Recent advancements in artificial intelligence have serious people discussing or even claiming the existence of artificial general intelligence (AGI). In particular, large language models (LLMs) demonstrate a major milestone. Are they a major step towards AGI or a statistical trick? During 'Machine Intelligence', we explore these topics from numerous perspectives.
While the possibilities with AGI emergence seem great, it also calls for safety concerns. On the show, Vahid Behzadan, an Assistant Professor of Computer Science and Data Science, joins us to discuss the complexities of modeling AGIs to accurately achieve objective functions. He touched on tangent issues such as abstractions during training, the problem of unpredictability, communications among agents, and so on.
Barry Smith and Jobst Landgrebe, authors of the book “Why Machines will never Rule the World,” join us today. They discussed the limitations of AI systems in today’s world. They also shared elaborate reasons AI will struggle to attain the level of human intelligence.
Fabricio Goes, a Lecturer in Creative Computing at the University of Leicester, joins us today. Fabricio discussed what creativity entails and how to evaluate jokes with LLMs. He specifically shared the process of evaluating jokes with GPT-3 and GPT-4. He concluded with his thoughts on the future of LLMs for creative tasks.
The application of LLMs cuts across various industries. Today, we are joined by Steven Van Vaerenbergh, who discussed the application of AI in mathematics education. He discussed how AI tools have changed the landscape of solving mathematical problems. He also shared LLMs' current strengths and weaknesses in solving math problems.
An assistant professor of Psychology at Harvard University, Tomer Ullman, joins us. Tomer discussed the theory of mind and whether machines can indeed pass it. Using variations of the Sally-Anne test and the Smarties tube test, he explained how LLMs could fail the theory of mind test.
On today’s show, we are joined by Michael Timothy Bennett, a Ph.D. student at the Australian National University. Michael’s research is centered around Artificial General Intelligence (AGI), specifically the mathematical formalism of AGIs. He joins us to discuss findings from his study, Computable Artificial General Intelligence.
Today on the show, we are joined by Lin Zhao and Lu Zhang. Lin is a Senior Research Scientist at United Imaging Intelligence, while Lu is a Ph.D. candidate at the Department of Computer Science and Engineering at the University of Texas. They both shared findings from their work When Brain-inspired AI Meets AGI.
Our guest today is Maciej Świechowski. Maciej is affiliated with QED Software and QED Games. He has a Ph.D. in Systems Research from the Polish Academy of Sciences. Maciej joins us to discuss findings from his study, Deep Learning and Artificial General Intelligence: Still a Long Way to Go.
The creators of large language models impose restrictions on some of the types of requests one might make of them. LLMs commonly refuse to give advice on committing crimes, producting adult content, or respond with any details about a variety of sensitive subjects. As with any content filtering system, you have false positives and false negatives.
In this episode, we are joined by Ryan Liu, a Computer Science graduate of Carnegie Mellon University. Ryan will begin his Ph.D. program at Princeton University this fall. His Ph.D. will focus on the intersection of large language models and how humans think. Ryan joins us to discuss his research titled "ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing"
We are excited to be joined by J.D. Zamfirescu-Pereira, a Ph.D. student at UC Berkeley. He focuses on the intersection of human-computer interaction (HCI) and artificial intelligence (AI). He joins us to share his work in his paper, Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts. The discussion also explores lessons learned and achievements related to BotDesigner, a tool for creating chat bots.
On today’s episode, we have Daniel Rock, an Assistant Professor of Operations Information and Decisions at the Wharton School of the University of Pennsylvania. Daniel’s research focuses on the economics of AI and ML, specifically how digital technologies are changing the economy.
Hongyi Wang, a Senior Researcher at the Machine Learning Department at Carnegie Mellon University, joins us. His research is in the intersection of systems and machine learning. He discussed his research paper, Cuttlefish: Low-Rank Model Training without All the Tuning, on today’s show.
In this episode, we are joined by Carlos Hernández Oliván, a Ph.D. student at the University of Zaragoza. Carlos’s interest focuses on building new models for symbolic music generation.
Today, We are joined by Petter Törnberg, an Assistant Professor in Computational Social Science at the University of Amsterdam and a Senior Researcher at the University of Neuchatel. His research is centered on the intersection of computational methods and their applications in social sciences. He joins us to discuss findings from his research papers, ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning, and How to use LLMs for Text Analysis.
Our guest today is Vid Kocijan, a Machine Learning Engineer at Kumo AI. Vid has a Ph.D. in Computer Science at the University of Oxford. His research focused on common sense reasoning, pre-training in LLMs, pretraining in knowledge-based completion, and how these pre-trainings impact societal bias. He joins us to discuss how he built a BERT model that solved the Winograd Schema Challenge.
We are joined by Maximilian Mozes, a PhD student at the University College, London. His PhD research focuses on Natural Language Processing (NLP), particularly the intersection of adversarial machine learning and NLP. He joins us to discuss his latest research, Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities.