Back to basics AI

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🧠 AI Terms You Need to Know (Part 1)

AI can seem complex, but most of it comes down to a few essential ideas. These 7 terms explain how AI models learn, improve, and power the tools we use every day. Master these, and you’ll understand how the AI engine actually runs.

1. AGI (Artificial General Intelligence) - AGI refers to AI that can think and work across many tasks, just like a human. It’s not just good at one thing, it’s designed to be useful in nearly any situation. Experts are still debating how close we are to reaching it.

2. AI Agent - An AI agent is a system that doesn’t just give you answers; it takes action. It can book flights, send emails, or write code without constant human input. Think of it as an intelligent assistant that can handle real tasks on your behalf.

3. Deep Learning - Deep learning is a way for AI to learn from large amounts of data using multi-layered algorithms. It helps AI recognize patterns like faces, voices, or even styles of writing. 

4. Neural Network - Neural networks are the structure behind deep learning, inspired by the way the brain works. They process data in layers, making connections and improving over time.

5. Training - Training is the process where AI learns from examples, like millions of books or images. The more data it sees, the better it becomes at predicting or generating something useful. 

6. Fine-Tuning - Fine-tuning adjusts a trained AI model to perform well on a specific task, like legal advice or customer support. It uses new, focused data to make the model more accurate in a chosen area. 

7. Transfer Learning - Transfer learning lets AI models reuse what they’ve already learned for new tasks. It saves time and resources by avoiding training from scratch. It’s especially useful when data is limited but performance still matters.

These terms explain how AI evolves from raw data to smart decisions. If you understand training, tuning, and learning, you can see how AI gets smarter and where to improve it. Knowing this helps you build, use, or trust AI more confidently.

Together with Motion

👏 Timeless Advertising Principles Meet Modern AI Tools In This Can’t-Miss Virtual Event

Over 11,000 marketers joined last year. This year, it’s your turn.

Join Motion’s 3rd annual Make Ads That Convert, happening May 27–June 4.

This isn’t just another virtual event, it’s a live, online masterclass series led by some of DTC’s top advertising minds, including AI experts Alex Cooper, Jimmy Slagle, and Jacob Posel.

🧑‍💻 Discover AI tools that accelerate classic market research, and even better, create your own ads during hands-on workshops, including access to new AI tools you haven't discovered yet.

Learn how to apply timeless advertising principles to modern tools. Each session builds on the last, allowing you to workshop new ads in real-time. 

Last year, over 11,000 people registered for Make Ads That Convert. This year, everyone (including your competitors) will be there.  

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🎬 DeepAgent by Abacus AI Can Now Create Monetizable TikTok + Reels Content , AutomaticallyInsights via x

DeepAgent isn’t just a text-to-video tool, it’s a full AI video agent that can generate, narrate, animate, and package short-form content for platforms like TikTok, Reels, and YouTube Shorts. And yes, it’s 100% legal to monetize.

Step-by-Step: How to Create Videos with DeepAgent

1. Go to deepagent.abacus.ai: Create an account and access the agent interface, designed for automated video workflows.

2. Choose a Topic or Upload a Script: You can input a topic like “How black holes form” or “Top 10 sci-fi movies of 2024,” or write your own script.

3. Select a Format: Choose from video formats like:

• AI Influencer (face + voice avatar)
• Top 10 explainer with narration
• Cartoon-style explainer
• Nature + travel showcase (visual-first)

4. Let the Agent Create: DeepAgent writes the voiceover, selects visuals, animates characters (if needed), and outputs the video in portrait format, perfect for TikTok or Reels.

5. Download and Monetize: Export the video and upload it directly. Since DeepAgent outputs royalty-free assets and avatars, it’s monetization-safe across platforms.

DeepAgent turns content into influence, no video editing, no filming, just prompts and publish. For creators, marketers, and educators, it’s a no-brainer shortcut to going viral (and getting paid).

🤖 AI Terms You Need to Know (Part 2)

AI systems don’t just learn, they also reason, make mistakes, and generate content in different ways. These next 7 terms explain how AI works in real-time, creates images or text, and sometimes produces errors. It’s everything you need to understand the risks and creativity behind today’s top tools.

The Decode:

1. Large Language Model (LLM) - LLMs are the brains behind chatbots like ChatGPT, Claude, and Copilot. They’re trained on billions of words and can predict what to say next based on your prompt. They use deep neural networks to understand and generate human-like text.

2. Inference -  Inference is when a trained AI uses its knowledge to give you a real-time response. It’s the final step where the model makes a prediction, writes a sentence, or solves a problem. 

3. Chain of Thought - Some problems need more than one step to solve, like math, code, or logic puzzles. Chain-of-thought helps the AI break a question down into smaller parts before answering. It takes longer, but it usually leads to better, more accurate results.

4. Hallucination - When AI makes up facts or gives wrong info, it’s called a hallucination. This happens when the model fills in gaps with guesses instead of real knowledge. 

5. GAN (Generative Adversarial Network) - GANs are made of two AIs: one generates content, and the other judges how real it looks. They’re powerful for visual content, but not ideal for general-purpose AI tasks.

6. Diffusion  - Diffusion models work by starting with random noise and learning how to turn it into something meaningful, like an image or piece of music. It’s a step-by-step reverse process, like restoring a blurry photo into a clear one. 

7. Distillation - Distillation compresses a big AI model into a smaller, faster one that keeps most of its smarts. It works like a “teacher-student” setup: the big model teaches the small one to act the same. 

These terms show how AI reasons, creates, and sometimes gets it wrong. From hallucinations to GANs, they explain what today’s models can (and can’t) do. Knowing them helps you use AI more safely, creatively, and effectively.

🏆 Tools you Cannot Miss:

🧪 ChatPlayground AI – The go-to platform for comparing AI models. Test, benchmark, and explore ChatGPT, Claude, Gemini, and more, side by side.

🎬 ShortsNinja – Create viral, faceless short-form videos in minutes. Perfect for YouTube Shorts, Reels, and TikTok.

📂 Skywork – Crush admin tasks with AI-powered productivity agents. Say goodbye to paperwork overload.

📰 AI Newsletter Generator – Generate high-converting newsletters on autopilot. Boost engagement and ROI without writing a word.

📧 Mailzen.ai – Draft emails, summarize meetings, and cut through inbox chaos. AI co-pilot for your daily communication flow.

🚀 Quick Hits

📦 What if ranking #1 on Amazon didn’t require massive ad spend? Brands like Magic Spoon and Blueland scaled micro-influencer campaigns using Stack Influence and saw a 13X revenue lift. Book a free call today and discover how Stack Influence multiplies your revenue in just 60 days!

😡 Marjorie Taylor Greene slammed Grok as “left-leaning” after it criticized her QAnon ties and Jan 6 defense, ironically following Grok’s own bug-fueled spread of Holocaust denial and white genocide conspiracies.

💨Microsoft’s Aurora AI, trained on over a million hours of data, predicts typhoons, air quality, and storms faster than traditional systems, and even outperformed the National Hurricane Center in cyclone forecasts.

🍟 Nvidia plans a cheaper Blackwell AI chip for China to comply with U.S. export restrictions, aiming to retain market share amid tightening regulations while still offering advanced AI capabilities, sources say.

💰Oracle plans to invest $40B in 400,000 Nvidia GB200 chips to lease compute power to OpenAI’s Stargate data center project, an aggressive move to dominate U.S. AI infrastructure amid global competition.

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