There are a lot of serious topics covered in Altered Carbon, a new science fiction series from Netflix. It delves into misogynistic power structures and the nature of identity. It touches on just how much of our morality is driven by the fact that we die and what might happen if death suddenly stopped being an endpoint and, instead, became a minor stopgap in an ultimately immortal life. But that is not what I'm here to talk to you about. Because while watching Altered Carbon -- even the stuff I didn't like all that much -- my primary critical reaction was, "This is so RAD!!!!" Imagine me sitting in the back of eighth-grade study hall, filling my notebook with scrawled images from this show (that my parents don't know I've seen, because if they did, my Netflix consumption would be seriously questioned), occasionally clicking over my four-color pen to red to write the word "rad" in all caps in the margins.

There's no doubt about it - Data Science is big news right now. We see it on the news every day, the increasing number of news stories about Big Data, the Internet of Things, Deep Learning, Artificial Intelligence, smart cars, smart cities, smart politicians. OK, maybe I went a bit too far with that last one... There's also a great appetite for learning about Data Science too. Every month I get an email from Udemy telling me which courses are their best sellers. The list isn't about Data Science, but there are always plenty of Data Science courses right up there at the top of the list.

The main goal of this reading is to understand enough statistical methodology to be able to leverage the machine learning algorithms in Python's scikit-learn library and then apply this knowledge to solve a classic machine learning problem. The first stop of our journey will take us through a brief history of machine learning. Then we will dive into different algorithms. On our final stop, we will use what we learned to solve the Titanic Survival Rate Prediction Problem. With that noted, let's dive in! As soon as you venture into this field, you realize that machine learning is less romantic than you may think. Initially, I was full of hopes that after I learned more I would be able to construct my own Jarvis AI, which would spend all day coding software and making money for me, so I could spend whole days outdoors reading books, driving a motorcycle, and enjoying a reckless lifestyle while my personal Jarvis makes my pockets deeper. However, I soon realized that the foundation of machine learning algorithms is statistics, which I personally find dull and uninteresting. Fortunately, it did turn out that "dull" statistics have some very fascinating applications. You will soon discover that to get to those fascinating applications, you need to understand statistics very well. One of the goals of machine learning algorithms is to find statistical dependencies in supplied data.

A new national survey commissioned by Northeastern University and conducted by Gallup finds most U.S. adults have an overall positive view of artificial intelligence, but believe they are ill-prepared to deal with AI's expected impact on the global digital economy. The survey comes on the heels of numerous international studies forecasting significant job loss resulting from AI. Overall, 22 percent of Americans with a bachelor's degree or higher level of education say their college or university studies prepared them well or very well to work with AI. Moreover, only 18 percent are extremely confident they could secure the education needed to obtain a comparable job should they lose their current position to advances in new technology. "The answer to greater artificial intelligence is greater human intelligence," said Northeastern President Joseph E. Aoun. "The AI revolution is an opportunity for us to reimagine higher education--to transform both what and how we teach. If colleges and universities can adapt and modernize, we can ensure that tomorrow's learners will be robot-proof."

The reality is that it's neither one. The fear is that automation is sweeping all before it, gobbling up jobs; displacing millions of workers and leaving them unemployed and, worse, unemployable; and exacerbating the income gap. It's reviled by many as a greater threat than jobs shipped overseas, even prompting some to suggest taxing robots to slow their spread. The counterview is that automation is not replacing jobs nearly fast enough. We don't have enough workers to do the jobs available now, and this will get worse as demographic trends play out.

BISHKEK – I was recently offered the presidency of a university in Kazakhstan that focuses primarily on business, economics, and law, and that teaches these subjects in a narrow, albeit intellectually rigorous, way. I am considering the job, but I have a few conditions. What I have proposed is to transform the university into an institution where students continue to concentrate in these three disciplines, but must also complete a rigorous "core curriculum" in the humanities, social sciences, and natural sciences – including computer science and statistics. Students would also need to choose a minor in one of the humanities or social sciences. There are many reasons for insisting on this transformation, but the most compelling one, from my perspective, is the need to prepare future graduates for a world in which artificial intelligence and AI-assisted technology plays an increasingly dominant role. To succeed in the workplace of tomorrow, students will need new skills.

There is increasing pressure in such fields as manufacturing, energy and transportation to adopt AI and machine learning to help improve efficiencies in operations, optimize workflows, enhance business decisions through analytics and reduce costs in logistics. We have talked about how industries like telecommunications and transportation are looking at recurrent neural networks for helping to better forecast resource demand in supply chains. However, adopting AI and machine learning comes with its share of challenges. Companies whose datacenters are crowded with traditional systems powered by CPUs now have to consider buying and bringing in GPU-based hardware that is better situated to handle machine learning inference work, and they have to find new employees in a relatively shallow pool of available AI talent. None of this is easy, but the trend is irreversibly toward AI, machine learning and deep learning, so decisions need to be made, according to Karim Beguir.

In this DZone Refcard, learn about TensorFlow, an open-source library with a rich set of application programming interfaces for most major languages and environments needed for deep learning programs like sentiment analysis and object detection.

The UK's population is ageing, and one of the outcomes of this is that more men are developing prostate cancer. In fact the number of deaths it causes among men has overtaken the number of deaths caused among women by breast cancer. And the latest available figures, from 2015, show that, overall, it killed 11,819 people - almost 400 more than breast cancer. However, the mortality rates for both diseases have fallen. But Angela Culhane, chief executive of the charity Prostate Cancer UK, says research on prostate cancer gets half the funding of that for breast cancer.

Why you should listen Noriko Arai is the program director of an AI challenge, Todai Robot Project, which asks the question: Can AI get into the University of Tokyo? The project aims to visualize both the possibilities and the limitation of current AI by setting a concrete goal: a software system that can pass university entrance exams. In 2015 and 2016, Todai Robot achieved top 20 percent in the exams, and passed more than 70 percent of the universities in Japan. The inventor of Reading Skill Test, in 2017 Arai conducted a large-scale survey on reading skills of high and junior high school students with Japan's Ministry of Education. The results revealed that more than half of junior high school students fail to comprehend sentences sampled from their textbooks.