As the winners of the world’s most famous IT awards meet this month in Germany, they share concerns about education, ed-tech resources and improving but still a low role for women.
HEIDELBERG, Germany—Every September, a group of the world’s most decorated computer scientists and mathematicians gather in this hot spot. They discuss the state of their field and guide 200 undergraduate, graduate and postgraduate students from around the world selected through a large competitive process.
Vinton Cerf, the vice president of Google and the Internet messenger, who is also known as one of the “fathers of the Internet” for development, and Robert Kahn, the architect of the Internet system known as Transmission, said, “It’s like coming home.” Control Protocol/Internet Protocol (TCP/IP). For this work, Cerf and Kahn won the Turing Prize, the so-called Nobel Prize for computing.
Young researchers who participated in this year’s Heidelberg Laureate Forum – that is the so-called event – can, for example, chat with Yann LeCun over coffee with Yann LeCun ( “father of intelligence”), walk with Whitfield Diffie (“Father of public-key cryptography”) or take a boat on the Nekar River with Shwetak Patel, a MacArthur Fellow who is an outstanding worker in human-computer interaction. the lives of millions have improved. The meeting is an intimate, invitation-only meeting inspired by the scientific community, the Lindau Nobel Laureate Meeting held every July in Lindau.
If the 28 winners who came this year gave and listened to each other’s conference with an optimistic name like “Computing for Social Good”, Inside Higher Ed took the opportunity to ask them questions about the challenges of computing and withdrawal from Higher Education.
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The luminaries are concerned about how computer science is taught today, because of the rapid development, the shortage of faculty, and the unnecessary need to incorporate ethics into the curriculum. They also have doubts about some ed-tech tools, cross-disciplinary discussions, and making women participate in numbers, but still low, especially given their role in developing tech products that are changing how people live.
Missing Seats and Tables Are Important
Researchers from all academic fields use computational tools to solve various problems in health care, weather forecasting, e-commerce, transportation, finance, agriculture, energy systems, manufacturing, environmental monitoring housing, national security, etc. But this does not mean that these researchers always go to computer scientists who provide computer tools.
“We’re seen as a bunch of geeks who give them resources, but not necessarily as equal players,” said Cherri Pancake, former president of the Association for Computing Machinery (ACM) and professor emeritus at Oregon State University. said. “What we need to bring to the table is not our software or our tools, but our different ways of approaching problems and finding solutions.”
Computer scientists have long warned that computer applications are dangerous. For example, British scientist Stephen Hawking has warned that intelligence can make people stop 카지노사이트. Last month, a paper published by scientists from Google and Oxford concluded that a well-designed product can cause “dangerous consequences”.
“As we try to solve these very real challenges for humanity, computer scientists will step up and bring a different way of looking at the universe.”
Brain Drain in the Private Sector
More than 7,500 students from Washington state, where Microsoft is headquartered, applied for admission to the University of Washington’s computer science and technology program this year. But without enough computer science professors to keep up, the UW accepted only 7% of applicants, an acceptance rate similar to that of graduate students at Brown and Yale. Such demand for students, along with a severe shortage of computer science majors, is evident at colleges across the United States.
“We eat our own corn,” Cerf said. “Expertise does not grow on trees. It is developed in universities and research schools. We have to deal with people.
“The salary model is a killer,” said Jeffrey Ullman, professor emeritus of computer science at Stanford and recipient of the Turing Award. “When you can make three times as much money, why teach a secret? It may not be a good idea to stick with your standard payment and take what you can afford.
“Every department is trying to figure out how to teach more students with the same population,” Brewer said. “They don’t have enough graduate students to [attend] all the classes, so they have college degrees [teaching assistants]. Next, you need to understand how to train undergraduate teaching assistants. We try to be inclusive and take on as many students as possible, but this is an ongoing challenge.
Cerf, who has spent all his time in academia, government and the private sector, hopes that the IT community can increase the opportunities for professionals to enter and exit academia during their careers.
“Perhaps some of the tools we’ve developed during this pandemic will be useful as they facilitate remote learning,” Cerf said.
Anticipation is Important and Negative
When Ralph Merkle, an undergraduate student at UC Berkeley in the 1970s, proposed a project to create a cryptographic system, his professor called his idea “complex”, according to Martin Hellman, professor emeritus of electrical engineering, at Stanford. Merkle left the teaching and worked alone. When he finally submitted a paper based on his results and a communication from the Association of Computing Machinery, it was not accepted.
“The reviewer dismissed it because ‘the report is not at the core of current cryptographic thought,'” Hellman said. “Of course not. It’s a change.
Merkle, working alone, with Hellman and Diffie working together, continued to develop public cryptography, technology that allows us, for example, to enter credit card numbers online with confidence. Hellman and Diffie won the Turing Award for this work, but Merkel’s award was rejected.
“Ralph invented half of the secret code – half of the code – on his own, independently of us, and actually a little bit ahead of us,” Hellman said. Like Merkle, Yann LeCun was a graduate student in the 1980s who also tried to make his opinion heard. At first, he told Inside Higher Ed, no faculty member would agree to work with him on research that was the embryo of neural networks — machine learning algorithms based on the structure and function of the brain. (The term “neural network” did not exist at the time.) Finally, he found a teacher who told him, “I don’t know what you’re working on, but you seem to know it well.”