Goto

Collaborating Authors

Text Analysis Machine Learning APIs From Algorithmia

#artificialintelligence

Helping us all make sense of, and enrich data that is moving along via our data pipes. It is common for our customers to perform sentiment analysis, enrich with tags, and extract names, dates, emails, and other relevant information for streams as they arrive, or as they are being delivered to other destinations. By adding additional tags, meaning, and other metadata, it makes it easier to connect and aggregate data across real-time streams, and transform existing streams into richer topical feeds. We are working on profiling, not just Algorithmia, but a number of other machine learning APIs. As we establish interesting collections of text analysis, deep learning, and other algorithms that can be applied to Streamdata.io streams, we'll publish here on the blog. If you have specific data and content, or machine learning model that you'd like to have delivered as part of your real-time infrastructure let us know. We are happy to prioritize specific types of data or profile more relevant machine learning APIs providers to help expedite your work. We are beginning to ramp up our efforts to profile relevant machine learning models, as the demand from our customers' increases, hoping to satisfy our customers demand for machine learning intelligence as they continue to optimize their streams of data across their organization.


Nudity Detection and Abusive Content Classifiers -- Research and Use cases

#artificialintelligence

Web 2.0 revolution has led to the explosion of content generated every day on the internet. Social sharing platforms such as Facebook, Twitter, Instagram etc. have seen astonishing growth in their daily active users but have been at their split ends when it comes to monitoring the content generated by their users. Users are uploading inappropriate content such as nudity or using abusive language while commenting on posts. Such behavior leads to social issues like bullying and revenge porn and also hampers the authenticity of the platform. However, the pace at which the content is generated online today is so high that it is nearly impossible to monitor everything manually. On Facebook itself, a total of 136,000 photos are uploaded, 510,000 comments are posted and 293,000 statuses are updated in every 60 seconds. At ParallelDots, we solved this problem through Machine Learning by building an algorithm that can classify nude photos (nudity detection) or abusive content with very high accuracy.


The Importance Of Tags In OpenAPI Definitions For Machine Learning APIs

#artificialintelligence

I am profiling APIs as part of my partnership with Streamdata.io, and my continued API Stack work. As part of my work, I am creating OpenAPI, Postman Collections, and APIs.json indexes for APIs in a variety of business sectors, and as I'm finishing up the profile for ParallelDots machine learning APIs, I am struck (again) by the importance of tags within OpenAPI definitions when it comes to defining what any API does, and something that will have significant effects on the growing machine learning, and artificial intelligence space. While profiling ParallelDots, I had to generate the OpenAPI definition from the Postman Collection they provide, which was void of any tags. I went through the handful of API paths, manually adding tags for each of the machine learning resources. Trying to capture what resources were available, allowing for the discovery, filtering, and execution of each individual machine learning model being exposed using a simple web API.


Automated Text Classification Using Machine Learning

@machinelearnbot

Digitization has changed the way we process and analyze information. There is an exponential increase in online availability of information. From web pages to emails, science journals, e-books, learning content, news and social media are all full of textual data. The idea is to create, analyze and report information fast. This is when automated text classification steps up.


Classifying Nudity and Abusive Content With AI - DZone AI

#artificialintelligence

The revolution of the web has led to an explosion of content generated every day on the internet. Social sharing platforms such as Facebook, Twitter, Instagram, etc. have seen astonishing growth in their daily active users, but have been at their split ends when it comes to monitoring the content generated by users. Users are uploading inappropriate content such as nudity or using abusive language while commenting on posts. Such behavior leads to social issues like bullying and revenge porn and also hampers the authenticity of the platform. However, the pace at which the content is generated online today is so high that it is nearly impossible to monitor everything manually. On Facebook itself, a total of 136,000 photos are uploaded, 510,000 comments are posted, and 293,000 statuses are updated every 60 seconds. At ParallelDots, we solved this problem through machine learning by building an algorithm that can classify nude photos (nudity detection) or abusive content with very high accuracy.