Rule-Based Reasoning

IRCAM, the world's largest research center dedicated to both musical expression and scientific research, is a partner of the Ars Electronica Festival 2019. The institution is also involved in the STARTS Initiative as coordinator of the STARTS Residencies and will present these activities at the STARTS Day. Hugues Vinet, head of research activities at IRCAM, told us in an interview how the institute works, why AI has social relevance and what role he and his team will play at the festival. Hugues Vinet: My background is signal processing. I worked from the mid 1980s at the Musical Research Group (GRM) in Paris on the first real-time audio workstations and I designed the early versions of the GRM Tools product which made creative audio processing tools broadly available on personal computers.

You can certainly write rules with machine learning models. That is one approach: to create a machine learning model that is a rule or a set of rules.

KDD: This was in fact the first term ever used to describe what we are still actively pursuing today: knowledge discovery from data(bases). Initially, this was the basic function assigned to what we call AI today: to get information out of databases by combining and comparing data, and to use this information to obtain new insights/knowledge. Actually, KDD goes all the way back to when SAS was originally founded in 1976! Data mining: This term refers to a next level of data discovery: the purpose-oriented search for meaningful patterns in data. Churn detection (analyzing customers' behaviour in regard to the competition) and association rule mining (studying grouped purchases of products) are two of the more widely known applications of data mining.

Airlines need to proactively monitor their loyal shoppers' membership accounts since the problem of loyalty fraud is on the rise. If on one hand airlines are offering more earning and redemption choices than ever, it also means that the overall loyalty earning and burning lifecycle has opened new avenues for fraud. "From a loyalty fraud standpoint, there is a lot of demand (for stolen loyalty currency among the fraudsters or in a marketplace on the dark web)," says Kevin Lee, Trust & Safety Architect, Sift. This is because over a period of time, prices for such items (stolen credentials, miles, points etc.) even though they fluctuate a bit still they are going up in value. Data breaches are a big issue, and a lot of sensitive information is being sold.

The combination of AI, robotic processing automation and predictive data analytics is redefining how businesses operate. The combination of artificial intelligence (AI), robotic processing automation and predictive data analytics is fundamentally redefining how businesses operate, how consumers engage with brands and, indeed, how we go about our daily lives. The field of insurance is no exception. Outlined here are three ways smart technology is affecting insurance, with a focus on identifying lessons learned and defining specific keys to success. The impact of rules-based robotic process automation (RPA) on insurance operations has been well-documented.

If IoT is going to deliver on its transformational promise, it will have to provide greater value and importance than a single internet enabled sensor such as a wearable device. The technology to create a central hub around a small collection of sensors, for example in home automation, has been around for decades. What is revolutionary today is that home automation is cheaper to implement and gives home owners better software to monitor and control their homes remotely. As I've said in a previous post, the real magic happens when a hub of managed sensors can easily communicate with other neighboring systems. Each hub has to be programmed to intelligently broadcast signals to its neighbors and also make intelligent decisions on how to process signals from its neighbors.

As per the market analysis in 2018 enterprise AI market is valued at 1211.60 million USD. The market is expected to grow to 15042.32 million by 2024 as end users are seeing immense potential in process automation. The operational efficiency has grown in organisations that have implemented artificial intelligence. This has also resulted in running businesses in an extremely cost-efficient way. Organisations are always up for reducing their operational costs and do regular work more efficiently.

Artificial intelligence and machine learning could be the next frontier for ETFs to outperform the market. So says Robert Tull, President of ProcureAM, an innovative exchange-traded product firm and wholly owned subsidiary of Procure Holdings. A veteran in the business, Tull has been involved in the ETF industry for decades, creating more than 400 ETFs across 18 different countries. Now, he's looking at new ways to beat the market by using big data as raw material, combined with machine learning, to build ETF portfolios that could potentially outperform active management -- even actively managed ETFs. "Active management has been out there for a long time, underperforming," he said on CNBC's "ETF Edge." "They haven't found a solution yet, and I think the technology that I've run into is going to help the marketplace today."

Every rule based system contains four basic components. Firstly, the system contains a set of rules, also known as the rule base, and acts as the domain of knowledge for the computer. Second, there is an interference engine, also called the semantic reasoner. This component is responsible for interpretation of the rules and taking action accordingly. The interference engine works in three steps: match, conflict-resolution, and act.

Most insurance companies depend on human expertise and business rules-based software to protect themselves from fraud. And the drive for digital transformation and process automation means data and scenarios change faster than you can update the rules. Machine learning has the potential to allow insurers to move from the current state of "detect and react" to "predict and prevent." It excels at automating the process of taking large volumes of data, analysing multiple fraud indicators in parallel – which taken individually may often be quite normal – and finding potential fraud. Generally, there are two ways to teach or train a machine learning algorithm, which depend on the available data: supervised and unsupervised learning.