Remove 2017 Remove Deep Learning Remove Supervised Learning
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DeepMind

Dataconomy

is dedicated to creating systems that can learn and adapt, a fundamental step toward achieving General-Purpose Artificial Intelligence (AGI). Technology and methodology DeepMind’s approach revolves around sophisticated machine learning methods that enable AI to interact with its environment and learn from experience.

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ChatGPT's Hallucinations Could Keep It from Succeeding

Flipboard

OpenAI has pioneered a technique to shape its models’ behaviors using something called reinforcement learning with human feedback (RLHF). Having a human periodically check on the reinforcement learning system’s output and give feedback allows reinforcement learning systems to learn even when the reward function is hidden. “I’m

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What Is a Transformer Model?

Hacker News

First described in a 2017 paper from Google, transformers are among the newest and one of the most powerful classes of models invented to date. They’re driving a wave of advances in machine learning some have dubbed transformer AI. Now we see self-attention is a powerful, flexible tool for learning,” he added. “Now

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Large language models (LLMs)

Dataconomy

LLMs are advanced AI systems that leverage machine learning to understand and generate natural language. By using deep learning and large datasets, LLMs can mimic human language patterns, providing coherent and contextually relevant outputs.

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Explosion in 2017: Our Year in Review

Explosion

spaCy In 2017 spaCy grew into one of the most popular open-source libraries for Artificial Intelligence. Highlights included: Developed new deep learning models for text classification, parsing, tagging, and NER with near state-of-the-art accuracy. spaCy’s Machine Learning library for NLP in Python. Released Prodigy v1.0,

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Supervised learning is great — it's data collection that's broken

Explosion

Prodigy features many of the ideas and solutions for data collection and supervised learning outlined in this blog post. It’s a cloud-free, downloadable tool and comes with powerful active learning models. Transfer learning and better annotation tooling are both key to our current plans for spaCy and related projects.

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The Full Story of Large Language Models and RLHF

Hacker News

The core process is a general technique known as self-supervised learning , a learning paradigm that leverages the inherent structure of the data itself to generate labels for training. Fine-tuning may involve further training the pre-trained model on a smaller, task-specific labeled dataset, using supervised learning.