The more artificial intelligence (AI) and machine learning (ML) is utilized across industry, the easier it is for business leaders to gain greater perspective on not only its possibilities, but also its limitations.
Nevertheless, in an industry such as finance, where the smallest of margins can have such a significant impact, leaders can end up putting data scientists under considerable pressure. Many data scientists often find themselves trying to explain why they have not been able to achieve the impossible in an afternoon.
So, ahead of her upcoming keynote presentation at this year's DATAx New York Festival, we spoke to Sarah Hoffman, vice president of AI and ML at Fidelity Investments, a multinational financial services corporation based in Boston. We discussed the importance of an AI strategy and how Hoffman perceives attitudes have changed since she first entered the tech field.
DATAx: How have industry attitudes toward AI and automation changed over your time in the industry?
Sarah Hoffman: I joined Fidelity this past July and I've been very impressed by Fidelity's focus on AI and technology. I started working in the AI field in 2007, as a natural language processing (NLP) engineer with FactSet Research Systems. I've definitely seen many significant changes since that time. The most substantial one is that I no longer need to define AI, NLP or ML to everyone I meet. Most companies today recognize, or at the very least are starting to recognize, the importance of AI and having an AI strategy.
DATAx: What are some of the ways you foresee ML use expanding in the New Year?
SH: We will see many more personalized services as individualized experiences have now become a customer expectation. We will also see more productivity from ML engineers and data scientists due to the increasing number of tools that automate some of the more time-consuming steps in the ML process.
DATAx: What is the biggest assumption financial institutions make when considering AI solutions?
SH: I would say the biggest assumption most people make when it comes to AI is that AI is magic and can solve (or cause) all problems. When I was heading up the ML efforts at FactSet, my team used to joke that we were the "Genie Team", since we got asked to perform all sorts of impossible tasks. Every business leader today really needs to understand what ML is and what it is not in order to understand how to make the best use of it.
DATAx: What points of progress do you think have been made in the gender equality struggle in tech and what do you think tech firms can do to be better?
SH: On the first day of my first technical internship, the initial comment my manager received after introducing me to the team was: "I didn't know it was take-your-daughter-to-work-day today." We've definitely come far since then. Most people today appreciate that it's possible to be in tech – and be good at it – and also be a woman.
I've also seen many tech companies work hard to be inclusive and actively seek out suggestions for improvement. There is still room to improve. Making sure the interview process is inclusive and fair, and that a diverse pool of candidates is being considered – especially for senior roles – is a good place to start. Also, making work/life balance a priority can really help employees who may have other responsibilities at home.
DATAx: What will you be discussing in your presentation at DATAx New York?
SH: I will be speaking about the future of ML including the issue of biased data, enabling more people in organizations to perform AI tasks, and how humans and machines can work together for more innovation.
Sarah Hoffman will be speaking on Day One of Innovation Enterprise's Machine Learning Innovation Summit, part of DATAx New York, on December 12–13, 2018. To attend and hear more great insights from AI and ML professionals across some of the biggest and most influential organizations, register here.