Eight AI Startups Winning the Race for Tech Talent https://
bit.ly/3YwVfRy #AI #MachineLearning #DeepLearning #LLMs #DataScience
MACHINE LEARNING
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Eight AI Startups Winning the Race for Tech Talent
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TokenSplit: Discrete Token Speech Separation Model at Interspeech
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At 3:30pm today, drop by the #Interspeech2023 Google booth to hear @ScottTWisdom & @HakanErdoganPhD discuss TokenSplit, a speech separation model that uses discrete token sequences representing audio signals & can improve output by using transcripts or separated audio inputs.
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Fighting Wildfire with Artificial Intelligence Technology
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Fighting wildfire with AI | LinkedIn https://
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Facial Emotion Recognition with VGG13 and FER+ Dataset
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🚀Blog-tastic Tuesdays🚀https://t.co/46uGOucL1F
— Satya Mallick (@LearnOpenCV) 22 août 2023
Our research article dives deep into the interdisciplinary nature of Facial Emotion Recognition (FER), bridging Computer Vision, Machine Learning, and Psychology. It explores a custom VGG13 model and the FER+ dataset to create a… pic.twitter.com/hs9Q4aShVqBlog-tastic Tuesdays https://
learnopencv.com/facial-emotion
-recognition/
… Our research article dives deep into the interdisciplinary nature of Facial Emotion Recognition (FER), bridging Computer Vision, Machine Learning, and Psychology. It explores a custom VGG13 model and the FER+ dataset to create a -
Limitations of Synthetic User Responses and API Constraints
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what are the limitations of generating synthetic user responses? API limits an issue?
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AI-Powered Dynamic UI/UX: Real-Time Interface Adaptation
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Who's using AI to create dynamic UI/UX? *not AI → design, but using AI to change interfaces for users on the fly
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Web scraping and model training data integration discussion
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i'll wget your post into my model's training data.
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ChatGPT vs ReST: A New Approach
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ChatGPT was trained using RLHF, while Google, through its DeepMind entity, proposes an alternative approach called ReST—and this could significantly accelerate many processes. Paper link here: https://arxiv.org/pdf/2308.08998.pdf
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Building Systems for ML: Storage, Compilers, and IR Design
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storage systems, compilers, … all will be useful. A talk I gave at Stanford a while back has some material on this (although focuses on building systems in the context of more traditional IR systems rather than ML systems).
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Software Engineering Skills Essential for ML Systems Development
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I think there's a lot of benefit in general software engineering and computer systems skills for people doing ML engineering. ML systems are often part of a larger system, so understanding good SW engineering practices, networking, performance engineering, OSes, …