Skip to content

Starmind Tech Talk at Google

GoogleLogo_fit2

We were recently invited by Google to hold a Tech Talk for Google engineers at their office in Zurich, their largest development office outside of Mountain View. After brainstorming, our AI team decided to present on topic extraction and creating expertise profiles.

Before we dive in, here's a quick quiz for you. It’s the same quiz we gave to the Google engineers in the presentation. The light bulb in the center is the person we are looking for and the tags around it are the topics that were extracted from political speeches. Can you guess which politician this expertise profile belongs to? Hint: It’s the expert profile of a famous US politician. (Solution at the end of the post!)

quiz

Now, let's dive into the Tech Talk: Why did we decide to give a presentation about the extraction of topics and the creation of expertise profiles? Because our approach of extracting the right information and building expert profiles differs fundamentally from Google. Therefore we could present a topic to the employees of Google that stands out from their daily business and is still relevant to them.

How are Starmind algorithms different from Google's algorithms? In short: while Google requires huge amounts of data, Starmind is able to produce phenomenal results even with small data.

There are many possible applications of Starmind algorithms in the business world. These are 3 of the main areas of use:

  • Finding the right experts to answer a question
  • Staffing of projects
  • Creating a detailed skills inventory of the workforce

In the presentation we detailed how we achieve this. To keep it brief, we will show you some snippets from the presentation to give you a glimpse of what we do.

topic extraction1

In this example you can see that nouns are high value words for topic extraction. Additionally, adjectives that are coupled with the relevant nouns are also of value for the topics that are extracted. Certain nouns however, such as students in this case are not relevant as a topic and therefore won't be extracted by the algorithm. In this case, the algorithm could extract artificial intelligence and algorithms as topics.

Then to assess expertise we use topic interactions. Take a look at this example:

Pascal (user) publishes an answer (action) about neuroscience, brain, and A.I (topics).

We do this by automatic detection of the topics a user interacts with from data sources such as

… calendar entries

… emails

… public chat messages

… project descriptions

… questions and answers in Q&A tools

… support tickets and so on.

In the following example you can see a potential expert profile, created by the questions and answers that this person has given. For example these interactions...

expert profile 1-1

...lead to this expert profile.

expert profile 2

That's it! We hope you enjoyed the sneak peek of our presentation at Google. For us the main challenges are to extract the most relevant topics and create expert profiles that accurately reflect the knowledge of each person. We are constantly working to improve our algorithm even further. 

We had a lot of fun presenting in front of an engaged and highly technical audience at Google. After the presentation, we answered questions on natural language processing, topic extraction, data security and many more subjects that led to interesting discussions. If you have a question about the underlying model of Starmind or how Starmind can be used in your organization, don’t hesitate to ask us! Just write an email to info@starmind.com and we’ll get back to you with all the answers you need.

Solution of the quiz:

solution quiz

John Fitzgerald Kennedy, better known as JFK. Congratulations if you got it right!

Bonus picture: This is us after the successful presentation at the Google offices. From left to right: Laura Mascarell, Mario Curschellas, Joachim Ott.
WhatsApp Image 2019-09-06 at 16.01.31-1

What is a corporate wiki?

A corporate wiki, also known as an enterprise wiki, is a knowledge management system that provides a central location where your company can collect, capture, and update organizational knowledge.

As an internet user, the chances are high that you’ve used Wikipedia to find and acquire information and knowledge. Corporate wikis are comparable because they use similar technology and processes for people to collaborate and share their knowledge. The main difference is that corporate wikis are confined to the people within your organization.

Wikis have been a favored solution for knowledge management because every employee has the ability to read, edit, and contribute new content and knowledge. Plus, they're relatively easy to use. If employees can create a word doc, they’ll have no issue contributing to a wiki. But, if you’ve already used a corporate wiki before, you’re probably familiar with the challenges they can bring and are ready for a more effective way to manage knowledge.

"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Fusce ut ligula lacus. Vestibulum interdum tempus vulputate. Maecenas varius venenatis nibh, eget lacinia felis vestibulum sed. Aenean vehicula erat tristique, hendrerit elit. Aenean ultrices leo id ornare rutrum. Etiam gravida mattis tempus." Name, Position, Company name
Get in touch

Get the latest to your inbox

Speak to an expert talk

We’re always ready to help, with support tailored to your business needs. Schedule a call with one of our team to:

  • Learn more about how Starmind can connect knowledge across your business.
  • Discover the use cases that best fit your needs.
  • See how you can bring all of your company’s knowledge into one central platform.
  • Discuss your bespoke pricing package.

Get in touch