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Cloud Computing and AI transform the Banking sector

#artificialintelligence

With the adoption and development of cognitive computing capabilities, the way customers interact with their banks will ultimately change for good. Artificial intelligence and cloud computing will empower banks to efficiently redefine the workflow, create innovative products and services, and transform customer experiences. Many banks have adopted AI, infusing it into their customer experience. This development is witness to AI's role in banking becoming increasingly crucial and visible over the next few years. The introduction of cloud computing and AI will permit the banking workforce to discard repetitive, process driven tasks towards the more strategic and innovative kinds of work that will ultimately drive the industry forward.


Deep learning can beat other forecast methods – Bank of Korea research - Central Banking

#artificialintelligence

Deep learning – an advanced form of artificial intelligence – can be more accurate in predicting outcomes, compared with conventional econometric approaches, according to research from Bank of Korea (BoK). The research paper tested predictions of monthly exports from Korea and daily Korean won-US dollar exchange rates. It found that deep learning approaches produced better results even with the sorts of non-granular data sets that are normally used for conventional econometric models.


Machine Learning Summarized in One Picture

#artificialintelligence

The following is a follow-up guest post by Jeremy Epstein, CEO of Never Stop Marketing, to his previous articles on blockchain marketing and blockchain brand promises. Jeremy currently works with several of the leading companies in the blockchain and decentralization space including OpenBazaar. Previously, he was VP of marketing at Sprinklr.


[P] Introduction to Learning to Trade with Reinforcement Learning • r/MachineLearning

@machinelearnbot

This is an interesting expository piece, and it seems to me that there are a lot of fundamental barriers that need to be addressed before RL can be successfully applied to trading, the most fundamental one appears to me to be the large number of competing agents in the environment.


Unsupervised Learning with Clustering Techniques w/Srini Anand

#artificialintelligence

As humans we are able to discern differences among different groups within a collection. We might group a collection by broad groups such as birds versus plants versus animals or detect subtle features to identify different makes and models of cars. Clustering techniques allow us to automate the process and apply them to data where groupings are not immediately obvious. These techniques are used for different purposes such as detecting market segments, identifying properties of online communities, fraud detection, and cybersecurity. Srini Anand is a Data Scientist at Ameritas Life Insurance Company and holds a Masters degree in Data Science from Indiana University.