There is a certain level of stigma that exists around using machine learning and location data in business applications, understandably due to risks inherent in exploitation of individual privacy. But if we look under the hood of society's daily web of interactions, we see that the location information economy--from GPS to radio signal based-triangulation to geo-tagged images and beyond--is now almost ubiquitous, from the moment we track our morning commute to the end-of-day search for healthy and convenient take-out for dinner.
We can infer numerous possible use-cases for each of these AI initiatives, in addition to the ways in which they were developed and implemented. The clearest use case is for their virtual assistant, which is likely a mature version of the pilot chatbot project they worked on with AI firm Kasisto in 2018 and earlier this year. Kasisto claims to have helped JP Morgan treasury services offer customers a customer service chatbot. The chatbot was purportedly made for the purpose of helping clients navigate JP Morgan's expansive website. Kasisto's platform, KAI, could be used to develop chatbots which can be deployed across multiple digital channels, such as employee dashboards and smartphone apps.
Over the last five months, we've combined a mix of interviews and surveys with some of our most experienced artificial intelligence guests – including computer science PhDs from Stanford, Georgia Tech, and more – as well as some of the most renowned AGI researchers in the world. We asked 33 AI researchers when they believe (with 90% confidence) that artificial intelligence will be capable of self-aware consciousness. Some of the answers surprised us. To explore the expert opinions, scroll in the interactive graphic below. Clicking on a date range (example: "2061-2100") will take you to all of the respondents who guessed that date range as realistic for AI consciousness.
Retailers and financial institutions are adopting artificial intelligence and machine learning in their business to solve various business problems such as cybersecurity and document digitization. However, many of these companies are also using AI to improve their payment processes for their clients and customers. These types of applications are usually layered into an existing payments technology stack, which could include straight-through processing (STP) or robotic process automation (RPA). In this article, we cover three companies that deal with large volumes of payments every day and how they leverage AI to make them faster, more accurate, or safer. We also infer how they most likely work, and discuss the benefits they saw after implementing their AI solution.