This whitepaper focuses on NexJ’s approach to AI and machine learning and looks at how our CRM deploys this technology towards action and recommendation engines such as NexJ Nudge.Continue scrolling to learn more
This whitepaper examines the evolution of AI, how it works, its importance and uses, challenges and limitations.
When it comes to Artificial Intelligence in financial services, there are all kinds of ways in which organizations deploy rules and algorithms. While a majority use it for customer engagement applications or in vertical-specific software, other use cases arise in the form of call center service and support, cybersecurity, supply chain management or asset performance management.Download White Paper
Jeff Bezos had an interesting thing to say about how automation is driven by rules. In Amazon’s 2017 Annual Report, he wrote: ‘Over the past decades, computers have broadly automated tasks that programmers could describe with clear rules and algorithms.’
A simple way of understanding the importance of AI is to think of it as the addition of intelligence to existing products. It can use rules or decision trees to help people make decisions.
AIs capabilities have been enhanced with advancements in computing power and big data which, in turn, has led to increased accuracy. The more we use a product like Alexa or Siri, the more accurate our interactions become because they are based on Machine Learning or Deep Learning.Download White Paper
The term ‘Machine Learning’ pretty much defines the goal, which is to get machines to learn a task.
The process depends upon the kind of task, kind of data and amount of data available, all of which then calls for either basic decision trees or clustering, layers of artificial neural networks or deep learning.
There are a number of methods used in Machine Learning, of which supervised learning and unsupervised learning are the most widely adopted.Download White Paper
Learning by example, something that comes naturally to human beings, is what Deep Learning tries to teach computers. It is a Machine Learning technique responsible for everything from driverless cars to voice control, teaching computers to distinguish being pedestrians and obstructions, or how to recognize stop signs.
It’s easy to see why Deep Learning is often mistaken for Machine Learning, given that it is a specialized form of the latter.Download White Paper
Watch as Adam Edmonds, VP of Products at NexJ Systems, describes the types of AI and machine learning we enable in our CRM and how they can help you improve your sales and service.Explore Resource
With just a few clicks, your advisors can stand out from the crowd. AI finds the right content from 10,000+ sources, matches it precisely with clients, runs it through a compliance check & posts it in the right channel at the right time.Explore Resource
Published on April 17, 2018Explore Resource
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