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Tired of the same stock photos next to each news article, blog post, and presentation? Me too. So when I wrote "The Art of Prompt Engineering with ChatGPT," I decided to build custom illustrations using MidJourney. I used a great trick that allowed me to rebuild my characters in different contexts throughout the book. Due to popular demand, I'm launching this book to teach you how to build your own consistent characters. In this book, you will learn: How text-to-image AI tools work How to get set up with MidJourney How to write basic prompts in MidJourney How to develop a character in MidJourney How to contextualise your character How to build backgrounds and compositions Not only that, but I've also added an extra section on using ChatGPT within your character building process, which covers: How to get ChatGPT to develop your characters and build them into MidJourney prompts How to get ChatGPT to contextualise your characters based on any text and build them into MidJourney prompts How to get ChatGPT to build backgrounds for your contextualised characters and build them into MidJourney prompts Get Certified and Show off Your Knowledge! Early Adopters of AI Tools should be able to have their expertise visible to their professional network. Chapter 17 of this book sets you up with a project to implement everything you have learned by creating a visual children's story with ChatGPT and MidJourney. ChatGPT Trainings has put together a certification program that lets you submit your project as proof of your new abilities, and in return you will receive a recognised and verifiable certification that can be added to your LinkedIn Profile. Head over to www.ChatGPTtrainings.com/certifications for more information

Master the skills required to become an AI product manager and drive the successful development and deployment of AI products to deliver value to your organization. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Build products that leverage AI for the common good and commercial success Take macro data and use it to show your customers you're a source of truth Best practices and common pitfalls that impact companies while developing AI product Book Description Product managers working with artificial intelligence will be able to put their knowledge to work with this practical guide to applied AI. This book covers everything you need to know to drive product development and growth in the AI industry. From understanding AI and machine learning to developing and launching AI products, it provides the strategies, techniques, and tools you need to succeed. The first part of the book focuses on establishing a foundation of the concepts most relevant to maintaining AI pipelines. The next part focuses on building an AI-native product, and the final part guides you in integrating AI into existing products. You'll learn about the types of AI, how to integrate AI into a product or business, and the infrastructure to support the exhaustive and ambitious endeavor of creating AI products or integrating AI into existing products. You'll gain practical knowledge of managing AI product development processes, evaluating and optimizing AI models, and navigating complex ethical and legal considerations associated with AI products. With the help of real-world examples and case studies, you'll stay ahead of the curve in the rapidly evolving field of AI and ML. By the end of this book, you'll have understood how to navigate the world of AI from a product perspective. What you will learn Build AI products for the future using minimal resources Identify opportunities where AI can be leveraged to meet business needs Collaborate with cross-function

Accessible Edition To make 'The Art of Prompt Engineering with ChatGPT' as beautiful as possible we designed the layout and published it here as a pdf. However, this wasn't the best option for those who used a kindle to read, or for those who had accessibility needs. So this is the accessible edition, rebuilt using a reflow able format. If you bought the original and need to use the accessibility features, just email us at nathan@ChatGPTtrainings.com. Let's move beyond basic examples and 'test this prompt.' ChatGPT is an amazing AI tool that can change the way we work. Bill Gates recently said that ChatGPT is as important an invention as the internet, and it could change the world. To make the most of ChatGPT and go beyond simple uses, you need to master prompt engineering. Check out a sample chapter: www.ChatGPTtrainings.com March Update - 2 New Sections with 34 More Pages of Content For this monthly update, we added a new section on Advanced Prompt Engineering to help you take your ChatGPT skills to a higher level once you've learned the basics. This section covers the co-creation approach, where you take control, and [format] your output, where you'll learn my favorite way to get the exact results you want. We also included a new section on GPT-4, with information on how to start, debunking past hype, and looking at some new improvements. This book will keep evolving as ChatGPT grows, making sure that everything you read and learn stays up-to-date and relevant. All updates are free and automatic for Kindle copies, and if you bought a hardcopy, you can email me at Nathan@ChatGPTtrainings.com with your proof of purchase to get a PDF update. Why This Book? This book helps you learn the art of working with ChatGPT to get much better results. This skill, prompt engineering, is what sets good apart from great when using ChatGPT. Learn 4 key techniques and tools for writing better prompts Master 2 advanced prompt engineering tools to take your skills further F

One of The Sunday Times Business Books of the Year Artificial intelligence is smarter than humans. It can process information at lightning speed and remain focused on specific tasks without distraction. AI can see into the future, predicting outcomes and even use sensors to see around physical and virtual corners. So why does AI frequently get it so wrong? The answer is us. Humans design the algorithms that define the way that AI works, and the processed information reflects an imperfect world. Does that mean we are doomed? In Scary Smart, Mo Gawdat, the internationally best-selling author of Solve for Happy, draws on his considerable expertise to answer this question and to show what we can all do now to teach ourselves and our machines how to live better. With more than 30 years' experience working at the cutting-edge of technology and his former role as chief business officer of Google [X], no one is better placed than Mo Gawdat to explain how the Artificial Intelligence of the future works. By 2049, AI will be a billion times more intelligent than humans. Scary Smart explains how to fix the current trajectory now, to make sure that the AI of the future can preserve our species. This book offers a blueprint, pointing the way to what we can do to safeguard ourselves, those we love and the planet itself.

Opera features an integrated AI called Aria that you can access from the sidebar. You can use a keyboard shortcut (CTRL or Command and /) to start using Aria as well. The AI is also available in Opera's Android browser. The AI stems from Opera's partnership with ChatGPT creator OpenAI. Aria connects to GPT to help answer users' queries. The AI incorporates live information from the web and it can generate text or code and answer support questions regarding Opera products. In addition, Opera One can generate contextual prompts for Aria when you right click or highlighting text in the browser. If you prefer to use ChatGPT or ChatSonic, you can access those from the Opera One sidebar too. Opera says users don't have to engage with the browser's AI features if they don't want to. For one thing, you'll need to be logged into an Opera account to use Aria.

Text to Cron: Text to Cron allows you to generate cron jobs through text descriptions of the command (text-to-cron.vercel.app).

This program includes an introduction read by the author. No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals its turbulent history and the recent surge of apparent successes, grand hopes, and emerging fears that surround AI. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent - really - are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant methods of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought that led to recent achievements. She meets with fellow experts like Douglas Hofstadter, the cognitive scientist and Pulitzer Prize - winning author of the modern classic Gödel, Escher, Bach, who explains why he is "terrified" about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much farther it has to go. Interweaving stories about the science and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and approachable accounts of the most interesting and provocative modern work in AI, flavored with Mitchell's humor and personal observations. This frank, lively book will prove an indispensable guide to understanding today's AI, its quest for "human-level" intelligence, and its impacts on all of our futures. PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

How you build, format, and annotate your training dataset has a direct impact on the model you create. In fact, poorly processed data is one of the most common reasons that machine learning projects fail. However, if you haven't worked with training data before, it can be difficult to know where to start. After all, data can be surprisingly complex. It's hard to figure out what a dataset should look like and how to improve it.

OpenAI Playground is a cutting-edge web-based tool designed to facilitate the construction and evaluation of predictive language models. Unlike ChatGPT, which excels at understanding context and can produce high-quality content even with vague prompts, OpenAI Playground requires specific prompts and settings for better-quality outputs. The tool boasts many purpose-specific preset prompts, ensuring seamless customisation through adjustable parameters such as the model, response length, and temperature. OpenAI Playground also accommodates diverse base language models, allowing users to handpick the one that perfectly resonates with their preferred conversational style. OpenAI Playground's Pros: Preset options for different tasks, such as text-to-command and translation Suited for specific use cases where preset models and settings are needed OpenAI Playground's Cons: Requires specific prompts and settings for better-quality outputs It can be challenging to use if you're not familiar with LLMs OpenAI Playground's Pricing: OpenAI Playground offers a comprehensive pricing structure based on the number of tokens used. 1 word = 1 token For example, Chat, model gpt-3.5-turbo costs $0.002 for 1K tokens, while InstructGPT, model Davinci costs $0.0200 for 1K tokens.

EVER had the feeling something is missing? If so, you're in good company. Dmitri Mendeleev did in 1869 when he noticed four gaps in his periodic table. They turned out to be the undiscovered elements scandium, gallium, technetium and germanium. Paul Dirac did in 1929 when he looked deep into the quantum-mechanical equation he had formulated to describe the electron. Besides the electron, he saw something else that looked rather like it, but different. It was only in 1932, when the electron's antimatter sibling, the positron, was sighted in cosmic rays that such a thing was found to exist. In 1971, Leon Chua had that feeling. A young electronics engineer with a penchant for mathematics at the University of California, Berkeley, he was fascinated by the fact that electronics had no rigorous mathematical foundation. So like any diligent scientist, he set about trying to derive one. And he found something missing: a fourth basic circuit element besides the standard trio of resistor, capacitor and inductor. Chua dubbed it the "memristor". The only problem was that as far as Chua or anyone else could see, memristors did not actually exist. Except that they do.