Building AI agents using LangChain
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LangChain
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LangChain
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“AI agent”—a fancy buzzword for AI model integration, or is there more to it? With increasing autonomy and decision-making capabilities, are we just rebranding models, or is this a true paradigm shift?
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When you ship AI into a vehicle, the consequences of failure aren’t abstract. They’re not a bad user review or a dip in engagement metrics. They’re a driver who takes their eyes off the road because the system said something confidently wrong, or a voice assistant that crashes mid-navigation and leaves someone lost on an unfamiliar highway. Shipping AI where failures have physical consequences changes how you think about deployment — permanently.
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TL;DR: We tested whether you actually need labeled training data to route queries between a cheap local LLM and an expensive cloud model. You don’t. Average token log-probability — available for free from the first query — matches supervised routing in-distribution and crushes it when the query distribution shifts. We tested across 3 model families, 2 datasets, ~4,500 queries, and $123 in cloud costs. All code and data are open.
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An in-progress lab-notebook post from the Autodidact project.
Published:
LangChain
Published:
“AI agent”—a fancy buzzword for AI model integration, or is there more to it? With increasing autonomy and decision-making capabilities, are we just rebranding models, or is this a true paradigm shift?
Published:
TL;DR: We tested whether you actually need labeled training data to route queries between a cheap local LLM and an expensive cloud model. You don’t. Average token log-probability — available for free from the first query — matches supervised routing in-distribution and crushes it when the query distribution shifts. We tested across 3 model families, 2 datasets, ~4,500 queries, and $123 in cloud costs. All code and data are open.
Published:
An in-progress lab-notebook post from the Autodidact project.
Published:
When you ship AI into a vehicle, the consequences of failure aren’t abstract. They’re not a bad user review or a dip in engagement metrics. They’re a driver who takes their eyes off the road because the system said something confidently wrong, or a voice assistant that crashes mid-navigation and leaves someone lost on an unfamiliar highway. Shipping AI where failures have physical consequences changes how you think about deployment — permanently.
Published:
I’m excited to share that our paper “A Novel Golden-Chip-Free Clustering Technique Using Backscattering Side Channel for Hardware Trojan Detection” won the Best Student Paper Award at the 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST).
Published:
TL;DR: We tested whether you actually need labeled training data to route queries between a cheap local LLM and an expensive cloud model. You don’t. Average token log-probability — available for free from the first query — matches supervised routing in-distribution and crushes it when the query distribution shifts. We tested across 3 model families, 2 datasets, ~4,500 queries, and $123 in cloud costs. All code and data are open.
Published:
An in-progress lab-notebook post from the Autodidact project.
Published:
I’m excited to announce that I will be giving a talk as part of the Cybersecurity Lecture Series at Georgia Tech on January 24, 2020.
Published:
When you ship AI into a vehicle, the consequences of failure aren’t abstract. They’re not a bad user review or a dip in engagement metrics. They’re a driver who takes their eyes off the road because the system said something confidently wrong, or a voice assistant that crashes mid-navigation and leaves someone lost on an unfamiliar highway. Shipping AI where failures have physical consequences changes how you think about deployment — permanently.
Published:
I’m excited to announce that I will be giving a talk as part of the Cybersecurity Lecture Series at Georgia Tech on January 24, 2020.
Published:
I’m excited to share that our paper “A Novel Golden-Chip-Free Clustering Technique Using Backscattering Side Channel for Hardware Trojan Detection” won the Best Student Paper Award at the 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST).
Published:
I’m excited to announce that I will be giving a talk as part of the Cybersecurity Lecture Series at Georgia Tech on January 24, 2020.
Published:
I’m excited to share that our paper “A Novel Golden-Chip-Free Clustering Technique Using Backscattering Side Channel for Hardware Trojan Detection” won the Best Student Paper Award at the 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST).
Published:
TL;DR: We tested whether you actually need labeled training data to route queries between a cheap local LLM and an expensive cloud model. You don’t. Average token log-probability — available for free from the first query — matches supervised routing in-distribution and crushes it when the query distribution shifts. We tested across 3 model families, 2 datasets, ~4,500 queries, and $123 in cloud costs. All code and data are open.
Published:
An in-progress lab-notebook post from the Autodidact project.
Published:
“AI agent”—a fancy buzzword for AI model integration, or is there more to it? With increasing autonomy and decision-making capabilities, are we just rebranding models, or is this a true paradigm shift?
Published:
I’m excited to share that our paper “A Novel Golden-Chip-Free Clustering Technique Using Backscattering Side Channel for Hardware Trojan Detection” won the Best Student Paper Award at the 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST).
Published:
When you ship AI into a vehicle, the consequences of failure aren’t abstract. They’re not a bad user review or a dip in engagement metrics. They’re a driver who takes their eyes off the road because the system said something confidently wrong, or a voice assistant that crashes mid-navigation and leaves someone lost on an unfamiliar highway. Shipping AI where failures have physical consequences changes how you think about deployment — permanently.
Published:
TL;DR: We tested whether you actually need labeled training data to route queries between a cheap local LLM and an expensive cloud model. You don’t. Average token log-probability — available for free from the first query — matches supervised routing in-distribution and crushes it when the query distribution shifts. We tested across 3 model families, 2 datasets, ~4,500 queries, and $123 in cloud costs. All code and data are open.
Published:
An in-progress lab-notebook post from the Autodidact project.
Published:
I’m excited to announce that I will be giving a talk as part of the Cybersecurity Lecture Series at Georgia Tech on January 24, 2020.
Published:
“AI agent”—a fancy buzzword for AI model integration, or is there more to it? With increasing autonomy and decision-making capabilities, are we just rebranding models, or is this a true paradigm shift?