One way of putting the human back in the loop. // @SnorkelAI // #ArtificialIntelligence #MachineLearning #deeplearning
MACHINE LEARNING
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McLaren Uses DataRobot AI to Analyze Racing Performance Data
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🏁 @McLarenF1 turned to DataRobot to analyze racing data and unlock actionable insights in an environment where one-tenth of a second can be the difference.#F1 #BahrainGPhttps://t.co/0BMyveNfsO pic.twitter.com/46rCXv0Vb1
— DataRobot (@DataRobot) 17 novembre 2022@McLarenF1 turned to DataRobot to analyze racing data and unlock actionable insights in an environment where one-tenth of a second can be the difference. #F1 #BahrainGP https://
datarobot.com/customers/mcla
ren/
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Fast Text-Conditional Discrete Denoising Vector-Quantized Latent
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Fast Text-Conditional Discrete Denoising on Vector-Quantized Latent Spaces Rampas et al.: https://
arxiv.org/abs/2211.07292 #ArtificialIntelligence #DeepLearning #MachineLearning -
Streamline Data Integration, Governance, and Virtualization on AWS
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Learn step-by-step how to streamline data integration, data governance, and data virtualization on @awscloud
: https://
ibm.co/3g5tvli
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#IBMDeveloper #machinelearning #AI -
Smart Cameras: AI Deep Learning for Safety Security Privacy
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Some #Smart Maths 🙂 #AI + #DeepLearning = Smart #cameras With benefits across #safety #security #privacy #mobility & #waste Meet AvidBeam ! https://
insight.tech/retail/intelli
gent-video-analytics-activate-smart-cameras?utm_source=twitter&utm_medium=organic&utm_campaign=2022-tdc-eaves
… #SmartCities #ITInfluencer #SmartNews #cities #IntelPartner #photography #CyberSecurity #IoT #SDGs #news -
Reliable Knowledge from Parametric Memory in AI Remains an Open Problem
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Yeah — getting reliable, truthful knowledge entirely from parametric memory seems like an open problem. The near-term solutions all involve grounding with queries to external resources.
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AI Prompting Techniques and Hallucination Challenges
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I spent some time trying to solve this problem through CoT/scratchpads but there were often secondary hallucinations, e.g. over-skepticism of non-trick questions. Nothing seemed to work as well as the zero-shot above.
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Language Models Continue Sequences from Prompts, Not Maximize Rewards
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they don't maximize rewards, they are given a prompt (a kind of inception) and continue the sequence
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Extending LLMs to Vision: Incremental Multimodal Integration with Flamingo
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Extending LLMs from text to vision will probably take time but, interestingly, can be made incremental. E.g. Flamingo (
https://
storage.googleapis.com/deepmind-media
/DeepMind.com/Blog/tackling-multiple-tasks-with-a-single-visual-language-model/flamingo.pdf
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Why LLMs Process Text Instead of Raw Pixels
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Interestingly the native and most general medium of existing infrastructure wrt I/O are screens and keyboard/mouse/touch. But pixels are computationally intractable atm, relatively speaking. So it's faster to adapt (textify/compress) the most useful ones so LLMs can act over them