Why AI is finally going mainstream
Introducing the O’Reilly Artificial Intelligence Conference.
Artificial Intelligence
The latest insights, ideas, and tools for building solutions that rely on machine intelligence.
Machines that dream
Understanding intelligence: An interview with Yoshua Bengio.
Building practical AI systems
As a technical founder at Siri, Sentient, and Viv Labs, Adam Cheyer has helped design and develop a number of intelligent systems. Drawing on specific examples, Adam reveals techniques he uses to maximize the impact of the AI technologies he employs.
Using computer vision to understand big visual data
Alyosha Efros discusses using computer vision to understand big visual data.
Summoning the demon: My perspective from the belly of the beast of AI
Jana Eggers has been in and around the field of artificial intelligence for more than 25 years, which gives her a unique perspective on what's been accomplished in AI and what we're still missing.
The future of machine intelligence
The field of machine learning is growing fast—read interviews with leading practitioners to learn where it's headed.
Emerging technology has a definition problem
The next big technologies are defined by their emerging market value.
TensorFlow for poets
How to build your own image classifier with no coding.
Training and serving NLP models using Spark MLlib
Beyond data processing: Utilizing Spark for individual predictions.
Compressed representations in the age of big data
Emerging trends in intelligent mobile applications and distributed computing.
Evolutionary computation: Stepping stones and unexpected solutions
An interview with Risto Miikkulainen.
Unsupervised learning, attention, and other mysteries
How to almost necessarily succeed: An interview with Google research scientist Ilya Sutskever.
Data has a shape
Using topology to uncover the shape of your data: An interview with Gurjeet Singh.
On the evolution of machine learning
From linear models to neural networks: An interview with Reza Zadeh.
An illustrated introduction to the t-SNE algorithm
This post is an introduction to a popular dimensionality reduction algorithm: t-distributed stochastic neighbor embedding (t-SNE).
Crowdsourcing at GoDaddy: How I Learned to Stop Worrying and Love the Crowd
Adam Marcus explains how to effectively benefit from crowd workers to solve your most challenging tasks. He uses examples from the wild and from his work at GoDaddy.
Thoughtful Machine Learning: Sentiment Analysis Using Support Vector Machines in Ruby
In this webcast Matthew Kirk will detect sentiment in tweets using the Support Vector Machines algorithm.
Wouldn’t it be fun to build your own Google?
Exploring open web crawl data — what if you had your own copy of the entire web, and you could do with it whatever you want?
In search of a model for modeling intelligence
True artificial intelligence will require rich models that incorporate real-world phenomena.
How to build and run your first deep learning network
Step-by-step instruction on training your own neural network.
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