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Model Tracking Update: 🔍 Time Frame: 6/30 - 7/22 Sport: MLB ⚾️ Bet Type: Hitters/Pitchers 🏟️ Picks: 263 🎯 Hits: 197 ✅ Misses: 66 ❌ Accuracy Average: 75% ⬆️ With a 75% accuracy rate over the last 3 weeks, our model is proving to be a GAME-CHANGER in the...

13,558 次观看 • 1 年前 •via X (Twitter)

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Soccer is booming in the United States and World Cup viewership shows there is MASSIVE interest, but it will never be the priority and always play 5th fiddle to the Big 4 sports in America unless Americans fundamentally change their approach to youth sports. American Soccer is behind the rest of the world for 3 reasons: 1. Cultural Sporting DNA In the USA, Soccer competes in a crowded field of Football, Basketball, Baseball and Hockey instead of dominating like abroad. Europe and the rest of the world built soccer through community clubs, local pride and it being their default sport. They are born with a soccer ball on their feet. 2. Pay-to-Play Barrier In the USA most youth are priced out of playing because the cost to be on the team is too expensive. Currently there is a 14% decline in youth soccer participation between ages 6 to 12. Europe and the rest of world’s accessible club model keeps more kids in the game longer. 3. Less participation in the sport An all time high 16.8 million youth played outdoor soccer in the USA in 2025, but per capita the USA is far behind other European countries like Belgium which has a 3x higher rate of participation for its population size. For American soccer to catch up, it has to change its sporting culture focus to development instead of making money and lower the cost barrier for participation through state and government funded clubs over the next 20 years. Development needs to be the priority, not maximizing how much money can be made off the athlete and their parents. Instead of focusing on the old statement of “if our best athletes played”, focus on making the best athletes want to keep playing and the ones who do play the beautiful game better at it. Then and only then can we find our Erling Haaland, Lamine Yamal, Messi, Ronaldo or Jude Bellingham. We have to build the structure for them to develop in. #FIFAWorldCup #FIFAWorldCup2026 #Football #Soccer #RG3 #OuttaPocket #USA

Robert Griffin III

275,350 次观看 • 11 天前

“Concentration - managing the game - basics of the game” A big thank you to Brentford head coach, Keith Andrews, for reinforcing the importance of mindset… As Coach Andrews is saying here, mindset is a basic of the game. It is central to the execution of every technical, tactical, and physical action. It constantly mediates the precision and accuracy of anticipation and decision-making… Attention Intensity Intent Coach Andrews bemoans players dropping in attention and shifting in intensity (Low Performance Mindset - LPM) and so subsequently not reading the game…not tracking the runners. And then he bemoans a low intent with actions (LPM again) - losing duels by not being aggressive and so unable to nullify the opposition. Oh yes Coach Andrews, yes sir, mindset lies at the heart of your players’ behaviour…and subsequent performance… Attention Intensity Intent “Our job on the pitch is to be in our High Performance Mindset (HPM). It’s to be in our HPM no matter what - high attention, optimal intensity, high intent. Every second in control and in charge. In control and in charge. Nothing and no one takes us away from our HPM. Nothing and no one…” Of course, every activity in every session counts. From a holistic warm-up to rondos and keep-balls to 11 v 11’s…all of these are opportunities to help players heighten their attention by strengthening their connections in the game and by making those connections precise and accurate…under pressure. Every activity in every session counts Every activity in every session counts Yes Coach Andrews…mindset is a basic of the game. But it’s a basic that must be worked on daily in every activity in every session… (Video from Sky Sports…please follow them on Sky Sports Premier League on X)

Daniel Abrahams

25,744 次观看 • 10 个月前

Introducing LifeGPT, showing that LLMs can simulate complex, Turing-complete systems like Conway's Game of Life with near-perfect accuracy—no prior topology needed.🌐This unlocks new potential for AI in modeling self-organizing systems in biology, materials science, & beyond.🔬🤖 #AI #LifeGPT. Cellular Automata (CA), like Conway's Game of Life ("Life"), are computationally irreducible, meaning their evolution is difficult to predict without an a-priori understanding of the rules of the game, including the topology on which it is played. LifeGPT is a topology-agnostic generative model that learns the rules of Life without prior knowledge of its grid structure or boundary conditions, from only a tiny number of game states. The success in simulating Life suggests promising avenues for scientific discovery, particularly in bridging the gap between AI, artificial life, and real-world biological systems, for both forward and inverse problems. The potential for universal computation within generative AI, including LLMs, through approaches like LifeGPT, represents an exciting area for future research, especially when combined with reinforcement learning. Model Convergence: LifeGPT exhibits rapid convergence during training, achieving high accuracy in predicting next-game-states. We attribute the non-zero cross-entropy loss to the lack of causal relationships within randomly generated ICs. Accuracy & Temperature: LifeGPT achieves near-perfect accuracy, particularly at lower sampling temperatures, but can be continually tuned towards higher creativity to discover patterns that the original ruleset would not be able to produce. This finding highlights the trade-off between model creativity (higher temperature) and accuracy in deterministic predictions, with high relevance to model real-world dynamical systems for which no closed-form rulesets exist. Zero/Few-Shot Learning: Trained on a small fraction of possible initial conditions, LifeGPT demonstrates strong zero/few-shot learning, accurately simulating Life for unseen initial conditions. However, rare prediction errors highlight that LifeGPT approximates rather than perfectly replicates the Life algorithm. Autoregressive Autoregressor: A recursive implementation of LifeGPT demonstrates the model's ability to simulate Life over multiple timesteps. LifeGPT is topology-agnostic with respect to its training data and our results show that a GPT model is capable of capturing the deterministic rules of a Turing-complete system with near-perfect accuracy, given sufficiently diverse training data. The work showcases the possibility for future models to synthesize stochastic generative capabilities with deterministic computational capabilities. Link to code, paper, etc. below. Podcast generated using #NotebookLM. LAMM@MIT DMSE at MIT

Markus J. Buehler

114,194 次观看 • 1 年前