#Generative AI course for developers

[callofdutymobileindia]

Beginner Generative AI Course: The Ultimate Guide to Start Your AI Journey in 2025

Generative AI has gone from buzzword to mainstream breakthrough. From AI writing tools like ChatGPT to image generators like DALL·E and Midjourney, generative AI is transforming how we work, create, and communicate. If you're curious about this technology but don’t know where to start, enrolling in a Beginner Generative AI Course is the perfect first step.

In this guide, we’ll explore what you can expect from a beginner-level course, the essential topics covered, who it’s for, and the best online programs you can join in 2025.

Why Take a Beginner Generative AI Course?

A Beginner Generative AI Course is ideal for anyone who wants to:

Understand what generative AI is and how it works

Learn how to use popular tools like ChatGPT, DALL·E, and Stable Diffusion

Gain in-demand skills without needing a deep technical background

Explore new career or creative opportunities in AI

Whether you’re a student, content creator, professional, or entrepreneur, this course will help you understand and apply generative AI in real-world scenarios.

What Will You Learn in a Beginner Generative AI Course?

Beginner courses focus on foundational knowledge and hands-on skills. Here’s what you’ll typically cover:

1. Introduction to Generative AI

What is Generative AI?

The difference between AI, ML, and Generative AI

Real-world use cases and applications

2. Understanding AI Models

Basics of machine learning and neural networks

How generative models (like GPT, DALL·E) are trained

Text, image, audio, and video generation

3. Prompt Engineering Basics

What is a “prompt” in generative AI?

How to write effective prompts

Prompt design for various tasks: summarization, Q&A, content creation

4. Hands-on with Popular Tools

Using ChatGPT for writing, brainstorming, and productivity

Generating visuals with DALL·E or Midjourney

Exploring Stable Diffusion for creative image generation

Simple Python-based projects (optional for non-coders)

5. Ethical AI Use

Understanding AI bias

Copyright, data privacy, and responsible AI usage

Who Should Enroll in a Beginner Generative AI Course?

Beginner courses are designed for non-technical and semi-technical audiences who want to understand and apply AI tools without writing complex code.

Ideal for:

Students & recent graduates looking to upskill

Content creators & marketers interested in automating tasks

Designers & artists exploring AI-generated visuals

Business professionals curious about AI’s potential in strategy or operations

Career switchers wanting to explore AI roles

No coding or advanced math is required—just curiosity and willingness to learn.

Best Beginner Generative AI Courses in 2025

Here’s a list of top-rated beginner courses that offer excellent value, structure, and outcomes:

Boston Institute of Analytics – Generative AI Foundation Course

Best For: Beginners seeking structured learning with hands-on practice

Highlights:

Live online classes with expert trainers

Introduction to LLMs, DALL·E, ChatGPT, and prompt engineering

No prior coding required

Real-world projects and capstone assignments

Certification & placement support

Why It’s Great: Offers a perfect balance of theory and practice, and is ideal for learners looking to build career-ready skills in generative AI.

What Makes a Great Beginner Generative AI Course?

Here are the must-have features:

✅ No-code or low-code friendly ✅ Real-world use cases (text, images, content) ✅ Hands-on tool walkthroughs (e.g., ChatGPT, DALL·E) ✅ Simple explanations of key AI concepts ✅ Project-based assignments ✅ Certification upon completion ✅ Supportive learning community or mentor access

What Can You Do After Completing a Beginner Generative AI Course?

Even an entry-level course gives you real-world applications:

Write better blogs, emails, and social posts using AI

Create stunning visuals and design ideas with AI tools

Build your portfolio with AI-generated content

Explore freelance gigs (prompt engineering, content AI, script generation)

Transition to more advanced AI courses and certifications

Next Steps:

Start working on small projects (e.g., chatbot using ChatGPT API)

Contribute to AI communities or forums

Experiment with fine-tuning prompts

Consider intermediate certifications (like those from Boston Institute of Analytics or DeepLearning.AI)

Final Thoughts

Taking a Beginner Generative AI Course is the smartest way to break into the exciting world of AI-powered creativity and innovation. With user-friendly tools, real-world applications, and minimal technical barriers, it’s never been easier to start learning.

If you're looking for a structured, beginner-friendly course with mentorship and projects, the Boston Institute of Analytics offers one of the best programs available online in 2025. It’s designed to make you confident, creative, and capable of using Generative AI in your career or personal pursuits.

[ethanparker9692]

What is the Best Certification for Generative AI Software Development?

If you're a developer or software engineer keeping an eye on the future (and who isn’t?), chances are you’ve already heard the buzz around generative AI in software development. From streamlining code generation to powering intelligent automation, generative AI software is reshaping how we build tech—and fast.

So, what's the smartest way to ride this wave? The Certified Generative AI in Software Development certification by GSDC is making serious noise as the go-to credential for anyone wanting to level up in the AI world.

🚀 Why This Cert Stands Out This isn’t just another AI development course. It’s a hands-on, cutting-edge certification designed specifically for developers. You’ll dive deep into generative AI development, understand practical use cases of generative AI software, and learn how to integrate AI software development tools into real-world projects.

🎯 It also covers:

Advanced generative AI for software development concepts

Implementation of AI-driven software engineering

Techniques from machine learning to LLMs (Large Language Models)

Best practices for code automation and optimization

👨‍💻 Whether you’re eyeing a Generative AI course for developers or aiming to add Software engineering with AI certification to your resume, this program has your back.

✅ Perfect for:

Software developers

AI enthusiasts

DevOps engineers

Tech leads looking to future-proof their skillset

For information visit: -

Contact : +41444851189

#GenerativeAI #AISoftwareDevelopment #GenerativeAICertification #GenerativeAIDevelopment #AIProgramming #GenerativeAIForSoftwareDevelopment #AIDrivenEngineering #AIForDevs #SoftwareEngineeringWithAI #MachineLearning #GSDCouncil #AIDevelopmentCourse

[beekeeperspicnic]

Giant hug for everyone who has been onto the game's Steam discussion board and replied with classy snark to people grumbling on there.

I'm reluctant to do that as a developer but when I see you guys doing it, just know that I'm cheering for you.

[jcmarchi]

The Importance of Investing in Soft Skills in the Age of AI

New Post has been published on https://thedigitalinsider.com/the-importance-of-investing-in-soft-skills-in-the-age-of-ai/

The Importance of Investing in Soft Skills in the Age of AI

I’ll set out my stall and let you know I am still an AI skeptic. Heck, I still wrap “AI” in quotes a lot of the time I talk about it. I am, however, skeptical of the present, rather than the future. I wouldn’t say I’m positive or even excited about where AI is going, but there’s an inevitability that in development circles, it will be further engrained in our work.

We joke in the industry that the suggestions that AI gives us are more often than not, terrible, but that will only improve in time. A good basis for that theory is how fast generative AI has improved with image and video generation. Sure, generated images still have that “shrink-wrapped” look about them, and generated images of people have extra… um… limbs, but consider how much generated AI images have improved, even in the last 12 months.

There’s also the case that VC money is seemingly exclusively being invested in AI, industry-wide. Pair that with a continuously turbulent tech recruitment situation, with endless major layoffs and even a skeptic like myself can see the writing on the wall with how our jobs as developers are going to be affected.

The biggest risk factor I can foresee is that if your sole responsibility is to write code, your job is almost certainly at risk. I don’t think this is an imminent risk in a lot of cases, but as generative AI improves its code output — just like it has for images and video — it’s only a matter of time before it becomes a redundancy risk for actual human developers.

Do I think this is right? Absolutely not. Do I think it’s time to panic? Not yet, but I do see a lot of value in evolving your skillset beyond writing code. I especially see the value in improving your soft skills.

What are soft skills?

A good way to think of soft skills is that they are life skills. Soft skills include:

communicating with others,

organizing yourself and others,

making decisions, and

adapting to difficult situations.

I believe so much in soft skills that I call them core skills and for the rest of this article, I’ll refer to them as core skills, to underline their importance.

The path to becoming a truly great developer is down to more than just coding. It comes down to how you approach everything else, like communication, giving and receiving feedback, finding a pragmatic solution, planning — and even thinking like a web developer.

I’ve been working with CSS for over 15 years at this point and a lot has changed in its capabilities. What hasn’t changed though, is the core skills — often called “soft skills” — that are required to push you to the next level. I’ve spent a large chunk of those 15 years as a consultant, helping organizations — both global corporations and small startups — write better CSS. In almost every single case, an improvement of the organization’s core skills was the overarching difference.

The main reason for this is a lot of the time, the organizations I worked with coded themselves into a corner. They’d done that because they just plowed through — Jira ticket after Jira ticket — rather than step back and question, “is our approach actually working?” By focusing on their team’s core skills, we were often — and very quickly — able to identify problem areas and come up with pragmatic solutions that were almost never development solutions. These solutions were instead:

Improving communication and collaboration between design and development teams

Reducing design “hand-off” and instead, making the web-based output the source of truth

Moving slowly and methodically to move fast

Putting a sharp focus on planning and collaboration between developers and designers, way in advance of production work being started

Changing the mindset of “plow on” to taking a step back, thoroughly evaluating the problem, and then developing a collaborative and by proxy, much simpler solution

Will improving my core skills actually help?

One thing AI cannot do — and (hopefully) never will be able to do — is be human. Core skills — especially communication skills — are very difficult for AI to recreate well because the way we communicate is uniquely human.

I’ve been doing this job a long time and something that’s certainly propelled my career is the fact I’ve always been versatile. Having a multifaceted skillset — like in my case, learning CSS and HTML to improve my design work — will only benefit you. It opens up other opportunities for you too, which is especially important with the way the tech industry currently is.

If you’re wondering how to get started on improving your core skills, I’ve got you. I produced a course called Complete CSS this year but it’s a slight rug-pull because it’s actually a core skills course that uses CSS as a context. You get to learn some iron-clad CSS skills alongside those core skills too, as a bonus. It’s definitely worth checking out if you are interested in developing your core skills, especially so if you receive a training budget from your employer.

Wrapping up

The main message I want to get across is developing your core skills is as important — if not more important — than keeping up to date with the latest CSS or JavaScript thing. It might be uncomfortable for you to do that, but trust me, being able to stand yourself out over AI is only going to be a good thing, and improving your core skills is a sure-fire way to do exactly that.

[growwithcertification]

Top Generative AI in Business Courses with Certification in 2025

Generative AI is rapidly transforming how businesses operate, innovate, and grow. Whether you're an entrepreneur, executive, or digital strategist, gaining expertise in generative AI for business is now essential. Here’s a curated list of top generative ai in business certification courses with certification that can supercharge your professional journey in 2025!

📘 1. Certified Generative AI in Business by GSDC

Elevate your AI literacy with this globally recognized generative AI for business certification from GSDC. This course covers key concepts, tools, and applications of generative AI for business development and decision-making strategies.

🎓 2. Coursera’s Generative AI for Business Specialization

Designed in collaboration with top universities, this generative AI for business course focuses on AI integration, process optimization, and creative business solutions using AI models. Ideal for managers and tech-savvy leaders looking to drive generative AI for business growth.

💡 3. LinkedIn Learning: Generative AI in Business Essentials

A quick and practical course for professionals aiming to explore generative AI in business use-cases. Learn how to improve workflows, customer engagement, and product innovation using AI-generated content and tools.

🧠 4. Udacity: Generative AI for Business Leaders

A tech-forward curriculum that dives into the mechanics and strategic use of generative AI. Great for executives and team leaders wanting to align AI tools with business KPIs and achieve measurable growth.

📊 5. edX: Applied Generative AI for Business

Explore hands-on labs, real-world business case studies, and industry projects. This generative AI in business course is perfect for anyone wanting practical AI application skills in sales, marketing, and operations.

💼 Make 2025 Your Year of AI-Driven Success!

Empower your career with the right skills and credentials. Enroll in a trusted and globally accepted generative AI for business course today and lead the change toward a smarter, AI-powered future!

📞 For more details, contact: +41444851189 🌐 Explore the certification: Certified Generative AI in Business – GSDC

#GenerativeAIforBusiness #BusinessAI #AICertification2025 #GenerativeAICourses #BusinessInnovation #GSDCCertified #AIforGrowth #FutureofBusiness #AILeadership

[bluewithpurplepolkadots]

Honestly not going to even to seriously check about remote/hybrid office jobs until I’ve finished my office skills course and perhaps had a break for a a bit*.

But like one of the roles I see that seems vaguely realistic to me is a certain role at a health company that keeps coming up and has been for the last few months.

Like I’m glad it…may have been there by the time I get over my fears of change? If I ever do? But is it a… bad sign that it keeps coming up? It’s possible they’re just like growing rapidly but maybe it implies high turnover.

[triotechseo]

Top 5 IT Skills That Will Get You Hired in 2025 🚀

1. Cloud Computing & DevOps ☁️

Companies are heavily investing in cloud platforms like AWS, Azure, and Google Cloud. Knowing cloud infrastructure, CI/CD pipelines, and DevOps tools like Kubernetes and Terraform can land you high-paying roles.

2. AI & Machine Learning ��

AI-driven automation is transforming every industry. Skills in Python, TensorFlow, and AI model deployment are highly sought after. Even non-technical roles now require a basic understanding of AI concepts.

3. Cybersecurity & Ethical Hacking 🔒

With cyber threats increasing, businesses need security professionals more than ever. Certifications like CISSP, CEH, or knowledge of SIEM tools and penetration testing can give you a competitive edge.

4. Data Science & Analytics 📊

Companies rely on data to make decisions. If you master SQL, Power BI, Tableau, and Python for data analysis, you’ll be in high demand across industries.

5. Full-Stack Development 💻

Web and software development are evolving, and full-stack skills (React, Node.js, Java, and databases like MongoDB) are essential. Businesses need developers who can build both the front-end and back-end.

[dhanasrivista]

How Generative AI is Changing Learning and Development Programs

The way we approach Learning and Development (L&D) is evolving quickly, and one of the main drivers of this change is generative artificial intelligence (AI). Generative AI refers to advanced machine learning models that can create new content, such as text, images, or even simulations, based on patterns it has learned from existing data. With the growing demand for expertise in this technology, Generative AI certification is becoming increasingly popular, helping professionals gain the necessary skills to leverage this powerful tool effectively. This technology is opening up new possibilities for creating engaging, personalized, and more effective learning experiences. Let’s explore how generative AI is reshaping L&D programs, the benefits it brings, and some of the challenges we need to watch out for.

Personalized Learning Made Easy

One of the biggest advantages of generative AI in L&D is its ability to offer personalized learning experiences. Traditional learning methods often follow a one-size-fits-all approach, which doesn’t work for everyone. Generative AI can change this by analyzing an individual’s past performance, learning style, and preferences to create a learning path that fits them. With the addition of generative AI course, learners can gain a deeper understanding of this technology while following a curriculum tailored to their needs and skill levels. For example, if a learner struggles with a specific topic, AI can generate additional resources, quizzes, or exercises to help them master it. This personalized touch keeps learners engaged and helps them understand complex topics at their own pace.

Faster and Smarter Content Creation

Creating training content can take a lot of time and effort, especially when it involves developing manuals, presentations, or e-learning modules. With generative AI, much of this process can be automated. AI can draft training materials, generate sample exercises, and even build interactive elements like quizzes and scenarios. This automation not only saves time for L&D teams but also ensures the content is consistent and aligned with the learning objectives.

Generative AI can also be used to create virtual instructors or chatbots that assist learners by answering questions, providing guidance, or simulating real-life scenarios. These AI-driven tools can handle routine inquiries, freeing up human instructors to focus on more complex tasks.

Keeping Up with Change: Continuous Learning and Upskilling

In today’s fast-paced world, continuous learning and upskilling are essential. Generative AI can help organizations stay ahead by identifying skill gaps and recommending training programs. For example, AI can analyze industry trends and suggest relevant courses to ensure employees are equipped with the latest skills.

Moreover, AI can create simulated environments where employees can practice new skills. For example, a sales professional could practice their pitch in an AI-generated virtual scenario, or a software developer could work on coding challenges in a simulated coding lab. These hands-on experiences can help learners become more confident and proficient in new skills without the pressure of real-world consequences.

Reducing Learning Fatigue and Increasing Engagement

One common issue in traditional L&D programs is learning fatigue — when learners get bored or overwhelmed. Generative AI can tackle this by making learning more engaging. It can adjust the difficulty of a course based on the learner’s progress, ensuring that the content stays challenging but not overwhelming. AI can also create diverse types of content, such as videos, infographics, and interactive quizzes, to keep the learning experience fresh and interesting.

Additionally, AI can introduce gamification elements, like badges, points, and leaderboards, to motivate learners. These small rewards can make the learning experience feel more like a game, which encourages participation and reduces fatigue.

Overcoming Challenges: Privacy and Bias

While the benefits of generative AI in L&D are clear, there are some challenges to consider. One major concern is data privacy. Personalized learning experiences require access to personal data, such as performance records and learning preferences. Organizations need to ensure that this data is handled securely and in compliance with regulations to protect learner privacy.

Another potential issue is content bias. AI models are only as good as the data they are trained on. If the data contains biases, the AI might produce biased or inappropriate content. L&D teams need to carefully monitor AI-generated materials to ensure they are accurate, fair, and inclusive.

The Future of AI in Learning and Development

The future of generative AI in L&D looks promising. As the technology continues to improve, it will be able to create even more personalized and interactive learning experiences. We might see AI working alongside other emerging technologies like augmented reality (AR) and virtual reality (VR) to create immersive learning environments where employees can practice real-world skills in a virtual setting.

While some may worry that AI could replace human roles in L&D, it’s more likely that it will act as a support tool. Human trainers and instructional designers will still be essential for developing strategies, overseeing AI-generated content, and providing the human touch that AI cannot replicate.

In conclusion, generative AI has the potential to transform L&D programs by making learning more personalized, efficient, and engaging. It’s a tool that can help organizations build a more skilled and agile workforce, ready to take on future challenges. However, it’s important to navigate the challenges of privacy and bias carefully to ensure that this technology benefits everyone.

[zsystems]

As a premier ed-tech platform, we empower working professionals to elevate their careers, enhance productivity, and achieve their professional goals.

Expand your expertise and refine your skills with courses in AI Tools, Excel enhanced by AI, Generative AI, and more.

Our meticulously crafted workshops prepare you for industry demands, helping you explore Artificial Intelligence skills and experience exponential career growth.

Embrace the opportunity to become a leader in your field with our comprehensive training.

[bhawaybhalla]

Unlock Your AI Potential: 10 Amazing Free AI Courses to Launch Your Learning Journey

Artificial intelligence (AI) is everywhere. From the way we interact with our smartphones to self-driving cars and groundbreaking medical diagnoses, AI’s impact is undeniable. Building a foundation in AI opens a world of career possibilities and empowers you to understand the technology shaping our future. The best part? You can dive into this fascinating field without spending a dime! In this…

[101blockchainss]

Improve your AI skills with the 101 Blockchains AI Development Course. Our experts will develop all the required skills and show you how to implement AI solutions in real-world case studies. This course provides you with practical knowledge so you can learn things fast.

[ethanparker9692]

What is the Best Generative AI Course for Developers?

If you're a developer trying to stay ahead of the curve, there's no better time to explore Generative AI for software development. Whether you're building intelligent apps, enhancing automation, or experimenting with Large Language Models (LLMs), finding the best Generative AI course for developers can shape your career for the future.

Let’s break down what to look for and how the right certification can take your skills to the next level.

🚀 Why Developers Need Generative AI Training

Generative AI in software development is no longer a buzzword—it's transforming how code is written, tested, and deployed. With tools like ChatGPT, Copilot, and other LLMs in action, developers are using generative AI software to boost productivity, accuracy, and innovation.

Enrolling in a Generative AI course for developers not only builds your AI coding capabilities but also prepares you to integrate AI-driven software engineering certification into your resume.

🎓 Top Certification Picks

If you're serious about upgrading your profile, look for credentials like:

Generative AI for Software Developers Specialization

Generative AI with LLMs Certification

Software engineering with AI certification

AI development course tailored for real-world applications

These certifications blend theoretical knowledge with hands-on experience in generative AI development and AI software development.

💡 What You'll Learn

With the right course, you’ll master:

Generative AI software development tools and practices

The impact of generative AI in software development projects

Real-world applications of generative AI software

LLM integration and AI software development patterns

✅ Final Thoughts

The future of tech is AI-powered—and the developers who understand generative AI for software development will lead the way. Choose a course that offers practical skills, a globally recognized credential, and strong industry relevance.

Ready to future-proof your career? Start with a trusted Generative AI course for developers and unlock your potential.

For information visit: -

Contact : +41444851189

#GenerativeAI #AIDevelopment #SoftwareEngineering #AIinDevelopment #GenerativeAISoftware #GenAISDLC #AIProgramming #AICertification #LLMTraining #MachineLearning #AIforDevelopers #GenerativeAIforSoftwareDevelopment #AITraining #SoftwareDevelopment #TechCareers

[digitalratul]

Machine Learning 101: A Beginner's Guide to the Basics

Part - 1

Intro Machine learning is a rapidly growing field of technology and is being used in a variety of applications. From Google Adsense to self-driving cars, machine learning is becoming increasingly important in our everyday lives. In this blog post, we will provide an overview of machine learning for beginners. We'll explain what it is, why it's important, and how it works. Whether you're a software developer, business analyst, or a curious individual, this post will give you a good starting point to understanding the basics of machine learning. What is Machine Learning? Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on enabling computer systems to learn and improve from experience without being explicitly programmed. It is based on the idea that machines can learn patterns, make predictions, and take actions on their own, with minimal human intervention. In simpler terms, Machine Learning is the process of teaching computers to think and make decisions like humans. It involves creating algorithms and models that allow computers to learn and make predictions or take actions based on large sets of data. These algorithms analyze and identify patterns, trends, and correlations within the data, allowing the machine to learn from them and improve its performance over time. Machine Learning can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a machine using labeled data, unsupervised learning involves training a machine using unlabeled data, and reinforcement learning involves training a machine to learn from its own actions and feedback. Machine Learning has found applications in various fields, such as healthcare, finance, marketing, and transportation. It can be used to predict diseases, detect fraud, recommend products, and optimize logistics, among other things. Its effectiveness lies in its ability to handle large amounts of complex data and make accurate predictions or decisions based on that data. However, Machine Learning is not without its limitations. It heavily relies on the availability and quality of data. Insufficient or biased data can lead to inaccurate or unfair predictions. Additionally, machine learning models can be susceptible to overfitting or underfitting, where they may perform well on training data but fail to generalize to new, unseen data. Overall, Machine Learning is a powerful tool that has the potential to revolutionize various industries. By continuously learning and improving from data, machines can become more intelligent and assist humans in making better decisions and solving complex problems.

[jcmarchi]

Starting reading the AI Snake Oil book online today

New Post has been published on https://thedigitalinsider.com/starting-reading-the-ai-snake-oil-book-online-today/

Starting reading the AI Snake Oil book online today

The first chapter of the AI snake oil book is now available online. It is 30 pages long and summarizes the book’s main arguments. If you start reading now, you won’t have to wait long for the rest of the book — it will be published on the 24th of September. If you haven’t pre-ordered it yet, we hope that reading the introductory chapter will convince you to get yourself a copy.

We were fortunate to receive positive early reviews by The New Yorker, Publishers’ Weekly (featured in the Top 10 science books for Fall 2024), and many other outlets. We’re hosting virtual book events (City Lights, Princeton Public Library, Princeton alumni events), and have appeared on many podcasts to talk about the book (including Machine Learning Street Talk, 20VC, Scaling Theory).

Our book is about demystifying AI, so right out of the gate we address what we think is the single most confusing thing about it: 

AI is an umbrella term for a set of loosely related technologies

Because AI is an umbrella term, we treat each type of AI differently. We have chapters on predictive AI, generative AI, as well as AI used for social media content moderation. We also have a chapter on whether AI is an existential risk. We conclude with a discussion of why AI snake oil persists and what the future might hold. By AI snake oil we mean AI applications that do not (and perhaps cannot) work. Our book is a guide to identifying AI snake oil and AI hype. We also look at AI that is harmful even if it works well — such as face recognition used for mass surveillance. 

While the book is meant for a broad audience, it does not simply rehash the arguments we have made in our papers or on this newsletter. We make scholarly contributions and we wrote the book to be suitable for adoption in courses. We will soon release exercises and class discussion questions to accompany the book.

Chapter 1: Introduction. We begin with a summary of our main arguments in the book. We discuss the definition of AI (and more importantly, why it is hard to come up with one), how AI is an umbrella term, what we mean by AI Snake Oil, and who the book is for. 

Generative AI has made huge strides in the last decade. On the other hand, predictive AI is used for predicting outcomes to make consequential decisions in hiring, banking, insurance, education, and more. While predictive AI can find broad statistical patterns in data, it is marketed as far more than that, leading to major real-world misfires. Finally, we discuss the benefits and limitations of AI for content moderation on social media.

We also tell the story of what led the two of us to write the book. The entire first chapter is now available online.

Chapter 2: How predictive AI goes wrong. Predictive AI is used to make predictions about people—will a defendant fail to show up for trial? Is a patient at high risk of negative health outcomes? Will a student drop out of college? These predictions are then used to make consequential decisions. Developers claim predictive AI is groundbreaking, but in reality it suffers from a number of shortcomings that are hard to fix. 

We have discussed the failures of predictive AI in this blog. But in the book, we go much deeper through case studies to show how predictive AI fails to live up to the promises made by its developers.

Chapter 3: Can AI predict the future? Are the shortcomings of predictive AI inherent, or can they be resolved? In this chapter, we look at why predicting the future is hard — with or without AI. While we have made consistent progress in some domains such as weather prediction, we argue that this progress cannot translate to other settings, such as individuals’ life outcomes, the success of cultural products like books and movies, or pandemics. 

Since much of our newsletter is focused on topics of current interest, this is a topic that we have never written about here. Yet, it is foundational knowledge that can help you build intuition around when we should expect predictions to be accurate.

Chapter 4: The long road to generative AI. Recent advances in generative AI can seem sudden, but they build on a series of improvements over seven decades. In this chapter, we retrace the history of computing advances that led to generative AI. While we have written a lot about current trends in generative AI, in the book, we look at its past. This is crucial for understanding what to expect in the future. 

Chapter 5: Is advanced AI an existential threat? Claims about AI wiping out humanity are common. Here, we critically evaluate claims about AI’s existential risk and find several shortcomings and fallacies in popular discussion of x-risk. We discuss approaches to defending against AI risks that improve societal resilience regardless of the threat of advanced AI.

Chapter 6: Why can’t AI fix social media? One area where AI is heavily used is content moderation on social media platforms. We discuss the current state of AI use on social media, and highlight seven reasons why improvements in AI alone are unlikely to solve platforms’ content moderation woes. We haven’t written about content moderation in this newsletter.

Chapter 7: Why do myths about AI persist? Companies, researchers, and journalists all contribute to AI hype. We discuss how myths about AI are created and how they persist. In the process, we hope to give you the tools to read AI news with the appropriate skepticism and identify attempts to sell you snake oil.

Chapter 8: Where do we go from here? While the previous chapter focuses on the supply of snake oil, in the last chapter, we look at where the demand for AI snake oil comes from. We also look at the impact of AI on the future of work, the role and limitations of regulation, and conclude with vignettes of the many possible futures ahead of us. We have the agency to determine which path we end up on, and each of us can play a role.

We hope you will find the book useful and look forward to hearing what you think. 

The New Yorker: “In AI Snake Oil, Arvind Narayanan and Sayash Kapoor urge skepticism and argue that the blanket term AI can serve as a smokescreen for underperforming technologies.”

Kirkus: “Highly useful advice for those who work with or are affected by AI—i.e., nearly everyone.”

Publishers’ Weekly: Featured in the Fall 2024 list of top science books.

Jean Gazis: “The authors admirably differentiate fact from opinion, draw from personal experience, give sensible reasons for their views (including copious references), and don’t hesitate to call for action. . . . If you’re curious about AI or deciding how to implement it, AI Snake Oil offers clear writing and level-headed thinking.”

Elizabeth Quill: “A worthwhile read whether you make policy decisions, use AI in the workplace or just spend time searching online. It’s a powerful reminder of how AI has already infiltrated our lives — and a convincing plea to take care in how we interact with it.”

We’ve been on many other podcasts that will air around the time of the book’s release, and we will keep this list updated.

The book is available to preorder internationally on Amazon.

[ms-demeanor]

Why reblog machine-generated art?

When I was ten years old I took a photography class where we developed black and white photos by projecting light on papers bathed in chemicals. If we wanted to change something in the image, we had to go through a gradual, arduous process called dodging and burning.

When I was fifteen years old I used photoshop for the first time, and I remember clicking on the clone tool or the blur tool and feeling like I was cheating.

When I was twenty eight I got my first smartphone. The phone could edit photos. A few taps with my thumb were enough to apply filters and change contrast and even spot correct. I was holding in my hand something more powerful than the huge light machines I'd first used to edit images.

When I was thirty six, just a few weeks ago, I took a photo class that used Lightroom Classic and again, it felt like cheating. It made me really understand how much the color profiles of popular web images I'd been seeing for years had been pumped and tweaked and layered with local edits to make something that, to my eyes, didn't much resemble photography. To me, photography is light on paper. It's what you capture in the lens. It's not automatic skin smoothing and a local filter to boost the sky. This reminded me a lot more of the photomanipulations my friend used to make on deviantart; layered things with unnatural colors that put wings on buildings or turned an eye into a swimming pool. It didn't remake the images to that extent, obviously, but it tipped into the uncanny valley. More real than real, more saturated more sharp and more present than the actual world my lens saw. And that was before I found the AI assisted filters and the tool that would identify the whole sky for you, picking pieces of it out from between leaves.

You know, it's funny, when people talk about artists who might lose their jobs to AI they don't talk about the people who have already had to move on from their photo editing work because of technology. You used to be able to get paid for basic photo manipulation, you know? If you were quick with a lasso or skilled with masks you could get a pretty decent chunk of change by pulling subjects out of backgrounds for family holiday cards or isolating the pies on the menu for a mom and pop. Not a lot, but enough to help. But, of course, you can just do that on your phone now. There's no need to pay a human for it, even if they might do a better job or be more considerate toward the aesthetic of an image.

And they certainly don't talk about all the development labs that went away, or the way that you could have trained to be a studio photographer if you wanted to take good photos of your family to hang on the walls and that digital photography allowed in a parade of amateurs who can make dozens of iterations of the same bad photo until they hit on a good one by sheer volume and luck; if you want to be a good photographer everyone can do that why didn't you train for it and spend a long time taking photos on film and being okay with bad photography don't you know that digital photography drove thousands of people out of their jobs.

My dad told me that he plays with AI the other day. He hosts a movie podcast and he puts up thumbnails for the downloads. In the past, he'd just take a screengrab from the film. Now he tells the Bing AI to make him little vignettes. A cowboy running away from a rhino, a dragon arm-wrestling a teddy bear. That kind of thing. Usually based on a joke that was made on the show, or about the subject of the film and an interest of the guest.

People talk about "well AI art doesn't allow people to create things, people were already able to create things, if they wanted to create things they should learn to create things." Not everyone wants to make good art that's creative. Even fewer people want to put the effort into making bad art for something that they aren't passionate about. Some people want filler to go on the cover of their youtube video. My dad isn't going to learn to draw, and as the person who he used to ask to photoshop him as Ant-Man because he certainly couldn't pay anyone for that kind of thing, I think this is a great use case for AI art. This senior citizen isn't going to start cartooning and at two recordings a week with a one-day editing turnaround he doesn't even really have the time for something like a Fiverr commission. This is a great use of AI art, actually.

I also know an artist who is going Hog Fucking Wild creating AI art of their blorbos. They're genuinely an incredibly talented artist who happens to want to see their niche interest represented visually without having to draw it all themself. They're posting the funny and good results to a small circle of mutuals on socials with clear information about the source of the images; they aren't trying to sell any of the images, they're basically using them as inserts for custom memes. Who is harmed by this person saying "i would like to see my blorbo lasciviously eating an ice cream cone in the is this a pigeon meme"?

The way I use machine-generated art, as an artist, is to proof things. Can I get an explosion to look like this. What would a wall of dead computer monitors look like. Would a ballerina leaping over the grand canyon look cool? Sometimes I use AI art to generate copyright free objects that I can snip for a collage. A lot of the time I use it to generate ideas. I start naming random things and seeing what it shows me and I start getting inspired. I can ask CrAIon for pose reference, I can ask it to show me the interior of spaces from a specific angle.

I profoundly dislike the antipathy that tumblr has for AI art. I understand if people don't want their art used in training pools. I understand if people don't want AI trained on their art to mimic their style. You should absolutely use those tools that poison datasets if you don't want your art included in AI training. I think that's an incredibly appropriate action to take as an artist who doesn't want AI learning from your work.

However I'm pretty fucking aggressively opposed to copyright and most of the "solid" arguments against AI art come down to "the AIs viewed and learned from people's copyrighted artwork and therefore AI is theft rather than fair use" and that's a losing argument for me. In. Like. A lot of ways. Primarily because it is saying that not only is copying someone's art theft, it is saying that looking at and learning from someone's art can be defined as theft rather than fair use.

Also because it's just patently untrue.

But that doesn't really answer your question. Why reblog machine-generated art? Because I liked that piece of art.

It was made by a machine that had looked at billions of images - some copyrighted, some not, some new, some old, some interesting, many boring - and guided by a human and I liked it. It was pretty. It communicated something to me. I looked at an image a machine made - an artificial picture, a total construct, something with no intrinsic meaning - and I felt a sense of quiet and loss and nostalgia. I looked at a collection of automatically arranged pixels and tasted salt and smelled the humidity in the air.

I liked it.

I don't think that all AI art is ugly. I don't think that AI art is all soulless (i actually think that 'having soul' is a bizarre descriptor for art and that lacking soul is an equally bizarre criticism). I don't think that AI art is bad for artists. I think the problem that people have with AI art is capitalism and I don't think that's a problem that can really be laid at the feet of people curating an aesthetic AI art blog on tumblr.

Machine learning isn't the fucking problem the problem is massive corporations have been trying hard not to pay artists for as long as massive corporations have existed (isn't that a b-plot in the shape of water? the neighbor who draws ads gets pushed out of his job by product photography? did you know that as recently as ten years ago NewEgg had in-house photographers who would take pictures of the products so users wouldn't have to rely on the manufacturer photos? I want you to guess what killed that job and I'll give you a hint: it wasn't AI)

Am I putting a human out of a job because I reblogged an AI-generated "photo" of curtains waving in the pale green waters of an imaginary beach? Who would have taken this photo of a place that doesn't exist? Who would have painted this hypersurrealistic image? What meaning would it have had if they had painted it or would it have just been for the aesthetic? Would someone have paid for it or would it be like so many of the things that artists on this site have spent dozens of hours on only to get no attention or value for their work?

My worst ratio of hours to notes is an 8-page hand-drawn detailed ink comic about getting assaulted at a concert and the complicated feelings that evoked that took me weeks of daily drawing after work with something like 54 notes after 8 years; should I be offended if something generated from a prompt has more notes than me? What does that actually get the blogger? Clout? I believe someone said that popularity on tumblr gets you one thing and that is yelled at.

What do you get out of this? Are you helping artists right now? You're helping me, and I'm an artist. I've wanted to unload this opinion for a while because I'm sick of the argument that all Real Artists think AI is bullshit. I'm a Real Artist. I've been paid for Real Art. I've been commissioned as an artist.

And I find a hell of a lot of AI art a lot more interesting than I find human-generated corporate art or Thomas Kincaid (but then, I repeat myself).

There are plenty of people who don't like AI art and don't want to interact with it. I am not one of those people. I thought the gay sex cats were funny and looked good and that shitposting is the ideal use of a machine image generation: to make uncopyrightable images to laugh at.

I think that tumblr has decided to take a principled stand against something that most people making the argument don't understand. I think tumblr's loathing for AI has, generally speaking, thrown weight behind a bunch of ideas that I think are going to be incredibly harmful *to artists specifically* in the long run.

Anyway. If you hate AI art and you don't want to interact with people who interact with it, block me.

[crazyforclones]

Girl dads 🥚🪨❤️

Thinking about Sage recently. I adore her characters in the games and how it really allows us to explore more of the emotional side of Dr eggman. So I’ve been thinking of ways Sage could fit into the movie universe and came to these conclusions.

Alternate universe: Sort of an accidental project that Robotnik had been working on for years because he never had any one person who stuck around. So he tried to make a responsive Ai that would deal with his ramblings. Once Stone came around, the project was put on hold and forgotten.

After the end of sonic movie three, Dr Robotnik survived and him and Stone continue to hide out. Robotnik now having some fresh generational trauma and daddy issues tried to keep his mind busy and continues the SAGE project. She is accessible only through the weird virtual reality headset thing they have and Robotnik starts to develop a connection to the AI. Using it in his own way to cope with his child neglect. By giving his own creation something he never had (he already treats them like his babies in the movies so it’s obviously he has no issue forming attachments to machines he creates). However she is unstable and they continue to struggle to preserve her uniqueness.

Thats all I really have as a base line of an idea. This was more thrown together cause I wanted some sweet Stone and Sage interactions and Robotnik of course. I love found family RAAAA- anyway. Enjoy tehee