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 offer internships or job opportunities.
The speakers were me and Katherine Li, the data scientist on 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, a 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 schedules and mid-term exams.
I first talked about Thomson Reuters, 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 the founding fathers of Thomson Reuters, beginning with Paul Reuter, who founded Reuters News in 1851, and Roy Herbert Thomson. They founded the company in the 1930s, which later became known as Thomson Corporation. Both companies later merged in 2008 and became Thomson Reuters. I reminded 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 entrepreneurs at a similar age as them.I then provided an overview of Thomson Reuters Labs and listed some innovative products, including the latest Westlaw Edge, the advanced legal research platform. 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 the machine learning revolution.
Through a couple of slides, I tried to make the point that students in 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 to contribute to the advancement of the core algorithms in artificial intelligence. That is great and important, but unfortunately, it does not always lead to the next innovation or another best product. 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 built the solution quickly 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 could focus more on the ideas and tie the different components into working prototypes.