Computers consist of on/off switches and process meaningless symbols. So how is it that we can hope that computers might understand the meaning of words, products, actions and documents? If most of us consider machine learning to be magic, it is because we don’t yet have an answer to this question. Here, I’ll provide an answer in the context of machines learning the meaning of words. But as we’ll see, the approach is the same everywhere.
Today I am going to talk about API documentation tools. Specifically the ones we use at Lateral to create our documentation. Now, I understand if you aren’t enthused by API documentation, I get that. But a lot of people are. I am. People who make APIs are. So maybe you should be too. You don’t want to be left behind not knowing what’s possible with today’s advanced API tools. What would you talk about at conferences? It’d be terrible. Imagine. You’d have no idea. Anyway. Here we go.
We are extremely excited to introduce you to NewsBot, the fastest way to find related articles and to stay up-to-date on the news that matters to you!
If a machine is to learn about humans from Wikipedia, it must experience the corpus as a human sees it and ignore the overwhelming mass of robot-generated pages that no human ever reads. We provide a cleaned corpus (also a Wikipedia recommendation API derived from it).
Having recently released our TED talks demo we felt another interesting application would be the thoughts of one person. No one fits that description more than Maria Popova’s excellent collection of ideas on her Brain Pickings site. With thoughts on music to philosophy we felt it would be an excellent exploration of how our technology represents thoughts.
By ignoring citation graphs and keywords, you can discover papers and researchers you never knew existed. Check it out here (on arXiv papers = ML, CS, math & physics).
TED is an awesome platform for ideas, so we thought an interesting experiment would be having our API provide recommendations based on the talks’ transcripts.
At Lateral we use PostgreSQL to store documents for our visualiser. Each document consists of a text column and a JSON column with meta data inside such as a title, date and URL. We wanted to create a fast search experience for the visualiser that lets you search the full text of documents as well as their titles to quickly find a document to get Lateral recommendations for.
Stack Overflow is a programming Q&A website with over 4M users and 9M posts. It is one of many such sites on a variety of topics run by StackExchange. Stack Overflow is highly successful at gameifying the answering of questions through a reputation system based on up-votes and bounties. Users can use the reputation points and badges they win to support job applications, and employers can use the reputation to find the best employees. So answering many questions and earning tons of points is something that users take very seriously. But how can I find questions that I can answer?
On Friday and Saturday (the 24th and 25th) was the joint APIDays Berlin & APIStrat Europe conference. The Lateral tech team was in attendance chatting away with various API management software providers and API providers, and attending some very interesting talks. Ben and I decided on the Saturday to enter the Speedhack challenge. This is a breakdown of the challenges that we completed and some opinions on the various APIs that we tried.