HEIDELBERG, Germany – Every September, a critical mass of the world’s most decorated computer scientists and mathematicians gather in this warm microclimate. They discuss the state of their fields and mentor 200 undergraduate, graduate and postgraduate students from around the world who have been selected in a highly competitive process.
“It feels like coming home,” said Vinton Cerf, Google’s vice president and chief internet evangelist, who is also known as one of the “fathers of the internet” because he developed, together with Robert Kahn, a series of known internet architecture and protocols. such as Transmission Control Protocol/Internet Protocol (TCP/IP). For this work, Cerf and Kahn won the Turing Award – the so-called Nobel in computer science.
The young researchers who attended this year’s Heidelberg Laureate Forum — as the event is known — were able, for example, to chat over coffee with Yann LeCun (“godfather of artificial intelligence”), take a walk with Whitfield Diffie (“father of cryptography public key”) or take a boat trip on the Nekar River with Shwetak Patel, a MacArthur Fellow whose pioneering work in human-computer interaction has improved the lives of millions. The forum is an intimate, invitation-only gathering modeled after its scientific counterpart, the Lindau Nobel Laureate Meetings held each July in Lindau, Switzerland.
Although this year’s 28 winners gave and listened to each other’s talks with optimistic titles such as “Computing for Social Good,” Inside Higher Ed took the opportunity to ask them questions about the challenges of computing in higher education.
These luminaries worry about how to teach computer science today, given the pace of innovation, faculty shortages and the unrealized need to integrate ethics into the curriculum. They also have doubts about some ed-tech tools, interdisciplinary dialogue and the improving but still low participation rates of women, especially given their role in developing technology products that change how people live.
Missing Seats at Important Tables
Researchers across academic departments are using computer science tools to tackle a variety of problems in health care, weather forecasting, e-commerce, transportation, finance, agriculture, energy systems, manufacturing, environmental monitoring, national security and more . But that does not mean that those researchers always consult the computer scientists who supply the computer equipment. Read also : Reading List: Summer 2022 reads to stimulate your imagination.
“We’re seen as a bunch of geeks who provide the raw materials for them but not necessarily as equal players,” says Cherri Pancake, past president of the Association for Computing Machinery (ACM) and professor emeritus at University State of Oregon. “What we really need to bring to the table is not our software or tools but our fundamentally different way of looking at problems and coming up with solutions.”
Computer scientists have long warned that computer applications carry risks. Leading British scientist Stephen Hawking, for example, warned that artificial intelligence could end humanity. Last month, a paper published by Google and Oxford scientists concluded that a sufficiently advanced artificial agent could lead to “catastrophic consequences.”
“As we move forward to try to solve these truly existential challenges for mankind, computer scientists need to step up and bring our fundamentally different ways of looking at the universe.”
Fast Pace of Developments Presents Teaching Challenges
Every hour in 2019, more than three artificial intelligence preprints were submitted to arXiv – an open access repository of electronic scientific preprints. That rate was over 148 times faster than in 1994, according to the Journal of Informmetrics study. On the same subject : Jackson Arts Council Launches Jackson Art Box – WBBJ TV. On the AI subtopic of deep learning alone, more than one preprint was submitted every hour—a 1,064-fold increase over the 1994 rate.
“It’s very similar in medical school when they talk about ‘the half-life of knowledge.’ The dean of the medical school tells graduates, ‘In five years, half of what we tell you will turn out to be false,’” said Alexei Efros, a computer science professor at the University of California, Berkeley. “The half-life of information in computing is quite short. In machine learning, it’s about three months.” Efros won an ACM Award in Computing for his innovative data-driven approaches to computer graphics and computer vision.
That makes teaching computer science challenging, according to Efros, who noted that he hadn’t had time to check arXiv during the month he was traveling but had since discovered that “five things had already changed while I was away.”
Questions About How to Teach Foresight and Ethics
Social media has brought people with common interests together, which sounds good, except that it has also connected, for example, supporters of terrorism, extremism and hate in ways that some argue have undermined democracy. For this reason and others, some computer scientists are trying to integrate ethics into their curricula. See the article : Riverside-based Empower You Edutainment aims to bring the arts to all Inland Empire students. But it is not clear how to do that.
“Do we do this by having a required ethics course?” said Barbara Liskov, Professor of Computer Science at the MIT Institute. “Or should every course have ethics in it?” Liskov is an early computer science pioneer who won the Turing Award for contributions to the practical and theoretical foundations of programming languages and systems design.
Training in ethics, however, is a necessary but not sufficient condition to avoid the unintended consequences of technology, says Liskov. Computer science students and practitioners also need to learn how to anticipate problems before they arise.
“We used to naively think, ‘Oh, isn’t it great that we could have these groups [on social media] where you could talk to people who are like you?’ Now we know this isn’t really work that well. ,” said Liskov.
“The days of being technically naive, which we had been for many years, are over,” said Liskov. “We need to open students’ minds so that they think about the harm that can come from what they are doing and so they ask, ‘What could I add that could act as a safeguard ?’ It is more than ethics. They need to think from a different perspective.”
Missed Opportunities to Include Experts From Other Fields
Computer scientists may be able to look back and recognize patterns in the development of their field, but they cannot predict its path looking forward.
“The No. 1 question every first-year or second-year graduate student asks me is, ‘What’s going to be hot in two years?'” Efros said. “The question presupposes determinism. It is not predetermined. It’s like evolution.”
Without clear goal posts, computer scientists engage in an evolutionary process, often in response to significant real-world needs. Technology enabled colleges to offer distance education during pandemic lockdowns, which was positive, except that it also magnified educational inequities experienced by low-income and underrepresented students.
However, the research community that enables the creation of technology does not always consult with, or does not always have access to, psychologists, anthropologists, sociologists, neuroscientists and other experts, according to Cerf. These individuals could help computer scientists understand how people may respond to new technology environments and applications.
“All of us in the online world who provide products and services need to be more aware than we have been about the impact of these technologies on our social and economic lives,” said Cerf.
Departments Spread Too Thin
Students in a variety of disciplines beyond computer science need computer skills that specialize in their subject areas. But that development is not without its challenges for already stretched thin computer science departments.
“How do you choose between teaching computer science students and students in other disciplines who also need and deserve some [computer science] education?” said Eric Brewer, professor emeritus of computer science at the University of California, Berkeley. “Do we have to choose? And if we’re going to choose, how do we choose?”
Berkeley, for example, offers three different courses in discrete mathematics – each tailored to different disciplines. When those departments help teach the courses, everyone wins, Brewer said. “They know what they want and, more importantly, they provide some female workforce or ability to teach it.”
This solution also helps reduce tensions with departments that may be jealous of computer science departments’ relatively high allocations in terms of faculty hiring and other resources.
“You can say it’s driven by undergraduate demand, but it doesn’t make it more desirable from the other departments,” Brewer said. “A joint model spreads that allocation a bit better.”
Questionable Social Surveillance Via Ed-Tech Tools
Some ed-tech products undermine educational goals, said Raj Reddy, a professor of computer science and robotics at Carnegie Mellon University, who won the Turing Award for pioneering work in artificial intelligence and human-computer interaction.
“The biggest use of data mining for student applications is plagiarism detection,” says Reddy. “In fact, we should promote copying. If you’re doing a great thing, I want to learn from you and copy it!” Reddy suggests that faculty members spend less time plagiarizing software and more time determining whether students understand concepts , even if their work is modeled after the work of others.
Shannon Vallor, chair of data ethics and artificial intelligence at the Edinburgh Institute for the Future at the University of Edinburgh, also encourages faculty members and students to think critically about extended social surveillance.
“As data-hungry models become the main trend in deep learning, what we see is that it induces a kind of social phenomenon,” Vallor said. “It encourages investment in expanded social surveillance systems and more intrusive methods of data extraction … We have to ask ourselves, ‘What does society look like at the end of that road?'”
Brain Drain to Private Sector
More than 7,500 students in Washington State – home of Microsoft’s headquarters – applied for admission to the University of Washington’s computer science and technology programs this year. But without enough computer science faculty to meet the demand, PC admitted only 7 percent of those applicants—an acceptance rate on par with undergraduate acceptance rates at Brown and Yale.
Such high student demand coupled with a significant shortage of computer science faculty is emerging at colleges across the United States.
“We eat our own corn seed,” Cerf said. “Expertise doesn’t grow on trees. It is growing in universities and research schools. We need to keep those populated.”
“The salary structure is a killer,” said Jeffrey Ullman, professor emeritus of computer science at Stanford and recipient of the Turing Award. “When you can earn three times as much by doing coding, why would you learn to code? It can’t be a good idea to stick to your standard pay scale and take what you can get.”
“Every department is trying to figure out how to teach more students with the same number of people,” Brewer said. “They don’t have enough grad students to [assist] all the classes, so they have undergraduate [teaching assistants]. Then you have to figure out how to train undergraduate teaching assistants. We try to be inclusive and take as many students as logistically possible, but that is an ongoing challenge.”
Cerf, who has logged full-time stints in academia, government and the private sector, hopes the computer science community can enable more professionals to transition seamlessly in and out of academia during their careers.
“Maybe some of the tools we have developed during the pandemic will turn out to be useful because it makes it possible to teach remotely,” said Cerf.
Resistance to Important, Unconventional Ideas
When Ralph Merkle, an undergraduate student at UC Berkeley in the 1970s, proposed a project to develop a cryptographic system, his professor called his idea “confused,” according to Martin Hellman, professor emeritus of electrical engineering there. Merkle left the class and worked on the project on his own. When he later submitted a paper based on the results to Communications of the Society for Computing Machinery, it was not accepted.
“One reviewer rejected it because ‘the paper is not in the mainstream of current cryptographic thinking,'” Hellman said. “Of course it wasn’t. It was groundbreaking.”
Merkle, working alone, and Hellman and Diffie working together, later developed public key cryptography — the technology that allows us, for example, to confidently enter credit card numbers online. Hellman and Diffie won the Turing Award for this work, but Merkel’s contribution was not recognised.
“Ralph created half of the public key cryptography – the privacy half – on his own, independently of us, and actually a little ahead of us,” Hellman said.
Like Merkle, Yann LeCun was a graduate student in the 1980s who also struggled to get his ideas 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 inspired by the structure and function of the brain. (The term “neural network” did not exist at the time.) Eventually, he found a faculty member who told him, “I have no idea what you’re working on, but you seem smart enough.”
Today, LeCun is a professor of data science, computer science, neural science and electrical engineering at New York University and chief AI scientist at Meta. He won the Turing Award, along with Yoshua Bengio and Geoffrey Hinton, “for conceptual and engineering advances that have made deep neural networks an essential part of computing.” The three computer scientists are referred to as the “godfathers of AI.”
Sometimes innovative ideas are only recognized as such after some time has passed. Before that, several of the computer science winners suggested they might be overlooked.
Problems With Underrepresentation
At least Merkle and LeCun identified avenues to ensure that their important ideas were heard, which begs the question of whose ideas are being heard.
Women participate in computer science higher education at one of the lowest rates across all fields of science and engineering, according to the National Science Foundation. Tim Cook, Apple’s chief executive, told the BBC this week that there were “no good excuses” for the under-representation of women in technology and that the sector “won’t achieve nearly what it could achieve” without a more diverse workforce.
That said, women’s participation is increasing. The number of women earning bachelor’s degrees in computer science doubled (from 7,580 to 16,000 students) between 1998 and 2018 (the most recent data available), as did the number of women earning doctoral degrees (from 140 to 430) during the same period; the number of women earning master’s degrees in computer science quadrupled (from 3,430 to 15,100), according to the NSF.
At the same time, the paucity of women receiving the Turing Award cannot be explained by the underrepresentation of women in the field. Since 1966, 75 computer scientists have won the Turing Award, only three of whom have been women: Barbara Liskov, Frances Allen (deceased) and Shafi Goldwasser. That means women make up 4 per cent of recipients of the prestigious award, although they won around 22 per cent of PhDs in 2018, down from the 1987 peak of 37 per cent.
Recipients of million dollar awards such as the Turing Award are often called upon to meet business leaders and advise politicians. They are also in great demand to inspire and mentor young researchers, as many volunteered to do in Heidelberg. That is a challenge when there is so little.
“It’s very sad,” Liskov said. As for living Turing Award recipients, “there are only two of us.”
