There is a huge amount of pressure on healthcare services today with much written about the resulting impact on patient care. You only need to reflect on the recent election campaign to see what an emotive issue it remains throughout the UK. What's also true is that advanced technology is taking a bigger role in healthcare than ever before, looking to alleviate some of the burdens on our health systems and provide greater support for our wellbeing. One clear theme underpinning this shift is applications. Whether being used by organisations to adapt and streamline processes to improve data access and patient care or by patients themselves looking at ways to better understand and manage their health, the sector is set to be transformed by the evolution of apps.

There has never been a better time to be a politician. But it's an even better time to be a machine learning engineer working for a politician. Throughout modern history, political candidates have had only a limited number of tools to take the temperature of the electorate. More often than not, they've had to rely on instinct rather than insight when running for office. Now big data can be used to maximise the effectiveness of a campaign.

My aim with this research is to allow me to quickly find the most relevant and appreciated articles about the topic of Artificial intelligence (A.I), Machine Learning(M.L.) and Deep Learning(D.L), so that I can improve my knowledge about the subject, without having to read 100 articles to find the 4–5 of them that are interesting and exciting for me. After scrapping 6 million posts from medium.com in the winter of 2016, I was able to filter just the posts that are tagged with either AI/ML or Deep Learning. In total, i got 6,546 articles, written by 3,528 users. All of this articles have a total of 153,567 recommends. So, to do this, I extracted the TOP 100 articles posted on medium, related to AI/ML/DL and created a list with all of them, so that I can read them in the coming weeks.

Quotage: "We are still decades from Star Trek-style artificial general intelligence that could pass the Turing test or outperform humans on a gamut of unrelated cognitive tasks. In the meantime, a promising compromise would be the ability to automate model selection and tuning based on the problem and available data, and then select the best options from a portfolio of deep learning software each designed for different applications." Thus the theme of Marko's useful offering, which gets into the practicalities of "automated algorithm selection," where the proper algorithm for a narrower use case is machine-determined. I can see why Marko argues that helping companies sort the right algorithms could make up for lack of internal data science expertise. More APIs and "metadata taxonomies" are needed.

About me @marijnvanzelst Marijn van Zelst • Software engineer at Luminis Amsterdam 3. Goal: • Practical knowledge about machine learning • What kind of problems it can solve • Do your own experiment 4. Why is machine learning interesting for Software Developers? • New opportunities • Builds on existing knowledge • Open-source communities and libraries 5. Tensorflow • Developed by Google • Open-source • Has api's for most major programming languages • High level api 6. Machine learning Learn from examples 7. Handwritten digit classification MNIST dataset 8. What is the difference between a 2 and a 3? 9. Neural networks Image recognition Speech recognition Natural language recognition Churn prediction Fraud detection Anomaly detection 10. Learning algorithm • While not done • pick a training example (input data, class) • run it through the neural network which produces a prediction • modify the weights in the neural network so that the prediction is closer to the actual class 14. Agile approach to machine learning • Theory takes a lot of time to explain and learn • Using high level api's you can do experiments without knowing all the mathematical details • When you have your first results and want to improve • Learn more • Ask for expert help 15. Using a high level api • Which features do I use? Neural nets intuition • Input: • Features (coordinates in feature space) • Output: • A predicted class at every coordinate in the feature space white black black y x 24.

Deep multi-task learning with low level tasks supervised at lower layers.

Industry 4.0 is much more than just another buzz word. The term Industry 4.0 refers to the fourth industrial revolution and is comprised of growing trends in automation, the internet of things, big data and cloud computing technologies. Just like steam power, electricity and digital automation of the past, cyber physical systems will create the factory of the future; the smart factory. Originating from Germany as part of a Governmental strategy for the computerization of factories, this is a revolution that will spread across industries globally. It is predicted that the adoption of Industry 4.0 will benefit production due to increased connectivity across entire businesses as manual factories are transformed into smart factories.

Blade Runner, the film inspired by Philip K Dick's book Do Androids Dream of Electric Sheep?, is 35 years old this year. Set in a dystopian Los Angeles, the story centres on the tracking down and killing of a renegade group of artificial humans – replicants – escaped from space and trying to extend their lifespans beyond the built-in four years. The story is set in 2019. Surprise, we aren't exactly there in terms of the future Hollywood envisioned in 1982 so it's seized the opportunity to hurl the idea even further into the future, 2049, with a new telling. Opportunism, sure, but while we are a million miles away from flying police cars and sushi bars, the stirrings of AI are evident.

"The development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded." This sounds like it could be an excerpt from the screenplay to the 1984 film "The Terminator," but it's actually physicist Stephen Hawking in late 2014, talking to the BBC. As part of the scientific advisory board for the Future of Life Institute based in Boston, Hawking and engineer/entrepreneur Elon Musk helped to create an open letter on artificial intelligence that highlighted both the positive and negative aspects of AI.

"Creativity is intelligence having fun." – Albert Einstein These days, artificial intelligence (AI) is being applied to nearly every aspect of our working lives. Creatives, however, have been mostly left alone because, after all, a robot can't create art, can it? As it turns out, there's more AI experimentation going on in artistic fields than you'd think, and it's on its way to making the lives of marketers, designers, journalists and advertising people easier. From the automation of monotonous everyday tasks to true autonomy, where robots may one day create their own artworks, AI is changing what it means to be creative and challenging concepts we thought were intrinsically human. Defining what we mean by'creativity' is a tall order.