Computational Intelligence and Operations Lab - CIOL

🚨 Excited to share that our paper, 𝐀𝐈 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐃𝐞𝐜𝐞𝐧𝐭𝐞𝐫𝐢𝐧𝐠 𝐨𝐟 𝐇𝐮𝐦𝐚𝐧 𝐂𝐫𝐞𝐚𝐭𝐢𝐯𝐢𝐭𝐲, has been published in 𝐀𝐂𝐌 𝐀𝐈 𝐋𝐞𝐭𝐭𝐞𝐫𝐬! 🇧🇩

Congratulations to Azmine Toushik Wasi and Sadia Tasnim Meem from Shahjalal University of Science and Technology for this contribution exploring the evolving relationship between human creativity and generative AI.

For centuries, Western philosophical traditions have framed creativity as an exclusively human capacity, grounding authorship and artistic agency in human consciousness, rationality, and intention. The rapid advancement of generative AI now challenges these assumptions by introducing computational systems capable of contributing meaningfully to creative production.

In this work, the authors examine how human–AI collaboration destabilizes traditional anthropocentric models of creativity. Drawing on posthumanist and new materialist perspectives, the paper argues that creativity should be understood as a distributed process emerging through interactions among human and non-human actors rather than as an exclusively human attribute.

The paper further explores how contemporary AI systems increasingly participate in creative workflows through idea generation, variation exploration, and iterative collaboration. By analyzing different levels of human–AI co-creation, ranging from AI as a digital tool to AI as a co-creator, the work highlights how creative agency is becoming increasingly distributed across computational and human networks.

The study also addresses broader philosophical and practical questions surrounding authorship, originality, aesthetic value, and responsibility in AI-mediated creative systems. Through a post-anthropocentric lens, the paper proposes a framework that repositions creativity as an emergent property of interactions among datasets, algorithms, interfaces, environments, and human interpretation.

This work contributes to ongoing discussions at the intersection of AI, philosophy, creativity, and human–computer interaction, highlighting how generative systems are reshaping established understandings of artistic production and creative agency.

🚨 Excited to share our work: 𝐏𝐨𝐬𝐢𝐭𝐢𝐨𝐧: 𝐓𝐢𝐦𝐞-𝐒𝐞𝐫𝐢𝐞𝐬 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬 𝐑𝐞𝐪𝐮𝐢𝐫𝐞 𝐄𝐱𝐩𝐥𝐢𝐜𝐢𝐭 𝐃𝐨𝐦𝐚𝐢𝐧-𝐋𝐞𝐯𝐞𝐥 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 has been accepted as a Position Paper at International Conference on Machine Learning (ICML) 2026! 🇧🇩

Huge congratulations to Md Asif Bin Syed, Md Younus Ahamed, and Azmine Toushik Wasi for this work!

Time-series foundation models are now widely used across domains like healthcare, finance, energy, transport, and retail. However, current evaluation benchmarks mix all these domains together, which can hide critical weaknesses in real-world settings.
Our study shows that model performance changes significantly across domains due to differences in data structure, noise levels, and temporal patterns. Experiments across 7 models and 72 datasets confirm that global leaderboard scores often do not reflect domain-specific reliability.
We argue that trustworthy deployment requires domain-level benchmarks, so models are evaluated where they are actually used, not only on aggregated global scores.

ICML is an A*-tier conference and widely regarded as one of the most prestigious venues in artificial intelligence and machine learning. ICML is also considered part of the AI/ML “grand slam” (the “big three”), alongside ICLR and NeurIPS!