Nearly half of Australian workers will find their role filled by a machine in the next 20 years, according to the Government's latest report on the future of the workforce. The battle of man vs. machine may come sooner than we think – with billions of dollars of venture capital being poured into Machine Learning. Rapidly advancing technology is allowing us to make tremendous strides in simulating human thinking, meaning that even jobs that involve complicated decision-making may soon be automated. With evolved smart machines, knowledge workers will soon find their career paths disrupted in the near future. However, this change needn't be seen as a negative.

I recently read an article in which the winner of a Kaggle Competition was not shy about sharing his technique for winning not one, but several of the analytical competitions. "I always use Gradient Boosting," he said. And then added, "but the key is Feature Engineering." A couple days later, a friend who read the same article called and asked, "What is this Feature Engineering that he's talking about?" It was a timely question, as I was in the process of developing a risk model for a client, and specifically, I was working through the stage of Feature Engineering.

Deep learning just may be the buzziest, most misunderstood term in the tech world. And October was a banner month for the enigmatic subject. The term "deep learning" is, essentially, a synonym for a type of neural network approach. And though voguish at the moment, the goal of the field has long been to create a system that can independently learn. Now, research into machine learning is rapidly accelerating, and those intellectual seeds are bearing widely applicable fruits.

Facebook has seen the success of sponsored ad messages and has decided to expand its reach. Starting this week, businesses and companies will be able to directly send ads to users on Facebook Messenger and start conversations with chatbots. With Facebook's advertising platform, businesses and companies will be able to display "highly-targeted, in-context" ads to a user's Messenger thread, according to The Next Web. Also, ads displayed on the News Feed will be able to redirect users to Messenger to start conversations. To avoid any potential pushback from users, Facebook has given some limitations on how these ads will pop up on Messenger.

A year ago, Danny Lange took over as the head of machine learning at Uber. The ride-sharing company, which launched in 2009, is, essentially, a tech company: It operates entirely through an app. Lange manages a team in San Francisco, and Uber has a smaller team in Seattle. And machine learning has become the underlying foundation for every part of the company. "Machine learning and AI technologies can really solve some very fundamental business problems that are really hard to create hardwired solutions to," said Lange.

Originally posted on Google Research blog It has been an eventful year since the Google Brain Team open-sourced TensorFlow to accelerate machine learning research and make technology work better for everyone. There has been an amazing amount of activity around the project: more than 480 people have contributed directly to TensorFlow, including Googlers, external researchers, independent programmers, students, and senior developers at other large companies. TensorFlow is now the most popular machine learning project on GitHub. With more than 10,000 commits in just twelve months, we've made numerous performance improvements, added support for distributed training, brought TensorFlow to iOS and Raspberry Pi, and integrated TensorFlow with widely-used big data infrastructure. We've also made TensorFlow accessible from Go, Rust, and Haskell, released state-of-the-art image classification models – and answered thousands of questions on GitHub, StackOverflow, and the TensorFlow mailing list along the way.

Data Analytics is one of the most sought-after skill sets today, with students and professionals alike aspiring to be enabled with the necessary skills to derive data-driven business insights in their careers. It also helps organisations attain a competitive advantage over others. Data Analytics is not limited to mathematicians, statisticians or IT professionals with programming skills. The need to analyse data has become so elementary today that a professional in any business is expected to know the necessary skills. While professionals today are aware of the need to be trained, some are unaware of how to embark on a career in analytics.

This repository contains the TSFRESH python package. "Time Series Feature extraction based on scalable hypothesis tests". The package contains many feature extraction methods and a robust feature selection algorithm. Data Scientists often spend most of their time either cleaning data or building features. While we cannot change the first thing, the second can be automated.

It has been said that to give a man a fish is to feed him for a day, whereas to teach a man to fish is to feed him for life. Forward-looking financial service companies are similarly finding that giving computers instructions is not nearly as fruitful as teaching them to write their own. From assessing credit risks to beefing-up the security of their own networks, fintech startups, in particular, are turning to machine learning finance-based solutions in order to work smarter rather than harder. Considering that over 200 leading financial institutions will attend the upcoming October 2016 Machine Learning Fintech Conference, investment in this subset of artificial intelligence (AI) seems to be a wise move, indeed, for companies that don't want to be left behind. With leading banks starting to invest in AI, and machine learning in particular, fintech companies will be significantly disadvantaged if they fail to do likewise.

It seems only yesterday that we were lying on our friend's sofa vowing to abstain from all alcohol for the entirety of 2016 before being coerced into a pub trip by our pesky co-workers the first Friday back in the office. Yes, the start of the year seems no time ago at all, but 2016 is nearly over. There are under 9 weeks left of the year and this, coupled with the arrival of the colder weather, has got us feeling all nostalgic. Let's recap some of the biggest tech innovations of 2016 and our predictions for the tech world in 2017. Well Pokémon Go launched in July and thanks to the enormous number of nostalgic noughties kids roaming the streets with little to do, it took off at an astonishing rate.