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Understanding Bigscreen Beyond, EP. 001 ↳ Wired vs. Wireless VR Headsets

26,412 Aufrufe • vor 3 Jahren •via X (Twitter)

10 Kommentare

Profilbild von tim
timvor 3 Jahren

the mic reveal was crazy

Profilbild von OPEN PC Reviews
OPEN PC Reviewsvor 3 Jahren

If you keep posting videos like this, I'm gonna have no choice but to purchase this headset.

Profilbild von Mosul Medic
Mosul Medicvor 3 Jahren

I agree with the sentiment on wireless headsets. I am excited to receive my headset

Profilbild von Dovah Kro
Dovah Krovor 3 Jahren

Seeing how comfortable it looks on your head while you're talking and moving your head, makes me all the more confident in the preorder. Thank you for making the video!

Profilbild von Christian Yang
Christian Yangvor 3 Jahren

@mylesdotapp "weight, all day comfort, clarity" spot on! the aspects I care the most about as well, can't wait to get beyond😍

Profilbild von Jedigoat
Jedigoatvor 3 Jahren

Been using bigscreen since day one on the oculus quest 2, glad to see how much growth the company has made. Will the beyond ever come to Australia?

Profilbild von Bigscreen
Bigscreenvor 3 Jahren

It’s already available to preorder in Australia today!

Profilbild von Stéphan Schamp
Stéphan Schampvor 3 Jahren

This was a great explainer. Can't wait to be able to buy this in EU (Belgium)!

Profilbild von Jason Moore
Jason Moorevor 3 Jahren

The second you guys implement eye tracking I'm there. Any chance?

Profilbild von The Tech Basement
The Tech Basementvor 3 Jahren

I have ran my original Vive on the wireless adapter for over 6 hrs without discomfort or changing the 🔋. The wireless argument here doesn't hold up. Did the team think of a modular option? @Valve I'm waiting for the Deckard!

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"Aydan why don't you make videos anymore?" Why? Because over the last two years every single time I get in vr to try and film. This shit happens. It makes it so fucking hard to get motivated to do it when I can't get one locked off angle shot. Before you say something like "Have you covered all the reflective surfaces?" "Have you re-paired the trackers?" "Try replacing the lighthouses?" "Try replacing the cables?" "The Dongles might be bad?" "The Trackers might be bad?" "The Controllers might be bad?" "Try a USB hub?" "Try USB Ports?" "Try this 7-1 in one dongle hub?" "Try Tundra Trackers?" "Try This 3-1 dongle hub?" "Try pairing one controller each to a dongle, and pair the trackers to a single dongle too?" "Have you tried reinstalling the software? "Have you tried Steam VR Beta?" "Try Slime trackers?" "Try Quest Pro?" "Try less 2.4ghz band devices?" "Maybe your Wifi bulbs are interfering?" "Try Bigscreen Beyond 2e?" "Try 2 Lighthouses?" "Try 3 Lighthouses?" "Try 4 Lighthouses?" "Maybe your computers bad? Get a new one?" YES. I have fucking tried all that shit. Literally have spent thousands of dollars trouble shooting this issues. I currently own 3 tracking headsets 9 lighthouses 6 vive trackers 3 tundra trackers 8 slime trackers 5 pairs of index controllers. And I've tried on 3 different computers. I have talked to like 20 different people. No one has an answer. I'm at the end of my rope at this point. Every time I get into VR I'm filled with dread because I know I'm going to get mad or frustrated. I'm just...sad. I want to make content, but I don't fucking know what to do at this point.

Aydan

12,723 Aufrufe • vor 4 Monaten

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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