Defining the Differences between MLOps, ModelOps, DataOps & AIOps https://
bit.ly/42f1zP7 By @ingliguori @kenovy_it #AI #MachineLearning #MLOps
MACHINE LEARNING
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Differences between MLOps, ModelOps, DataOps and AIOps
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Text prediction machinery has cognitive quirks, not ideal simulation
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You should care about weird machinery underlying text predictions that has its own cognitive quirks, not imagine it as an ideal simulacrum plus noise.
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Outer Optimizers and Inner Optimizers: Beyond Naive Reward Function Pursuit
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From my perspective, the point of raising the example of natural selection is that it debunks the naive belief that if in general an outer optimizer trains on reward function, it gets an inner optimizer that pursues that reward function OOD. Saying "But SGD is first-order and
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Direct Preference Optimization: Language Models as Reward Models
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Direct Preference Optimization: Your Language Model is Secretly a Reward Model Rafailov et al.: https://
arxiv.org/abs/2305.18290 #ArtificialIntelligence #DeepLearning #MachineLearning -
Direct Preference Optimization: Language Model as Reward Model
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Direct Preference Optimization: Your Language Model is Secretly a Reward Model Rafailov et al.: https://
arxiv.org/abs/2305.18290 #ArtificialIntelligence #DeepLearning #MachineLearning -
ML Systems Cannot Magically Recover Hidden Information
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A prevalent cognitive fallacy around ML systems is the belief that they can magically recover hidden information that is out of reach for human experts — "the AI detected that those Apollo pictures are fake!", "the AI can predict if the person in the picture is a criminal!",
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Linear Regression Should Stop Being Called AI
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can finally stop calling Linear Regression as AI.
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DocLLM: Visual Document Reasoning with Bounding Box Spatial Layout
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8/ DocLLM – an extension to traditional LLMs for reasoning over visual documents; focuses on using bounding box information to incorporate spatial layout structure; demonstrates SoTA on 14 of 16 datasets across several document intelligence tasks.
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LLM Augmented LLMs: Composing Models for Expanded Capabilities
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5/ LLM Augmented LLMs – explore composing existing foundation models with specific models to expand capabilities; introduce cross-attention between models to compose representations that enable new capabilities.
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Fast Inference of Mixture-of-Experts Models via Offloading
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6/ Fast Inference of Mixture-of-Experts – achieves efficient inference of Mixtral-8x7B models through offloading; designs a MoE-specific offloading strategy that enables running Mixtral-8x7B on desktop hardware and free-tier Google Colab instances.