How do machines learn meaning?

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.

You’ll never guess which API documentation tools we use

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.

The Unknown Perils of Mining Wikipedia

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).

Exploring your thoughts with machine learning

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.

TED Talks Visualizer

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.

Full text search in milliseconds with PostgreSQL

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.

APIDays Berlin & APIStrat Europe

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.