This paper critically examines the pervasive yet often unsubstantiated notion that we are living in an algorithmically determined world. While popular discourse and some academic literature suggest a monolithic takeover by algorithmic systems, this research argues for a more nuanced and empirically grounded understanding. Through a comprehensive literature review, analysis of recent empirical data, and the introduction of three novel theoretical frameworks—the Algorithmic Influence Matrix, the Algorithmic Social Contract, and the Algorithmic Uncertainty Principle—this paper deconstructs the determinism narrative. We find that while algorithmic influence is significant and growing in specific, high-penetration domains, the broader picture is one of uneven adoption, significant implementation gaps, and persistent human agency. The research reveals a critical “determinism-reality gap,” where public perception, fueled by media narratives, far outpaces the empirical evidence of widespread algorithmic control. Key findings from recent studies on social media platforms and financial markets demonstrate that algorithms can amplify divisive content and introduce new systemic risks, yet their power is neither absolute nor universally applied. This paper concludes that the critical question is not if we are algorithmically determined, but how, where, and to what extent algorithmic systems are shaping our world, and how we can develop more effective governance and regulatory frameworks to navigate this complex and evolving landscape.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
It is proved that any global minimizer of the trajectory balance objective can define a policy that samples exactly from the target distribution, and empirically demonstrate the benefits of the trajectories balance objective for GFlowNet convergence, diversity of generated samples, and robustness to long action sequenc...
Esmeralda S. Whitammer, Moksh Jain, Emmanuel Bengio et al.· Neural Information Processin...· 302 citations· ⚡60
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 7, 2026
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026