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Difficulty = Value

33,959 次观看 • 1 年前 •via X (Twitter)

7 条评论

heiswize 的头像
heiswize1 年前

@grok can you elaborate more on this?

AVANZA FREELANCING AGENCY 的头像
AVANZA FREELANCING AGENCY1 年前

True

Terrence 的头像
Terrence1 年前

@Cobratate

AmandaPanter 的头像
AmandaPanter1 年前

@EthanMillerX2 this is crazy good read it like a script structured position hit my yearly goal

GP_SKY 的头像
GP_SKY1 年前

Scarcity = value

Egbe⚽️🕊 的头像
Egbe⚽️🕊1 年前

Pure Truth

no 的头像
no1 年前

Some woman just gotta go tho, you have to know what is valuable

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One of my all-time favourite games is the original Civilization from 1991. The number of hours I spent playing it is way too high, but the memories are all worth it. At some point, I set myself challenges, for example playing on the highest difficulty against the maximum number of opponents, and limiting my tech choices. Right at the start, the options "Horseback Riding" and "The Wheel" are dead-end choices. It doesn't make them obsolete (Horseback Riding will get you to Chivalry – but the road ends there… and The Wheel will eventually come into play when you research Engineering), but obviously there are much better choices at the start such as "Alphabet", "Masonry", and "Pottery" (though a dead-end tech, you still need it for Granaries). My #1 goal was usually to get to the wonders of Darwin's Voyage (2 tech advances, incredible value) and J.S. Bach's Cathedral (reducing the number of unhappy citizens in your cities by 2, also incredible value). The little animation below shows my standard tech-tree path towards both those goals. What made Civilization so incredibly wonderful and gave it such a high level of replayability was the optionality. Apart from setting planet size and conditions, you could adjust difficulty levels and then also handicap yourself through deliberate self-imposed limitations, just to see what would happen and if you could still win. Man, I love this game. What were your first two or three tech choices and what wonders did you aim for early on?

exQUIZitely 🕹️

13,029 次观看 • 8 个月前

My spoiler free thoughts on Saros so far: I’m definitely finishing this way faster than Returnal. I’m almost at 10 hours now, and if tomorrow goes well, I might actually wrap it up. The game is clearly more approachable, especially if you already understand this style of gameplay. That doesn’t mean it’s “easy”. There’s still plenty of challenge, and the boss fights and the whole gameplay loop have been really good so far. It’s just not the same level of punishment as Returnal, where one death could wipe basically everything and make a whole run feel wasted if you didn’t beat the biome boss. The game looks incredible, and the haptic feedback is on point again. Housemarque really knows how to get the most out of the DualSense and make every action feel great. The atmosphere also deserves a mention. The way the environments, sound design, and pacing come together makes every area feel alive. It’s not just pretty visuals, it’s the whole vibe of the world pulling you in. I’ll admit I would’ve loved the difficulty to match Returnal, but I get why they toned it down. A huge percentage of players didn’t even beat the first boss in Returnal, and Saros gives you ways to adjust the experience. You can push it harder or make it more forgiving depending on how you want to play. It’s also the type of game where the quality genuinely matches the price. You can feel the production value everywhere. And without going into spoilers, the story has my attention. It’s not something spectacular, but it’s engaging enough that I actually care about what’s happening, which rarely happens for me in games. I’m genuinely glad I picked it up. I had a great time today and didn’t even notice the hours passing. If you were interested in Returnal but bounced off the difficulty, or you always wanted to finish it but couldn’t, Saros is absolutely worth trying. The boss fights and the gameplay so far have been really good.

𝙉𝙄𝙆𝙁𝙊𝙍

20,019 次观看 • 5 个月前

Neuroscientist Dr. Jeff Beck from Noumenal Labs discusses the fundamental nature of representation, understanding, and modelling, comparing biological intelligence with current artificial intelligence. Jeff argues that *how* information is represented dictates predictive ability and that LLMs, while impressive at symbol manipulation and pattern matching (like next-word prediction), lack the *grounded*, causal understanding of the world inherent in biological systems. Timestamps: 00:00 - Cat visual cortex experiments & discovering orientation sensitivity (slide projector analogy) 01:49 - Representation choice and neural coding (orientation vs. feature intensity) 02:30 - Choice of representation impacts predictions; generative models 03:15 - Importance of choosing the right generative model for predictions 03:35 - The problem: We don't know the brain's true generative model 03:55 - Theory of Mind (ToM) in LLMs 04:05 - Jeff Beck's ToM tests on early ChatGPT (stapler example) 05:40 - ChatGPT recognizing the ToM test vs. passing it 06:32 - Analogy: LLMs recognizing known problems vs. generalizing (sum/product riddle) 07:25 - Do LLMs implicitly build world models? Vicarious experience analogy 07:59 - The difference: Grounding symbols in reality outside language 08:35 - AI Alignment: Difficulty in capturing human reward functions & belief formation 09:21 - Nightmare scenario: Humans as "complacent value function selectors" 09:44 - Hope: AI enhancing human understanding, not replacing thought 10:08 - Philosophy of science: Science realism vs. modeling pockets of regularity 10:39 - Noise in models as ignorance or deliberate exclusion (design choice) 11:00 - Design choices in science, controlled experiments, and induced bias 11:29 - Are there true, discoverable mathematical laws of the universe? 11:41 - Is there a "true" ground truth distribution (P)? Beck's answer: No (with nuance) 12:55 - Ontological vs. Epistemological divide: Perfect models vs. models of regularities 13:21 - Are scientific models "false by definition"? The Bayesian perspective 14:07 - "All knowledge is conditional"; Are foundational theories (e.g., FEP) true or just perspectives? 14:51 - FEP as a mathematical framework, not a theory; models are just models 15:54 - Legibility vs. Utility: Useful but illegible AI models 16:01 - Prediction vs. Explanation: Trusting black boxes can be unsatisfying 16:30 - Why understanding AI matters: Ensuring alignment with human decisions/values 17:08 - Line-of-sight legibility as an alignment approach 17:14 - Benefits of explainable AI: Human understanding and value alignment verification 18:21 - RL components: Prediction engine, reward function, policy; the alignment challenge 19:25 - Trusting AI = Trusting its policy aligns with our reward + its superior beliefs 19:57 - Language: Intrinsic representation vs. pointers between shared minds 20:21 - Why language works: Shared internal models and common grounding 21:00 - Basis of shared understanding: Not linguistic, but shared experience/intuitive physics 22:44 - Consciousness and language as lossy, simplified summaries of complex brain processes 23:22 - Evidence for simplification: Brain regions, perception vs. representation; limits of language models 24:32 - Counterpoint: Language captures complex/ambiguous human concepts 24:54 - Language as massive compression: The information bottleneck (Meister's paper) 26:22 - Implication: Language/actions are poor representations of internal understanding 27:03 - Can language models understand? The mimicry argument (Piantadosi) 27:33 - Beck's skepticism: LLMs excel at prediction/mimicry, not true understanding 28:09 - LLM explanations replicate structure but lack grounding 28:54 - Beck's test for LLM understanding: Genuine novelty beyond training data 29:19 - Summary: Symbol manipulation is not understanding; grounding is key 30:06 - Abstraction and Idealization in scientific modeling ("The Brain Abstracted") 30:45 - Revisiting Newton: Intuitive physics is correct for our world; idealizations are simplifications 32:01 - Sophistication & boundaries: Nested systems vs. one complex system? 32:32 - The boundary problem in FEP/Markov Blankets: Where to partition? 33:41 - Beck's research: Finding principled partitions based on interaction dynamics 35:44 - Beyond direct experience: Imagination, language, and learning 36:16 - Human creativity: Creating new *things* by combining modeled objects (Systems Engineering) 37:43 - Goal for AI: Automating systems engineering for creative combination 38:17 - Sutton's "Reward is Enough" paper 38:25 - The challenge of "Reward is Enough": Defining and obtaining the *right* reward function 39:02 - Difficulty of eliciting individual reward functions 39:52 - The core alignment problem: Accessing and representing individual reward functions 40:13 - Impossibility: Disentangling beliefs and rewards from observed actions 41:51 - Argument analogy: Disagreements stem from different beliefs or values 43:00 - Prerequisite for value inference: Understanding belief formation 43:13 - Building aligned systems: Sparsity of data, meta-models vs. base system modification 43:46 - Proposed solution: AI layer that models the human's belief formation system 44:40 - Alignment process: Align beliefs first, then address value differences 45:00 - Conclusion CC Maxwell Ramstead

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17,828 次观看 • 1 年前

Have We Changed our Approach to Hamstring Injuries? 🎯 Five key points for clinical practice: 1⃣ - Strength and Flexibility are weak risk factors for hamstring injuries and continue to form a small but important part of the causal pathway. Although the evidence for stretching is lacking, multi-faceted prevention programmes might consider including these components to be successful in the prevention of hamstring injuries. 2⃣ - Intrinsic neuromuscular function may be altered post-injury. Clinicians should focus on returning the player to full function during the rehabilitation of hamstring injuries, which may include specific targeted intervention even after return to play. 3⃣ - Our common strength and flexibility tests have poor predictive value, and do not possess the characteristics needed to successfully identify individual players at greater risk of hamstring injury. This is evident in the large amount of variability between seasons, and poor sensitivity and specificity demonstrated for these measurements. The wide overlap in distribution of pre-season strength between injured and uninjured players demonstrates the difficulty in identifying a subgroup of at-risk players that might benefit from targeted intervention. It is therefore recommended that prevention programs be implemented for all players. 4⃣ - Workload monitoring provide another potential way in which we may reduce the risk of injury. The aggregation of chronic workload, as well as careful planning of acute workload increases, may reduce the risk of hamstring injury. However, high-level evidence to support this is lacking. 5⃣ - Understanding the context in which injuries occur and appreciating the complex nature of these injuries are important considerations to better manage players with hamstring injuries 🔗Read the full article 👉 ✍️ Roald Bahr & Nicol van Dyk

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