Research

Robust adversarial inputs

We’ve created images that reliably fool neural network classifiers when viewed from varied scales and perspectives. This challenges a claim from last week that self-driving cars would be hard to trick maliciously since they capture images from multiple…

Uncategorised

Predict Stock Prices Using RNN: Part 1

This is a tutorial for how to build a recurrent neural network using Tensorflow to predict stock market prices. The full working code is available in github.com/lilianweng/stock-rnn. If you don’t know what is recurrent neural network or LSTM cel…

Uncategorised

An Overview of Deep Learning for Curious People

(The post was originated from my talk for WiMLDS x Fintech meetup hosted by Affirm.)
I believe many of you have watched or heard of the games between AlphaGo and professional Go player Lee Sedol in 2016. Lee has the highest rank of nine dan and many w…

Safety & Alignment

Learning from human preferences

One step towards building safe AI systems is to remove the need for humans to write goal functions, since using a simple proxy for a complex goal, or getting the complex goal a bit wrong, can lead to undesirable and even dangerous behavior. In collabor…

Research

Learning to cooperate, compete, and communicate

Multiagent environments where agents compete for resources are stepping stones on the path to AGI. Multiagent environments have two useful properties: first, there is a natural curriculum—the difficulty of the environment is determined by the skill of …

Research

OpenAI Baselines: DQN

We’re open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with published results. We’ll release the algorithms over upcoming months; today’s release includes DQN and three of its va…

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