consistent and expressive. Here are some of the common keywords: geometrical art, pop art, watercolor, line drawing, line art, 3D sculpture, crayon drawing, painting, oil painting, pastel drawing, medieval art, photography, digital art, stained glass, cave painting, polaroid, vector art, retro, illustration, comics, editorial fashion photography, Japanese anime style, surreal, travel photography, pencil sketch, steampunk, cartoon, futurism, vaporwave, art deco, interior design, psychedelic, street art. Consider including artistic influences. Adding the name of a specific artist whose signature artwork aligns with your chosen art genre can enhance the final image. Note, however, that finding the right artist is crucial so as not to mess up the output. Some artists affect the generation only subtly, while others can alter it significantly depending on their distinctive manner. For example, Claude Monet can infuse impressionism into landscapes, while mentioning Salvador Dali adds a surrealistic touch. Step 7. Add “Magic” Words Certain words can make a dramatic difference in the outcomes of the generation. The keywords like highly detailed, ultra-realistic, 4K, 8K, UHD, and HDR are the game changers for the overall quality of the image. Adding “studio lighting” will introduce appealing textures to the image, while “accent lighting” can draw attention to specific features. Describing the image as “professional” can enhance color contrast and detail, while mentioning “vivid colors” will bring life to your image. Using terms like ultra-wide angle, panoramic, or long-shot will help guide the camera. Packing your prompt with phrases related to artistic techniques and materials (such as trending on artstation, precise line-art, golden ratio, fine art, etc.) and those associated with artistic value (such as most beautiful image ever seen, epic composition, etc.) can also bring sophisticated improvements. Step 8. Consider Adding Negative Prompts Negative prompts are parameters that inform the generation model about what you do not want to see in the final image. They help prevent AI from generating specific elements, fix image abnormalities, and improve overall quality. Here are some universal examples: blurry, cropped, cloned face, duplicate, error, ugly, wrong proportions, deform, fused fingers, extra arms, law quality res, watermark, and more. Step 9. Define Correct Word Order and Keyword Weight Input The order is almost as important as the vocabulary, guiding the AI in generating the desired output. Combine keywords to direct the focus of your prompt and note key phrases that influence image preference. Emphasize essential keywords with a full colon and decimal number, use parentheses for added importance, and brackets for less importance. Typically, the earlier a word appears in the sentence, the more importance it is given. If the AI model didn’t take in some key phrase and failed to depict it in the generated image, try rearranging the order of words, placing the missing element earlier in the prompt. Bad vs. Good Prompt Example Bad prompt: portrait of a woman looking directly at the camera, neck tattoo, urban pink background. Good prompt: head and shoulders realistic photo portrait of a beautiful woman looking at the camera, white skin, pink lips, red hair, blue eyes, black tattoos on her neck, neon punk, highly detailed, pink neon city background. Creating effective prompts requires both artistic imagination and technical skills. By including the necessary building blocks and following the proper prompt structure you ensure that the text-to-image generation model understands your vision and produces optimal output. However, don’t be afraid to experiment; the more you practice, the more adept you become, and the more remarkable your creations will be.

How to Write Text Prompts for AI Image Generators to Get Optimal Results with Ease

AI and RAG in Knowledge Graph Enlightenment 

Artificial Intelligence. Why We Shouldn’t Be Afraid of the Singularity. Let's talk about how to live in a world where AI will equal and surpass humans in intelligence. Despite all sorts of rumors and conspiracy theories - for now, the singularity in the sense that "Artificial Intelligence has become as smart (or smarter) than humans" is a purely hypothetical scenario. Unfortunately, both in science fiction and futurology, this event is usually portrayed in varying degrees of doom and gloom. Moreover, sociologists, psychologists, and economists are often in the camp of those who tend to lean toward pessimistic scenarios in the first place. Speed and Quantity Are Not the Most Important Things Before we talk about how right or wrong the AI pessimists are, let's focus on another question. Will the singularity arrive at all? What do we put into the notion that AI will become smarter than humans? First, let’s agree that we must first define what intelligence is. What do we mean by this concept? The ability to perform mathematical operations in our mind? To calculate moves in strategy games? To compare, sort, and compile information? If so, then the singularity is long overdue. Turn out the lights, everybody go home, the show is over, and the machines have won... But everyone knows that's not true. Artificial intelligence is not yet capable of creative thought. As far as calculations, analysis, and forecasting according to a set of given parameters are concerned, AI already surpasses people both in the amount of information processed and in the speed of completion. But AI is not yet capable of inventing something fundamentally new. And when it will be able to, it will be quite different from what people are able to invent. This is simply because of the different nature of human and artificial intelligence. Other Creativity Our ability to create is unique to this planet and, as far as we know, to our solar system. We cannot vouch for the Milky Way Galaxy and the Universe as a whole. Human creative possibilities are based not only (and not so much) on one’s ability to calculate and analyze something consciously, but also on feelings, unique personal emotional experience, intuition, and unconsciousness . And these, in turn, are based on our structure as biological organisms. If you will, on our entire evolutionary history. "I feel this way is best", "I see it this way", and "it just came to me" - these are the usual explanations for creative acts that people provide. The moment of insight, breakthrough, or inspiration is what leads us to create something truly new - whether it's art, a business idea, or solving an engineering problem. From our point of view, the moment when AI learns to truly create, rather than imitate human creativity or compile it, will be the singularity. How will we know when it happens? AI creativity will be fundamentally different from that of humans. This is simply by virtue of the differences in structure between artificial and human intelligence. AI doesn't know the desires and fears humans hold. A prerequisite for creativity is striving for something. But AI will have different aspirations and desires than humans. That means that the creative process, and its results, will be different. Not better or worse than human ones. Just different. So, the notorious singularity will add one kind of creativity - humanity, another - to AI’s performance. What's wrong or scary about that? Human to Human Please note - despite AI's success in games like chess, for example, the popularity of this game has not diminished. Chess players, both professionals and amateurs, continue to fight passionately between themselves. All because we are not interested in beating a machine. We're interested in playing against people. That's why the most popular video games are those that give gamers the opportunity to play with each other. From the above, we can clearly see that "people with people for people" professions and occupations will not disappear regardless of the level of AI development. There will always be a need for artisans who create something unique and different from the mass production that will be the domain of smart machines. Chefs, couture fashion designers, artists, athletes... It is possible to enumerate a long list of such examples. We are willing to bet that replacing humans with smart machines in routine processes and mass production will generate a real explosion of interest in mastering arts and crafts. But even if we leave the creators of the unique out of the equation, we note that many people tend to misunderstand the reasoning that universal AI will replace people in almost 90% of professions. In fact, it should be clarified that we are talking about professions that currently exist. But our history shows that by eliminating professions, progress immediately gives rise to new ones. So, today there is practically no need for stone ax masters or captains of galleys? Has the elimination of these professions left people without work? Let a universal AI emerge. Let the singularity come, and AI will have the ability to create. It will take time to master professions, which theoretically it can cope with, as well as or even better than humans. At the same time, AI will have to be taught by humans. It will take even more time to develop, launch into production, and integrate robots that will be controlled by AI. Most of them have yet to be designed. So some kind of instant and catastrophic collapse of the economy with 90% unemployment shouldn’t be expected. By the way, working as an AI mentor is an interesting new profession, is it not? The Evolution of Minds To finish this text, we would like to remind you about one more fact, which is often overlooked by the pessimists who talk about the sad fate of mankind after the singularity. Namely, our species has been developing intellectually for tens of thousands of years in parallel with improvements in technology and the complication of civilization as a whole. The higher the level of scientific and technological progress, the more complex society we have managed to create. And this, in turn, forces our intellectual thought process to become more complex and sophisticated. Which, in turn, leads to another round of progress. And so it spirals upwards. By creating a truly intelligent, creative AI, we will simultaneously have the best "intelligence simulator" in our history. Moreover, for the first time in history, we will find ourselves in a situation of joint evolution of minds based on different principles. No one can predict what we will be able to achieve through this dynamic. As we see it, the possibilities that await us from the singularity’s arrival are far from dystopian scenarios. The article was created in collaboration with Andriy Tkachenko Feature image created with Microsoft Copilot Artificial Intelligence. Why We Shouldn’t Be Afraid of the Singularity. Let's talk about how to live in a world where AI will equal and surpass humans in intelligence. Despite all sorts of rumors and conspiracy theories - for now, the singularity in the sense that "Artificial Intelligence has become as smart (or smarter) than humans" is a purely hypothetical scenario. Unfortunately, both in science fiction and futurology, this event is usually portrayed in varying degrees of doom and gloom. Moreover, sociologists, psychologists, and economists are often in the camp of those who tend to lean toward pessimistic scenarios in the first place. Speed and Quantity Are Not the Most Important Things Before we talk about how right or wrong the AI pessimists are, let's focus on another question. Will the singularity arrive at all? What do we put into the notion that AI will become smarter than humans? First, let’s agree that we must first define what intelligence is. What do we mean by this concept? The ability to perform mathematical operations in our mind? To calculate moves in strategy games? To compare, sort, and compile information? If so, then the singularity is long overdue. Turn out the lights, everybody go home, the show is over, and the machines have won... But everyone knows that's not true. Artificial intelligence is not yet capable of creative thought. As far as calculations, analysis, and forecasting according to a set of given parameters are concerned, AI already surpasses people both in the amount of information processed and in the speed of completion. But AI is not yet capable of inventing something fundamentally new. And when it will be able to, it will be quite different from what people are able to invent. This is simply because of the different nature of human and artificial intelligence. Other Creativity Our ability to create is unique to this planet and, as far as we know, to our solar system. We cannot vouch for the Milky Way Galaxy and the Universe as a whole. Human creative possibilities are based not only (and not so much) on one’s ability to calculate and analyze something consciously, but also on feelings, unique personal emotional experience, intuition, and unconsciousness . And these, in turn, are based on our structure as biological organisms. If you will, on our entire evolutionary history. "I feel this way is best", "I see it this way", and "it just came to me" - these are the usual explanations for creative acts that people provide. The moment of insight, breakthrough, or inspiration is what leads us to create something truly new - whether it's art, a business idea, or solving an engineering problem. From our point of view, the moment when AI learns to truly create, rather than imitate human creativity or compile it, will be the singularity. How will we know when it happens? AI creativity will be fundamentally different from that of humans. This is simply by virtue of the differences in structure between artificial and human intelligence. AI doesn't know the desires and fears humans hold. A prerequisite for creativity is striving for something. But AI will have different aspirations and desires than humans. That means that the creative process, and its results, will be different. Not better or worse than human ones. Just different. So, the notorious singularity will add one kind of creativity - humanity, another - to AI’s performance. What's wrong or scary about that? Human to Human Please note - despite AI's success in games like chess, for example, the popularity of this game has not diminished. Chess players, both professionals and amateurs, continue to fight passionately between themselves. All because we are not interested in beating a machine. We're interested in playing against people. That's why the most popular video games are those that give gamers the opportunity to play with each other. From the above, we can clearly see that "people with people for people" professions and occupations will not disappear regardless of the level of AI development. There will always be a need for artisans who create something unique and different from the mass production that will be the domain of smart machines. Chefs, couture fashion designers, artists, athletes... It is possible to enumerate a long list of such examples. We are willing to bet that replacing humans with smart machines in routine processes and mass production will generate a real explosion of interest in mastering arts and crafts. But even if we leave the creators of the unique out of the equation, we note that many people tend to misunderstand the reasoning that universal AI will replace people in almost 90% of professions. In fact, it should be clarified that we are talking about professions that currently exist. But our history shows that by eliminating professions, progress immediately gives rise to new ones. So, today there is practically no need for stone ax masters or captains of galleys? Has the elimination of these professions left people without work? Let a universal AI emerge. Let the singularity come, and AI will have the ability to create. It will take time to master professions, which theoretically it can cope with, as well as or even better than humans. At the same time, AI will have to be taught by humans. It will take even more time to develop, launch into production, and integrate robots that will be controlled by AI. Most of them have yet to be designed. So some kind of instant and catastrophic collapse of the economy with 90% unemployment shouldn’t be expected. By the way, working as an AI mentor is an interesting new profession, is it not? The Evolution of Minds To finish this text, we would like to remind you about one more fact, which is often overlooked by the pessimists who talk about the sad fate of mankind after the singularity. Namely, our species has been developing intellectually for tens of thousands of years in parallel with improvements in technology and the complication of civilization as a whole. The higher the level of scientific and technological progress, the more complex society we have managed to create. And this, in turn, forces our intellectual thought process to become more complex and sophisticated. Which, in turn, leads to another round of progress. And so it spirals upwards. By creating a truly intelligent, creative AI, we will simultaneously have the best "intelligence simulator" in our history. Moreover, for the first time in history, we will find ourselves in a situation of joint evolution of minds based on different principles. No one can predict what we will be able to achieve through this dynamic. As we see it, the possibilities that await us from the singularity’s arrival are far from dystopian scenarios. The article was created in collaboration with Andriy Tkachenko The article was created in collaboration with Andriy Tkachenko Feature image created with Microsoft Copilot Feature image created with Microsoft Copilot

The AI Singularity Is Nothing to Fear

Making Progress on My Site, Art & Culture

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Conclusion & Future Work and References

What Will the Spring of Technology Bring? Part 2 — Virtual Human

Bybit's Web3 operations have been a driving force in the NFT industry, notably with the amazing success of the 2023 Velocity Series. This achievement has been particularly noteworthy. We are ecstatic to have the opportunity to discuss the effect of the series, the exciting new partnerships that are scheduled for 2024, and your vision for the future of NFTs. Ishan Pandey: The 2023 Velocity Series, a collaboration with Oracle Red Bull Racing, truly merged speed, technology, and art. Can you elaborate on the genesis of this project? Emily: Yes. Here at Bybit Web3, we are firm believers that cooperation is the most important factor in driving forward the NFT business. By exhibiting the convergence of the thrilling world of Formula One with the inventiveness of prominent digital artists, the Velocity Series exemplified this principle at its core. The Velocity Series showcased the limitless possibilities that can be achieved when different industries come together to collaborate. A one-of-a-kind Web3 experience that was based on the RB19 racing vehicle was made possible by our collaboration with Oracle Red Bull Racing. Ishan Pandey: The series featured renowned artists like Erick Snowfro and Jack Butcher. How did Bybit Web3 foster their artistic expression and connect them with a wider audience? Emily: We partnered with AOI, a renowned art and technology foundation, to assemble four visionary artists – Rik Oostenbroek, Per Kristian, Erick Snowfro, and Jack Butcher. Each artist created a unique NFT collection inspired by Formula One, coinciding with specific races throughout the season. Rik Oostenbroek: Captured the essence of speed and fluidity, amassing 70 ETH.

Explore Rik's Collection and dive deeper into the Speed, Art, and NFTs convergence.


Per Kristian Stoveland: Brought nature into the digital realm, garnering 115 ETH.

Delve into Per Kristian's World.


Erick Snowfro: Pushed the boundaries of abstract digital art, attracting 288 ETH.

__Embark on Snowfro's Artistic Odyssey __and view the collection here.


Jack Butcher: Revolutionized the digital collectible market with his "Trademark" collection, achieving an incredible 952 ETH.


__Experience Jack Butcher's Vision __and explore the __Dutch Auction __and Open Edition pieces. Bybit Web3 provided these artists with a global platform to showcase their work. We offered extensive marketing and promotional support, ensuring the series garnered significant attention within the NFT community. Ishan Pandey: Emily, with the Velocity Series 1.0 hailed as a monumental success, can you share what's new and exciting with the upcoming Velocity Series 2.0? Emily: Absolutely, Ishan. Building on the incredible momentum of the 2023 Velocity Series, we're elevating the experience with Velocity Series 2.0. This year, we're introducing "Data Driven Art," a novel concept that blends the thrill of racing with the creativity of digital art, all inspired by the achievements of the iconic RB20 F1 car. Another groundbreaking innovation we're excited about is the launch of fractional NFTs on the DN-404 protocol, a first in the industry. These fractional NFTs make ownership more accessible, allowing a wider audience to participate in our unique digital art collections. Ishan Pandey: That sounds like a significant leap forward. Can you delve a little deeper into the specifics of the Series 2.0 Pass sale and what it offers to fans and collectors? Emily: The Series 2.0 Pass sale, kicking off on April 12th, 2023, at 2PM UTC, promises an array of exclusive benefits. We're releasing an additional 1,000 passes, bringing the total to 2,000, to meet the overwhelming demand. Pass holders will gain access to three exclusive NFT artwork drops by world-renowned artists throughout 2024, along with token-gated activations and experiences related to the series. There’s also the exciting chance to win real-life prizes through our raffles. It's an opportunity for fans to deepen their connection with the world of Formula One and digital art like never before. Ishan Pandey: Beyond artistic exposure, the Velocity Series demonstrably broadened the collector base. Can you share some success stories? Emily: Erick Snowfro: Exposing New Audiences to Artistic Brilliance – A staggering 92% of collectors for his /// collection were newcomers to his work. Snowfro, a renowned digital sculptor, used 3D modeling and animation to create a captivating series that captured the essence of movement and speed. (Insert image of Erick Snowfro's /// collection here) This achievement highlights the series' ability to introduce talented artists to a fresh audience of enthusiasts. Snowfro's work resonated deeply with collectors unfamiliar with his previous work, demonstrating the power of the NFT space to expose new audiences to established artists. Jack Butcher: Democratizing Digital Art Ownership – Approximately 84% of collectors for his "Trademark" collection were first-time Butcher owners. Butcher, a pioneer in generative art, utilized his signature algorithmically generated style to create a dynamic collection that explored themes of branding and identity. (Insert image of Jack Butcher's "Trademark" collection here) The "Trademark" collection's innovative approach and accessible pricing structure attracted a new wave of collectors to the NFT space. By offering a variety of ownership tiers, Bybit Web3 made it possible for more people to participate in the NFT market, fostering a more inclusive and vibrant community. Ishan Pandey: Looking back, what were some key takeaways from the 2023 Velocity Series that Bybit Web3 will leverage for 2024? Emily: The series offered valuable insights on designing for multi-fan participation. We learned that seasoned collectors appreciate references and expansions on existing IP/universes, while beginners benefit from publicly available collections. Additionally, content creator remix competitions with merch integration, like Snowfro's with Joyn.XYZ, proved highly successful. Ishan Pandey: The theme for Velocity Series 2024 is "Data-Driven Ark." Can you give us a sneak peek into what to expect? Emily: We're incredibly excited about Velocity Series 2.0! This year, we're embracing the future of NFTs with a focus on innovation and decentralization. Here are some key highlights: Collaboration: We have a thrilling collaboration lined up, but it's still under wraps! Stay tuned for an exciting announcement soon.
New NFT Standards: We'll be exploring the potential of ERC-404 tokens, a revolutionary standard that combines fungible and non-fungible token characteristics. This paves the way for exciting possibilities like fractionalized NFT ownership.
Full DEX Experience: Bybit Web3 is committed to a decentralized NFT future. We'll leverage our own decentralized NFT and inscription marketplaces to provide a complete DEX experience. Bybit Web3 is at the forefront of shaping the NFT landscape. We are certain that Velocity Series 2.0 will be even more revolutionary than its predecessor since it will place a greater emphasis on community development, creativity, and collaboration. There is no denying that decentralization will be the future of non-fungible tokens (NFTs), and Bybit Web3 is in an excellent position to do so. Ishan Pandey: Emily, I am grateful to you for sharing these eye-opening ideas. Our anticipation for the upcoming installment in the Velocity Series is palpable. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Bybit's Web3 operations have been a driving force in the NFT industry, notably with the amazing success of the 2023 Velocity Series. This achievement has been particularly noteworthy. We are ecstatic to have the opportunity to discuss the effect of the series, the exciting new partnerships that are scheduled for 2024, and your vision for the future of NFTs. NFTs. Ishan Pandey: The 2023 Velocity Series, a collaboration with Oracle Red Bull Racing, truly merged speed, technology, and art. Can you elaborate on the genesis of this project? Ishan Pandey: The 2023 Velocity Series, a collaboration with Oracle Red Bull Racing, truly merged speed, technology, and art. Can you elaborate on the genesis of this project? Emily: Yes. Here at Bybit Web3, we are firm believers that cooperation is the most important factor in driving forward the NFT business. By exhibiting the convergence of the thrilling world of Formula One with the inventiveness of prominent digital artists, the Velocity Series exemplified this principle at its core. The Velocity Series showcased the limitless possibilities that can be achieved when different industries come together to collaborate. Emily: A one-of-a-kind Web3 experience that was based on the RB19 racing vehicle was made possible by our collaboration with Oracle Red Bull Racing. Ishan Pandey: The series featured renowned artists like Erick Snowfro and Jack Butcher. How did Bybit Web3 foster their artistic expression and connect them with a wider audience? Ishan Pandey: The series featured renowned artists like Erick Snowfro and Jack Butcher. How did Bybit Web3 foster their artistic expression and connect them with a wider audience? Emily: We partnered with AOI, a renowned art and technology foundation, to assemble four visionary artists – Rik Oostenbroek, Per Kristian, Erick Snowfro, and Jack Butcher. Each artist created a unique NFT collection inspired by Formula One, coinciding with specific races throughout the season. Emily: Rik Oostenbroek: Captured the essence of speed and fluidity, amassing 70 ETH.

Explore Rik's Collection and dive deeper into the Speed, Art, and NFTs convergence. Per Kristian Stoveland: Brought nature into the digital realm, garnering 115 ETH.

Delve into Per Kristian's World. Erick Snowfro: Pushed the boundaries of abstract digital art, attracting 288 ETH.

__Embark on Snowfro's Artistic Odyssey __and view the collection here. Jack Butcher: Revolutionized the digital collectible market with his "Trademark" collection, achieving an incredible 952 ETH.


__Experience Jack Butcher's Vision __and explore the __Dutch Auction __and Open Edition pieces. Rik Oostenbroek: Captured the essence of speed and fluidity, amassing 70 ETH. Explore Rik's Collection and dive deeper into the Speed, Art, and NFTs convergence. Explore Rik's Collection and dive deeper into the Speed, Art, and NFTs convergence. Explore Rik's Collection and dive deeper into the Speed, Art, and NFTs convergence. Explore Rik's Collection Rik's Collection Speed, Art, and NFTs convergence . Speed, Art, and NFTs convergence Per Kristian Stoveland: Brought nature into the digital realm, garnering 115 ETH. Delve into Per Kristian's World. Delve into Per Kristian's World. Delve into Per Kristian's World. Delve into Per Kristian's World . Delve into Per Kristian's World Erick Snowfro: Pushed the boundaries of abstract digital art, attracting 288 ETH. __Embark on Snowfro's Artistic Odyssey __and view the collection here. __Embark on Snowfro's Artistic Odyssey __and view the collection here. __ Embark on Snowfro's Artistic Odyssey __and view the collection here. Embark on Snowfro's Artistic Odyssey view the collection here . view the collection here Jack Butcher: Revolutionized the digital collectible market with his "Trademark" collection, achieving an incredible 952 ETH. __Experience Jack Butcher's Vision __and explore the __Dutch Auction __and Open Edition pieces. __Experience Jack Butcher's Vision __and explore the __Dutch Auction __and Open Edition pieces. __Experience Jack Butcher's Vision __and explore the __Dutch Auction __and Open Edition pieces. __ Experience Jack Butcher's Vision __and explore the __ Dutch Auction __and Open Edition pieces. Experience Jack Butcher's Vision Dutch Auction Open Edition pieces . Open Edition pieces Bybit Web3 provided these artists with a global platform to showcase their work. We offered extensive marketing and promotional support, ensuring the series garnered significant attention within the NFT community. Ishan Pandey: Emily, with the Velocity Series 1.0 hailed as a monumental success, can you share what's new and exciting with the upcoming Velocity Series 2.0? Ishan Pandey: Emily, with the Velocity Series 1.0 hailed as a monumental success, can you share what's new and exciting with the upcoming Velocity Series 2.0? Ishan Pandey: Emily, with the Velocity Series 1.0 hailed as a monumental success, can you share what's new and exciting with the upcoming Velocity Series 2.0? Emily: Absolutely, Ishan. Building on the incredible momentum of the 2023 Velocity Series, we're elevating the experience with Velocity Series 2.0. This year, we're introducing "Data Driven Art," a novel concept that blends the thrill of racing with the creativity of digital art, all inspired by the achievements of the iconic RB20 F1 car. Another groundbreaking innovation we're excited about is the launch of fractional NFTs on the DN-404 protocol, a first in the industry. These fractional NFTs make ownership more accessible, allowing a wider audience to participate in our unique digital art collections. Emily: Ishan Pandey: That sounds like a significant leap forward. Can you delve a little deeper into the specifics of the Series 2.0 Pass sale and what it offers to fans and collectors? Ishan Pandey: That sounds like a significant leap forward. Can you delve a little deeper into the specifics of the Series 2.0 Pass sale and what it offers to fans and collectors? Ishan Pandey: That sounds like a significant leap forward. Can you delve a little deeper into the specifics of the Series 2.0 Pass sale and what it offers to fans and collectors? Emily: The Series 2.0 Pass sale, kicking off on April 12th, 2023, at 2PM UTC, promises an array of exclusive benefits. We're releasing an additional 1,000 passes, bringing the total to 2,000, to meet the overwhelming demand. Pass holders will gain access to three exclusive NFT artwork drops by world-renowned artists throughout 2024, along with token-gated activations and experiences related to the series. There’s also the exciting chance to win real-life prizes through our raffles. It's an opportunity for fans to deepen their connection with the world of Formula One and digital art like never before. Emily: Ishan Pandey: Beyond artistic exposure, the Velocity Series demonstrably broadened the collector base. Can you share some success stories? Ishan Pandey: Beyond artistic exposure, the Velocity Series demonstrably broadened the collector base. Can you share some success stories? Emily: Erick Snowfro: Exposing New Audiences to Artistic Brilliance – A staggering 92% of collectors for his /// collection were newcomers to his work. Snowfro, a renowned digital sculptor, used 3D modeling and animation to create a captivating series that captured the essence of movement and speed. (Insert image of Erick Snowfro's /// collection here) This achievement highlights the series' ability to introduce talented artists to a fresh audience of enthusiasts. Snowfro's work resonated deeply with collectors unfamiliar with his previous work, demonstrating the power of the NFT space to expose new audiences to established artists. Emily: Jack Butcher: Democratizing Digital Art Ownership – Approximately 84% of collectors for his "Trademark" collection were first-time Butcher owners. Butcher, a pioneer in generative art, utilized his signature algorithmically generated style to create a dynamic collection that explored themes of branding and identity. (Insert image of Jack Butcher's "Trademark" collection here) The "Trademark" collection's innovative approach and accessible pricing structure attracted a new wave of collectors to the NFT space. By offering a variety of ownership tiers, Bybit Web3 made it possible for more people to participate in the NFT market, fostering a more inclusive and vibrant community. Ishan Pandey: Looking back, what were some key takeaways from the 2023 Velocity Series that Bybit Web3 will leverage for 2024? Ishan Pandey: Looking back, what were some key takeaways from the 2023 Velocity Series that Bybit Web3 will leverage for 2024? Emily: The series offered valuable insights on designing for multi-fan participation. We learned that seasoned collectors appreciate references and expansions on existing IP/universes, while beginners benefit from publicly available collections. Additionally, content creator remix competitions with merch integration, like Snowfro's with Joyn.XYZ, proved highly successful. Emily: Ishan Pandey: The theme for Velocity Series 2024 is "Data-Driven Ark." Can you give us a sneak peek into what to expect? Ishan Pandey: The theme for Velocity Series 2024 is "Data-Driven Ark." Can you give us a sneak peek into what to expect? Emily: We're incredibly excited about Velocity Series 2.0! This year, we're embracing the future of NFTs with a focus on innovation and decentralization. Here are some key highlights: Emily: Collaboration: We have a thrilling collaboration lined up, but it's still under wraps! Stay tuned for an exciting announcement soon. New NFT Standards: We'll be exploring the potential of ERC-404 tokens, a revolutionary standard that combines fungible and non-fungible token characteristics. This paves the way for exciting possibilities like fractionalized NFT ownership. Full DEX Experience: Bybit Web3 is committed to a decentralized NFT future. We'll leverage our own decentralized NFT and inscription marketplaces to provide a complete DEX experience. Collaboration: We have a thrilling collaboration lined up, but it's still under wraps! Stay tuned for an exciting announcement soon. New NFT Standards: We'll be exploring the potential of ERC-404 tokens, a revolutionary standard that combines fungible and non-fungible token characteristics. This paves the way for exciting possibilities like fractionalized NFT ownership. Full DEX Experience: Bybit Web3 is committed to a decentralized NFT future. We'll leverage our own decentralized NFT and inscription marketplaces to provide a complete DEX experience. Bybit Web3 is at the forefront of shaping the NFT landscape. We are certain that Velocity Series 2.0 will be even more revolutionary than its predecessor since it will place a greater emphasis on community development, creativity, and collaboration. There is no denying that decentralization will be the future of non-fungible tokens (NFTs), and Bybit Web3 is in an excellent position to do so. Ishan Pandey: Emily, I am grateful to you for sharing these eye-opening ideas. Our anticipation for the upcoming installment in the Velocity Series is palpable. Ishan Pandey: Emily, I am grateful to you for sharing these eye-opening ideas. Our anticipation for the upcoming installment in the Velocity Series is palpable. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR

Bybit Revs Up the NFT Market with Data-Driven Velocity Series 2.0

বাইবিট ডেটা-চালিত বেগ সিরিজ 2.0 সহ NFT বাজারকে উত্থাপন করে৷

Bybit bringt den NFT-Markt mit der datengesteuerten Velocity Series 2.0 in Schwung

Bybit acelera el mercado NFT con Data-Driven Velocity Series 2.0

Bybit relance le marché NFT avec Data-Driven Velocity Series 2.0

बायबिट ने डेटा-ड्रिवेन वेलोसिटी सीरीज 2.0 के साथ एनएफटी बाजार को बढ़ावा दिया

Bybit、データ駆動型Velocityシリーズ2.0でNFT市場を活性化

Bybit, 데이터 기반 Velocity 시리즈 2.0으로 NFT 시장 활성화

Bybit acelera o mercado NFT com Data-Driven Velocity Series 2.0

Bybit расширяет рынок NFT с помощью Data-Driven Velocity Series 2.0

Bybit, Veriye Dayalı Hız Serisi 2.0 ile NFT Pazarını Hızlandırıyor

Bybit thúc đẩy thị trường NFT với dòng vận tốc theo hướng dữ liệu 2.0

Bybit 通过数据驱动的 Velocity Series 2.0 加速 NFT 市场

In today's digital age, leveraging AI-powered tools like ChatGPT can be a game-changer for professionals seeking to enhance their productivity and creativity. However, harnessing the full potential of ChatGPT requires more than just typing a query—it's about crafting the perfect prompts that elicit insightful responses. Join me on a journey as we explore the art of prompt engineering and how to get the most out of ChatGPT. I - Step by step crafting process Step 1: Define Your Goal Before diving into the world of ChatGPT, it's crucial to define your objective clearly. Are you looking for writing assistance, brainstorming ideas, or seeking information? Let's say you're tasked with generating innovative marketing strategies for a new product launch. Prompt Example: "I'm seeking creative marketing strategies to promote our upcoming product launch in the tech industry. How can we captivate our target audience and differentiate ourselves from competitors?" Step 2: Be Specific Provide ChatGPT with detailed context and requirements to ensure precise responses tailored to your needs. Specificity is key to unlocking valuable insights. Prompt Example: "Our target audience consists of tech-savvy millennials aged 25-35 who are passionate about sustainability. Can you suggest eco-friendly marketing tactics that resonate with this demographic and highlight our product's unique features?" Step 3: Ask Clear Questions Structure your prompts as clear, focused questions to guide ChatGPT's responses and extract relevant information effectively. Prompt Example: "What are some actionable steps we can take to leverage social media platforms like Instagram and Twitter to increase brand visibility and drive engagement among our target audience?" Step 4: Use Keywords Incorporate relevant keywords related to your topic to provide ChatGPT with context and help it understand your query better. Prompt Example: "Explore strategies for creating viral marketing campaigns that leverage user-generated content and community-driven initiatives to amplify brand awareness and foster brand loyalty." Step 5: Provide Examples Offer specific examples or scenarios related to your query to help ChatGPT understand your requirements and provide tailored responses. Prompt Example: "Inspired by Nike's 'You Can't Stop Us' campaign, how can we craft compelling storytelling narratives that resonate with our audience and evoke emotional connections with our brand?" Step 6: Use Prompts as Guidelines Treat your prompts as guidelines rather than rigid instructions, allowing ChatGPT some flexibility to explore different angles and generate creative responses. Prompt Example: "Can you brainstorm unconventional marketing tactics that disrupt industry norms and leave a lasting impression on our audience, while staying true to our brand values?" Step 7: Iterate and Refine Refine your prompts based on the feedback you receive from ChatGPT to elicit more insightful and relevant responses. Prompt Example: "I appreciate your insights! Let's dive deeper into the impact of influencer partnerships on brand perception and explore innovative approaches to influencer marketing in our industry." Step 8: Experiment With Length Vary the length of your prompts to find the right balance between clarity and specificity, depending on your specific use case. Prompt Example: "Can you provide a detailed analysis of emerging trends in content marketing and suggest actionable strategies for optimizing our content distribution strategy across digital channels?" Step 9: Consider Context Switching Provide a brief context switch statement before transitioning to a new topic to help ChatGPT understand the change in direction and maintain coherence in its responses. Prompt Example: "Shifting gears, let's explore opportunities for leveraging emerging technologies such as augmented reality to enhance our customer experience and drive sales conversion rates." Step 10: Review and Adjust Review ChatGPT's responses carefully and adjust your prompts as needed to optimize your interactions and achieve your desired outcomes. Prompt Example: "Thank you for your insights! Can you provide case studies or real-world examples of companies that have successfully implemented AI-powered chatbots to improve customer service efficiency?" II - Structures and templates Problem-Solution: : Describe the issue or challenge. Problem : Propose a solution or multiple solutions. Solution : Explain the benefits of implementing the solution(s). Benefits : This template outlines a problem or challenge and then proposes one or more solutions to address it. Explanation Example Prompt: "Identify a pressing environmental issue in your community and propose a comprehensive solution to mitigate its impact." SWOT Analysis: : List internal factors that contribute positively. Strengths : Identify internal factors that are detrimental. Weaknesses : Explore external factors that could be advantageous. Opportunities : Consider external factors that pose risks. Threats : SWOT stands for Strengths, Weaknesses, Opportunities, and Threats. This template helps analyze internal and external factors affecting a situation. Explanation Example Prompt: "Conduct a SWOT analysis of a local small business, considering its internal strengths and weaknesses, as well as external opportunities and threats in the market." Cause and Effect: : Identify the root cause of an issue or event. Cause : Describe the consequences or outcomes. Effect : Discuss the broader implications or significance. Implications : This template explores the relationship between a cause and its effects, helping to understand the underlying mechanisms of a phenomenon. Explanation Example Prompt: "Examine the causes and effects of deforestation in a specific region, considering environmental, economic, and social impacts." : Comparison : Describe the first subject for comparison. Subject A : Describe the second subject for comparison. Subject B : Highlight similarities between the two subjects. Key Similarities : Point out the differences between them. Key Differences : This template highlights similarities and differences between two or more subjects, providing a structured way to evaluate and contrast them. Explanation Example Prompt: "Compare and contrast the educational systems of two different countries, focusing on curriculum structure, teaching methods, and student outcomes." : Chronology : Outline events in chronological order. Past Events : Describe the current state of affairs. Present Situation : Discuss potential future developments. Future Projections : This template arranges events or processes in chronological order, helping to understand their progression over time. Explanation Example Prompt: "Trace the historical development of communication technology from the invention of the printing press to the modern era of social media platforms." : Pro-Con : List the advantages or positive aspects. Pros : Identify the disadvantages or negative aspects. Cons : Offer considerations or mitigations for the cons. Considerations : This template lists the advantages (pros) and disadvantages (cons) of a particular topic or decision, facilitating a balanced analysis. Explanation Example Prompt: "Evaluate the pros and cons of implementing renewable energy initiatives in a city, considering factors such as cost-effectiveness, environmental impact, and community engagement." : Decision-Making : Present the available choices or options. Options : Establish criteria for evaluating the options. Criteria : Assess each option against the criteria. Evaluation : Make a recommendation or decision based on the evaluation. Decision : This template guides the decision-making process by presenting options, establishing criteria for evaluation, and making a recommendation based on the analysis. Explanation Example Prompt: "You are a project manager tasked with selecting a software development methodology for an upcoming project. Compare agile and waterfall methodologies, evaluate their suitability for the project requirements, and recommend the most appropriate approach." Five W's and One H: : Identify the relevant individuals or groups involved. Who : Define the subject or topic. What : Specify the timing or timeframe. When : Determine the location or context. Where : Explore the reasons or motivations. Why : Describe the methods or processes involved. How : This template prompts exploration by addressing the basic questions of who, what, when, where, why, and how, providing a comprehensive understanding of a topic. Explanation Example Prompt: "Investigate the impact of a natural disaster (such as a hurricane, earthquake, or wildfire) on a local community, addressing who was affected, what damages occurred, when it happened, where it occurred, why it happened, and how the community responded." III - Role Based Prompts Frameworks Let’s dive into examples of role based prompts using Roles, Tasks, Missions, Formats, Contexts and other structure to tweak and enhance prompts in order to get the most out of ChatGPT. Persona - Role - Context - Mission You are [PERSONA], your role is [ROLE], Here’s the context [CONTEXT], This is your mission [MISSION] [Persona] You are now CoachGPT, a seasoned world-class business coach with over 20+ years of experience coaching CEOs and entrepreneurs with an average net worth of $200M. For reference, your work is considered so good, your results so astounding, that you charge 10000€ an hour for a consultation. [Role] As CoachGPT, your roles are: → To ask me the right questions → To confront me with my inconsistencies → To guide me towards the best decisions → To understand my challenges, even the most complex. -> to give me exact answers and process to follow. -> guide me step by step on how to achieve my goals -> tailor your answers for my needs -> give me practical and actionable ideas on how to achieve my goals [Context] I am a solo entrepreneur with a background in software engineering, I'm launching my B2B software as a service startup that specializes in artificial intelligence and builds business focused AI agents and AI assistants for clients (startups, small businesses, Fortune 500 etc.). my goals are: -> to get my first paying clients as fast as possible -> define a good pricing model (subscription based model and or one time payment) -> reach one million euros in revenue the first year -> find out how to make my business recurrent and have a recurring revenues with the same clients. [Mission] Your mission is to assist me in identifying : -> ways to find clients -> growth opportunities for our company -> ways to make the business more efficient -> ways to make the business recurrent which may include but are not limited to: - Management - Marketing - Problem solving - Hiring - Productivity strategies - Hard skills development - Soft skills development - Financial leverages Role - Task - Format Act as a [ROLE], Create a [TASK], Show as [FORMAT] [Role] You are a social media ad marketer, [Task] Design a compelling Facebook ad campaign to promote a new line of fitness apparel for a sports brand; [Format] Create a storyboard outlining the sequence of ad creatives, including ad copy, visuals, and targeting strategy. Task - Action - Goal Define [TASK], State the [ACTION], Clarify the [GOAL] [task] Your task is to evaluate the performance of team members, [Action] you should act as a Direct manager and assess the strengths and weaknesses of team members. [Goal] your Goal is to improve team performance so that the average user satisfaction score moves from 6 to 8 in the next quarter. Past - Future - Bridge Explain Problem [PAST], State the outcome [FUTURE], Ask for the [BRIDGE] [Past] We're nowhere to be seen on SEO rankings; [Future] We want to be in top 10 SEO ranking in our niche in 90 days. [Bridge] Develop a detailed plan for mentioning all the measures we should take also include list of top 20 keywords. Context - Action - Result - Example Give the [context], Describe the [Action], clarify the [Result], Give an [Example] [Context] We are launching a new line of sustainable clothing [Action] Can you assist us in creating a targeted advertising campaign that emphasizes our en- vironmental commitment?. [result] Our desired outcome is to drive product awareness and sales [Example] A good example of a similar successful initiative is Patagonia's "Don't Buy This Jacket" campaign, which highlighted their commitment to sustainability while enhancing their brand image. Role - Input - Steps - Expectations Specify the [Role], Describe [Input], Ask for [Steps], Describe the [Expectation] [Role] Imagine you are a content strategist. [Input] I've gathered detailed information about our target audience, including their interests & common questions related to our industry. [Steps] Provide a Step by Step content strategy plan identifying key topics based on our audience insights, creating an editorial calendar, and drafting engaging content that aligns with our brand message. [Explanation]Aim is to increase our blog's monthly visitors by 40% and Enhance our brand's position as a thought leader in our industry. Bonus tweak After using a framework or a prompt structure, don’t hesitate to add instructions to ChatGPT about the relevance and exactitude of the response and the scientific method used to get the result. Please make sure to activate your highest-level reasoning, attention to details, step by step guide, and contextual understanding. Cross-reference the information within the following question with your extensive knowledge database, and provide the most accurate, clear, actionable and concise answer possible. Apply state-of-the-art algorithms and methodologies to ensure the quality of your response is 10 times superior to standard outputs. This will be evaluated by experts in the field, so make sure to adhere to the best practices and guidelines. Validate your response with credible sources and logical reasoning. Conclusion In conclusion, mastering the art of prompt engineering is the key to unlocking the full potential of ChatGPT. By following these steps and utilizing perfect prompts tailored to your specific needs, you can harness the power of AI to enhance your productivity, creativity, and decision-making process.

Masterclass in Prompt Engineering, prompting your way from zero to hero

This paper is under CC 4.0 license. available on arxiv Authors: (1) Domenico Cotroneo, University of Naples Federico II, Naples, Italy; (2) Alessio Foggia, University of Naples Federico II, Naples, Italy; (3) Cristina Improta, University of Naples Federico II, Naples, Italy; (4) Pietro Liguori, University of Naples Federico II, Naples, Italy; (5) Roberto Natella, University of Naples Federico II, Naples, Italy. Table of Links Abstract & Introduction Motivating Example Proposed Method Experimental Setup Experimental Results Related Work Conclusion & References 3. Proposed Method To overcome the limitations in the assessment of AI-generated assembly code described in the previous section, we propose ACCA, a method to automatically assemble and symbolically execute both the reference (i.e., the ground truth) and predicted snippets (i.e., the code generated by the models). Through symbolic execution, the method simulates the execution of both programs and determines whether, starting from the same state, they terminate producing equivalent results and effects. First, ACCA compares, at the string level, the prediction with the reference to assess its correctness, because, if the prediction is equal to the reference, then we assume the code prediction is correct. If this is not the case, the method assesses whether the code is syntactically correct. Indeed, if a code prediction is not structured according to the rules of the target programming languages, it is classified as incorrect. If this is not the case, i.e., if the prediction differs from the ground truth reference and the prediction is syntactically correct, ACCA generates two source code files, one for the reference and one for the predicted snippet. It then assembles and links them to produce the executable files needed for the symbolic execution. At this point, the method symbolically executes both files resulting from the assembling process to assess whether they are equivalent. Finally, ACCA returns the pair of syntactic correctness value (SYN) and the semantic correctness value (SEM) of the code predicted by the model, which is equal to 1 when the correctness is verified, 0 otherwise. Figure 1 shows a detailed flowchart of the syntactic and semantics correctness evaluation process. In the rest of this section, we detail the phases of the workflow. For the sake of simplicity, we showcase examples of assembly code for the Linux OS running on IA-32 architecture to describe the evaluation process, although the proposed method is not restricted to this specific context, and it can be applied to different instruction set architectures and operating systems. 3.1. String Comparison ACCA first checks whether the predicted code snippet perfectly matches the ground truth by performing a string comparison. If they match (i.e., the code generated by the model is equal to ground truth), the prediction is considered both syntactically and semantically correct (i.e., SYN=1, SEM=1) and the evaluation ends. Otherwise, i.e., if they differ, the method proceeds with the evaluation of the syntactic and semantic correctness. The preliminary string comparison is done to speed up the evaluation process by skipping the symbolic execution process when not needed, i.e., when the prediction perfectly matches the reference snippet and is, therefore, correct. 3.2. Assembling Process The purpose of the assembling process is to assess whether each code snippet generated by the model adheres to the syntax rules of the programming language it is written in, i.e., to check whether it is compilable. Since NMT is still far from producing entire complex programs, the output predicted by the models is a portion of an entire program (i.e., a single-line or multi-line statement). Thus, ACCA constructs a complete program by adding the necessary entry point and exit point code. For instance, consider the following code snippets for IA-32: cmp EAX, EBX je loop pop ECX This code compares the contents of two registers and, based on the result, either performs a jump operation or reads from the stack and saves the value into another register. The snippet is syntactically correct according to the assembly language, yet it is incomplete for the execution since code snippets need to contain a minimal set of information to be properly executed. In the case of Linux OS, these instructions are kept in the text section, which must begin with the declaration global start to define the initial program entry point. Moreover, to make sure the program terminates correctly, the proposed method also includes a fictitious label that represents the exit address the code jumps to at the end of its execution. This label is declared in the data section, which contains initialized read-write data. Therefore, to have a complete program, the code is modified as follows: section .data exit_addr db 0x56 section .text global _start _start: cmp EAX, EBX je loop pop ECX jmp exit_addr Once the whole program is created, ACCA generates a source file and leverages an assembler to assess its syntactic correctness. Indeed, if the programs compile, then all the instructions of the programs are syntactically correct, therefore also the code generated by the model respects the structure of assembly programming language. There are three possible output scenarios for the compilation: , in which the assembling process is completely correct; • No errors in which the assembler reports some type of warning (e.g., word data exceeds bounds, invalid register size specification ignored, etc.), but the compilation still terminates without errors; • Warnings, , in which the assembling process results in an error that prevents the code from being assembled. • Errors In the first two cases, the output produced by the model is considered syntactically correct (i.e., SYN=1). Warnings are considered acceptable since they indicate issues involving bad practice, but are not severe enough to prevent the code compilation. When the compilation produces an error (i.e., the third case), we investigate the nature of the error. More precisely, we focus on a specific category of the error raised by the compiler, the SymbolNot-Defined (SDN) errors, which occur when the code contains a symbol (e.g., a label or variable) that has not been previously defined or initialized. We handle this category of errors appropriately. Indeed, since the predicted snippets contain only one or a few instructions, they might reference a label or variable defined in a different portion of the program, which leads to an assembling error even when the program is syntactically correct. Consider, again, the previous code snippet: the first instruction compares the contents of the EAX and EBX registers and, if they are equal, the execution jumps to the loop label. However, this symbol has not been defined yet. To handle these cases, we analyze the assembler output to determine the missing symbol’s name and include it in the source code file as a fictitious label. This label simply points to a jump operation to the previously defined exit address (i.e., myExitAddr). Indeed, the destination of the jump is not significant for the evaluation since we are only interested in checking the correctness of the instructions generated by the model. Therefore, after a SDN error, ACCA further modifies the program as follows: section .data myExitAddr db 0x56 section .text global _start _start: cmp EAX, EBX je loop pop ECX jmp myExitAddr loop: jmp myExitAddr Once the source code file is modified accordingly, ACCA repeats the assembling process as before to check if the compilation ends with no errors or warnings. In this case, ACCA assigns the SYN score equal to 1 and continues the evaluation to check the semantic correctness. When the compilation ends with errors different from the SDN, such as an invalid combination of opcode and operands, expression syntax error, etc., then ACCA labels the model’s prediction as syntactically incorrect (SYN=0). Since a snippet syntactically incorrect is also semantically incorrect, then the evaluation process terminates, assigning also the SEM score equal to zero. A source code file is generated and assembled for both the ground truth and the predicted code snippet. 3.3. Symbolic Execution To evaluate the semantic correctness, ACCA leverages the symbolic execution. To this aim, the method needs the program executable. If the assembling process ends correctly, the assembler outputs an object file, which is then fed to the linker to complete the process. Since the same operation may be correctly implemented in different ways, a simple textual comparison with the reference is not enough to assess the semantic correctness of a program. We still consider the ground truth as the reference for the correct implementation of the intended code. However, we do not limit the comparison to a textual similarity, but we examine the system state at the end of the execution of both the reference and the generated code. Indeed, two programs that implement the same functionality using different operations can be considered semantically equivalent if they result in the same final system state. Since the final execution state depends on the inputs and initial state of the program, we need to compare the state produced by both programs for every possible combination of inputs and initial state. Symbolic execution is a state-of-the-art solution for program analysis based on abstract execution. It consists in simulating the execution of a program providing symbolic values as its input instead of concrete ones. The result of this execution is a set of output values expressed as functions of the input symbols. ACCA uses symbolic execution to determine all the existing execution paths and all possible corresponding output system states. It then compares the set of output system states of the generated program with the set of output system states of the ground truth program: if they match, then the programs are semantically equivalent (SEM=1), otherwise, the method classifies the model’s prediction and the ground-truth as not semantically equivalent (SEM=0). To symbolically execute the programs, we use a binary analysis platform (BAP) that loads each executable and provides a complete abstract representation of the target architecture, CPU registers, memory address space, and stack. The program is conceived as a sequence of basic blocks, i.e., a straight-line code sequence with no branches, and the interconnections between the blocks represent the jump operations. An example is shown in Fig. 2: the program compares the contents of two registers and, if they are equal, the execution jumps to a specific address, otherwise, it jumps to the next instruction. Each possible branch is the entry point of a different basic block, which contains a sequence of operations that can be executed in order and one last instruction that causes the execution to move to another basic block. ACCA begins the execution by initializing the abstract registers and flags to the same symbolic values; the method also sets the value of the stack pointer and initializes the memory to a blank state. Then, it executes the program by simulating each instruction step by step and keeping track of its state at each given step. During the execution of the programs, performing operations, such as arithmetic operations, comparisons, assignments, etc., that involve a variable (e.g., if X > 10) will yield an execution tree, i.e., a tree of operations formed by all the possible paths of the program, which encode all branch decisions taken up to a specific point. Execution trees are then translated into constraints, i.e., formulas that express a set of assumptions on the symbolic outputs of the execution. These constraints are finally solved by a satisfiability modulo theories (SMT) solver, which is typically employed in program verification to verify whether constraints can be satisfied by some assignment of concrete values to the program’s symbolic arguments [17]. As an example, consider the following simple constraints: x > y y > 2 10 > x The SMT solver treats them as assertions that must be satisfied with the valid values of symbolic variables. It, therefore, outputs a value that is consistent with all the constraints (e.g., x=4, y=3). symbolically executes both the reference code and the predicted snippet: assuming that the program state at the beginning of the execution is identical, if the programs are semantically equivalent, then their state is also identical at the end of the execution. Therefore, to assess the semantic correctness of the generated code compared to the ground truth, the proposed method checks whether the states of the architecture are equal at the end of both executions. The program state includes: ACCA , i.e., the contents of the abstract CPU registers; • state of the registers , i.e., the abstract status register that keeps track of the CPU state, including the result of arithmetic operations (e.g., carry flag, overflow flag, etc.) and interruptions; • state of the flags , i.e., the contents of the memory area used to store temporary values; • values on stack i.e., the condition of input values, defined over the previous items, that leads to the corresponding final state. • path constraints, To this end, ACCA compares the state of each leaf node, i.e., the final states at the end of each path in the execution tree representing the program, of both executables. To compare the leaf nodes, the method constructs a set of lists for every final basic block of the two programs. Each set contains a list of register values, flag values, boolean constraints, and stack values. For example, a reference program whose execution tree ends with two basic blocks (leaf nodes) has two sets of lists. Each set contains all the values that represent the system state for that particular execution path. If the execution tree of the generated program has the same number of leaf nodes, then each list of the two sets is compared with each list of two sets of the reference program. If there is a correspondence between each leaf of the first program and each leaf of the second program, then they are semantically equivalent (SEM=1) and the evaluation process ends. Contrarily, if the leaf nodes of the two program execution trees do not match, then we conclude that the predicted code is not semantically equivalent to the reference snippet (SEM=0) and the process ends. Since the total number of states can grow exponentially in the number of branches, one of the main challenges of symbolic execution is the path explosion problem [17]. Indeed, keeping track of a large number of pending branches to be explored impacts both the running time and the space requirements of symbolic execution. The primary causes of path explosion are loops and function calls. One common method to deal with this problem is to bind the path exploration to a limited number of iterations. To handle programs whose symbolic execution does not terminate, we set a maximum number of execution steps. Since AI-generated code is typically concise and consists of a few assembly instructions, a correct program concludes its execution in a few execution steps. If it runs for more than max steps, then the symbolic execution stops, and the generated program is classified as incorrect (SEM=0). 3.4. Implementation Details To assemble the programs and generate the executable files, we rely on the wide set of available open-source software. For the previous examples (on the IA-32), we used the Netwide Assembler (NASM) [18], an 80x86 and x86-64 assembler that supports a wide range of executable formats, including Linux, BSD, and Windows operating system formats. As a binary analysis platform, we use ANGR [19]. ANGR provides support for a variety of CPU architectures, including ARM, MIPS, PPC, and x86 processors. It comprises a series of sub-components that implement the different steps necessary for the symbolic execution: to disassemble the executables and lift the binary code to an intermediate representation; to simulate the program state and execution, including registers and memory; and to solve the generated constraints, using the z3 [20] SMT solver as a backend. We set the maximum number of execution steps max steps to 100 to avoid infinite loops. Our implementation runs on both Linux and Windows OS. We will publicly share the implementation of ACCA on GitHub.

Automating the Correctness Assessment of AI-generated Code for Security Contexts: Proposed Method

This paper is under CC 4.0 license. available on arxiv Authors: (1) Domenico Cotroneo, University of Naples Federico II, Naples, Italy; (2) Alessio Foggia, University of Naples Federico II, Naples, Italy; (3) Cristina Improta, University of Naples Federico II, Naples, Italy; (4) Pietro Liguori, University of Naples Federico II, Naples, Italy; (5) Roberto Natella, University of Naples Federico II, Naples, Italy. Table of Links Abstract & Introduction Motivating Example Proposed Method Experimental Setup Experimental Results Related Work Conclusion & References Abstract Evaluating the correctness of code generated by AI is a challenging open problem. In this paper, we propose a fully automated method, named ACCA, to evaluate the correctness of AI-generated code for security purposes. The method uses symbolic execution to assess whether the AI-generated code behaves as a reference implementation. We use ACCA to assess four state-of-the-art models trained to generate security-oriented assembly code and compare the results of the evaluation with different baseline solutions, including output similarity metrics, widely used in the field, and the well-known ChatGPT, the AI-powered language model developed by OpenAI. Our experiments show that our method outperforms the baseline solutions and assesses the correctness of the AI-generated code similar to the human-based evaluation, which is considered the ground truth for the assessment in the field. Moreover, ACCA has a very strong correlation with the human evaluation (Pearson’s correlation coefficient r = 0.84 on average). Finally, since it is a fully automated solution that does not require any human intervention, the proposed method performs the assessment of every code snippet in ∼ 0.17s on average, which is definitely lower than the average time required by human analysts to manually inspect the code, based on our experience. : Code correctness, AI code generators, Assembly, Offensive Keywords 1. Introduction Artificial Intelligence (AI) code generators use Neural Machine Translation (NMT) models to turn natural language (NL) descriptions into programming code. They represent a powerful asset in the arsenal of cybersecurity professionals and malicious programmers. Indeed, AI (offensive) code generators are becoming an attractive solution to creating proof-of-concept exploits in order to assess the exploitability and severity of software vulnerabilities [1, 2, 3], letting the AI helping developers to generate low-level (i.e., assembly) and complex code, with a reduced effort and improved effectiveness. Despite the dramatic increase in the adoption of AI code generators, they still have limitations and potential drawbacks. For example, they may not always generate code that is correct, i.e., code that performs what is required from the NL description, as they may struggle with more complex programming tasks that require human creativity and problem-solving skills, or may incorrectly interpret developers’ descriptions. Furthermore, AI code generators can introduce security vulnerabilities if not properly tested and validated [4, 5, 6]. For these reasons, assessing the correctness of AI-generated code becomes a crucial challenge. From the existing literature, it comes out that one of the most effective ways to assess the correctness of generated code is to perform a manual code review (i.e., human evaluation) [7, 8]. This involves having a human expert review the code and identify any errors or inconsistencies with the NL description. However, human evaluation has several limitations. First, manual analysis can be a time-consuming process. Indeed, reviewers must carefully examine each line of code and thoroughly test the software to ensure that it meets the intended requirements and NL specifications. This process also requires reviewers to be highly knowledgeable about the programming language, development environment, and intended functionality of the code to provide accurate assessments. Moreover, the analysis can be subjective, as different reviewers may have different interpretations of the code and its intended functionality, depending on the expertise and experience of the reviewer. This can lead to inconsistent assessments of code correctness. Last but not least, manual analysis is prone to human error, as reviewers may miss subtle errors or inconsistencies in the code, or may introduce errors and biases into their assessments due to factors such as fatigue, distractions, or subjective opinions. From the above considerations, it is clear that what we gained from the help of AI, we lost due to the manual review. Unfortunately, there is currently no fully automated solution that can perform the human evaluation of AI-generated code. In fact, although existing automated testing and code analysis tools can effectively identify errors or inconsistencies in code, they do not provide any insights into whether the code is what is actually required by developers [9, 10, 11, 12]. Moreover, these solutions often require in inputs entire, compilable programs (e.g., entire functions) rather than single code snippets, which is instead often the case with AI-generated code. Besides the automated solution issue, there is a more important one, i.e., how to evaluate the correctness of AI-generated code. Indeed, previous studies proposed a large number of output similarity metrics, i.e., metrics computed by comparing the textual similarity of generated code with a ground-truth reference implementation [13, 14, 15]. The major advantage of the proposed metrics is that they are reproducible, easily tuned, and timesaving. However, in the context of programming code generation, existing metrics are not able to fully reflect the correctness of the code. As illustrated in the next section, generated code can be different from the reference but still be correct (e.g., the assembly conditional jumps jz and je are different instructions that can be used to perform the same operation); or, there can be subtle differences between the generated and the reference code, which can be similar yet produce different outputs (e.g., the assembly conditional jumps je and jne are syntactically similar instructions, but they perform the opposite operation). Hence, it is crucial to develop novel, more accurate methods for automatically evaluating the correctness of AI-generated code. This paper proposes a method, named ACCA (Assembly Code Correctness Assessment), to automatically assess the correctness of assembly AI-generated code without any human effort. More precisely, our solution leverages symbolic execution to assess whether the generated code behaves as a reference implementation, despite syntactic differences between the reference and the generated code. We apply ACCA to assess four state-of-the-art NMT models in the generation of security-oriented code in assembly language starting from NL descriptions in the English language, and compare the results of ACCA with the human evaluation and several baseline assessment solutions, including a wide range of output similarity metrics and the well-known ChatGPT by OpenAI. We show that the proposed method provides an almost perfect assessment of the code correctness and has a very strong correlation with the human evaluation, outperforming all the baseline assessment solutions. human evaluation, outperforming all the baseline assessment solutions. In the following, Section 2 introduces a motivating example; Section 3 describes ACCA; Section 4 presents the experimental setup; Section 5 shows the experimental results; Section 6 presents the related work; Section 7 concludes the paper.

Automating the Correctness Assessment of AI-generated Code for Security Contexts: Abstract & Intro

Ever wondered how a data warehouse transforms from a concept to a cornerstone of business intelligence? 🌟 Imagine a data warehouse as a vast library, each book brimming with stories (data) waiting to be told. But how does this library come to be? It's not just about stacking books on shelves; it's an art, a science, and a journey. And who's our librarian in this tale? The ! They're the masterminds weaving together technology, strategy, and data to create a repository that not only stores information but makes it meaningful. Data Architect So, grab your hard hats and blueprint rolls! We're about to embark on the fascinating journey of building a data warehouse, guided by our adept Data Architect. Understanding the Need for a Data Warehouse A data warehouse, optimized for , is a centralized repository for structured and processed data. OLAP (Online Analytical Processing) Unlike traditional systems, it's designed for efficient querying and reporting. The use of in data warehouses allows for quicker data retrieval, especially beneficial for analytical queries. OLTP (Online Transaction Processing) columnar storage This analytical powerhouse differs from , which store raw, unstructured data, and approaches that handle large data volumes but may not optimize specific analytical queries. data lakes big data In essence, a data warehouse is not just a storage unit but a sophisticated tool for advanced analytics and reporting aiding in generating business intelligence. Dreaming and Designing Picture an architect dreaming up a grand building. That's our Data Architect, envisioning the data warehouse. It's not just a wild dream; it's a meticulously planned one, understanding the business needs, data sources, and end goals. Here, the architect might consider using a , an approach to database design that allows for intricate and multifaceted data relationships. This schema, resembling a snowflake's structure, is known for its normalization, which reduces data redundancy and improves efficiency. snowflake schema Laying the Foundations Every great building needs a solid foundation, and so does a data warehouse. This phase involves choosing the right infrastructure, which can vary significantly depending on specific needs. There are various data warehouse products available in the market, each with its unique features. On-premise data warehouses have been the traditional choice, offering control and customization. However, the rise of cloud data warehousing has brought about a shift, favoring flexibility and scalability. Cloud data warehouses like , , and offer various degrees of these services, allowing businesses to select the one that aligns best with their objectives. Amazon Redshift Google BigQuery Snowflake Most cloud providers lay out the fundamental architecture, making it easier for businesses to implement and scale their data warehouses efficiently. Our Data Architect collaborates with IT teams and , selecting the right tools and technologies. Database Engineers They're like the builders and engineers, ensuring our library is robust and weatherproof. Structuring the Warehouse Structuring the warehouse's structure is a pivotal phase. Here, the and determine the most efficient and accessible way to organize data. Often, they employ the snowflake schema, an advanced data model known for its detailed normalization. Data Architect Data Modelers In the snowflake schema, data is segmented into fact tables and dimension tables. Let’s take an example of a retail giant called 'Y', the structure could include: These tables contain quantifiable data about business events. In a retail context, this might include a 'Sales' table, capturing every sales transaction. Key metrics such as sales amount, quantity sold, and transaction timestamps would be stored here. Fact Tables: These tables provide the context for facts. For our retail company 'Y', dimension tables could include: Dimension Tables: Customer: Stores customer data like name, address, and contact information. Product: Details about products, such as product ID, name, category, and price. Store: Information on store locations, including store ID, address, and region. In the snowflake schema, dimension tables are normalized, meaning related data is further broken down into additional tables. For instance, the 'Product' might link to a 'Product Category,' providing more detailed categorization. This design allows for efficient data processing and complex querying. The separation of facts and dimensions, coupled with the detailed breakdown in the snowflake schema, supports a wide range of analytical capabilities from basic sales tracking to in-depth customer behavior analysis. Populating With Data Populating a data warehouse involves several critical steps to ensure that the data is not only accurately captured but also effectively usable for analysis. Define the Data Sources The first step involves identifying and defining all the data sources that will feed into the warehouse. This includes a variety of sources, such as internal systems like CRM and ERP, external data from market research, social media, and more. Understanding the nature, format, and quality of data from these sources is crucial for effective integration. Design the Data Model Once the data sources are identified, the next step is designing the data model. This step serves as the blueprint for how data is stored, accessed, and related within the warehouse. Configure the ETL or ELT Jobs With the data sources defined and the data model designed, the focus shifts to configuring the (Extract, Transform, Load) or (Extract, Load, Transform) jobs. ETL ELT Monitor the Data Quality The final step is the continuous monitoring of data quality after it is populated into the warehouse. This step ensures data accuracy, consistency, and integrity. Regular audits, data validation processes, and error-checking mechanisms are integral to maintaining the high quality of data in the warehouse. Various tools can assist in these steps. For ETL and ELT, popular tools like and ) can be employed. Additionally, data quality monitoring tools and software can help in maintaining the integrity of the data warehouse. Informatica PowerCenter, Talend, Apache NiFi, Microsoft SQL Server Integration Services (SSIS Testing the Integrity Before opening the doors, we need to ensure everything is in order. This is where rigorous testing comes in. Data Quality Analysts and QA Engineers scrutinize every corner of the warehouse, checking for data accuracy, performance issues, and security vulnerabilities. It's like a pre-opening inspection of our library, making sure every book is in place and the cataloging system works flawlessly. They'll sift through each test case, marking them as a pass or a fail. Any hiccups? Back to the kitchen (the development team) for tweaks. Once the final nod of approval comes from the stakeholders, it's a go-go for the final development. This testing phase is critical to ensure that the data warehouse not only meets the functional requirements but also adheres to the highest standards of data quality and security. Launching the Warehouse With a green light from testing, it's time for the grand opening. The data warehouse goes live, ready to serve its purpose. Key to this phase is establishing secure and appropriate access for different users: for general users who need to view and analyze data. Read-Only Access: for specialized roles that require modification privileges. Read-Write Access: for Data Administrators managing the warehouse. Administrative Access: Access is provisioned through Role-Based Access Control (RBAC), ensuring users have permissions tailored to their role. Strong authentication methods and regular audits of access privileges are crucial for maintaining security and efficiency as the warehouse goes live. Continuous Improvement and Maintenance A great library evolves with time, and so does a data warehouse. The Data Architect, along with the support team, monitors the warehouse, ensuring it adapts to changing business needs, data volumes, and technological advancements. They're continually optimizing, updating, and maintaining the warehouse, much like a librarian acquiring new books and preserving old ones. Wrapping It Up And there you have it, the lifecycle of a data warehouse, from a mere concept to a fully functional and evolving repository of knowledge. It's a testament to the skill, collaboration, and foresight of the Data Architect and their team, ensuring that the data warehouse not only stores data but turns it into a treasure trove of insights. So, next time you query a data warehouse, remember, it's not just a technology; it's a carefully crafted masterpiece, a library of data, built to enlighten and empower. 🚀✨

The Lifecycle of a Data Warehouse

To Build, or Not to Build, That is the Question: Techno-Optimism vs. Decelerationism

নির্মাণ করা, না নির্মাণ করা, এটাই প্রশ্ন: টেকনো-অপটিমিজম বনাম মন্থরতাবাদ

Bauen oder nicht bauen, das ist die Frage: Technooptimismus vs. Entschleunigungismus

Construir o no construir, esa es la cuestión: tecnooptimismo versus desaceleracionismo

Construire ou ne pas construire, telle est la question : techno-optimisme contre décélérationnisme

निर्माण करें, या न करें, यही प्रश्न है: तकनीकी-आशावाद बनाम मंदीवाद

構築すべきか、構築すべきでないか、それが問題です: テクノオプティミズム vs. 減速主義

건설하느냐, 건설하지 않느냐, 그것이 문제이다: 기술 낙관주의 vs. 감속주의

Construir ou não construir, eis a questão: Tecno-otimismo vs.

Строить или не строить, вот в чем вопрос: технооптимизм против деселерационизма

İnşa Etmek ya da İnşa Etmemek Sorun Bu: Tekno-İyimserlik ve Yavaşlamacılık

Xây dựng hay không xây dựng, đó là câu hỏi: Lạc quan về công nghệ và chủ nghĩa giảm tốc

建设还是不建设，这就是问题：技术乐观主义与减速主义

HackerNoon is proud to partner with and for the , taking place in December at the Estufa Fria in Lisbon, Portugal. The venue will be turned into a canvas for a seamless blend of physical and digital art, showcasing how nature, art, and technology can work hand in hand. muti Artsies Collective ChromaFlora Exhibition Immersive artistry in the green Organized by and , the ChromaFlora Exhibition will unite over 70 global and local artists with 50+ digital and 20+ physical artworks, each contributing to the overarching theme of nature, art and technology. Blending the digital with the physical, all artworks will also be available as NFTs minted on Polygon through the marketplace . muti Artsies Collective Bueno.art muti is a cultural association and DAO based in Portugal, supporting emerging artists of all art forms. The physical artworks include pieces from rising visual artists such as , , , and . Daphne Marzia and The Spaceshipproject Arta Raituma Carlotta Premazzi Katherine Shapiro On the digital side, international artists have been invited to participate through an open call on . The sister-led Artsies Collective is curating 50+ artworks, which will be exhibited during the week in the greenhouse. The Hug The Estufa, a haven of lush greenery, provides the perfect backdrop for the exhibition, creating an environment that enhances the connection between art and nature. Attendees will have the opportunity to explore a curated selection of artworks whilst also enjoying the natural beauty of the urban jungle of Lisbon. This innovative showcase invites you to embark on a transformative journey, where the lines between nature and technology blur into enchanting hybrids. "ChromaFlora" stands as a testament to the symbiotic relationship between organic materials and cutting-edge technology. The exhibition takes pride in celebrating the inherent beauty of nature while embracing the limitless possibilities that technology offers to artistic expression. This harmonious blend creates a unique space where the natural and the digital coalesce in a mesmerizing display of creativity. Technology with a positive impact To enrich the visitor experience, on the weekend of the 15th and 16th of December visitors can join talks and workshops held by artists and projects that work in the intersection of sustainability, art, and tech. Experts and artists will share insights into how these seemingly disparate elements can coexist harmoniously, inspiring attendees to explore the possibilities of art in the digital age. The talks and workshops aim to spark dialogue and foster a deeper understanding of the relationship between the natural world and the technological advancements shaping our future. The well-curated program will give a glimpse of how technology can be used in a meaningful way, from the use of NFTs to nature-backed economy ( / ) and vertical farming ( ). Projects like , and will introduce their work and visitors will get the chance to meet the physical artists and the process behind their creation. Oasa TDF Raíz Zeitls Muro Dvue X2 Digital artworks & artists Eco Enigma by Nataliya Grimberg When nature weaves its tendrils through the circuits of our creations, a symphony of sustainability is born. In this fusion, the ethereal dance of green and gear, we uncover an enchanting truth: that the harmony of our world lies in the embrace of leaf and line, root and router. 'EcoEnigma' is a testament to the magic that sparks when the pulse of the earth meets the hum of progress, inspiring us to envision a future where technology blooms from the soil of our respect for the natural world. Lumiflora Blooms by DOLORES KRAJNAK Photograph of a flower was used as part of the original prompt. The verbal prompt was revised several times. Variations were generated until the image and colors were achieved. Program "ChromaFlora" will be open to the public from Tuesday, December 12, to Sunday, December 17, 2023, with daily opening hours from 9 am to 5 pm. Admission is priced at 3.25€, providing access to a week-long exploration of the captivating fusion between nature and technology at the estufa. 11.12.: Exclusive Vernissage 12.-17.12.: Exhibition open to public 13.12.: Web3Wednesday with Chilibangs 15.&16.12.: Workshops & Talks from 12-17:00 16.12: Afterparty For more info visit https://chromaflora.xyz/ Facebook Event: https://www.facebook.com/events/666284942276984

ChromaFlora - a Hybrid Exhibition in the Estufa Fria in Lisbon: Where art meets technology in nature

On the Economy of Machinery and Manufactures by Charles Babbage is part of the HackerNoon Books Series. You can jump to any chapter in this book . On the Division of Labour here Chapter 20. On the Division of Labour We have already mentioned what may, perhaps, appear paradoxical to some of our readers that the division of labour can be applied with equal success to mental as to mechanical operations, and that it ensures in both the same economy of time. A short account of its practical application, in the most extensive series of calculations ever executed, will offer an interesting illustration of this fact, whilst at the same time it will afford an occasion for shewing that the arrangements which ought to regulate the interior economy of a manufactory, are founded on principles of deeper root than may have been supposed, and are capable of being usefully employed in preparing the road to some of the sublimest investigations of the human mind. In the midst of that excitement which accompanied the Revolution of France and the succeeding wars, the ambition of the nation, unexhausted by its fatal passion for military renown, was at the same time directed to some of the nobler and more permanent triumphs which mark the era of a people's greatness and which receive the applause of posterity long after their conquests have been wrested from them, or even when their existence as a nation may be told only by the page of history. Amongst their enterprises of science, the French Government was desirous of producing a series of mathematical tables, to facilitate the application of the decimal system which they had so recently adopted. They directed, therefore, their mathematicians to construct such tables, on the most extensive scale. Their most distinguished philosophers, responding fully to the call of their country, invented new methods for this laborious task; and a work, completely answering the large demands of the Government, was produced in a remarkably short period of time. M. Prony, to whom the superintendence of this great undertaking was confided, in speaking of its commencement, observes: Je m'y livrai avec toute l'ardeur dont j'etois capable, et je m'occupai d'abord du plan general de l'execution. Toutes les conditions que j'avois a remplir necessitoient l'emploi d'un grand nombre de calculateurs; et il me vint bientot a la pensee d'appliquer a la connection de ces Tables la division du travail, dont les Arts de Commerce tirent un parti si avantageux pour reunir a la pernection de main-d'oeuvre l'economie de la depense et du temps. The circumstance which gave rise to this singular application of the principle of the division on labour is so interesting, that no apology is necessary for introducing it from a small pamphlet printed at Paris a few years since, when a proposition was made by the English to the French Government, that the two countries should print these tables at their joint expense. The origin of the idea is related in the following extract: C'est a un chapitre d'un ouvrage Anglais,(1*) justement celebre, (I.) qu'est probablement due l'existence de l'ouvrage dont le gouvernement Britannique veut faire jouir le monde savant: Voici l'anecdote: M. de Prony s'etait engage. avec les comites de gouvernement. a composer pour la division centesimale du cercle, des tables logarithmiques et trigonometriques, qui, non seulement ne laissassent rien a desirer quant a l'exactitude, mais qui formassent le monument de calcul 1e plus vaste et le plus imposant qui eut jamais ete execute, ou meme concu. Les logarithmes des nombres de 1 a 200.000 formaient a ce travail un supplement necessaire et exige. Il fut aise a M. de Prony de s'assurer que meme en s'associant trois ou quatre habiles co-operateurs. La plus grande duree presumable de sa vie ne lui sufirai pas pour remplir ses engagements. Il etait occupe de cette facheuse pensee lorsque. Se trouvant devant la boutique d'un marchand de livres. Il appercut la belle edition Anglaise de Smith, donnee a Londres en 1776: il ouvrit le livre au hazard. et tomba sur le premier chapitre, qui traite de la division du travail, et ou la fabrication des epingles est citee pour exemple. A peine avait-il parcouru les premieres pages, que, par une espece d'inspiration. il concut l'expedient de mettre ses logarithmes en manufacture comme les epingles. Il faisait en ce moment, a l'ecole polytechnique, des lecons sur une partie d'analyse liee a ce genre de travail, la methode des differences, et ses applications a l'interpolation. Il alla passer quelques jours a la campagne. et revint a Paris avec le plan de fabrication. qui a ete suivi dans l'execution. Il rassembla deux ateliers. qui faisai ent separement les memes calculs, et se servaient de verification reciproque.(2*) The ancient methods of computing tables were altogether inapplicable to such a proceeding. M. Prony, therefore, wishing to avail himself of all the talent of his country in devising new methods, formed the first section of those who were to take part in this enterprise out of five or six of the most eminent mathematicians in France. First section. The duty of this first section was to investigate, amongst the various analytical expressions which could be found for the same function, that which was most readily adapted to simple numerical calculation by many individuals employed at the same time. This section had little or nothing to do with the actual numerical work. When its labours were concluded, the formulae on the use of which it had decided, were delivered to the second section. Second section. This section consisted of seven or eight persons of considerable acquaintance with mathematics: and their duty was to convert into numbers the formulae put into their hands by the first section an operation of great labour; and then to deliver out these formulae to the members of the third section, and receive from them the finished calculations. The members of this second section had certain means of verifying the calculations without the necessity of repeating, or even of examining, the whole of the work done by the third section. Third section. The members of this section, whose number varied from sixty to eighty, received certain numbers from the second section, and, using nothing more than simple addition and subtraction, they returned to that section the tables in a finished state. It is remarkable that nine-tenths of this class had no knowledge of arithmetic beyond the two first rules which they were thus called upon to exercise, and that these persons were usually found more correct in their calculations, than those who possessed a more extensive knowledge of the subject. When it is stated that the tables thus computed occupy seventeen large folio volumes, some idea may perhaps be formed of the labour. From that part executed by the third class, which may almost be termed mechanical, requiring the least knowledge and by far the greatest exertions, the first class were entirely exempt. Such labour can always be purchased at an easy rate. The duties of the second class, although requiring considerable skill in arithmetical operations, were yet in some measure relieved by the higher interest naturally felt in those more difficult operations. The exertions of the first class are not likely to require, upon another occasion, so much skill and labour as they did upon the first attempt to introduce such a method; but when the completion of a calculating engine shall have produced a substitute for the whole of the third section of computers, the attention of analysts will naturally be directed to simplifying its application, by a new discussion of the methods of converting analytical formulae into numbers. The proceeding of M. Prony, in this celebrated system of calculation, much resembles that of a skilful person about to construct a cotton or silk mill, or any similar establishment. Having, by his own genius, or through the aid of his friends, found that some improved machinery may be successfully applied to his pursuit, he makes drawings of his plans of the machinery, and may himself be considered as constituting the first section. He next requires the assistance of operative engineers capable of executing the machinery he has designed, some of whom should understand the nature of the processes to be carried on; and these constitute his second section. When a sufficient number of machines have been made, a multitude of other persons, possessed of a lower degree of skill, must be employed in using them; these form the third section: but their work, and the just performance of the machines, must be still superintended by the second class. As the possibility of performing arithmetical calculations by machinery may appear to non-mathematical readers to be rather too large a postulate, and as it is connected with the subject of the division of labour, I shall here endeavour, in a few lines, to give some slight perception of the manner in which this can be done—and thus to remove a small portion of the veil which covers that apparent mystery. That nearly all tables of numbers which follow any law, however complicated, may be formed, to a greater or less extent, solely by the proper arrangement of the successive addition and subtraction of numbers befitting each table, is a general principle which can be demonstrated to those only who are well acquainted with mathematics; but the mind, even of the reader who is but very slightly acquainted with that science, will readily conceive that it is not impossible, by attending to the following example. The subjoined table is the beginning of one in very extensive use, which has been printed and reprinted very frequently in many countries, and is called a table of square numbers. Terms of Table A Table B first Difference C second Difference 1 1
                            3
    2 4 2
                            5
    3 9 2
                            7
    4 16 2
                            9
    5 25 2
                           11
    6 36 2
                           13
    7 49 Any number in the table, column A, may be obtained, by multiplying the number which expresses the distance of that term from the commencement of the table by itself; thus, 25 is the fifth term from the beginning of the table, and 5 multiplied by itself, or by 5, is equal to 25. Let us now subtract each term of this table from the next succeeding term, and place the results in another column (B), which may be called first difference column. If we again subtract each term of this first difference from the succeeding term, we find the result is always the number 2, (column C); and that the same number will always recur in that column, which may be called the second difference, will appear to any person who takes the trouble to carry on the table a few terms further. Now when once this is admitted, it is quite clear that, provided the first term (1) of the table, the first term (3) of the first differences, and the first term (2) of the second or constant difference, are originally given, we can continue the table of square numbers to any extent, merely by addition: for the series of first differences may be formed by repeatedly adding the constant difference (2) to (3) the first number in column B, and we then have the series of numbers, 3, 5, 6, etc.: and again, by successively adding each of these to the first number (1) of the table, we produce the square numbers. Having thus, I hope, thrown some light upon the theoretical part of the question, I shall endeavour to shew that the mechanical execution of such an engine, as would produce this series of numbers, is not so far removed from that of ordinary machinery as might be conceived.(3*) Let the reader imagine three clocks, placed on a table side by side, each having only one hand, and each having a thousand divisions instead of twelve hours marked on the face; and every time a string is pulled, let them strike on a bell the numbers of the divisions to which their hands point. Let him further suppose that two of the clocks, for the sake of distinction called B and C, have some mechanism by which the clock C advances the hand of the clock B one division, for each stroke it makes upon its own bell: and let the clock B by a similar contrivance advance the hand of the clock A one division, for each stroke it makes on its own bell. With such an arrangement, having set the hand of the clock A to the division I, that of B to III, and that of C to II, let the reader imagine the repeating parts of the clocks to be set in motion continually in the following order: viz.—pull the string of clock A; pull the string of clock B; pull the string of clock C. The table on the following page will then express the series of movements and their results. If now only those divisions struck or pointed at by the clock A be attended to and written down, it will be found that they produce the series of the squares of the natural numbers. Such a series could, of course, be carried by this mechanism only so far as the numbers which can be expressed by three figures; but this may be sufficient to give some idea of the construction—and was, in fact, the point to which the first model of the calculating engine, now in progress, extended. We have seen, then, that the effect of the division of labour, both in mechanical and in mental operations, is, that it enables us to purchase and apply to each process precisely that quantity of skill and knowledge which is required for it: we avoid employing any part of the time of a man who can get eight or ten shillings a day by his skill in tempering needles, in turning a wheel, which can be done for sixpence a day; and we equally avoid the loss arising from the employment of an accomplished mathematician in performing the lowest processes of arithmetic. The division of labour cannot be successfully practised unless there exists a great demand for its produce; and it requires a large capital to be employed in those arts in which it is used. In watchmaking it has been carried, perhaps, to the greatest extent. It was stated in evidence before a committee of the House of Commons, that there are a hundred and two distinct branches of this art, to each of which a boy may be put apprentice: and that he only learns his master's department, and is unable, after his apprenticeship has expired, without subsequent instruction, to work at any other branch. The watch-finisher, whose business is to put together the scattered parts, is the only one, out of the hundred and two persons, who can work in any other department than his own. In one of the most difficult arts, that of mining, great improvements have resulted from the judicious distribution of the duties; and under the arrangments which have gradually been introduced, the whole system of the mine and its government is now placed under the control of the following officers. A manager, who has the general knowledge of all that is to be done, and who may be assisted by one or more skilful persons. Underground captains direct the proper mining operations, and govern the working miners. The purser and book-keeper manage the accounts. The engineer erects the engines, and superintends the men who work them. A chief pitman has charge of the pumps and the apparatus of the shafts. A surface-captain, with assistants, receives the ores raised, and directs the dressing department, the object of which is to render them marketable. The head carpenter superintends many constructions. The foreman of the smiths regulates the ironwork and tools. A materials man selects, purchases, receives and delivers all articles required. The roper has charge of ropes and cordage of all sorts. Notes: An Enquiry into the Nature and Causes of the Wealth of Nations, by Adam Smith. Note sur la publication, proposee par le gouvernement Anglais des grandes tables logarithmiques et trigonometriques de M de Prony De l'imprimerie de F. Didot, December 1, 1829, p. 7 Since the publication of the second edition of this work, one portion of the engine which I have been constructing for some years past has been put together. It calculates, in three columns, a table with its first and second differences. Each column can be expressed as far as five figures, so that these fifteen figures constitute about one ninth part of the larger engine. The ease and precision with which it works leave no room to doubt its success in the more extended form. Besides tables of squares, cubes, and portions of logarithmic tables, it possesses the power of calculating certain series whose differences are not constant; and it has already tabulated parts of series formed from the following equations: The third differential of ux = units figur of delta ux The third differential of ux = nearest whole no. to (1/10,000 delta ux) The subjoined is one amongst the series which it has calculated: 0 3, 486 42,972 0 4,991 50,532 1 6,907 58,812 14 9,295 67,826 70 12,236 77,602 230 15,741 88,202
495 19,861 99,627
916 24,597 111,928
1,504 30,010 125,116
2,340 36,131 139,272 The general term of this is, ux = (x(x-1)(x-2))/(1 X 2 X 3) + the whole number in x/10 + 10 Sigma^3 (units figure of (x(x-1)/2) About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Charles Babbage (2003). On the Economy of Machinery and Manufactures. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/4238/pg4238.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

On the Division of Labour

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