How to Build Your Own Intelligent Assistant Using RASA

  • Posted by Daitan Innovation Team
  • On August 1, 2020
  • AI, Chatbot, Open Source, RASA

Chatbots are everywhere. So are the tools that promise easy development and deployment of these applications. Frameworks like Google DialogFlow, Microsoft Luis, and Amazon Lex are fighting (badly) each-other to control this growing market.

In this blog post, we describe an...

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 Building a Voice Recognition System with PyTorch by Taking Advantage of Computer Vision Techniques

Building a Voice Recognition System with PyTorch by Taking Advantage of Computer Vision Techniques

  • Posted by Daitan Innovation Team
  • On June 18, 2020
  • AI, Biometrics, Computer Vision, Deep Learning, NLP, PyTorch, Voice Recognition
In this piece we describe how we built a reasonably performing Voice Recognition System with PyTorch, using deep learning Computer Vision techniques. With results as good as 90.2% accuracy using different training and testing samples, with only 25% of the original dataset size, we demonstrate how it is currently possible for different AI domains to leverage knowledge from each other to improve their techniques and outcomes.
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 1
 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
 How To Build a Deep Audio De-Noiser Using TensorFlow 2.0

How To Build a Deep Audio De-Noiser Using TensorFlow 2.0

  • Posted by Daitan Innovation Team
  • On December 1, 2019
  • AI, Audio, Audio De-noiser, Deep Learning, Tensor Flow 2.0
In this article, we tackle the problem of speech de-noising using Convolutional Neural Networks (CNNs). Given a noisy input signal, we aim to build a statistical model that can extract the clean signal (the source) and return it to the user. Here, we focus on source separation of regular speech signals from ten different types of noise often found in an urban street environment.
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 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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 Fundamentals of Time Series Data and Forecasting

Fundamentals of Time Series Data and Forecasting

  • Posted by Daitan Innovation Team
  • On July 25, 2019
  • AI, Data, Time Series Data, Time Series Forecast
Time series forecasting is the use of statistical methods to predict future behavior based on historical data. This is similar to other statistical learning approaches, such as supervised or unsupervised learning. However, time series forecasting has many nuances that make it different from regular machine learning. From data processing all the way to model validation, time series forecasting is a different beast.Many companies are exploring time series forecasting as a way of making better business decisions.
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