It seems like there isn't a day which goes by at the moment without a new robotic invention in the news, with promises around how these inventions will not only revolutionise our lives, but threaten our jobs. In the outsourcing sector robots are most definitely on the way, or in some cases, already here. And it is, therefore, vital that businesses operating in this sector seriously consider how some robotic processes can enhance their operations – there's no doubt competitors are also considering the same issue. These robotic innovations can be roughly split into two separate areas: robotic process automation (RPA) and artificial intelligence (AI). RPA, simply speaking, is a process which enables computer software to partially or fully automate human activities which are manual, repetitive and rule-based.

This meetup is a gathering place for professionals who use or (or want to learn about using) machine learning to solve messy optimization problems where "hard coded" solutions like deterministic grammars and rules-based systems just don't cut it. If you have a working expertise in machine learning, suspect that you may have a problem where you need to develop a program that "learns" from the data to provide an adequate solution, or you just want to learn more about machine learning and how it might benefit you, this is the place to be! Regular meetup topics will run the gauntlet of supervised, unsupervised, and reinforcement learning approaches and range from natural language processing to computer vision and everything in between.

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chat bots, or search engines. Given high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow). Today, machine learning has come to play an integral role in many phases of the financial ecosystem, from approving loans, to managing assets, to assessing risks. Yet, few technically-savvy professionals have an accurate view of just how many ways machine learning finds it's way into their daily financial lives.

Wee-Hyong Tok has decades of database systems experience, spanning academia and industry, including deep experience driving and shipping products and services that span distributed engineering teams from Asia and the United States. Before joining Microsoft, Tok worked on in-database analytics, demonstrating how association rule mining can be integrated into a relational database management system, Predator-Miner, which enables users to express data mining operations using SQL queries and provides opportunities for better query optimization and processing. Tok is instrumental in driving data mining boot camps in Asia and was honored as a Microsoft SQL Server Most Valuable Professional for several consecutive years because of his active contribution to the database community in Asia. He has co-authored several books, including the first book on Azure machine learning, Predictive Analytics with Microsoft Azure Machine Learning, and has also published more than 20 peer-reviewed academic papers and journals. Tok holds a Ph.D. in computer science from the National University of Singapore.

A Learning System Based on Genetic Adaptive Algorithms.

The ultimate goals of serious education games (SEG) are to facilitate learning and maximizing enjoyment during playing SEGs. In SEG development, there are normally two spaces to be taken into account: knowledge space regarding learning materials and content space regarding games to be used to convey learning materials. How to deploy the learning materials seamlessly and effectively into game content becomes one of the most challenging problems in SEG development. Unlike previous work where experts in education have to be used heavily, we proposed a novel approach that works toward minimizing the efforts of education experts in mapping learning materials to content space. For a proof-of-concept, we apply the proposed approach in developing an SEG game, named \emph{Chem Dungeon}, as a case study in order to demonstrate the effectiveness of our proposed approach. This SEG game has been tested with a number of users, and the user survey suggests our method works reasonably well.

The decision has provided a solid legal foundation on which a rules-based approach for resolving disputes in the South China Sea can be built,

With the increasingly vast volumes of data generated by enterprises, relying on static rule-based decision systems is no longer competitive; instead, there is an unprecedented opportunity to optimize decisions, and adapt to changing conditions, by leveraging patterns in real-time and historical data. The very size of the data however makes it impossible for humans to find these patterns, and this has lead to an explosion of industry interest in the field of Machine Learning, which is the science and practice of designing computer algorithms that, broadly speaking, find patterns in large volumes of data. ML is particularly important in digital marketing: understanding how to leverage vast amounts of data about digital audiences and the media they consume can be the difference between success and failure for the world's largest brands. MediaMath's vision is for every addressable interaction between a marketer and a consumer to be driven by ML optimization against all available, relevant data at that moment, to maximize long-term marketer business outcomes. In this series of blog posts we will present a very basic, non-technical introduction to Machine Learning.

SoftBank Group Corp.'s former Chief Operating Officer Nikesh Arora, whose 8 billion package topped the list, hails from India. Higher wages in Japan were typically earned by sticking around, thanks to rigid corporate promotion systems based on tenure. In the U.S., executives have reaped the benefits of a shift from cash to equity-based compensation tied to their companies' performance -- a change that sent pay packages spiraling in recent decades as the stock market soared. Interlocking stock ownership between companies listed on the Tokyo Stock Exchange fell to 16 percent in 2015 from 50 percent in 1990, according to data from Nomura Holdings Inc. Last year's biggest pay packages for Japan executives born in the country were Fanuc Corp. CEO Yoshiharu Inaba's 690 million and Sony Corp. CEO Kazuo Hirai's 513 million, data compiled by Bloomberg show.

To predict and prevent fraud online even more quickly than cybercriminals adopt new tactics, Sift Science has raised 30 million in a Series C round of venture funding in a round led by Insight Venture Partners. According to the U.S. Internet Crime Complaint Center (IC3) 2015 annual report, reported internet crimes alone, ranging from personal and corporate data breaches to credit card fraud, phishing and identity, theft cost victims 1.07 billion. The financial losses to U.S. businesses as a result of such crimes go well beyond what is reported to IC3, of course. Certain types of sites and apps are under more frequent attacks than others, with digital gift card businesses, money transfer services and on-demand marketplaces rampant with fraud attempts. Sift Science uses machine learning and artificial intelligence to automatically surmise whether an attempted transaction or interaction with a business online is authentic or potentially problematic.