Jennifer Bone isn't a computer scientist. But she's learning to work with Artificial Intelligence by teaming with students from the Machine Learning department.
"So we're trying to actually take patient's own cells and bioprint them into new organs It has so many factors, so many variables that go into the process that the human brain is just, it's very difficult for us to optimize. So we're using machine learning as a way to help ustune the perfect way to print these organs so that they're repeatable and have high fidelity each time."
This is one of the ways that Carnegie Mellon University has been leading education in AI to produce innovative research and new technology.
With the proliferation of AI and its potential to transform all sectors of society,
governments over the past year have been rushing to set up university courses and graduate schools in the field.
Many are looking to the U.S. as an example, as the country is perceived to have the highest proportion of AI professionals compared to demand from businesses.
Many attribute the country's success to the early adoption of research programmes in the field.
Carnegie Mellon in 1958, offered the first university-level course in computer programming. It later created the first Ph.D. program in robotics, as well as the world's first Machine Learning department.
The school has produced many world class researchers and spin-off firms, that have driven innovation across a wide range of sectors.
"So this is still a relatively new field, and the students who go into the major in AI, tend to be ones who are more forward thinking. Employment in artificial intelligence doesn't have quite as long a track record as employment in other areas. So some of the students are a little wary about what the job prospects are, but the ones who are willing to take that chance I think are going to be very well rewarded."
In addition to courses on machine learning, robotics and decision-making, the school has been encouraging multidisciplinary collaboration.
"A big part of this is actually learning about the problems themselves and domain information from the sciences to try to understand them better and see how we can apply machine learning to them."
We're seeing a global talent search for young experts in Artificial Intelligence, but not everyone needs a rigorous knowledge of coding the way forward is collaboration.
Oh Soo-young, Arirang News, Pittsburgh.