Machine learning is at the forefront of AI. With applications to computer vision, natural language processing, and more, ML has enormous implications for the future of tech! However, as our reliance on ML increases, so does our concerns about ML security.

I’m not talking about a robot uprising, but rather something much more realistic — the threat of Adversarial Examples.

What are Adversarial Examples?

In short, Adversarial Examples are model inputs that are specifically designed to fool ML models (e.g. neural networks). What’s scary about this is that adversarial examples are nearly identical to their real life counter parts — by adding a small…

HalfCheetah Model featured by OpenAI Gym + MujoCo

A cheetah might be a bit of an exaggeration, but using Deep Reinforcement Learning, I was able to train a cheetah based physics model to run!

While this might not seem immediately exciting, let me put it this way — before training, the agent (cheetah) didn’t have any prior knowledge about movement. It had access to its action space and observation space, and knew that it had to move as far as possible, but that’s it! Any coordinated movement was solely a product of training using a reinforcement learning algorithm.

In this article, I’ll be breaking down how I trained…

During recent years, deep learning has become somewhat of a buzzword in the tech community. We always seem to hear about it in news regarding AI, and yet most people don’t actually know what it is! In this article, I’ll be demystifying the buzzword that is deep learning, and providing an intuition of how it works.

Building the Intuition

Generally speaking, deep learning is a machine learning method that takes in an input X, and uses it to predict an output of Y. …

Doom Deathmatch

Deep reinforcement learning is at the forefront of AI. In combining recent advances in Deep Learning with the psychology based reinforcement learning, we’ve been able to create crazy AI that’s been able to outperform humans in complex environments- all without any labelled data!

To experiment with the power of DRL, I built a program that allows an agent to self-play doom deathmatch — in this article, I’ll be breaking down the techniques used in creating my agent, alongside with a few algorithms that are at the forefront of DRL.

Github link for program: Doom

Summary: Deep Q Networks

In Q Learning, the Q value…

James Liang

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