Best of 2015
What's wrong with Deep Learning?
(Very) long and detailed powerpoint presentation by Yann Lecun at the CVPR 2015 conference. Recommended.
Why 2015 was a breakthrough year in AI
A few interesting charts on the acceleration in the number of AI projects, systems and usage.
The state of Artificial Intelligence in six visuals
Overview of the market and companies working on Artificial Intelligence (split by geography, split by category, funding details and company age)
"Deep Learning Conspiracy" (Nature)
Jürgen Schmidhuber, famed ML researcher, replies to a recent paper in Nature by Lecun, Bengio and Hinton. The fight is getting ugly...
A full hardware guide to Deep Learning
If you consider using Deep Learning at some point, have a look at this guide. Interesting information to help you choose between GPUs, CPU specs, RAM size and more.
Top 10 machine learning APIs
This API list covers image tagging, face recognition, document classification, speech recognition, predictive modeling, sentiment analysis, and pattern recognition.
Interactive introduction to neural networks
Simulate a neural network within your browser. This is a useful introduction to people new to neural networks who want to "see" activation functions, bias nodes, synapses etc.
Understanding LSTM networks
Long short-term memory (LSTM) is a type recurrent neural network architecture. It is well-suited to learn from experience to classify, process and predict time series when there are very long time lags of unknown size between important events. This is great walk-through.
What to do with “small” data?
Many technology companies now have teams of smart data-scientists, versed in big-data infrastructure tools and machine learning algorithms, but every now and then, a data set with very few data points turns up and none of these algorithms seem to be working properly anymore. What the hell is happening? What can you do about it?
Inside Facebook's Artificial Intelligence lab
Deep-dive into Facebook's AI work and its quest to tackle the problem of emulating general intelligence — that is, getting computers to think less like linear, logical machines, and like us free-form humans — with a multi-prong approach. While the Facebook Artificial Intelligence Research (FAIR) team works on solving generalized AI problems, smaller groups like Language Technology and Facebook M deploy practical features to users.
How to become a unicorn data scientist
What makes a good data scientist? And if you are a good data scientist, how much should you expect to get paid?
The (perfect) startup story of Wit.ai, acquired by Facebook in 21 months
You don’t often get to meet a co-founder of a startup that follows, by all means, the “perfect successful startup” path
Want an open-source deep learning framework? Take your pick
There are many deep learning frameworks out there. Here is a list of the main ones, along with their specificities.
The fourth generation of machine learning: Adaptive learning
Adaptive learning combines the previous generations of rule-based, simple machine learning, and deep learning approaches to machine intelligence.
Google is said to be working on new algorithm - Thought Vectors
Professor Geoff Hinton, who was hired by Google two years ago to develop intelligent operating systems, said that the company is on the brink of developing algorithms with the capacity for logic, natural conversation and even flirtation.
Goodbye apps, hello smart agents: Are you ready for the post-app world?
What will the post-app world look like? This piece argues that smart agents will progressively replace apps.
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