Grokipedia will exceed the content breadth, depth and accuracy of Wikipedia by >1000%DogeDesigner (@cb_doge):BREAKING: More pages from Grokipedia are now ranking #1 on Google ahead of woke Wikipedia.Keep sharing and citing Grokipedia links. https://t.co/VEYWec4zQy ...

Speed and accuracy ...

5/As AI becomes the layer users rely on for facts, accuracy is only the baseline.Institutions need AI systems where every conclusion can show its work.That’s exactly what @PolyhedraZK is building: AI that must verify its own reasoning before its answers ever reach the public. ...

And it becomes exponentially more accurate ...

voyage-3-large embedding model just topped the RTEB leaderboard!It's a big deal because it:- ranks first across 33 eval datasets- outperforms OpenAI and cohere models- supports quantization to reduce storage costsHere's another reason that makes this model truly superior:Most retrieval benchmarks test models on academic datasets that don’t reflect real-world data.RTEB, on the other hand, is a newly-released leaderboard on HuggingFace that evaluates retrieval models across enterprise domains like finance, la ...

Grokipedia will exceed Wikipedia by several orders of magnitude in breadth, depth and accuracyGrummz (@Grummz):I think we are all underestimating the real future impact of Grokipedia.The more I think about it, the more in awe I am.It’s not just a wiki website. ...

Model Calibration Importance - Modern neural networks can be misleading due to overconfidence in predictions [1][2] - Calibration ensures predicted probabilities align with actual outcomes, crucial for reliable decision-making [2][3] - Overly confident but inaccurate models can lead to suboptimal decisions, exemplified by unnecessary medical tests [3] Calibration Assessment - Reliability Diagrams visually inspect model calibration by plotting expected accuracy against confidence [4] - Expected Calibration Error (ECE) quantifies miscalibration, approximated by averaging accuracy/confidence differences across bins [6] Calibration Techniques - Calibration is important when probabilities matter and models are operationally similar [7] - Binary classification models can be calibrated using histogram binning, isotonic regression, or Platt scaling [7] - Multiclass classification models can be calibrated using binning methods or matrix and vector scaling [7] Experimental Results - LeNet model achieved an accuracy of approximately 55% with an average confidence of approximately 54% [5] - ResNet model achieved an accuracy of approximately 70% but with a higher average confidence of approximately 90%, indicating overconfidence [5] - ResNet model thinks it's 90% confident in its predictions, in reality, it only turns out to be 70% accurate [2]

If anyone wants to read it from May - I still think this is pretty accuratehttps://t.co/obeeCNhQRPIcoBeast.eth🦇🔊 (@beast_ico):https://t.co/OIr34DuX7Z ...

It highlighted it’s purely for testing purposes and will be reviewed by a human.But I suspect this could evolve as more trust can be placed in the accuracy of the process. https://t.co/SMNzD2fvDz ...

RT vittorio (@IterIntellectus)prediction markets outperform polls because when you put skin in the game, you select for accuracy over narrativecapital is realizing thismarkets are becoming mechanisms for reality itself ...