Heraklion, Crete, 1976. A ten-year-old girl is fascinated by the world of numbers and dreams of becoming a mathematician when she grows up. Ten years later he moved to Athens and studies at the School of Mathematics of the Hellenic Academy of Sciences. But her gaze is fixed far away, on the opposite coast of the Atlantic. After graduating, she leaves for America. She comes to Rhode Island, begins her graduate studies at elective Brown University, and in 1993 receives a prestigious doctorate in applied mathematics and business studies. And although her original goal was to return to Greece, the “land of great opportunity” won her over, and in 1998 she began teaching at the Massachusetts Institute of Technology, better known as the Massachusetts Institute of Technology.

Georgia Peraki, having outlined a path that deservedly represents the peaks that Greece often conquers in the diaspora (often without us, in the “homeland”, knowing this), tells us not only about her academic and research journey, but also about this journey in which we all recently set off: what’s going on in the yet uncharted waters of artificial intelligence. In addition, she is one of the authorities in this field and currently holds two important positions at the Massachusetts Institute of Technology: the position of director of the Center for Operations Research, and the position of associate dean of the newly built Schwartzman College of Computing.

“Thank you for your interest – so many years in America and for the first time the Greek press is interested in my work and role here,” he says at the beginning of our conversation with the directness, kindness and humility that distinguished the words of two other leading professors of the Massachusetts Institute of Technology with whom we I recently had the pleasure of meeting you. We recently spoke on behalf of K, David Kaiser and Marsha Bartusiak.. Is this humility characteristic of those who prefer to work in silence, of those who speak only of their own affairs? “At MIT, we all have to be humble, because we know that for each of us there will always be someone much better. Those who understand this are successful and happy.”

But how did a girl from a Greek village end up on the scientific top of the world? What are the starting points and what stops does such an adventure have? As is often the case, simple gestures from a parent or teacher can be the start of a career. “There were two people who inspired me to love math,” she recalls, recalling her childhood in Crete in the 1970s and 1980s. “One of them was my father, who, with the help of matches, taught me in childhood to perceive mathematics as something interesting and enjoyable. The second was my math teacher in middle and high school. As a teenager, I spent hours in his office, where he gave me complex mathematical problems, which for me were a challenge, a game. That’s what mathematics was for me: a game I wanted to play for the rest of my life.”

Female role model

She remembers her professor well from her early years in America: “Stella Dufermow was the first woman to teach applied mathematics at Brown University. He became a role model for me, but, unfortunately, at the end of my third year there, he died of cancer. So I ended up as a faculty member in her department who needed an assistant, and at the same time I began new doctoral studies under the guidance of a professor from the Massachusetts Institute of Technology. This was also my acquaintance with the Institute: one door closed, but another opened. My beloved teacher is gone, but her death brought me here, where I have been for 25 years.”

Does the “game” of mathematics that she dreamed of all her life continue today at MIT? She answers yes, but explains that it is now hidden in complex algorithms.

“MIT has recognized that artificial intelligence should be at the center of its research. That’s why he founded the College of Computer Science.”

So what’s going on in this new College of Computer Science? What needs have created him, what research is he doing? “MIT has recognized that artificial intelligence should now be at the center of its research. That’s why he created the College of Computer Science three years ago, a “horizontal” academic structure that focuses heavily on research in artificial intelligence and its interdisciplinary applications, and uses technology to bring together all five of its departments, the School of Architecture and Urban Planning, the Faculty of Engineering, the Faculty of Sciences, Faculty of Humanities, Arts and Social Sciences and Faculty of Management.

Thus, academic fields that were traditionally considered separate are now fruitfully combined with computer science and artificial intelligence at the Massachusetts Institute of Technology. “Indeed, this is a trend in our program today. For example, our undergraduate students can opt for “double” majors (dual majors) such as, for example. “Decision Making and Artificial Intelligence”, as well as “mixed” specializations (mixed specialties), such as, for example. “Computer Science and Molecular Biology”. In addition, there is the concept of “point of contact” where students take courses (such as the Climate Change course) that simultaneously correspond to many different MIT schools.”

Life with algorithms

When we ask her about her students, she speaks warmly. “We are like a family,” she emphasizes, and when we ask her if any of them are Greeks, she replies that of her nine graduate students, four are Europeans (two Greeks, one Spaniard, one Romanian) and five are from the north. America (four Americans and a Canadian). “All of them, and of course the Greeks, make me very proud.” But are these students learning to practice professions that artificial intelligence will make redundant tomorrow? “This is something that has happened before. This is an inevitable part of technological development. But I do not believe that (artificial intelligence) will be able – at least not soon – to work successfully autonomously, without our help.

Let’s take a look at how the first steps ChatGPT it worked with autocomplete technology like Google does when we type in keywords and it guesses what to do with our search. However, along this path, human participation was considered necessary for its evolution. In the various large projects that we are working on, I see that in the end it is the person who helps artificial intelligence, and not vice versa.”

By asking her to share some relevant examples with us, she gives us a glimpse into the future: “One example is in retail and our collaboration with the well-known Spanish clothing company Zara, which is heavily accused of producing a lot of waste fabric. . Therefore, we use artificial intelligence to determine exactly how many pieces need to be produced so that there is no surplus. But it is not enough to analyze market data – you also need to consult with local managers. Another example comes from our partnership with a local public health facility, UMass Memorial Hospital, which works closely with the University of Massachusetts School of Medicine.

Therefore, we have built a system that can use artificial intelligence to calculate how long the patient will wait in the hospital waiting room until competent doctors admit him for examination and treatment. This, as you understand, is very important, as some incidents are more urgent than others.” OUR technologiestherefore in the service of the environment and health, but always with our own instructions.

At this point, an MIT professor shares something very interesting with us: “One day it occurred to me that, as a woman myself, going to the emergency room, I waited longer than a man. Was it my paranoia or not? The answer was somewhere in between: when the incident was really urgent, there was no difference. But if he was middleweight, he existed.” So, algorithms, unlike us, do not have prejudices? “They have as much as we give them the right to have,” he answers us. “If we give them biased information, they will give us a biased result.” He continues, reminding us of the 2018 Amazon hiring algorithm: “Its results weren’t particularly ‘fair’ for women. The reason was that it was just an artificial intelligence system trained on a database containing only men.”

moral responsibility

For AI to help us properly, we must first help it properly. “It’s a two-way street, as is the case with any beneficial relationship. That’s why our educational goal at MIT has another important pillar that we call “SERC” (Social and Ethical Responsibility of Computing), the Social and Ethical Responsibility of IT. The new generation must be taught the correct use of these tools, otherwise the results can be horrendous. I cite nuclear power as an example: it can provide energy sufficiency (as is the case in France), but it can also lead to Hiroshima.