AI learns to write its own code by stealing from other programs
Generating Object Proposals
This is a followup post to A Seismic Shift in Object Detection. In the earlier post I discussed the resurgence of segmentation for object detection, in this post I go into more technical detail about the algorithms for generating the segments and object proposals. If you haven’t yet, you should read my previous post first. First, a brief historical overview. In classic segmentation the goal was to
Google's Hand-Fed AI Now Gives Answers, Not Just Search Results
Google answers these questions with the help from deep neural networks, a form of artificial intelligence rapidly remaking not just Google's search engine but the entire company and, well, the other giants of the internet, from Facebook to Microsoft. Deep neutral nets are pattern recognition systems that can learn to perform specific tasks by analyzing vast amounts of data. In this case, they've l
A brand new dance partner: Teaching AI to dance
As data scientists gradually inch machines toward human levels of intelligence, they’ve begun to tackle some very human endeavours. At Peltarion, an artificial intelligence (AI) expert and dance choreographer teamed up to find out if an AI model can not only learn a dancer’s style, but create its very own artistically compelling choreography. Art has always worked hand in hand with technology to p
Neural Networks Are Impressively Good At Compression
Neural Networks Are Impressively Good At Compression by Malte Skarupke I’m trying to get into neural networks. There have been a couple big breakthroughs in the field in recent years and suddenly my side project of messing around with programming languages seemed short sighted. It almost seems like we’ll have real AI soon and I want to be working on that. While making my first couple steps into th
Gym
Gym is a standard API for reinforcement learning, and a diverse collection of reference environments¶ The Gym interface is simple, pythonic, and capable of representing general RL problems: import gym env = gym.make("LunarLander-v2", render_mode="human") observation, info = env.reset(seed=42) for _ in range(1000): action = policy(observation) # User-defined policy function observation, reward, ter
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