At Talla, we build products that help people find and make use of an organization’s internal knowledge. While we've already implemented technology for enterprises with models that are successfully in production, we continuously experiment with different data and methods that have less certain outcomes. In my talk at the Machine Learning Conference, which you can watch below, I shared what happened when we experimented with using chat data for issue detection and matching.
A couple of weeks ago, we launched Talla the Task Assistant, it’s a subset of the full Talla functionality coming later this year, and focuses on natural language management of to-do/task lists in Slack (and Hipchat soon). We’re a startup, and have been building the foundation of our larger platform over the past few months. It’s exciting to have shipped our first product, and since we’ve been so focused on that, at this point, we’ve shifted from trying to answer “What should we build?” to “Have we been successful?”
The White House recently requested information on the future of artificial intelligence. Since it's our job to think about these things, as builders of AI-powered assistants for businesses, our Chief Data Scientist and Co-founder, Byron Galbraith, was an awesome person to provide insights on the topics they're asking about. Below are his answers.
It's been a while since we wrote on this blog because we've been running an experiment and only posting on Medium.com. Now that we have digested the results of that experiment, we intend to publish on both places. Before we get started writing again here on Talla.com, I want to update you on some of the things we've done the past few months, in case you missed it.
The Talla Question & Answer Assistant is an internal company expert for teams on Slack. Every company has lots of frequently asked questions that are unique to them:
We build intelligent automated assistants for teams. More updates are coming soon—stay tuned, or contact us below.
When I started a machine intelligence company, the first thing a VC said to me was "You can't do it. There aren't enough good data scientists out there, and you won't be able to find any." He was wrong of course. But, I knew that as we went to raise money, VCs would ultimately ask about the team. How do I know that the Talla data science team is up to the task? The majority of VCs like to back teams that have Stanford engineering degrees, Harvard MBAs, and then a stint at Google or Facebook, but I prefer to hire weirdos, and I knew VCs wouldn't accept them on their credentials alone, because I'm not hiring the kind of person they want. So one way to get around this VC question was to do some sort of task that shows we have a good team. And that is why we decided to participate in the Allen A.I. Challenge on Kaggle. This post is about how we approached that problem.