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.

California voters decided last year that the sale of recreational marijuana should be made legal, beginning on Jan. 1, 2018. But Proposition 64 left many of the details to local governments and state regulators. So the last several months have been a race against the calendar, as officials have sought to develop rules governing where, when and how businesses may grow, transport and sell marijuana to adults. Last month, the state unveiled 276 pages of regulations for the new recreational pot marketplace. Among other things, the rules set hefty licensing fees, regulate how much THC will be allowed in edibles and other cannabis products, and require marijuana businesses to track their product from seed to sale.

"It gets better over time" may be the leading slogan for artificial intelligence (AI) these days. Is "better later" acceptable in today's marketplace? How long should we wait for AI to become great? For a company that is trying to decide whether to use chatbots to serve customers, those questions matter. Because companies know that interactions are probably going to begin with a question, they need to program customer service chatbots to determine the intent of the message -- i.e., what it is the customer wants.

The Basel committee rules have been an ongoing international response to the 2007-2009 financial crisis that saw the bankruptcy of U.S. investment bank Lehman Brothers and taxpayer bailouts of big banks. The financial crisis was the prelude to the Great Recession that saw many people lose their jobs and homes. Governments in the United States, Europe and elsewhere were pushed to rescue banks to prevent a cutoff of credit to businesses that would further harm the economy and increase unemployment.

Sponsored You've got an application or workflow that needs to do lots of repetitive work typically done by people in the past. Or you want to branch out into some new area of digital business or customer experience. It might just be a perfect fit for an artificial intelligence algorithm, perhaps using a form of machine learning or a rules-based system. AI has been a long-promised concept but has been held back by, among other factors, a lack of the kinds of raw computer power required to process vast amounts of data and to crunch complex algorithms. It's only now, thanks to cloud, these resources are becoming available, as Intel and service providers make available this kind of power available through their massive server farms.

In order to submit a claim to insurance companies, a doctor needs to code a patient encounter with both the diagnosis (ICDs) and procedures performed (CPTs) in an Electronic Health Record (EHR). Identifying and applying relevant procedures code is a cumbersome and time-consuming task as a doctor has to choose from around 13,000 procedure codes with no predefined one-to-one mapping. In this paper, we propose a state-of-the-art deep learning method for automatic and intelligent coding of procedures (CPTs) from the diagnosis codes (ICDs) entered by the doctor. Precisely, we cast the learning problem as a multi-label classification problem and use distributed representation to learn the input mapping of high-dimensional sparse ICDs codes. Our final model trained on 2.3 million claims is able to outperform existing rule-based probabilistic and association-rule mining based methods and has a recall of 90@3.