When pursuing a career in STEM, Silvie Spreeuwenberg, founding director of LibRT and program manager of Simacan, warns that job seekers need to be aware of biased individuals.
“They’ll always look for a copy of themselves,” she says. “It is proven that a non-homogenous group is more likely to find optimal results. Don’t blame your colleagues but make them aware of this fact and the consequences. Biases result in sub-optimal behavior. Since most people working in STEM are male, they tend to overlook female workers and underestimate the value of the female touch.”
Silvie also recommends that startups avoid the pitfalls of a technology-focused strategy versus one that is focused on creating business value.
“Many tech startups fail because the driving force is technology instead of offering value for businesses,” she says. “Women have the tendency to look at an issue from multiple perspectives. Promote this way of thinking.”
And remember that less isn’t always more.
“Always try to be together,” she advises. “Better to have two women in a team than one because one woman is not able to change the culture, while two would be enough. Women should help each other more and be less critical of each other’s behavior.”
Confidence is also an important part of career success.
“Be overconfident, because your male colleagues are overconfident as well, and look for additional training programs when you feel unsure,” she says. “It is always worth the investment.”
Silvie also had some advice that applies to both men and women, regardless of their career path.
“It is never a problem to combine parenting with working,” she says. “But it’s easier when you are young!”
One of her clients at LibRT includes the Schiphol Airport, a large airport in Europe. Silvie assisted with its integrated planning and forecasting, which she says is very much related to AI.
“We had lots of data about all flights, passengers, and how they behave – you have to find the balance between using historic data to forecast the future and using human knowledge expressed by rules,” she says. You can imagine that in exceptional times, like with the COVID-19 pandemic, historic data becomes useless and knowledge more important. “Assisting in the management of decision support systems is one of the things I do. I also do assessments on such systems, sometimes for the government. The return on investment for such IT systems in general is disappointing. Many projects fail. The cause is not related algorithms and IT, but more to whether the system is integrated in your organization and whether it is accepted.”
Regarding Simacan, a SaaS platform to control and digitize transport operations for retail logistics, Silvie says that her work is related to applying AI and algorithms in a very practical way.
“To be able to do that, you need to have a holistic view and understand the challenges in an industry. This way you find a way to create real value,” she says. “Women are very important in this process. It’s a man’s world, but what you see a lot in the industry is that they push technology instead of business value. Of course, there are some men that understand this challenge. But not the other way around: I have never witnessed a woman push technology for the sake of technology. We are more holistic and less attracted by gadgets.”
Silvie’s first encounter with AI began in college, when she embraced mathematics and enjoyed physics and philosophy.
“I found the study of artificial intelligence and it was a combination of philosophy, psychology, mathematics, information science (I didn't know what that was), biology, and language theory,” she recalls. “It had everything I liked – so I didn’t have to choose. No one knew what I was studying. My parents didn’t know if I’d be able to get a job. In my study, there were less than 10% of women around, and to my surprise that is still the case. The majority of the time, I was the only woman in all my working environments.”
Hoping to better explain AI to others, she wrote a book, “AIX: Artificial Intelligence needs explanation: Why and how transparency increases the success of AI solutions.”
“One of the reasons I wrote it was to demystify the decision based on artificial intelligence,” she says. “I think it's very, very dangerous if people think that this is some kind of God-like machining that makes perfect decisions, which is not true. I believe it's very important that people understand that these machines are man-made, and only do what they are trained for.”
Silvie dismisses the notion that AI is a “magic box.” She says the firms that promote this message only make money from those who do not understand the technology.
“That is not sustainable,” she says. “In the end, it doesn’t make sense. If you don’t understand a system, then you don’t know when to update it if the environment changes. The human is in constant control and must remain responsible.”