Collaborating Authors

2020 IoT Trends and Predictions: Be prepared for the IoT Tsu-nami


We must be prepared for the Internet of Things (IoT) Tsunami, but it will not be in 2020 as many of us believed. The good news is that it is approaching but now more quietly than a few years ago. For the most pessimistic or unconvinced of the IoT, I must say that much of the hype around the Internet of Things is not really hype anymore. I will say one more time: "IoT's here to stay". In Ten Trends of IoT in 2020, Ahmed Banafa considers that in the year 2020 we will hit all 4 components of IoT Model: Sensors, Networks (Communications), Analytics (Cloud), and Applications, with different degrees of impact.

Embedding Inference for Structured Multilabel Prediction

Neural Information Processing Systems

A key bottleneck in structured output prediction is the need for inference during training and testing, usually requiring some form of dynamic programming. Rather than using approximate inference or tailoring a specialized inference method for a particular structure---standard responses to the scaling challenge---we propose to embed prediction constraints directly into the learned representation. By eliminating the need for explicit inference a more scalable approach to structured output prediction can be achieved, particularly at test time. We demonstrate the idea for multi-label prediction under subsumption and mutual exclusion constraints, where a relationship to maximum margin structured output prediction can be established. Experiments demonstrate that the benefits of structured output training can still be realized even after inference has been eliminated.

Zipari How can you make smart predictions about data you don't have?


You've heard the term, and you probably nod in agreement when someone tells you how important it is. But secretly you may not be sure what it is or how it works. Ask your data scientists to explain, and you may get lost in a sea of specialist talk about forks, leaf nodes, split points, and recursions. The only thing you need to know is that machine learning applies statistical models to the data you have in order to make smart predictions about data you don't have. Those predictions can help you find signals in the noise and extract value from all the data you're collecting.

Oscar Nomination Predictions and Filmmaker Charles Burnett


On this episode of Represent, Aisha is joined by Mark Harris, a Vulture writer and author of Pictures at a Revolution and Five Came Back, to help predict this year's Oscar nominees. Then, Aisha talks to revered director Charles Burnett, who emerged from the period of groundbreaking black independent filmmaking known as the L.A. Rebellion.

These VCs nailed their 2017 health-care predictions. Here's what they see for 2018.


For what is now an annual tradition, we are once again attempting to be healthcare soothsayers. We are proud to share with you our 10 healthcare predictions for 2018. But first, we look back on our 2017 predictions.