The "willing suspension of disbelief" is the idea that we (the audience, readers, viewers, content consumers) are willing to suspend judgment about the implausibility of the narrative for the quality of our own enjoyment. We do it all the time. Two-dimensional video on our screens is smaller than life and flat and not in real time, but we ignore those facts and immerse ourselves in the stories as if they were real. We have also learned the "conventions" of each medium. While we watch a movie or a video, we don't yell to the characters on the screen "Duck!" or "Look out!" when something is about to happen to them.

A great example of vertical vs horizontal machine learning applications can be found in chat bots. There are some horizontal chat bot assistants that help you with any and all requests (viv.ai, Magic, and Awesome to name a few). It would seem obvious that building NLP and intelligent capabilities across all conceivable tasks and requests could be a long slow training slog of manual human validation. These companies are also at a heavy disadvantage to incumbent players tackling the horizontal assistant space.

Hospitals in Paris are trialling Big Data and machine learning systems designed to forecast admission rates – leading to more efficient deployment of resources and better patient outcomes. It's just one more way in which cutting-edge data science is being applied to real-world problems in healthcare, along with creating personalized medicines, fighting cancer and streamlining pharmaceutical trials. At four of the hospitals which make up the Assistance Publique-Hôpitaux de Paris (AP-HP), data from internal and external sources – including 10 years' worth of hospital admissions records has been crunched to come up with day and hour-level predictions of the number of patients expected through the doors. The core of the analytics work involves using time series analysis techniques – looking for ways in which patterns in the data can be used to predict the admission rates at different times. Machine learning is employed to determine which algorithms provide the best indicator of future trends, when they are fed data from the past.

Autonomous vehicles are already making profound choices about whose lives matter, according to experts, so we might want to pay attention. "Every time the car makes a complex manoeuvre, it is implicitly making trade-off in terms of risks to different parties," Iyad Rahwan, an MIT cognitive scientist, wrote in an email. The most well-known issues in AV ethics are trolly problems -- moral questions dating back to the era of trollies that ask whose lives should be sacrificed in an unavoidable crash. For instance, if a person falls onto the road in front of a fast-moving AV, and the car can either swerve into a traffic barrier, potentially killing the passenger, or go straight, potentially killing the pedestrian, what should it do? Rahwan and colleagues have studied what humans consider the moral action in no-win scenarios (you can judge your own cases at their crowd-sourced project, Moral Machine).

Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. It is a very simple idea that can result in accurate forecasts on a range of time series problems. In this tutorial, you will discover how to implement an autoregressive model for time series forecasting with Python. Autoregression Models for Time Series Forecasting With Python Photo by Umberto Salvagnin, some rights reserved. A regression model, such as linear regression, models an output value based on a linear combination of input values.

Diabetes is one of deadliest diseases in the world. It is not only a disease but also a creator of different kinds of diseases like heart attack, blindness, kidney diseases, etc. The normal identifying process is that patients need to visit a diagnostic center, consult their doctor, and sit tight for a day or more to get their reports. Moreover, every time they want to get their diagnosis report, they have to waste their money in vain. But with the rise of Machine Learning approaches we have the ability to find a solution to this issue, we have developed a system using data mining which has the ability to predict whether the patient has diabetes or not.

The fact that these are mere electrical components means that they can be adapted to more than brain-emulating computers. This technology can hold special significance for devices considered to be a part of the Internet of Things. According to co-author Dr Themis Prodromakis, "This new type of hardware could find a diverse range of applications in pervasive sensing technologies to fuel real-time monitoring in harsh or inaccessible environments; a highly desirable capability for enabling the Internet of Things vision."

When it comes to robots replacing humans, we might think we have the upper hand since we're the ones who build and program them but that's not neccesarily the case anymore. Google is taking a different approach to training its robots – it's letting them teach each other. Researchers at Google have released a report showing how they connected 14 robotic arms together and used convolutional neural networks to let them teach themselves how to pick things up. The approach mimics how young children learn between the ages of one and four years old, and is essentially helping the robots to develop reliable hand-eye coordination. Typically, a robot would be programmed to carry out specific tasks, but this method shows how they can learn through trial-and-error in combination with a neural network – the same way a child learns how to do something by watching other people.

Welcome to the Fourth Industrial Revolution, or what the World Economic Forum calls the "fusion of technologies that is blurring the lines between the physical, digital, and biological spheres." One aspect of it, Artificial Intelligence, is poised to change our lives dramatically. In this ongoing series, we will explore what the impact of AI will be on us as humans and designers. Our first installment, by Rob Girling, takes a look at what makes the stakes higher than ever before. Already, artificial intelligence is all around us, from self-driving cars and drones to virtual assistants and software that translate or invest.

I love to visit Machine Learning meetups organized in Madrid (Spain) and I'm a regular attendant to Tensorflow Madrid and Machine Learning Spain groups. At least I was until the begining of the Self-Driving Car course, but that is another story. The fact is that too often, during "pizza & beer" time or networking I heard people talking about Deep Learning. Sentences like "where should I begin? Tensorflow is the most popular, isn't it?",