It's Christmas Day!! How many carol singers have you encountered so far this? Or how many times have you heard Michael Bublé on repeat? Whilst there's a new Christmas Number One every year, they're never particularly festive thanks to the X-Factor, and no one seems to be dedicating their time to creating a new generation of Christmas classics. What to do when humans are failing at a task? Last Christmas, Researchers from University of Toronto developed an AI system that composes and then sings a Christmas song through analysing an image that you upload.

With pressure constantly on profit margins, organisations need to strike a balance between improving cost efficiencies and customer satisfaction. This has led to a growing interest in the rapidly advancing area of robots to create a virtual workforce; transforming business processes by automating manual, rules-based, back-office administrative processes. This also has benefits for future workers – there's now a strong argument for reducing workers' pension age as a result of boosting UK productivity through artificial intelligence (AI). The re-engineering of processes through automation, known as robotic process automation (RPA), is being applied to a wide range of industries to improve speed, quality and consistency of service delivery. There's no question that RPA technology provides a number of straightforward and powerful business benefits.

President Donald Trump banned the Centers for Disease Control (CDC) from using a list of words, which include "transgender" and "evidence-based." Needless to say, social media users did not take lightly to the president's decision. In a meeting, led by Alison Kelly, a senior leader in the agency's Office of Financial Services, CDC policy analysts, who are tasked with preparing the 2019 budget that is expected to be released in February, were handed out a list of words that were forbidden to use in official documents for the budget. The mandate from the Trump administration banned the use of seven words – "vulnerable," "entitlement," "diversity," "transgender," "fetus," "evidence-based," "science-based." The news of the ban was originally reported by the Washington Post, which caused immediate outrage among Twitter users.

Email marketing is in for a complete overhaul. With AI, email marketing isn't limited to rule-based triggers but has evolved into a means of more in-the-moment personalization. AI has filled in the missing gap between traditional shopping and online shopping with 1:1 personalization. Here are four of the ways AI has changed email marketing for better. You have three seconds to seize the shopper's attention.

U.S. District Judge Wendy Beetlestone in Philadelphia issued a preliminary injunction preventing enforcement of rules the administration announced in October that allowed businesses or non-profits to obtain exemptions on moral or religious grounds.

Many of the technology trends that drove us into 2017 will continue into 2018: connected devices, digital transformation, the internet of things (IoT), machine learning, artificial intelligence, and automation. These hot-button issues will remain part of the technology vocabulary in 2018 and beyond. Where I see a substantive difference is in the union of the technologies. AI and IoT are transformative by themselves; now imagine digital transformation in a connected and automated world empowered by an artificial intelligence of things. Going into 2018, I see two common technology characteristics: intelligence and automation.

When it comes to digital business, Andrew McAfee knows a thing or two. A principal research scientist at MIT, prolific writer and management expert, McAfee is a leader in understanding and explaining how digital technologies are changing business, the economy, and society. At the recent SAP Leonardo Live event in Chicago that focused on digital transformation, McAfee urged his audience to throw out the business playbook they've been using for the past 30 years. "The right way to run a factory in the steam era became a really, really bad way to run it in the era of electrical power," he said. "Similarly, during a technology transition -- and afterwards -- the advice you used to follow becomes bad advice."

Years ago our team built self-improving machine technology that could achieve bid and budget management faster and better than any human, using more data than one person could ever consider. The technology was created by rocket scientists with other early AI patents and includes more than 30 sophisticated algorithms. Not just an automated, rule-based system, this one actually self-improves constantly, making it the closest thing we have in the SEM industry to artificial intelligence. We get asked a lot about our machine learning technology known as Acquisio TuringTM and what type of results marketers can expect from it because agencies and marketers are sick of claims from martech providers. So our team set out to put our machine to the test.

The contraception mandate was implemented as part of the 2010 Affordable Care Act, former Democratic President Barack Obama's signature healthcare legislation, popularly known as Obamacare. Republicans, who control the U.S. House of Representatives, Senate and White House, have so far failed to repeal the law, a top presidential campaign promise of Trump.

Anomaly detection is a very pervasive problem applicable to a variety of domains including network intrusion, fraud detection, and system failures. It is a crucial task in many applications because failure to detect anomalous activity could result in highly undesirable outcomes. For example, (i) detection of anomalous medical claims is important to identify fraud; (ii) detection of fraudulent credit card transactions is necessary to help prevent identity theft; and (iii) detection of abnormal network traffic is necessary to identify hacking. Many techniques have been developed for anomaly detection. These methods can be broadly classified into two categories: (i) rule-based systems, and (ii) statistical datadriven approaches. The rule-based systems are based on domain expertise and look for specific types of anomalies while the data-driven approaches look to identify anomalies by identifying statistically rare patterns. Examples of datadriven methods include parametric methods that assume a known family for the nominal (non-anomalous) distribution and nonparametric methods such as those using unsupervised or semi-supervised support vector machines (SVMs) [1], [2] or based on minimum volume set estimation [3], [4], [5]. The advantage of data-driven approaches over rule-based methods is that they can identify novel types of anomalies that are unknown to the domain expert.