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Large Transformer Model Inference Optimization

Large transformer models are mainstream nowadays, creating SoTA results for a variety of tasks. They are powerful but very expensive to train and use. The extremely high inference cost, in both time and memory, is a big bottleneck for adopting a powerful transformer for solving real-world tasks at scale.
Why is it hard to run inference for large transformer models? Besides the increasing size of SoTA models, there are two main factors contributing to the inference challenge (Pope et al. ....

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Kaj Labs: Lithosphere (LITHO) Blockchain Developer Plans to Extend $830M Credit Facility to Genesis, Digital Currency Group (DCG)

Seattle, Washington (Newsfile Corp. - January 19, 2023) - Lithosphere blockchain core developer KaJ Labs is willing to help Digital Currency Group (DCG) with its insolvency issues by extending a credit ....

Dorothy Marley , Kostenloser Wertpapierhandel , University Way , Washington Newsfile Corp , Digital Currency Group , Kaj Labs Foundation , Neural Networks , Kaj Labs , Deep Neural Networks , Currency Group ,

Kaj Labs: Lithosphere (LITHO) to Launch Finesse P2E Game & NFT Collection on Tron Network

New York, New York (Newsfile Corp. - January 11, 2023) - Lithosphere developer KaJ Labs will utilize TRON for the Finesse P2E game and the upcoming NFT collection from the game. Lithosphere announced ....

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Insights on the Mobile Biometrics Global Market to 2028 - Increase in Platforms Using Biometric Authentication Drives Growth

/PRNewswire/ The "Global Mobile Biometrics Market Size, Share & Industry Trends Analysis Report By Industry, By Technology, By Authentication Mode (Single. ....

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Frontiers | Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It

Artificial Neural Networks have reached ‘Grandmaster’ and even ‘super-human’ performance’ across a variety of games, from those involving perfect-information, such as Go ((Silver et al. (2016)); to those involving imperfect-information, such as ‘Starcraft’ (Vinyals et al. (2019)). Such technological developments from AI-labs have ushered concomitant applications across the world of business, where an ‘AI’ brand-tag is fast becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong - an autonomous vehicle crashes; a chatbot exhibits ‘racist’ behaviour; automated credit-scoring processes ‘discriminate’ on gender etc. - there are often significant financial, legal and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “. all the impressive achievements of deep learning amount to just curve fitting”. The key, Pearl suggests (Pearl and ....

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