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"After such a tough season, it's tolling on the mental. ... It's just completely changed my game so much." AUSL Utah Talons player Bri Ellis shares how she benefited from a four-month break and seeing a sports psychologist.

“While theater is not therapy, it can be incredibly therapeutic,” says psychologist Jenny Shields. That therapeutic effect is something participants in verbatim theater might experience, as they perform their own lived experiences rather than a fictional script.

.@pbborganization gives incarcerated individuals the chance to raise service dogs for wounded veterans, first responders, and school psychologists. The NFL is proud to partner with them to help raise three future service dogs. 🐾

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or @alayastd. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

Tom Brady reveals the brutally honest talk he had with a Michigan sports psychologist that turned him from a benchwarmer into the GOAT. Brady's transformation started with one change: the way he thought. "I sit on the bench my first year, and I really had—I would call it—a lot of self-defeating attitudes and behaviors. I always had an excuse. 'Coach doesn't want me in there.'" "I had a sports psychologist. His name was Greg Harden. I would go into his office every Tuesday, and he would say, 'Tom, I like you. You work hard, but you have a shitty attitude.'" "'How about you start worrying about what you can control and stop talking about the other quarterbacks, stop talking about the coaches not putting you in.'" "If they give you three reps, you do the best with the three you get. Quit bitching about you only getting three or you going in there with the backup receivers. No one cares.'" "'You treat practice like it's a game. If you throw a touchdown in the two-minute in practice, you celebrate like it's the game.'" Brady never approached it that way before. Then "sure enough" everything changed. "Mmy energy started getting way better. I was bringing juice; I had the right attitude." "Then all of a sudden, I'm bringing the juice, man. Every day, boom." "That [mindshift] really helped me get better." The road to 7 Super Bowl rings didn't start on the practice field; it started in that very office…

Bombshell: Oliver Sacks (a humane man & a fine essayist) made up many of the details in his famous case studies, deluding neuroscientists, psychologists, & general readers for decades. The man who mistook his wife for a hat? The autistic twins who generated multi-digit prime numbers? The institutionalized, paralyzed man who tapped out allusions to Rilke? Made up to embellish the stories. Probably also: the aphasic patients who detected lies better than neurologically intact people, including Ronald Reagan's insincerity.

Consider the Flash, with the speed and reaction time to pluck bullets out of the air. That wouldn’t keep you from getting shot in the back of the head with a supersonic round with no warning to react to. He could constantly look around, swiveling his head at thousands of RPM to look everywhere, which would be an interesting visual effect, but another option would be to compress a 360 degree visual field into the straight ahead view. There have been novelty “see behind you glasses” forever, but AFAIK, there haven’t been any attempts at giving full 360 degree vision. There have been experiments where psychologists put mirror glasses that flipped the visual field on people to test the adaptability of the brain’s visual system, and it was found that the view change was initially incapacitating, but over the course of a couple weeks, people could adapt to it. Taking them off then took a couple weeks to fully retrain for normal vision. For the Flash, it couldn’t be a camera-and-screen pass-through VR system, because you would need thousands of frames per second to accurately estimate the trajectory of a bullet, but a purely reflective system of curved mirrors could do it.  A non-linear warping would be harder to adapt to than a simple mirroring, because different eye positions would have different distortions. For conventional human reaction speeds, pass-through VR systems with eye tracking could compress everything into the peripheral vision and leave the fovea untouched, which would probably speed adaptation significantly. I can imagine special forces teams having to wear their 360 vision helmets for a couple days prior to a mission, akin to deep sea divers having an extended prebreath and decompression phase. @PalmerLuckey The comic-friendly fix is to just assume that the Speed Force gives the Flash an awareness of anything moving fast in his vicinity, although another thought I had is that while the speed of sound in air is only 330 m/s, the speed of sound in the ground is almost 10x that, so you might be able to “hear a gunshot” with your feet well before you could with your ears.

8/ These examples illustrate how prompts 1: matter and 2: are not trivial, and why today it makes sense to be a "prompt engineer" (e.g. @goodside ). I also like to think of this role as a kind of LLM psychologist.