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 ...

Accuracy of Economic Data - The public, CEOs, and the Fed need accurate and timely economic numbers from agencies like the BLS for decision-making [3] - Removing individuals who create noise against the president is expected to improve the accuracy of economic numbers [4] Political Influence on Agencies - There is a desire to replace individuals perceived as being against the president with those who support the administration's goals [2] - The administration aims to have agencies that are supportive and focused on producing correct numbers [1] Economic Outlook - The expectation is for the US economy to experience significant growth [4]

Of course, there is a trade-off between accuracy and size. As we reduce the size, its accuracy drops (check the video).But in most cases, accuracy is not the only metric we optimize.Instead, several operational metrics like efficiency, memory, etc., are the key factors. https://t.co/zoivU2E627 ...

Neural Network Performance - Removing 74% of neurons from a neural network only decreased accuracy by 0.50% [1]