Generative AI In Creative Jobs

Drone shows are increasingly incorporating AI technologies to enhance their performance. What do you think about this one? Here are several ways in which #AI is being utilized in drone shows: 1. Autonomous Navigation: Path Planning: AI algorithms assist drones in planning and optimizing flight paths for intricate aerial displays. Collision Avoidance: AI enables real-time analysis of the environment, helping drones avoid collisions and maintain safe distances. 2. Formation Flying: Coordination Algorithms: AI algorithms coordinate the movements of multiple drones to achieve precise formations. Real-Time Adjustments: Drones can dynamically adjust their positions in response to environmental factors or unexpected changes. 3. Swarm Intelligence: Collective Behavior: AI-driven swarm intelligence allows drones to exhibit collective behavior, creating synchronized and mesmerizing patterns. Adaptability: Drones in a swarm can adapt their behavior based on the actions of neighboring drones. 4. Real-Time Data Analysis: Environmental Sensors: Drones equipped with sensors provide real-time data on weather conditions, wind speed, and other factors. Adjusting Performances: AI analyzes this data to make real-time adjustments to the drone show, ensuring optimal performance. 5. Light and Color Choreography: Dynamic Lighting: AI algorithms control the lighting elements on drones, creating dynamic and customizable light shows. Color Synchronization: Drones can synchronize their colors and lighting patterns in real time for visually stunning effects. 6. AI-Generated Patterns: Generative Algorithms: AI is used to generate unique and artistic patterns for drone formations. Variability: Each show can be different, adding an element of surprise and creativity. 7. Gesture Recognition: Audience Interaction: AI-powered gesture recognition systems allow drones to respond to audience movements or gestures. Interactive Shows: Audience members can influence the show in real time. 8. Dynamic Choreography: Learning Algorithms: AI can learn from previous performances, adjusting choreography based on audience reactions and preferences. Continuous Improvement: Drones can adapt and improve their performances over time. 9. Logistics Optimization: Efficient Deployment: AI assists in optimizing the deployment and retrieval of drones before and after shows. Battery Management: Algorithms manage drone battery usage for extended performances. 10. Safety Measures: Emergency Protocols: AI can implement emergency protocols to ensure the safety of the drone show, such as automated landing in case of malfunctions. Monitoring Systems: AI monitors drones for any irregularities in flight behavior. 11. Sound Integration: Audio-Synchronized Displays: AI synchronizes drone movements with music or other audio elements for a fully immersive experience. #ai #innovation via @ zzmenx #drone #dronetechnology

A team of researchers from Google Research, Google DeepMind, and Tel Aviv University has developed a groundbreaking AI application capable of recreating and simulating parts of existing video games, including the iconic game Doom. In a fascinating advancement for gaming and AI, researchers have modified a machine learning model to recreate video game environments and actions. Named GameNGen, this new system uses neural rendering techniques based on diffusion models to simulate realistic gameplay. The team trained the AI by feeding it video footage of Doom, allowing it to generate new gameplay frames nearly indistinguishable from the original. This development marks a significant step in the intersection of AI and gaming, opening up new game development and simulation possibilities. 🎮 Recreating Games with AI: The research team successfully used a modified diffusion model, GameNGen, to simulate sections of the video game Doom, highlighting the potential of AI in game development. 🧠 Neural Rendering Techniques: The process relies on neural rendering, where AI learns to recreate the imagery and the actions within a game, pushing the boundaries of what AI can achieve. 🖼️ Diffusion Models in Action: Building on the Stable Diffusion 1.4 model, GameNGen is explicitly trained on video game footage, allowing it to generate new, realistic gameplay frames. ⚙️ Realistic Gameplay Simulation: The AI-generated frames were shown to human raters, who often could not distinguish them from real game footage, demonstrating the model's effectiveness. 🚀 Impact on Game Development: This technology could revolutionize the gaming industry by enabling more efficient game development and even the possibility of creating entirely new games through AI. #GameNGen #AIinGaming #NeuralRendering #MachineLearning #VideoGameAI #GenerativeAI #DoomSimulation #GoogleResearch #DeepMind #GameDevelopment 

If your AI brainstorming starts with an AI prompt  such as “give me ideas about for X,” you’re limiting your imagination.  I learned this while working through IDEO U’s Human-Centered Design and AI certificate program, which keeps reminding me that AI only supports creativity when humans stay actively involved. To test this, I ran a small experiment tied to my design challenge: how can nonprofit professionals use AI to augment their thinking so their work becomes more strategic, creative, and human-centered? Here’s what happened. When I began with human-only ideation (my own brain or a brainstorming session with other humans), the ideas were grounded in mission, constraints, and real community needs. When I switched to AI with a clear creative direction to generate ideas, I asked for absurdity.  AI delivered: costume-based learning scenes, dramatic falling sequences, Play-Doh brains, even a human–AI tango. These weren’t solutions or a waste of time. They were creative provocations that loosened up the tight mental space we often operate within. The best ideas emerged only after I cycled through several layers of human grounding, AI variation, and human synthesis. It felt like a club sandwich of thinking modes. Humans brought mission and ethics. AI widened the possibility space. Humans shaped meaning. The infographic (created in Nano Banana) shows the practices that made this work: 💡Begin with human insight. 💡Give AI a clear creative direction. 💡Separate idea expansion from idea selection. 💡Use reflective checkpoints. 💡Treat AI as a partner, not a replacement. This experiment makes me think that the real value of AI in nonprofit brainstorming is less about efficiency and more about expanding imagination. When humans guide the process, AI becomes a thought-partner for more human-centered creativity. What would open up in your work if your organization treated AI as a creative partner instead of a shortcut?

Last week, Google DeepMind launched Genie 3 - and the tech community lit up like it was Christmas in August. Why? Because Genie isn’t just another text-to-video model. It is a real‑time world model that turns a short text or image prompt into a navigable 3D scene that streams at 720p/24fps, preserves state and layout for minutes, and supports “promptable world events” (e.g., add rain, drop in characters) without resetting the scene. Until now, AI video tools could only show you something like magic cameras that spit out short clips. Genie 3 is different. It doesn’t just render a video, it creates a world you can step into, explore, and change in real time. Imagine taking a photo of a beach, and suddenly you’re not looking at a picture - you’re walking on the sand, watching waves roll in. It’s the difference between a postcard and a playable simulation. This jump from flat video to interactive, persistent worlds may sound subtle, but it’s seismic. Because once AI can generate worlds, 5 things happen: (1) AI gets a childhood. Humans learn by acting in environments - stacking blocks, knocking them over, testing cause and effect. AI has lacked that loop, trained instead on frozen internet data. Genie provides infinite synthetic childhoods where agents can practice safely and cheaply. DeepMind has long argued that environments are the missing ingredient in intelligence; Genie 3 makes them infinite. (2) Media transforms from consumption to participation. Content stops being watched and starts being lived inside. Games, stories, even ads become simulations you can enter, alter, and exit. A trailer becomes playable; a memory video becomes explorable. Platforms built on “time spent watching” will need to measure something new: decisions made. The line between game, story, and commerce will collapse. (3) Creation flips from production to direction. Today’s games are assembled painstakingly - asset by asset, rule by rule. Genie collapses that pipeline into intention: describe → generate → interact. The innovation isn’t graphics; it’s velocity. The next billion “games” may not come from studios but from anyone with a sketchpad and imagination. (4) Google’s latent advantage becomes visible. If worlds are the new data, Google owns the richest video corpus (YouTube) to train them, the devices (Android, Chromebooks) to deploy them, and an agent stack (SIMA) hungry for environments. OpenAI has the leading chat product; DeepMind may end up owning the “world layer” - the operating system for synthetic environments. (5) Governance will be harder than generation. A bad video disappears after 30 seconds. A bad world persists. Mis-specified physics, harmful content, or deepfake locations won’t just vanish. Moderating evolving, interactive environments will make today’s AI alignment debates look quaint. Thanks to Genie, everyone’s now humming I can show you the world - shining, shimmering, splendid. Strap in for this magic carpet ride.

I meet with hundreds of creators around the world every year, from independent designers to global creative directors. In conversation after conversation, one thing is clear: creators aren’t just experimenting with AI – they’re embedding it into their workflows to expand their range, accelerate production and unlock capacity for more ambitious work.    A few consistent priorities stand out: ✅ Creators want a one-stop shop for AI models. Keeping up with all the new models – and juggling apps and accounts – slows them down. That’s why we built Adobe Firefly as an all-in-one creative AI studio, bringing leading models from Adobe, Google, OpenAI and others together in one place, at one price. ✅ Models are powerful, but in the right tools they're transformative. Using Google’s #NanoBanana directly inside Photoshop lets creators reshape a pose or reimagine a background by pairing breakthrough AI with the world's best imaging tools. ✅ Ideation with AI is liberating. Designers tell me Firefly Boards helps them take an initial sketch in hundreds of directions in minutes. Creative directors say it’s reshaping how their teams explore concepts at the earliest stages. ✅ Modular, composable workflows are the future. Project Graph, the creative system we previewed at #AdobeMAX, lets creators mix and match creative tools to move beyond prompts into visual workflows they can build, customize and reuse across Adobe's ecosystem.    I’m continually inspired by how intentionally creators adopt new technology to elevate their craft. It motivates us at Adobe to build tools that remove friction, expand possibility and help creators move from idea to impact even faster.    Learn more about why creators are excited about Project Graph ➡️ https://lnkd.in/gmMhvnbn

AI is no longer just trained on data. It’s being raised in environments. Billions are flowing into synthetic training grounds where AI agents practice: – Virtual offices to learn scheduling and negotiation. – Simulated warehouses to master logistics. – Entire artificial marketplaces to test decision-making. This is the shift: from studying the past (datasets) to shaping behavior in invented environments. And here’s the power play: whoever builds these environments sets the rules. Not just what AI knows — but how it acts. That’s the new empire being built. It’s not about bigger models or faster chips anymore. It’s about who controls the environments where AIs learn how to operate — and what hidden incentives and biases get baked in. Are we ready for a handful of companies to design the operating conditions of the machines that will run our future? And who has a role to play in this? #AI #Business #Transformation #Next https://lnkd.in/eSnz6bjy

A very useful in-depth study out this week on how humans work with AI agents. The research analyzed 2,200 participants in the process of creating 11,000 advertisements, analyzing speed, quality, and creative variety. The findings: 🚀 AI agents supercharge individual output. Collaborating with AI agents increases individual productivity by 50% per worker compared to human-only teams. These teams also maintain higher completion rates for specific tasks like headlines and primary text. AI appears to effectively substitute for a second human collaborator. 🏔️ Skills follow a jagged frontier of capability. Collaborating with AI improves the quality of ad text but results in lower image quality compared to human-human teams. This uneven capability means that AI-assisted ads perform better on click-through rates and view duration, while human-designed ads are more effective at lowering cost-per-click. 🤖 Social maintenance disappears in AI collaboration. Humans + AI teams send 25% more task-oriented messages but 18% fewer interpersonal messages than human-only teams. Because AI requires no rapport-building, encouragement, or conflict resolution, communication becomes more instrumental and directive. ✍️ Work shifts from doing to directing. Humans make 62% fewer direct text edits when working with AI, choosing instead to delegate 17% more of the workload to their digital partners. This creates a "delegation workflow" where the human moves into a supervisory role, spending less time on manual production and more time providing instructions and feedback. 📉 Automation leads to a collapse in creative diversity. Humans + AI teams produce outputs that are more self-similar and less unique than those produced by human pairs. While delegating to AI can improve the average quality of text, it consistently reduces the variety and atypicality of the final content, leading to a homogenization of creative output. 🧐 Knowing your partner is AI changes how you work. Recognition of a partner's identity acts as a key behavioral moderator; participants who correctly identify their partner as AI are significantly more task-oriented and more likely to delegate work. Awareness of AI’s capabilities allows humans to override social defaults like politeness or turn-taking, and adopt more efficient, calibrated collaboration strategies. A really solid study, but there is a lot more to uncover in the realities of human-AI agent work and complementarity.

I built an automated AI game design team with multi-agents 🤯 That helps you design game concepts from start to finish. Each AI agent has a specific role: 1. Story Agent  ↳ Creates compelling narratives and characters ↳ It builds entire worlds and storylines automatically 2. Gameplay Agent  ↳ Designs core mechanics ↳ It balances fun and challenge perfectly 3. Visual Agent  ↳ Handles all art direction ↳ From UI design to character aesthetics 4. Tech Agent  ↳ Plans the technical architecture ↳ It knows exactly what tools to use Led by a Task Agent that coordinates everything: ↳ Ensures all ideas work together  ↳ Manages the creative flow  ↳ Keeps the vision cohesive Think of it like a real game studio: The Task Agent is your project lead, Making sure everyone's ideas align with the vision. When the Story Agent pitches an epic narrative, The Task Agent checks if it fits the gameplay goals. When the Tech Agent suggests an engine, The Task Agent ensures it supports the art direction. It's like having a virtual game design director. Just input your requirements: → Game type → Target audience → Art style → Technical constraints And watch as the Task Agent orchestrates the design process. This isn't about replacing game designers. It's about enhancing the creative process. Small studios can explore ideas faster. Independent developers can validate concepts. Students can learn game design principles. The future of game design is collaborative. Want to try it yourself? Link to the tutorial and GitHub repo in the comments. P.S. I create these tutorials and opensource them for free. Your 👍 like and ♻️ repost helps keep me going. Don't forget to follow me Shubham Saboo for daily tips and tutorials on LLMs, RAG and AI Agents.

Ever wondered how to elevate your AI-generated videos from looking generic to truly cinematic? In my latest video, I dive into how applying real-world filmmaking techniques, tools, and terminology can help you create stunning visuals that stand out. By referencing actual film stocks, lenses, and camera angles, you can transform AI-driven imagery into something that feels authentically handcrafted. In this video, I show an example by describing a scene in #Sora a “feather-footed rooster floating in a futuristic spaceship”—and then enhance it with cinematic attributes like Kodak 5274 film stock, anamorphic lens flares, and an orange-teal color grade. The result is a richer, more compelling final image. If you’re interested in pushing the boundaries of what AI visuals can do, start by studying how your favorite films were shot. Learn about camera equipment, lighting techniques, and framing styles. The more you understand cinema’s language, the more persuasive and visually arresting your own AI-generated creations can become. Check out the video and let’s explore the future of filmmaking together. #AI OpenAI

Microsoft just unveiled Muse — an AI model that can generate minutes of cohesive gameplay from a single second of frames and controller actions. The implications for gaming are absolutely massive. Muse is the first World and Human Action Model (WHAM), and the scale of training is mind-blowing: — Trained on 1B+ gameplay images — Used 7+ YEARS of continuous gameplay data — Learned from real Xbox multiplayer matches From a single second of gameplay + controller inputs, Muse can create multiple unique, playable sequences that follow actual game physics, mechanics, and rules. The version shown in research was trained on just a single game (Bleeding Edge). We're watching the beginning of a major shift in how games are created. Game development traditionally takes months/years of character design, animation, and gameplay testing. Scaled up models like Muse could eventually compress this cycle from months to minutes. Microsoft's research is published in Nature today, and researchers will also be releasing Muse's model weights, training data samples, and sharing the testing interface. Another win for open-source! Hot take: We'll see the first fully AI-assisted indie game hit top 10 on Steam within 18 months. Not because AI makes better games, but because it lets creative people iterate 100x faster.