Financial software and digital solutions provider Objectway has revealed a chatbot that utilises artificial intelligence (AI) to answer users' wealth management questions, reports Jane Connolly. "The advisor's relationship with the customer remain fundamental" Designed with the specific requirements of the financial sector in mind, the Objectway project aimed to implement a new service model for financial institutions, in which AI can be used to deliver information and implement orders. To keep the interactions clear and simple, the chatbot uses Natural Language Processing (NLP), acts in a reactive manner, uses questions to manage word disambiguation and avoids asking for information that it already possesses. "In addition to this conversational agent, we are working to support the advisory activity with predictive analytics," says Georgios Lekkas, Objectway's chief product officer. "In the wealth segment, automation must support service customisation, in an area where the ability of artificial intelligence to justify the suggestions provided and the advisor's relationship with the customer remain fundamental."

The US Army has installed PRIDE (Pulse Radar Intelligent Diagnostic Environment), a diagnostic expert system developed by Carnegie Group (Pittsburgh, PA), in Saudi Arabia in support of Operation Desert Storm. PRIDE is said to be the first diagnostic expert system ever put in the field by the US Army. American Airlines (Dallas, TX) has developed an expert system - Maintenance Operation Control Advisor (MOCA) - to ensure that every Super 80 (DC-9) airplane meets the airline's and the FAA's aircraft maintenance requirements. Nova Technology (Bethesda, MD), a new company founded by Naval Research Center scientist Harold Szu, plans to commercialize neural networks made from high-performance superconductors. Inference (El Segundo, CA) has named Peter Tierney CEO and president.

This book looks at some of the results of this synergy among AI, cognitive science, and education. Examples include virtual students whose misconceptions force students to reflect on their own knowledge, intelligent tutoring systems, and speech recognition technology that helps students learn to read. Some of the systems described are already used in classrooms and have been evaluated; a few are still laboratory efforts. The book also addresses cultural and political issues involved in the deployment of new educational technologies. ISBN 0-0-262-56141-7 To order call 800-405-1619.

Much has changed in the wealth management industry since robo-advisors first made their appearance a decade ago. Talk of disrupting the advisor-client relationship has given way to a new generation of innovative technology solutions that enhance and augment the fundamental ways that investors invest and advisors advise. Today, robo-advisors can be seen as part of a broader movement to digitize wealth management, embodied in the term: WealthTech. Technology derived from wealth management firms, research tools that generate investment solutions, and platforms to support financial advisors -- all fall under WealthTech. Finance is becoming a technology play.

As I leaned back in my chair, I began to realise that the only way you could provide unlimited, free, specific and scalable advice was if, it was… not human. The promise is that it will reduce costs, save time and even save lives. So I began to think about how AI could help my industry and give millions of business owners their own personal business coach. That plane ride was two years ago and that's why I'm so very pleased to announce we have launched BRiN, the world's first artificially intelligent business advisor.

Investment banks are evaluating possible avenues for efficiency improvement either through artificial intelligence (AI) or through automation. In fact Robo Advisors are disrupting the traditional wealth management model by attracting large number of millennial customers by providing a differentiated Investment management experience. At the same time new regulations are being introduced to ensure safety of investor funds from improper selling of financial products to gullible investors. There have been a number of instances where financial intermediaries had to pay huge penalties for not being able to appreciate the risks and bring in the right product features while selling the products to customers. The topic of reviewing and selecting a product for the client is largely the judgement of an Advisor.

There are several different types of goals, and each has a corresponding set of parameters. For example, the goal (obtain (coefficient 6 x 2)) means to obtain an expression for the coefficient of x2 in g6, either to print it out or pass as argument to some MACSYMA command. Note that this can be done either by finding an already computed expression (stored, say, as the value of some variable) or by computing it anew. Either implementation is satisfactory so long as it computes the desired expression.

Examining the refinement structure created by the automatic programmer makes possible justifications of the code. This chapter describes XP LAIN and outlines additional advantages this approach has for explanation. The significance of Swartout's work is not just its use of a s_vstem design technique that makes explanation possible. His work reveals how principles (here, domain strategies by which specific treatment methods are apphed) are part of explanation. It is useful to supply not just an "audit trail" of what a problem solver did (on perhaps

This paper explores whether the addition of costly, imperfect, and exploitable advisors to Berg's investment game enhances or detracts from investor performance in both one-shot and multi-round interactions.We then leverage our findings to develop an automated investor agent that performs as well as or better than humans in these games.To gather this data, we extended Berg's game and conducted a series of experiments using Amazon's Mechanical Turk to determine how humans behave in these potentially adversarial conditions.Our results indicate that, in games of short duration, advisors do not stimulate positive behavior and are not useful in providing actionable advice.In long-term interactions, however, advisors do stimulate positive behavior with significantly increased investments and returns.By modeling human behavior across several hundred participants, we were then able to develop agent strategies that maximized return on investment and performed as well as or significantly better than humans.In one-shot games, we identified an ideal investment value that, on average, resulted in positive returns as long as advisor exploitation was not allowed.For the multi-round games, our agents relied on the corrective presence of advisors to stimulate positive returns on maximum investment.

As people engage in increasingly complex conversations with computers, the need for generality and flexibility in spoken dialogue systems becomes more apparent. This pa­per describes how three different spoken dialogue systems for the same task reason with knowledge and certainty as they seek to understand what people want. It advocates sys­tems that exploit partial understanding, consider credibility, and are aware both of what they know and of their certainty that it matches their users’ intent.