The CS Department mourns the loss of Dragomir R. Radev, a 1999 computer science PhD graduate who unexpectedly passed away on March 29th in his home in New Haven, Connecticut. He was 54 years old and leaves behind his wife, Axinia, and children, Laura and Victoria.
Dragomir R. Radev
Radev worked with Professor Kathleen McKeown on seminal multi-document text summarization research, the topic of his PhD dissertation. His first job after Columbia was at IBM TJ Watson Research in Hawthorne, New York, where he worked for a year as a Research Staff Member. Then he spent 16 years on the computer science faculty at the University of Michigan before joining Yale University in 2017 as the A. Bartlett Giamatti Professor of Computer Science and led the Language, Information, and Learning (LILY) Lab at Yale University.
His research and work were influential, from his widely cited paper on LexRank to his most recent papers providing datasets, benchmarks, and evaluation of metrics for text summarization. His wide-ranging research touched many areas beyond summarization. He worked on graph-based methods for natural language processing (NLP), question answering, interfaces to databases, and language generation.
Over his career, Radev received many honors, including Fellow of the Association for Computational Linguistics (2018), the American Association for the Advancement of Science (2020), the Association for Computing Machinery (2015), and the Association for the Advancement of Artificial Intelligence (2020). He served as the Secretary of ACL from 2006-2015 and was awarded the ACL Distinguished Service Award in 2022.
Radev co-founded the North American Computational Linguistics Open Competition, an annual competition in which high school students solve brain teasers about language. He organized the contest and traveled with top-ranked students to the International Linguistics Olympiad every year.
“Drago was a very special, incredible person who touched all of us with his energy, his love for NLP, and his kindness,” said Kathleen McKeown. “He touched so many people and has had a huge impact on the field and on the ACL, the primary organization for our field.”
Dragomir R. Radev and family. Left to right: Laura, Dragomir, Axinia, and Victoria.
Fundraising note: A small group of faculty members from Columbia University, Yale University, and the University of Michigan have joined forces to raise money and set up a GoFundMe to help the Radev family support Victoria, who has a disability. The fund will help Axinia and the family continue to provide Victoria with the care she needs. If you are interested in and capable of donating in any way, please consider giving to the fundraiser.
The Distinguished Lecture series brings computer scientists to Columbia to discuss current issues and research that are affecting their particular fields.
An architect, a former Communications Officer for the United States Marine Corps, and a linguist will receive scholarship aid from a program that supports students from non-computational backgrounds to gain the knowledge and skills necessary to build careers in technology.
As one of the highest honors for young faculty, the NSF CAREER award recognizes outstanding research. Meet the Columbia Engineering professors who earned the award.
The chatbot has made waves over the past couple of months for being able to answer queries in a conversational tone. CS professors discuss what it can and cannot do correctly.
OpenAI’s ChatGPT is an artificial intelligence (AI) chatbot that is trained to follow the instruction in a prompt and give a detailed response. It is built upon GPT-3, a type of large language model (LLM) that predicts and generates text. Given a sequence of words, it will predict the word that has the highest probability of following next (kind of like autocomplete). These models are trained on huge datasets that allow them to generate answers to questions. ChatGPT works quickly and gives answers within seconds, and it also learns from every interaction and improves daily.
It can create a letter to your super asking for a repair to be done, write code and fix bugs, and suggest plot summaries for novels. But that does not mean that it is perfect. The problem with LLMs is that they can “hallucinate” and make things up. ChatGPT is guilty of this; some of the answers in its outputs do not even exist. It is also not trained to be truthful and it answers queries with a lot of confidence and authority, which is worrisome.
It is being compared to the last great tech disruption–the internet’s onset in the 1990s. We asked CS professors what the technology could do and how to use the tool the right way.
The original interface was cumbersome and needed an analyst who could use specialized programming languages to access the answer.
We developed AskCricInfo, which takes human input–questions or search queries–and converts the queries into a structured language like SQL that machines understand. The technology can “translate” the question into a programming language, find the answer, and quickly send it back to the user.
It is an excellent example of the power of underlying technology and what the tool can do. ChatGPT is very interesting. It is the first chatbot that makes “intelligent” and engaging conversations. There are definite use cases for making it a very effective teaching tool. It is up to the instructors to imagine a world where something like ChatGPT will always be available and teach students how to use it effectively. A lot can go wrong with ChatGPT- primarily because of its design that makes it bullshit confidently. But that is a problem with all LLMs, not just ChatGPT.
Kathy McKeown ChatGPT and the technology it is built on, GPT3, are game changers. With them and the other large language models that have recently emerged, models can now produce extremely fluent text. They are also able to perform a variety of tasks in a way that was not previously possible. And there are many situations in which they can be used to help us do things that we previously found tedious. But they are not able to do as much as we might initially have imagined. They have limitations that are not always immediately obvious, and researchers are taking the time to study them so that we can accurately characterize what they can and cannot do. Nonetheless, they change how we do research in the natural language field and how we build new models to carry out other tasks.
I think we need to learn how to incorporate them into our teaching because they are here to stay. Students know about them and will know how to use them. We will need to think creatively about how to teach students what these tools do well and what they do poorly, when they can be used as a tool to aid in research, and situations in which it would be dishonest. I imagine situations where we ask ChatGPT to answer an essay question, ask the student to critique the essay and identify what it does poorly, and then ask the student to write an essay that goes beyond what ChatGPT can do.
The worst problem with ChatGPT is that it is not always truthful. In fact, it may often lie or hallucinate facts. It does this very fluently, making its lies sound very plausible. This is quite a barrier to use in a real-world situation unless it is used by someone who knows what is correct, what is not, and can sift the good from the bad. Technology constantly changes, though, and it is a problem that researchers are actively working on.
Itsik Pe’er I asked ChatGPT about AI language models, and this is the output:
AI language models like ChatGPT, its competitors and successors represent a significant advancement in the field of Natural Language Processing (NLP). These models have the ability to generate human-like text based on a massive amount of input data, making them useful for various applications such as chatbots, question-answering systems, and content generation.
However, it is important to note that despite their sophisticated capabilities, they still have limitations and may generate outputs that are inaccurate, biased, or inappropriate. It is crucial to ensure responsible development and deployment of these models to maximize their benefits while minimizing potential harm.
The above demonstrates my thoughts more than I could otherwise express. In particular: it is already useful, but often not out-of-the-box, but I am a techno-optimist and believe even more usefulness is coming.
As a former judge in high school debate competitions, it reminds me of the most eloquent contestants, who would, if they needed to, lie through their teeth without missing a heartbeat. For instance, in response to this prompt, “What could go wrong with tools like ChatGPT?”, it writes:
There are several potential risks associated with the use of AI language models like ChatGPT: Bias: AI models are trained on large amounts of text data, which may reflect societal biases and lead to biased outputs. Misinformation: AI models may generate false or misleading information, which could have serious consequences if relied upon. Privacy Concerns: AI models process large amounts of personal information, raising privacy and security concerns. Misuse: AI models may be used for malicious purposes, such as spreading hate speech, fake news, or conducting phishing attacks. Job Loss: AI models may automate certain tasks, leading to job loss in industries that rely on those tasks. Therefore, it is important to carefully consider the potential risks and consequences of AI models and to develop and deploy them responsibly to minimize potential harm.
These are all valid points, but more context needs to be considered:
When it comes to bias, chatbots are worse than the best other (human?) alternatives but much better than the median ones.
Misinformation is a big problem when compounded by misuse, and we can see that happening now with stories posted online.
Privacy concerns depend on who has access to the data in ChatGPT and what can be done with it. But given that we all carry powerful tracking and sensing devices 24/7, our privacy is already weakly protected against powerful actors.
Some attention-seeking blogs and websites overstate the job loss point. Many jobs will become more efficient; many jobs will change; many jobs will be created, and, yes, many will be lost. People will adapt, and we will all be better for it.
Empathy is one of the most important leadership traits for managers. It helps build trust and connection among teams and demonstrates a leader’s ability to understand the needs of employees.
She represents just one of the many female talents at Columbia Engineering. Meet computer science undergrad Kennedy Salamat, who shares her experience & achievements as a woman in STEM in celebration of Women’s History Month.
Paparrizos is recognized for breakthroughs in time series data management, as well as contributions to adaptive methodologies for data-intensive and machine learning applications.
The award supports early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.
Dean Boyce's statement on amicus brief filed by President Bollinger
President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”
This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.
I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.
Sincerely,
Mary C. Boyce
Dean of Engineering
Morris A. and Alma Schapiro Professor
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