How will machine learning shape the future of writing?

Machine learning is a widely used application of AI that allows programmes to learn from extensive datasets without being programmed manually.

It can replace, as the paragraph itself implies, certain writing tasks being automated, leading to job loss for low-cost/low-skilled writers.

Imagine this: it takes almost half a lifetime for a human being to read enough to be able to pick up the art of writing and then actually write and get published, let alone be exceptionally adept in it.

Human labour has value, and that is why we still patronise such labour.

If you cannot differentiate the text written by a human author from that written by a machine, would you be willing to pay for it as much as you did before?

Human creativity, apart from following others and learning certain strategies, also requires raw feelings and emotions.

The only hope I see for the near future is collaboration between machines and human writers where, rather than competing with each other, both would complement each other’s skills and continue to produce great reads.

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Why do some people avoid news? Because they don’t trust us — or because they don’t think we add value to their lives? Nieman Journalism Lab

In 2017, 29 percent of those surveyed worldwide said they “Often or sometimes avoid the news,” including 38 percent in the United States and 24 percent in the U.K. By 2019, those numbers had increased to 32 percent worldwide, 41 percent in the U.S., and 35 percent in the U.K. Why do people avoid news? In the 2017 data, the leading causes for Americans were “It can have a negative effect on my mood” and “I can’t rely on news to be true”.

LinkedIn senior editor-at-large Isabelle Roughol wrote a short piece Saturday summarizing this year’s Digital News Report, highlighted the news avoidance data in the headline, and asked readers about their own experience with news avoidance.

Mainstream news is a waste of time and energy – so yes, I avoid the news.

News organizations have become dependent on sensationalism and shocking news.

My question to you is why would I waste my energy and psychological wellbeing looking at grotesque pictures or reading depressing draining news? I would much rather see a magazine full of ads and no news.

Regular news consumption can engender a kind of learned helplessness that make clear the appeal of ideologically slanted news – which offers up a clear cast of good guys and bad guys with no moral gray – and just avoiding news entirely.

News consumption used to be about daily habits – reading the paper every morning, watching the 6 o’clock news every night.

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How I used Deep Learning to Optimize an Ecommerce Business Process with Keras

Nowadays in the era of deep learning and computer vision, checking manually web content is considered as a flaw and very time consuming, furthermore it can lead to many mistakes such as this one below, where moderators had accepted a laptop ad in phone category which is wrong and affect search engine quality, while this work could be done in a second by a Deep Learning model.

In this blog post I will cover how I optimized this process by building a simple Convolutional Neural Network using Keras framework, that can classify if an uploaded image is for a phone or a laptop and tell us if the image is matching the ad category or not.

2.2 Image resizingThis step is absolutely depending on the adopted Deep Learning architecture, for example when using Alexnet model to classify images, the input shape should be 227 x 227, while for VGG-19 the input shape is 224 x 224.Since we are not going to adopt any pre-built architecture, we will build our own Convolutional Neural Network model, where the input size is 64 x 64, like shown in the code snapshot below.

For this model, we will discuss each component how it was implemented using Keras and its own parameters starting from convolutions to fully connected layer, but first of all, let’s discover the full architecture of the built-in model.

We have to compile the network that we have just built by calling compile function, it is a mandatory step for every model built using Keras.

Analyzing Model with TensorBoardIn this step, we will see how we can analyse our model behavior using TensorBoard.

ConclusionTo conclude with, this blog post shows a complete computer vision pipeline by building a Deep Learning model that can predict the class of an uploaded image applied on eCommerce context, starting from the Data Collecting to the Data Modeling and finishing by Model Deployment as a web app.

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