The science-fiction writer Robert Heinlein once wrote, "Each generation thinks it invented sex." He was presumably referring to the pride each generation takes in defining its own sexual practices and ethics. But his comment hit the mark in another sense: Every generation has to reinvent sex because the previous generation did a lousy job of teaching it. In the United States, the conversations we have with our children about sex are often awkward, limited, and brimming with euphemism. At school, if kids are lucky enough to live in a state that allows it, they'll get something like 10 total hours of sex education.1

THE TVET EXPERT OF THE WEEK, p18 Madeleine Anne Decker is information and knowledge management specialist for the Canadian Vocational Association/Association canadienne de la formation professionnelle. The Canadian Vocational Association (CVA) was created in 1960 to promote and foster education and training which leads to occupational competence. The CVA is also a world leader in DACUM training, development and research, and Canada's premier voice and expert in competency-based learning and training. Madeleine joined the CVA team in 2011 to increase the visibility by publishing a monthly newsletter and creating two TVET online databases. She added to these tasks the role of community manager by feeding the Association's Twitter and LinkedIn accounts.

The following is an interview with Daniel Wagner, ahead of the release of his new book, AI Supremacy: Winning in the Era of Machine Learning. Can you briefly explain the differences between artificial intelligence, machine learning, and deep learning? Artificial intelligence (AI) is the overarching science and engineering associated with intelligent algorithms, whether or not they learn from data. However, the definition of intelligence is subject to philosophical debate-even the terms algorithms can be interpreted in a wide context. This is one of the reasons why there is some confusion about what AI is and what is not, because people use the word loosely and have their own definition of what they believe AI is. People should understand AI to be a catch-all term for technology which tends to imply the latest advances in intelligent algorithms, but the context in how the phrase is used determines its meaning, which can vary quite widely. Machine learning (ML) is a subfield of AI that focuses on intelligent algorithms that can learn automatically (without being explicitly programmed) from data.

Facebook begins publicly testing its online-dating product, called Dating, in Colombia today. The service was first announced at the annual F8 conference in May this year, and will likely be available in other locations in the future. For now, users aged 18 and older in Colombia will be able to create dating profiles and, once those reach a critical mass, find some matches. WIRED got to preview an early version of the service, and it looks promising--especially for users looking for meaningful long-term relationships rather than hookups. In other words, you can expect to find exactly zero swiping.

Developing a new drug can cost billions of dollars and take a dozen or more years to bring to market. Two Israeli researchers have applied artificial intelligence (AI) and deep learning to shave time and money off the drug-discovery process. Instead of searching for the appropriate molecules to use in a new medicine, as is done today, they enabled a computer to make smart predictions without human guidance. Shahar Harel and Kira Radinsky at the Technion-Israel Institute of Technology fed into their computer system hundreds of thousands of known molecules as well as the chemical composition of all FDA-approved drugs up until 1950. Aided by AI, the computer came up with new potential molecules by making sometimes unexpected correlations from within this massive sample.

China will be the world's dominant player in artificial intelligence by 2030. A State Council document, issued in July last year, resolved to position China as the world's pre-eminent practitioner of artificial intelligence (AI) in both research and application within the next 12 years. Governments across the world are rushing to support innovation in AI, but none has published as coherent a plan as China and – more importantly – has the ability to get things done: the Chinese government can implement policy in ways that are impossible in western democracies. Intent, however, is one thing: to paraphrase the boxer Mike Tyson, everyone has a plan until they get punched in the mouth. The Chinese not only have a strategy, they have a track record of delivering on large-scale, ambitious projects.

A World Food Programme convoy carries humanitarian aid to Aleppo, Syria. Getting food into conflict zones is a major hurdle -- and a topic of discussion at the WFP's Innovation Accelerator. A World Food Programme convoy carries humanitarian aid to Aleppo, Syria. Getting food into conflict zones is a major hurdle -- and a topic of discussion at the WFP's Innovation Accelerator. Let's figure out how to end hunger forever.

Companies and cities all over world are experimenting with using artificial intelligence to reduce and prevent crime, and to more quickly respond to crimes in progress. The ideas behind many of these projects is that crimes are relatively predictable; it just requires being able to sort through a massive volume of data to find patterns that are useful to law enforcement. This kind of data analysis was technologically impossible a few decades ago, but the hope is that recent developments in machine learning are up to the task. There is good reason why companies and government are both interested in trying to use AI in this manner. As of 2010, the United States spent over $80 billion a year on incarations at the state, local, and federal levels. Estimates put the United States' total spending on law enforcement at over $100 billion a year. Law enforcement and prisons make up a substantial percentage of local government budgets.

Another option is at the very least to cooperate in order to increase the power of negotiation. A lot of the development of AI is based on the accumulation of enormous amount of data. This data is being accumulated from throughout the world, from countries like India, Brazil or Mexico, and is centralized in places like California or China. You can say that to some extent what we see today in the world is a kind of data colonization. Just as in the 19th century (when) raw material from South America, Africa, Asia, was used to fuel the industrial revolution in Europe and the countries that provided the raw material gained almost nothing in return, a similar thing may be happening now with data.

Elon Musk is one of the world's most prominent business figures. His latest headline-grabber is the announcement of the first paying passenger his SpaceX venture plans to fly around the Moon. There are also his extraordinary pioneering achievements include creating online payment platform PayPal, running electric car maker Tesla, as well as SpaceX. His innovative and wide-ranging interests include solar energy and artificial intelligence, and he has promised the Hyperloop, a super-high speed magnetic train travel, in an underground tube and he has designs on Mars. But this year, things seem to have turned sour for the tech entrepreneur.