Facial Recognition | Authentication

Facial Recognition | Authentication

  • Posted by Wilker Amorim
  • On December 8, 2019
  • AI, Facial Recognition, Symphony Platform
Currently, face authentication is one of the most reliable and seamless biometric technologies in the tech market. It offers a fast and natural mechanism to prevent fraudulent actions caused by stolen personal credentials.
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 4
 Machine Learning for Anomaly Detection

Machine Learning for Anomaly Detection

  • Posted by Wilker Amorim
  • On December 8, 2019
  • AI, Communications, Machine Learning, Symphony Platform, Telecommunications, Time Series Forecast
Build and implement an AI solution to monitor, analyze and alert Communications companies of deviations from normal traffic behavior, detecting robocalling and fraud.
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 3
 Leveraging Deep Learning on the Browser for Face Recognition

Leveraging Deep Learning on the Browser for Face Recognition

  • Posted by Daitan Innovation Team
  • On August 20, 2019
  • AI, Chatbot, Facial Recognition
Face recognition is probably one of the long-awaited technologies of recent decades. From Hollywood movies and TV sci-fi series to actual cell phone solutions, the face seems to be the perfect authenticator. But, despite the hype, the tech didn’t look ready for a long time. However, recent advances in machine learning seem to be worth the wait. To get an idea, let’s take a look at what the big four tech companies are doing in this area.
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 A Visual Guide to Time Series Decomposition Analysis

A Visual Guide to Time Series Decomposition Analysis

  • Posted by Daitan Innovation Team
  • On August 2, 2019
  • AI, Analytics
Last time, we talked about the main patterns found in time series data. We saw that, trend, season, and cycle are the most common variations in data recorded through time. However, each of these patterns might affect the time series in different ways. In fact, when choosing a forecasting model, after identifying patterns like trend and season, we need to understand how each one behaves in the series. With this goal in mind, let’s explore two different pre-processing techniques — additive and multiplicative decomposition.
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 An AI-Powered Customer Experience Requires Breaking Down Data Silos

An AI-Powered Customer Experience Requires Breaking Down Data Silos

  • Posted by Daitan
  • On September 6, 2018
  • AI, Architecture, Customer Experience, Data
BLOG - Today’s consumer expectations cannot be ignored. A customer calling a company on their mobile phone, texting via a message window, or emailing to a support system, expects that company to know instantly who they are without asking
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 Practical Facts About the Current State of Machine Learning and Artificial Intelligence

Practical Facts About the Current State of Machine Learning and Artificial Intelligence

  • Posted by Daitan
  • On August 11, 2018
  • AI, Data, Digital Transformation, Machine Learning
Over the past 2 years, Daitan has helped a number of our clients implement advanced technology solutions, as well as provided Data Science, Data Engineering, Customer Experience and DevOps resources to successfully implement it. In our experience, companies are taking measured steps to take advantage of Machine Learning and Artificial Intelligence technologies based on clear business objectives that intelligently further growth.
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