A rule-based system may be viewed as consisting of three basic components: a set of rules [rule base], a data base [fact base], and an interpreter for the rules. In the simplest design, a rule … can be viewed as a simple conditional statement, and the invocation of rules as a sequence of actions chained by modus ponens.
– from The Origin of Rule-Based Systems in AI. Randall Davis and Jonathan J. King, reprinted as Ch. 2 of Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley Series in Artificial Intelligence). Bruce G. Buchanan and Edward H. Shortliffe (Eds.). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1984.
Cosmos Bank in India recently had $13.5 million siphoned off by hackers linked to the Lazarus Group in North Korea. They exploited and succeeded in compromising two of the bank's payment systems – the ATM Switch and the SWIFT payments system. The group is also alleged to have orchestrated the $81 million cyber heist at Bangladesh Bank by siphoning off SWIFT payments from the bank's Federal Reserve account. These attacks emphasize the need for layered fraud defences and controls that effectively mitigate such risks going forward. As the adage says: "There is no silver bullet!"
This blog post was created in partnership with André Burrell who is the Banking & Capital Markets Strategy Leader on the Worldwide Industry team at Microsoft. In our last blog post Anti-money laundering – Microsoft Azure helping banks reduce false positives, we alluded to Microsoft's high-level approach to a solution--which automates the end-to-end handling of anti-money laundering (AML) detection and management. Due to the growing number of fines issued, there is now an increased drive to hold compliance officers, senior executives, and board members personally liable for failing to have an adequate AML program and transaction monitoring system (TMS). Any alert generated and not closed by the TMS must be reviewed by a human. Current technologies cannot assess a transaction in context.
Automated game design has remained a key challenge within the field of Game AI. In this paper, we introduce a method for recombining existing games to create new games through a process called conceptual expansion. Prior automated game design approaches have relied on hand-authored or crowd-sourced knowledge, which limits the scope and applications of such systems. Our approach instead relies on machine learning to learn approximate representations of games. Our approach recombines knowledge from these learned representations to create new games via conceptual expansion. We evaluate this approach by demonstrating the ability for the system to recreate existing games. To the best of our knowledge, this represents the first machine learning-based automated game design system.
From facial recognition for unlocking our smartphones to speech recognition and intent analysis for voice assistance, artificial intelligence is all around us today. In the business world, AI is helping us uncover new insight from data and enhance decision-making. For example, online retailers use AI to recommend new products to consumers based on past purchases. And, banks use conversational AI to interact with clients and enhance their customer experiences. However, most of the AI in use now is "narrow AI," meaning it is only capable of performing individual tasks.
The insurance industry consists of more than 7,000 companies that collect more than $1 trillion in premiums annually, providing fraudsters with huge opportunities to commit fraud using a growing number of schemes. Fraudsters are successful too often. According to FBI statistics, the total cost of non-health insurance fraud is estimated at more than $40 billion a year. Fighting fraud is like aiming at a constantly moving target, since criminals constantly hone and change their strategies. As insurers offer customers additional ways to submit information, fraudsters find a way to exploit new channels, and detecting issues is increasingly challenging because threats and attacks are growing in sophistication.
Of all the facets of enterprise IT impacted by the growing trend towards machine intelligence, those pertaining to network availability could well prove the most pivotal in terms of cost savings, resource efficiency, and business continuity--especially when analyzed over the long term. Although most organizations simply consider availability in relation to network failures or disaster recovery, the dynamic routing of computational resources for the foregoing benefits can create just as much, if not more, of a positive effect when utilizing the automation capabilities for which artificial intelligence is acclaimed. "Artificial intelligence is really about code getting smarter and being able to carry out repeatable tasks," DH2i CTO OJ Ngo said. "Smart availability is the capability where we say you define the rule, then we'll carry out the rule on your behalf when you're not looking, when you go on vacation. It is really the stepping stone for AI."
The traditional insurance business model is going to fundamentally and permanently change from what was invented in the Lloyd's coffee shop in 1668 and which has been the basis of insurance ever since: Risk Mitigation via Indemnity and minimising interactions with customers. The next 10 years will see unprecedented change in the insurance industry. Traditional insurance companies selling and servicing the old style product model are being replaced by IT enabled, risk management companies selling profitable, long term contracts for valuable services delivered as RMAAS (risk management as a service). The experience of other industries offers a stark warning to the insurance industry: banks suffering death by a thousand cuts from tech companies in payments, cards, lending and now open banking, show the way it will go. Even software is sprinting to a cloud based, software as a service (SAAS) model.
Companies are using AI to prevent and detect everything from routine employee theft to insider trading. Many banks and large corporations employ artificial intelligence to detect and prevent fraud and money laundering. Social media companies use machine learning to block illicit content such as child pornography. Businesses are constantly experimenting with new ways to use artificial intelligence for better risk management and faster, more responsive fraud detection -- and even to predict and prevent crimes. While today's basic technology is not necessarily revolutionary, the algorithms it uses and the results they can produce are.
As business process management systems (BPMS) mature, we have moved beyond systems that merely execute pre-defined process models to more intelligent systems that dynamically support, guide and automate processes. To drive innovation and maintain operational efficiencies, we need to augment processes and human intelligence with machine intelligence. How do all of these technologies fit together, and how can they help businesses achieve operational benefits? In 2016, I was asked to contribute to the Workflow Management Coalition's book "Best Practices for Knowledge Workers." My section, "Beyond Checklists", called for more intelligent adaptive case management to drive innovation while maintaining operational efficiency.
A great example of ecommerce and dynamic inventory visibility working well together came from a discussion with a partner: Episerver. As part of a solid partnership with OrderDynamics, Episerver develops an ecommerce platform as part of the full Digital Experience Cloud system. Our discussions resulted in a great business opportunity for existing and new clients. Part of the Episerver solution is the geolocation capabilities the ecommerce platform (ECP). Simply, this can pinpoint the location of a customer.