Connect with us

Future of AI & Trends

Playable World Models: The Ultimate AI Revolution in Gaming & Simulation

Published

on

Playable World Models

The line between playing a video game and creating one is about to blur into oblivion. A recent flurry of activity, kicked off by a cryptic tweet from Google DeepMind CEO Demis Hassabis, has pulled back the curtain on the next frontier of artificial intelligence: Playable World Models. This isn’t just about generating videos; it’s about generating entire, interactive, and explorable 3D environments from a simple prompt. As the technology behind models like Google’s Veo 3 becomes indistinguishable from high-end game engines, we’re witnessing a paradigm shift that could redefine not only the gaming industry but the very path toward Artificial General Intelligence (AGI).

Demis Hassabis's tweet hinting at Playable World Models, showing a cyberpunk video game world.
Demis Hassabis hints at the exciting future of generative interactive environments, sparked by Google’s latest AI video technology.

What Are Playable World Models?

The conversation exploded when AI enthusiast Jimmy Apples asked a simple question to Google’s Logan Kilpatrick: “playable world models wen?” Demis Hassabis jumped in with a sly reference to Tron: Legacy, “now wouldn’t that be something…” The video that sparked this all, a demo from Google’s Veo 3, showcases a cyberpunk city so detailed and fluid it looks like a scene from a AAA video game. This is the core of the concept: AI that doesn’t just create a static image or a linear video, but generates a dynamic, playable 3D world you can actually interact with.

This idea builds on an open secret in the AI industry: game engines are the training grounds for AI. For years, companies have used synthetic data from engines like Unreal Engine to train models. OpenAI’s Sora was rumored to be trained on such data, and it’s been used to create realistic simulations for training self-driving cars. Now, the tables are turning. Instead of just learning from games, AI is beginning to build them.

Google’s Groundbreaking Work: From Veo 3 to Genie 2

Google DeepMind is at the forefront of this revolution with several astonishing projects that demonstrate the power of generative interactive environments.

Veo 3: When Video Generation Looks Like a Game

The latest demonstrations from Veo 3 show its incredible capability to generate high-fidelity, game-like videos. The seamless camera movements, consistent character models, and dynamic environments are so advanced that they naturally lead to the question: “When can I play this?”

Genie 2: Creating Playable Worlds from a Single Image

This is where things get truly mind-blowing. Google’s Genie 2 is an AI model that can take a single input—a text prompt, a real-world photo, or even a simple hand-drawn sketch—and generate a fully playable, interactive 2D world based on it. The model, trained on over 200,000 hours of internet gaming videos, learns the cause-and-effect of player actions without any specific labels. You can walk, jump, and interact within a world that was literally dreamed up by an AI moments before.

Examples of Google's Genie 2 creating playable world models from a text-to-image prompt, a hand-drawn sketch, and a real-world photo.
Genie 2 can generate playable 2D platformers from any image, heralding a new era of on-the-fly game creation.

The Neural Dream: Simulating Entire Games Like DOOM

Pushing the concept further is GameNGen, another Google DeepMind project. This is not a game engine; it’s a neural model that simulates the game DOOM entirely on its own. It’s not running the original game’s code. Instead, it’s generating the next frame in real-time based on the player’s inputs. For short bursts, its output is indistinguishable from the actual game. It’s like an AI dreaming a game into existence, responding to your every move. This proves that a neural network can learn the complex rules and physics of a game world purely through observation.

Beyond Creation: Training Generalist AI Agents with SIMA

While creating games on the fly is incredible, the ultimate goal is much larger. Google’s SIMA (Scalable, Instructable, Multiworld Agent) is a generalist AI agent designed to learn and operate across numerous 3D virtual environments. SIMA was trained on a variety of commercial video games, from No Man’s Sky to Goat Simulator 3.

What makes SIMA different is its ability to understand natural language commands. A human can tell it to “collect wood,” and the AI, simply by looking at the screen like a human player, will figure out how to navigate to a tree and perform the necessary actions. It’s learning to map language to complex behaviors within diverse game worlds, a crucial step for creating truly intelligent agents. For more on how AI is learning to interact with complex systems, you can explore the latest in AI Technology Explained.

The Bigger Picture: Why Playable World Models Matter for the Future of AI

This technology has two monumental implications that extend far beyond entertainment.

1. Revolutionizing Game Development

For game developers, this technology promises to drastically lower development costs and supercharge creativity. Tools like Microsoft’s Muse, designed for “gameplay ideation,” will allow creators to rapidly prototype and test ideas. Non-coders could soon be able to generate entire game levels and mechanics with a simple sketch or a few lines of text, democratizing game creation for everyone.

2. The Ultimate Goal: Simulations and the Path to AGI

The most profound application is in creating massive-scale simulations, or “world models.” These are not just video games; they are complex, dynamic digital twins of reality. By creating millions of these virtual environments, we can:

  • Generate limitless data to train more advanced AI agents and robotics.
  • Run complex scientific simulations, like modeling the spread of a disease, as was unofficially done by studying a plague in World of Warcraft years ago.
  • Test economic and social policies in a safe, controlled environment before implementing them in the real world.

This is the path to AGI. The ability to create and understand these simulated realities is fundamental to building an AI that can generalize its knowledge across any task or environment, whether virtual or physical. [SUGGESTED INTERNAL LINK: You can follow the latest developments in this area in our Future of AI & Trends section.]

The Visionaries: From Demis Hassabis to John Carmack

It’s fascinating that the brightest minds in AI are all converging on this idea. While Demis Hassabis and Google DeepMind are pushing the boundaries of generative worlds, another legend is tackling it from a different angle. John Carmack, the creator of DOOM, is now working on AGI with his company Keen Technologies. His approach? To have physical robots learn by playing video games. By grounding AI learning in both the virtual and physical worlds, he aims to create agents that can truly generalize their understanding.

Whether it’s AI generating games or robots playing them, the message is clear: the rich, complex, and rule-based environments of video games are the perfect sandbox for forging the next generation of artificial intelligence. What we are seeing with playable world models is not just the future of gaming, but a foundational step towards a simulated reality that could help us solve some of the world’s most complex problems. It truly is “something.”

For an in-depth look at one of these projects, read Google DeepMind’s official post on Genie.

AI News & Updates

Google Veo 3 Tutorial: The Ultimate Guide to AI Video

Published

on

Google Veo 3 Tutorial

What if you could turn your wildest imagination into stunning, cinematic reality just by typing a sentence? Google’s latest innovation is making that possible. Welcome to the complete beginner’s Google Veo 3 tutorial, where we’ll walk you through exactly how to use this mind-blowing AI video generator, from your first prompt to your final masterpiece.

Google just released Veo 3, an AI video tool that transforms simple text prompts into high-quality, cinematic videos. In this guide, we’ll cover how to get started, write effective prompts, and unlock the most powerful features of this game-changing technology—even if you’re brand new to AI video creation.

Turn simple prompts into cinematic reality with Google Veo 3.
Turn simple prompts into cinematic reality with Google Veo 3.

Table of Contents

  1. What is Google Veo 3?
  2. How to Get Access to Google Veo 3
  3. How to Use Google Veo 3: A Step-by-Step Guide
  4. Advanced Prompting: Let Gemini Be Your Creative Partner
  5. How to Find and Download Your Generated Videos
  6. Final Thoughts: The Future is Here

What is Google Veo 3?

Veo 3 is Google’s latest and most advanced AI video generation model, developed by the brilliant minds at DeepMind. It allows you to create incredibly polished videos from nothing more than a text prompt.

Unlike many other AI tools, Veo 3 has a deep understanding of cinematic language. It comprehends concepts like:

  • Camera Movement: Specify drone shots, slow pans, or time-lapses.
  • Lighting & Composition: Describe the mood with terms like “dramatic lighting,” “golden hour,” or “eerie twilight.”
  • Visual Styles: Generate everything from photorealistic scenes to animated shorts.

But what truly sets it apart is its ability to generate a complete audio-visual experience. Veo 3 doesn’t just create silent clips; it automatically adds background music, ambient sound effects, and even voice narration that matches the scene, making the results feel incredibly natural and complete.

How to Get Access to Google Veo 3

To use Veo 3, you need a paid Google One AI Premium plan. The good news is that you can get a free trial for the first month, giving you a chance to explore everything this powerful tool can do.

Both the Google AI Pro and Google AI Ultra plans include access to Veo 3. In addition to video generation, these plans bundle other premium features like advanced Gemini capabilities directly in Google Docs and Gmail, plus a massive 2TB of cloud storage.

How to Use Google Veo 3: A Step-by-Step Guide

Once you’ve signed up for a plan, this part of our Google Veo 3 tutorial will show you just how easy it is to start creating.

  1. Go to Gemini: Head over to gemini.google.com and sign in with your Google account.
  2. Activate the Video Tool: At the bottom of the chat interface, you’ll see a prompt field. Below it, click on the tool labeled “Video”. This activates Veo 3 for your next prompt.
  3. Write Your Prompt: This is where the magic happens. Be as descriptive as possible. The more detail you provide, the closer the result will be to your vision. For example:A cinematic slow-motion shot of freshly baked chocolate chip cookies being pulled out of the oven in a cozy, sunlit kitchen. Warm lighting, soft focus, steam rising, and gentle background music.
  4. Submit and Generate: Hit the submit button and let Veo 3 work its magic. In a short time, your video will be ready to view!
Simply click the "Video" button in Gemini to start your creation.
Simply click the “Video” button in Gemini to start your creation.

Adding Narration to Your Videos

One of Veo 3’s coolest features is adding custom narration. To do this, simply include the word Narration: in your prompt, followed by the text you want spoken enclosed in quotation marks.

For example: ...Narration: "History is being made — the Kevin Cookie Company unveils the world’s largest chocolate chip cookie."

Veo will generate a fitting voice to speak your lines, complete with appropriate background music and sound effects, creating a truly impressive final product.

Advanced Prompting: Let Gemini Be Your Creative Partner

Not sure how to phrase your prompt to get that epic, cinematic feel? Just ask Gemini for help!

Since Veo 3 is integrated into Gemini, you can use the same chat interface to brainstorm and refine your ideas. Before activating the video tool, simply ask Gemini for help. For example, you could type:

"Can you help me write a cinematic video prompt about a team of bakers making the world’s largest cookie?"

Gemini will provide you with several detailed options, including suggestions for strong adjectives (epic, colossal), camera shots (close-up, wide shot), lighting, and sound. You can then copy, paste, and tweak these suggestions to create the perfect prompt. It’s a fantastic trick to get the best results.

 “For more great tips, check out our other AI How-To’s & Tricks.”

How to Find and Download Your Generated Videos

If you ever want to revisit a video you created earlier, it’s incredibly simple.

On the left-hand side of the Gemini interface, you’ll see a list of your “Recent” chats. Simply click on the chat conversation where you generated the video. The video will be right there in the chat history.

To download it, hover your mouse over the video, and a download icon will appear in the top-right corner. Click it to save an MP4 file of your creation directly to your computer.

Final Thoughts: The Future is Here

With tools like Google Veo 3, we’ve officially entered an era where professional-quality video creation is accessible to everyone. The line between what’s real and what’s generated by AI is becoming increasingly blurry.

As you start your journey with this incredible tool, you’ll unlock a new level of creative freedom. So go ahead, give it a try, and see what you can bring to life from your imagination.

Continue Reading

AI News & Updates

Weekly AI News: Ultimate Reveal of Shocking AI Updates

Published

on

Weekly AI News

The Attention Economy Shift: ChatGPT’s App Downloads Threaten Social Media Giants

In a surprising turn of events, the application for OpenAI’s ChatGPT is on the verge of eclipsing the combined iOS downloads of social media titans like TikTok, Facebook, and Instagram. This isn’t just a fleeting trend; it signals a fundamental shift in user behavior. Users are migrating from passive “doomscrolling” on entertainment platforms to engaging with intelligent tools that boost their productivity.

A chart showing ChatGPT app downloads nearing the total of other social media apps, illustrating the latest weekly AI news.

Data from Similarweb shows ChatGPT’s downloads (black line) rapidly approaching the combined total of leading social apps.

According to data from Similarweb, OpenAI’s tool has garnered 29 million installs compared to the 33 million for the dominant social trio. This trend shows that deep value is now challenging viral reach. We are witnessing the dawn of a new era where the center of digital gravity is shifting from mere content consumption to the adoption of smart, productive tools. For more analysis on AI’s impact, you can explore our Future of AI & Trends section.

New Research Agents Break Records

The race for the most powerful research agent is heating up, with a new contender from China making waves.

Kimi Researcher: The New Benchmark King

Moonshot AI’s new research agent, Kimi Researcher, has shattered records on the “Humanity’s Last Exam” (HLE) benchmark, scoring an impressive 26.9%. This performance surpasses established models like Google’s Gemini Deep Research and OpenAI’s DeepSearch. Kimi’s success lies in its sophisticated training, utilizing end-to-end agentic Reinforcement Learning (RL). The agent performs 23 reasoning steps and explores over 200 links for a single task, showcasing its depth. In our test, it provided a highly detailed and well-structured report on global investment opportunities, proving its powerful analytical capabilities.

Bar charts comparing the performance of Kimi Researcher against Gemini and OpenAI on various AI benchmarks.
Kimi Researcher’s performance on HLE and other benchmarks compared to its competitors.

A Prompt to Create Your Own Research Agent for Free

You don’t need a paid tool to get powerful, web-enabled research. We’re sharing an exclusive prompt that transforms any free LLM with search capabilities (like the free version of Gemini) into a dedicated research agent. This technique, which we use to gather our weekly AI news, automates comprehensive research without the filler. You can find this powerful prompt in our AI How-To’s & Tricks section (coming soon!).

Google Shakes Up the Developer World with Gemini CLI

In a strategic move set to redefine the developer landscape, Google has launched the Gemini CLI. This open-source, command-line interface (CLI) tool puts the immense power of Gemini models directly into a developer’s terminal—completely free of charge. This move is a direct challenge to paid tools like Anthropic’s Claude Code and OpenAI’s Codex.

The Gemini CLI is not just another addition; it’s a competitive weapon. It offers:

  • Integration with Google Search for web-enabled queries.
  • Direct interaction with local files and command execution.
  • An enormous 1 million token context window, allowing it to process entire codebases.

This launch democratizes access to top-tier AI coding assistance, raising the bar for competitors and putting immense pressure on their paid business models.

Controversies and High Stakes in the AI Race

Elon Musk’s “History Sieving” Project

Elon Musk recently unveiled a new, and frankly alarming, project for xAI. The goal is to use Grok 3.5 to “sieve” the entire corpus of human knowledge—all written information available online—to correct errors and fill in missing information. While the stated aim is to create a refined knowledge base, the project raises a critical question: Who gets to define “truth”? The idea of a single entity curating human history and knowledge is deeply problematic, as what one group considers a myth, another may hold as a foundational belief. This project is one of the most concerning pieces of weekly AI news we’ve encountered.

Apple Faces Fraud Lawsuit Over Siri

Apple is now facing a class-action lawsuit from shareholders accusing the company of fraud. The plaintiffs allege that Apple’s leadership, including Tim Cook, knowingly exaggerated Siri’s AI capabilities and misled investors about the timeline for its integration. This gap between the company’s grand promises and the technical reality has allegedly cost the company approximately $900 billion in market value. The case highlights the immense pressure in the AI race, which can lead major players to make costly, overblown claims.

More Groundbreaking AI Updates

  • Perplexity Video Generation: Perplexity now allows free video generation directly on X (formerly Twitter) using the VEO-3 model. Simply mention their account @AskPerplexity in a tweet with your prompt.
  • FLUX.1 Kontekt [dev] Release: Black Forest Labs has released an incredibly powerful open-source image editing model that outperforms giants like Google and OpenAI while maintaining facial identity.
  • AlphaGenome by DeepMind: This revolutionary AI model can predict the likelihood of diseases by “reading” DNA sequences. It represents a massive leap from reactive medicine to proactive, predictive healthcare.
  • ElevenLabs Voice Design V3: Creating custom, expressive AI voices is now easier than ever. This new tool allows users to generate voices with specific emotions like crying, laughing, and even singing, simply from a text prompt.
Continue Reading

AI How-To's & Tricks

ChatGPT Reasoning Models: The Ultimate Guide to Stop Wasting Time

Published

on

ChatGPT Reasoning Models

OpenAI is rolling out new ChatGPT features at a dizzying pace, making it tough to keep up, let alone figure out which updates are actually useful. Between “reasoning models,” “deep research,” and “canvas,” it’s easy to get lost in meaningless jargon. This guide cuts through the noise and gives you a simple framework to understand the most crucial new updates, starting with the difference between Chat Models and the powerful new ChatGPT Reasoning Models.

We’ll show you exactly when to use each feature with practical, real-world examples, so you can stop wasting time and start getting better results from AI.

                                             The simple decision tree for choosing the right ChatGPT model.

Choosing the Correct ChatGPT Model: The #1 Most Important Update

The most significant recent change in ChatGPT is the introduction of distinct model types. While the names and numbers (like oX, o-mini, GPT-4o) change quickly, the core concept is what matters: knowing when you need a Chat Model versus a Reasoning Model.

The Simple Rule: Chat vs. Reasoning Models

Here’s the only rule you need to remember. Ask yourself one question: “Is my task important or hard?”

  • If the answer is YES (the task is complex, high-stakes, or requires deep thought), use a Reasoning Model (e.g., oX). You might wait a few extra seconds, but the quality of the answer is worth the trade-off.
  • If the answer is NO (the task is simple, low-stakes, and you need a fast response), use a Chat Model (e.g., GPT-4o).

Think of it like choosing a partner: pick the one with the cleanest name (like oX) and avoid the ones with extra baggage at the end (like oX-mini). The models with simpler names are generally the most powerful reasoning engines.

Real-World Examples: When to Use a Chat Model

A Chat Model is perfect for low-stakes tasks where speed is more important than perfect accuracy.

Example 1: Basic Fact-Finding
Prompt: “Which fruits have the most fiber?”
For this, a chat model is perfect. It will give you a quick, helpful list. We don’t really care if one of the numbers is off by a single gram.

Example 2: Finding a Quote
Prompt: “Who was the guy who said ‘success is never final’ or something like that?”
The chat model will quickly identify this quote is widely attributed to Winston Churchill and provide the full context.

Real-World Examples: When to Use a Reasoning Model

For any task that requires nuance, multi-step thinking, or high-quality output, a Reasoning Model is your best bet. These models “think through” the problem before giving an answer.

Example 1: Complex, Multi-Constraint Task
Prompt: “Act as a nutritionist and create a vegetarian breakfast with at least 15 grams of fiber and 20 grams of protein.”
This is a hard task with multiple requirements. A reasoning model will analyze the constraints, calculate the nutritional values, and provide a detailed, accurate meal plan, including a grocery list.

Example 2: Nuanced Historical Analysis
Prompt: “Act as a British Historian. Explain why Winston Churchill was ousted even after winning a world war.”
This question requires deep, nuanced understanding. A reasoning model will break down the complex socio-economic factors, political landscape, and public sentiment to provide a comprehensive analysis that a simple chat model couldn’t.

Example 3: High-Stakes Email Drafting
While a simple email can be handled by a chat model, what about a messy, 20-message email thread where a stakeholder is upset? You should use a reasoning model. You can upload the entire thread as a PDF and ask it to “Write a super polite email explaining why this is a terrible idea.” The model’s ability to reason through the context and sentiment is critical for a diplomatic reply.

Internal Link Suggestion: To learn more about getting the most out of AI, check out our other guides in the AI How-To’s & Tricks section.

Pro-Tips for Prompting ChatGPT Reasoning Models

To get the best results from these advanced models, follow these three tips:

  1. Use Delimiters: Separate your instructions from the content you want analyzed. For example, put your instructions under a ## TASK ## heading and the text or data under a ## DOCUMENT ## heading. This helps the model differentiate what you want it to do from what it should analyze.
  2. Don’t Include “Think Step-by-Step”: This phrase is a crutch for older chat models. Reasoning models already do this by default, and including the phrase can actually hurt their performance.
  3. Examples Are Optional: This is counter-intuitive, but reasoning models excel at “zero-shot” prompting (giving instructions with no examples). Only add examples if you’re getting wrong or undesirable results and need to guide the model more specifically.
Structuring your prompt with delimiters helps reasoning models perform better.
Structuring your prompt with delimiters helps reasoning models perform better.

Mastering Other Powerful ChatGPT Features

Beyond choosing the right model, here’s how to leverage other key ChatGPT features.

When to Use ChatGPT Search vs. Google Search

The trap here is forgetting that Google Search still exists and is often better. Here’s the rule:

  • For a single fact (e.g., stock price, weather today): Use Google Search. It’s faster.
  • For a fact with a quick explainer: Use ChatGPT Search. For example, instead of just asking for NVIDIA’s stock price, ask: “When was NVIDIA’s latest earnings call? Did the stock go up or down? Why?” ChatGPT will provide the stock chart and a detailed analysis of the context.

How to Use ChatGPT Deep Research Effectively

Deep Research is like an autonomous agent that spends 10-20 minutes browsing dozens of links to produce a detailed, cited report on a topic. It’s perfect for when you need to synthesize information from many sources.

Instead of manually researching NVIDIA, AMD, and Intel’s earnings reports, you could use Deep Research with this prompt: “Analyze and compare the AI chip roadmaps for these three companies based on their latest earnings calls.”

Pro-Tip: Deep Research works best with comprehensive prompts. To save time, use a custom GPT to generate a detailed prompt template for you.

External Link Suggestion: This Deep Research Prompt Generator GPT by Reddit user u/Tall_Ad4729 is a fantastic starting point.

Unlocking the ChatGPT Canvas Feature

The rule for Canvas is simple: Toggle it on if you know you’re going to edit and build upon ChatGPT’s response more than once.

It’s ideal for tasks like drafting a performance review. You can upload a document (like a performance rubric), ask ChatGPT to draft an initial outline, and then edit it in the standalone Canvas window. You can fill in your achievements, delete sections, and even ask ChatGPT to make in-line edits, such as rephrasing a sentence or generating an executive summary based on the content you’ve added. Once finished, you can download the final document in PDF, DOCX, or Markdown format.

Bonus: My 3 Favorite Text-to-Text Commands

For any text-generation task, keep these three powerful command words in your back pocket:

  1. Elaborate: Use this to add more detail. “Elaborate on these 3 bullet points.”
  2. Critique: Use this to spot problems early and pressure-test your ideas. “I’m arguing for more headcount based on this data; critique my approach.”
  3. Rewrite: Use this to improve previous content. “Rewrite the second paragraph using a friendly tone of voice.”

By understanding when to use ChatGPT Reasoning Models and leveraging these advanced features, you can significantly improve the quality and efficiency of your AI-powered work.

Continue Reading

Trending