Whether you’re a scientist analyzing earthquake data to predict the next “big one”, or are in healthcare analyzing patient wait times to better staff your ER, understanding time series data is crucial to making better, data informed decisions.
This gentle introduction to time series will help you understand the components that make up a series such as trend, noise, and seasonality. It will also cover how to remove some of these components and give you an understanding on why you would want to. Some common statistical and machine learning models for forecasting and anomaly detection will be explained and we’ll briefly dive into how neural networks can provide better results for some types of analysis. Read More…
We’ve all heard about racial bias in artificial intelligence via the media, whether it’s found in recidivism software or object detection that mislabels African American people as Gorillas. Due to the increase in the media attention, people have grown more aware that implicit bias occurring in people can affect the AI systems we build.
Early this week, I was honored to give a talk on Racial Bias in Facial Recognition at PyCascades, a new regional Python conference. Last week I wrote a blog post on learning facial recognition through OpenFace where I went into deeper detail about both facial recognition and the OpenFace architecture, so if you want to give that a read through before checking out this talk, I highly encourage it. Read More…
Facial recognition has become an increasingly ubiquitous part of our lives.
Today smartphones use facial recognition for access control while animated movies such as Avatar use it to bring realistic movement and expression to life. Police surveillance cameras use face recognition software to identify citizens that have warrants out for their arrest and these models are also being used in retail stores for targeted marketing campaigns. And of course we’ve all used celebrity look-a-like apps and Facebook’s auto tagger that classifies us, our friends, and our family.
Face recognition can be used in many different applications, but not all facial recognition libraries are equal in accuracy and performance and most state-of-the-art systems are proprietary black boxes.
OpenFace is an open source library that rivals the performance and accuracy of proprietary models. This project was created with mobile performance in mind, so let’s look at some of the internals that make this library fast and accurate and think through some use cases on why you might want to implement it in your project. Read More…
Apache Spark is one of the most useful tools for large scale data processing. It allows for data ingestion, aggregation, analysis and more on massive amounts of data and has been widely adopted by data engineers and other professionals.
With Spark Streaming and Spark SQL you can perform ETL operations in real-time on data coming from a variety of sources such as Kafka or Flume. And now if you want to do some basic machine learning, you can do that with SparkML, which is a library where they bring core statistical models like KMeans or decision tree models to users in a high level API.
But what if you want to analyze thousands of Tweets in real time, yet you don’t have a trained dataset to discover the sentiment of those tweets. Or maybe you want to classify documents on the fly or remove profanity from text or nudity from images?
Algorithmia’s over 4,000 pre-trained models and functions cover all of the above use cases and perfectly compliment Spark’s core functionality. These pre-trained models can easily integrate into Spark via a REST API endpoint. And just like Spark, Algorithmia has Python, R, Java, and Scala clients so you can stay in the language you’re familiar with while building robust machine and deep learning pipelines that scale with your data.
In this hands-on micro workshop, Jon will show you how to create a chatbot using Dexter, a company that makes building chatbots easy and accessible. Then he’ll show you how to make the chatbot emotionally aware using Algorithmia. Our open marketplace that hosts over 4,000 algorithms and microservices that are all available via a scalable API endpoint.
Jon will also go through some use cases covering why you would need a chatbot, especially one enabled with machine learning and provide some examples of other machine learning algorithms that work well in chatbots, but aren’t covered in the demo.
Please join us for a fun evening of food and drinks provided by Algorithmia and learn how to build an emotionally intelligent chatbot!
For more information or to RSVP check out the Seattle Building Intelligent Applications Meetup.