‘Applied AI/ML in the Workplace – Geek Food for Thought’ – UT Austin presentation

2 minute read

Last Thursday we presented “Applied AI/ML in the Workplace – Geek Food for Thought” at the University of Texas in Austin Computer Science department. Thomson Reuters is one of the Friends of the University of Texas at Austin that gives students an excellent opportunity to engage with the industry and learn more about companies that offers internships or job opportunities.

The speakers were me and Katherine Li, data scientist in my team. Special thanks to our co-workers and UT Austin alumni, Cameron Humphries, Director of software engineering, and Matthew Hudson, software engineer at Thomson Reuters. Also special thank you to Jennifer Green, senior talent acquisition partner in Thomson Reuters HR, and Ana Lozano, events program coordinator at UT Austin who helped set up the talk. More importantly, thank you UT Austin students for attending the event knowing that we missed more students because of conflicting class schedule and mid term exams.

I first talked about Thomson Reuters the company with a 100-year history, a global company, and its top-notch technology and careers development programs. I ran a video of our CEO and president, Jim Smith, explaining what makes Thomson Reuters Thomson Reuters. I then highlighted who are founding fathers of Thomson Reuters, beginning with Paul Reuter who founded Reuters News in 1851 and Roy Herbert Thomson who founded the company in the 1930’s which later became known as Thomson Corporation. Both companies later merged in 2008 and became Thomson Reuters. I hope to have made my point to the young audience that the Thomson Reuters founding fathers, Paul Reuter, who pioneered telegraphy and news reporting starting with pigeon posts, and Roy Herbert Thomson, First Baron Thomson of Fleet, were both entrepreneurs at a similar age as them.

I then provided an overview of Thomson Reuters Labs and listed some of the key innovative products including the latest WestLaw Edge, the most advanced legal research platform ever. I then moved to talk about AI and ran a video for our TR Labs CTO, Mona Vernon, speaking to The Economist early this year about AI and machine learning revolution. That was a great segue way to the main topic of the presentation, and that is applied AI in the workplace.

Through a couple of slides, I tried to make the point that students in the field of machine learning and artificial intelligence need to consider applying existing algorithms for their projects or their next start-up idea instead of building everything from scratch. It is quite understandable that students need to understand or even seek too contribute to the advancement of the core algorithms in the artificial intelligence. That is great and is very important but, unfortunately, it does not always lead to the next innovation or the next best product out there. The markets are hungry for applying artificial intelligence in the quickest time possible and in all the different ways that would have a societal impact. To illustrate the point, Katherine Li and I showcased four projects that leverage machine learning and natural language processing algorithms. We managed to get the applications working in a short time because we leveraged available cloud-based solutions notably Amazon AWS and Google Cloud and added our code for the projects. By spending less time building machine learning algorithms, we were able to focus more on the ideas and tie the different components into working prototypes.

You can check the presentation on this DropBox link

note: please download the deck and run the slides in presentation mode so that you can access the videos