Posts Tagged ‘machine learning’

Join us and our partner Amazon Web Services (AWS) for a virtual Q&A session on Wednesday, April 15. AWS will feature one of our experts to speak on a panel about the evolution and progress being made to solve critical business problems such as customer personalization and forecasting through the use of data science. Perficient […]

Previously, I analyzed how dirty, or bad data is the enemy of machine learning. The final blog of this series describes the advantages of artificial intelligence and machine learning in financial services. Many financial services organizations have already begun to take advantage of ML technology because of its proven ability to reduce operational costs, increase […]

Previously, I discussed machine learning and the traits that separate it from artificial intelligence. This blog analyzes how dirty, or bad data, is the enemy of machine learning. While entirely accurate and complete data is the goal of a comprehensive data management program, many firms fall somewhat short. Siloed governance projects and the lack of […]

The term artificial intelligence (AI) was first coined in 1956 by the computer scientist, John McCarty, when he held the first academic conference on the subject at Dartmouth College. McCarty, who is widely recognized as the father of AI, defined it as “the science and engineering of making intelligent machines.” Over time, the meaning of […]

Previously, I dove into how artificial intelligence helps review, and provide statistical analysis to data. The final blog of this series outlines how to be successful with an artificial intelligence implementation. Setting initial expectations and not promising a magic bullet is a key factor in determining the success of an initiative that focuses on deploying […]

Previously, I analyzed fraud detection and content management methods, with the assistance of artificial intelligence (AI). In my final post of this blog series, I explain the opportunities artificial intelligence provides to retirement services transformations. For many 401(k) plans, passing ADP/ACP testing can be a very difficult and challenging endeavor, and there can be severe […]

  Perficient Presents at Oracle OpenWorld 2019 – Live from the show floor, a director in our analytics team delivers an overview of the machine learning capabilities of Oracle Analytics Cloud with its built-in ML algorithms. Machine Learning is a science that existed for several decades. However, it wasn’t until the last 10 years that […]

Many recent machine learning challenges winners are predictive model ensembles. We have seen this in the news. Data science challenges are hosted on many platforms. Techniques included decision trees, regression, and neural networks. And, winning ensembles used these in concert. But, let’s understand the pros and cons of an ensemble approach. Pros of Model Ensembles […]

I like seeing patterns across domains. Consider machine learning models and employees. Both models and people can be appraised. What is an employee appraisal? Employee “performance appraisal has three basic functions: (1) to provide adequate feedback to each person on his or her performance; (2) to serve as a basis for modifying or changing behavior […]

My last blog discussed how AI assists to create human-computer systems. This next blog in the series analyzes clinical data review platforms. While machine learning (ML) and natural language processing (NLP) functionalities are not currently available in today’s clinical data review platforms (CDRP’s), we know they offer tremendous benefits to the clinical data review and […]

Previously, I discussed artificial intelligence (AI) enhancing clinical data review processes. This blog discusses how AI assists to create a human-computer system. Humans and machines each have their own strengths. On the one hand, machines are good at processing and analyzing large volumes of data with high speed and accuracy. On the other hand, humans […]

Ensuring that clinical trial data is accurate and that clinical trials are safe and effective, is a time-consuming and a manually intensive process. Many life sciences companies have implemented home-grown or off-the-shelf clinical data review platforms and defined the reviewing and cleaning processes to be used with them. Many of these systems have proven to […]