According to the motto: "A picture says more than a thousand words" some useful slides with a short explanation are shown below. Analytics is the discovery, interpretation, and communication of meaningful patterns in data; and the process of applying those patterns towards effective decision making. In other words, analytics can be understood as the connective tissue between data and effective decision making, within an organization. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Organizations may apply analytics to business data to describe, predict, and improve business performance.

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For readers unfamiliar with the drug discovery and development process, it is worthwhile to point out that the pharmaceutical industry is one of the most inefficient and risky industries on the planet. The efficiency of the industry has been on the decline since the 1950s. It costs over $2.6 Billion to bring the New Molecular Entity (NME) to the market. And despite the many advances disrupting other industries including personal computing, the Internet, and genome sequencing, the cost to develop a drug is steadily increasing. This is one of the reasons why most industry experts are skeptical about the promises of deep learning.

As you might know, supervised machine learning is one of the most commonly used and successful types of machine learning. In this article, we will describe supervised learning in more detail and explain several popular supervised learning algorithms. Remember that supervised learning is used whenever we want to predict a certain outcome from a given input, and we have examples of input/output pairs. We build a machine learning model from these input/output pairs, which comprise our training set. Our goal is to make accurate predictions for new, never-before-seen data. Supervised learning often requires human effort to build the training set, but afterwards automates and often speeds up an otherwise laborious or infeasible task. There are two major types of supervised machine learning problems, called classification and regression. In classification, the goal is to predict a class label, which is a choice from a predefined list of possibilities.

Artificial intelligence is one of a series of technologies that are on the radar for implementation in Chile's mining industry. AI, which, simply put, is the ability of a computer program or machine to think and learn from observing large quantities of data, to identify trends and make recommendations to improve decision making, all in a matter of milliseconds. The impending tsunami of data that will be collected from sensors and internet of things (IoT) devices will be too overwhelming for humans to compute. Businesses that are able to compute and extract value from huge volumes of data are expected to have a key advantage over their competitors by being able to improve efficiency, productivity and lower costs as well as identify new business opportunities. A recent study by consultancy Accenture, showed 82% of executives in the global mining industry expecting to increase investment in digital technology over the next three years.

The government's peak advisory body on tech and science has turned its attention to the development of an artificial intelligence ethics framework and lifelong learning of STEM skills. The National Science and Technology Council met for the third time in Brisbane last week, after it was launched to replace the Commonwealth Science Council in February this year. The meeting was chaired by Industry Minister Karen Andrews, with education minister Dan Tehan also in attendance. Council members include Professor Genevieve Bell, Professor Barbara Howlett, Professor Debra Henly and Professor Brian Schmidt. They were briefed on the government's progress in developing a national artificial intelligence ethics framework, and the "strong engagement" from stakeholders during consultation.

It's one of our most distinctive features, but it seems that your voice isn't safe from cybercriminals, if a recent case is anything to go by. In the case, cybercriminals developed an AI that mimicked a CEO's voice so well, that it was able to scam an energy firm out of hundreds of thousands of pounds. The Wall Street Journal reported the scam, which happened back in March, and saw criminals swindle a staggering $243,000 (£201,000). The fraudsters used AI to mimic a chief executive from the German parent company of an unnamed UK energy firm. This voice was so believable that the UK-based CEO was tricked into making a large transfer of money to the chief executive, via a Hungarian supplier.

A blue-collar worker is a working class person who performs manual labor. Blue-collar work may involve skilled or unskilled manufacturing, mining, sanitation, custodial work, textile manufacturing, power plant operations, farming, commercial fishing, landscaping, pest control, food processing, oil field work, waste disposal, recycling, electrical, plumbing, construction, mechanic, maintenance, warehousing, shipping, technical installation, and many other types of physical work. Blue-collar work often involves something being physically built or maintained. In contrast, the white-collar worker typically performs work in an office environment and may involve sitting at a computer or desk. A third type of work is a service worker (pink collar) whose labor is related to customer interaction, entertainment, sales or other service-oriented work.

A new iOS app called Zao, developed by Chinese developer MoMo, was the most-downloaded China over the weekend, as it takes deepfakes to the next level by letting users superimpose their faces onto celebrities in just seconds rather than hours or even days. So far, the only issue users have had is with their privacy policy, which states that the developers could keep and reuse their images forever. In case you haven't heard, #ZAO is a Chinese app which completely blew up since Friday. Best application of'Deepfake'-style AI facial replacement I've ever seen. Here's an example of me as DiCaprio (generated in under 8 secs from that one photo in the thumbnail) pic.twitter.com/1RpnJJ3wgT

In this episode, Audrow Nash interviews Zhuoran Zhang, PhD student at the University of Toronto, about how robots can be used to assist in artificial insemination. Zhang discusses how precise robotic manipulators can be used to extract a single sperm and how sperm can be evaluated for fitness using computer vision.