Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.
- Like oil leads to global warming, data leads to social cooling
If you feel you are being watched, you change your behavior. Big Data is supercharging this effect. - Deal or no deal? Training AI bots to negotiate
To date, existing work on chatbots has led to systems that can hold short conversations and perform simple tasks such as booking a restaurant. But building machines that can hold meaningful conversations with people is challenging because it requires a bot to combine its understanding of the conversation with its knowledge of the world, and then produce a new sentence that helps it achieve its goals. Researchers at Facebook have open-sourced code and published research introducing dialog agents with the ability to negotiate. - The mathematicians who want to save democracy
With algorithms in hand, scientists are looking to make elections in the United States more representative. - 17 success factors for the age of AI
How do we, as investors, evaluate early stage software companies that put ML at the heart of their value proposition? Below, we introduce our ML Investment Framework. - Google advances AI with ‘one model to learn them all’
Google quietly released an academic paper that could provide a blueprint for the future of machine learning. Called “One Model to Learn Them All,” it lays out a template for how to create a single machine learning model that can address multiple tasks well. - Advantages of Using R Notebooks For Data Analysis Instead of Jupyter Notebooks
From the perspective of a former Apple Software QA Engineer. - Don’t use a blockchain unless you need to
An easy way to spot a startup that won’t provide return on investment is to look for the words “blockchain” or “decentralized” on their landing pages. - Engineering extreme event forecasting at Uber with recurrent neural networks
“At Uber, event forecasting enables us to future-proof our services based on anticipated user demand. The goal is to accurately predict where, when, and how many ride requests Uber will receive at any given time.” - The Limits of Artificial Intelligence
Companies are too reluctant to talk about the limits of AI. - Media Companies Are Getting Sick of Facebook
News outlets are complaining about Facebook’s terms for TV-quality videos meant to compete with YouTube. - Make R a production-ready language for deployable machine learning
Syberia is a new development framework for R. - Google Will Stop Reading Your Emails for Gmail Ads
The move is designed to ease concerns of enterprise customers. - Exploring LSTMs
Lengthy and very visual exploration of LSTM networks. - Using Deep Learning to Reconstruct High-Resolution Audio
There is recent interest in using deep neural networks to accomplish upsampling on raw audio waveforms. - DeepChatModels: Conversation models in TensorFlow
Implementation of conversational models in TensorFlow. - Andrew Ng cryptically unveils new project: deeplearning.ai
“Explore the frontier of AI” - Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees (paper)
“We introduce a new method called differentiable boundary tree which allows for learning deep kNN representations. We build on the recently proposed boundary tree algorithm which allows for efficient nearest neighbor classification, regression and retrieval.” - Poincaré Embeddings for Learning Hierarchical Representations (paper)
“Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically learn embeddings in Euclidean vector spaces, which do not account for this property. For this purpose, we introduce a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space — or more precisely into an n-dimensional Poincare ball.”
