Michael Watson: Text data is probably one of the easiest to start training AI with
Michael will be set to speak at Quant Strats 2024 at 9.10 am on the Keynote Panel: Will recent technological innovations unlock the next source of Alpha? The panel will take place on March 12 at the Quorum by Convene, New York.
Please give us a little introduction to your current role and what you do
As a Director of Field Application Engineering for Supermicro, I manage East Coast Field Engineering, Solutions & Technology Enablement teams responsible for understanding large enterprise, Cloud GPU/AI, and service provider customer requirements for deployment of optimized solutions to meet customer needs. I travel often to drive strategic partner initiatives, customer satisfaction, and lead technology & roadmap discussions with leaders of organizations and strategic customers.
What do you consider your biggest professional achievement to date?
Early in my career, I was lucky enough to have done chip design at Intel, followed by the architecting of a Department of Energy Pacific Northwest National Labs cluster and as of late I have been involved with the deployment and usage of one of the largest commercial AI/ML clusters in the world.
What has been you/your firm’s top 3 priorities for the coming year?
Understand how AI/ML is reshaping the global enterprise marketplace, articulate industry-wide customer pain points, and provide optimized AI/ML compute solutions to address industries evolving needs.
What do you think are the biggest challenges facing data scientists/AI experts/quantitative practitioners/portfolio managers for 2023 and beyond?
AI-based tools and insights can potentially be game-changing for organizations, but until AI becomes more mainstream, , a lack of trust will be a major obstacle to more widespread adoption.
With some semblance of stability returning to the US and European markets – where do you think the next source of Alpha is?
I’m personally very bullish on AI/ML and believe that we’ve just begun to scratch the surface of the types of insights that AI/ML can provide when combined with large data sets. I’m of the opinion that the AI/ML space will continue to outperform the market as a whole.
What is the biggest potential economic banana skin that could shock the system?
War, large-scale supply chain disruptions, and massive socio-economic disparities could lead to sizable systemic shocks to the system.
With Gen.Ai being the word on everyone's lips and vigorous debate about the impact it has and will have on financial markets – do you think that it will seismically change the landscape or will it just automate the ‘boring stuff’
I believe that AI will likely provide deep meaningful insights within a multitude of industries, but it’s probably a bit early to predict which industries will be the big winners or the magnitude of change we’ll see. Regardless, I’m very bullish on what we can expect in the coming months and years ahead.
The data market seems to be incredibly saturated at present, with more data being available than any other time in human history – do novel data sources still exist?
The data market may seem saturated, but as we begin to more fully understand what AI/ML can do for us, we’ll most likely start looking at the existing data we already have in new ways and begin collecting data from alternative sources to unlock additional understanding of the world around us.
Text data seems to be an area where firms are focusing, are there particular risks to this and how would you go about extracting the most value?
Text data is probably some of the easiest/lowest-hanging fruit to start training AI with, but moving forward I expect audio and video sources augmented with supplemental data to hold a great deal of promise for AI/ML training in the future.