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AI promises faster, cheaper drug discovery but gains are conditional: large firms adopt via partnerships, cultural transformation, or productionized tools while startups exploit pre‑trained models and cloud services, yet durable democratized discovery hinges on interoperable data, robust validation, skilled teams and regulatory clarity.
n/a commentary semantic_scholar
AI as the Catalyst for a New Paradigm in Biomedical Research · R. Pajon · Fetched Mar 18, 2026
AI acts chiefly as cheaper prediction: it boosts productivity by augmenting human judgment rather than simply automating jobs, shifting value toward complementary skills, data-rich firms and new organizational practices.
n/a theoretical semantic_scholar
PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery · Weize Xu, Erwin Poussi, Quan Zhong, Zehua Zeng, Christopher Zou, Xuehai Wang, Yi... · Fetched Mar 18, 2026
Decentralized Autonomous Organizations can mobilize tokenized capital and community governance to accelerate early-stage drug discovery, but regulatory uncertainty, health-data privacy and token-market incentives threaten long-run effectiveness and patient protection.
low review_meta semantic_scholar
Decentralized Autonomous Organizations in the Pharmaceutical Sector: A Paradigm Shift in Research, Development, and Governance · Rajendra V. Patil, G. M. Poddar, S. M. Rajput, D. Bhadane, A. Suryavanshi, Shrav... · Fetched Mar 18, 2026
Seeing food as soft matter reframes R&D: digital twins and AI can sharply speed formulation and embed sensory knowledge, but they also risk locking in consensus and erasing cultural nuance unless paired with interdisciplinary, language-aware practices.
low theoretical semantic_scholar
At the table with Wittgenstein: How language shapes taste and texture · Florian Nettesheim · Fetched Mar 18, 2026
AI monitoring and predictive models can flag risky online gambling behaviour and enable tailored interventions, but evidence is mostly retrospective and short-term; without randomized evaluations, transparency, and stronger data governance the systems remain promising prototypes rather than proven public‑policy tools.
low review_meta semantic_scholar
Deep technologies and safer gambling: A systematic review. · L. G. Cardoso, Beatriz C R Barroso, G. Piccoli, Miguel Peixoto, Pedro Morgado, A... · Fetched Mar 18, 2026
Deep learning is poised to speed materials discovery and cut R&D cycle times by enabling end-to-end prediction and inverse design; realizing those gains requires calibrated, interpretable models and standardized multimodal data plus automated labs to turn predictions into validated materials.
medium review_meta semantic_scholar
Machine Learning-Driven R&D of Perovskites and Spinels: From Traditional Models to Deep Learning. · Mengxue Sun, Yingquan Song, Zhengxin Chen, Lanze Xiao, Hairui Zhou, Jia Lin · Fetched Mar 18, 2026
AI for ecology has moved beyond simple automation to produce methodological advances and tangible conservation value when computer scientists and ecologists collaborate closely. To realize scientific and policy impact, projects must couple technical innovation with ecological relevance, shared data infrastructure, and long-term partnerships.
n/a review_meta semantic_scholar
Towards ‘digital ecology’: Advances in integrating artificial intelligence from data generation to ecological insight · D. Tuia, Sara Beery, Blair R. Costelloe, Ruth Y. Oliver, Nicolas Lecomte · Fetched Mar 18, 2026
Finance professionals who report using AI also report sizable gains: survey respondents link AI adoption to materially higher decision‑making efficiency, operational efficiency and resilience. These results are based on cross‑sectional self‑reports, so causal claims require follow‑up with objective, longitudinal evidence.
low correlational semantic_scholar
From Data to Decisions: Harnessing Artificial Intelligence for Smarter, Faster, and More Resilient Financial Systems · Islam Belhaoua, I. Hanif · Fetched Mar 18, 2026
AI can raise radiology accuracy and throughput, but benefits are conditional on real-world integration—poorly designed deployments risk automation bias, deskilling and workflow disruption that can erode clinical and economic gains.
low review_meta semantic_scholar
Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation. · B. Koçak, Renato Cuocolo · Fetched Mar 18, 2026
Organizational backing and managerial mentoring determine whether AI delivers value: firms that invest in strategic support and developmental mentoring are more likely to realize productivity and innovation gains from human–AI collaboration.
n/a theoretical semantic_scholar
Revolutionizing Human Resource Development: A Theoretical Framework for Enhancing Effective Human–AI Collaboration · Chieh‐Peng Lin · Fetched Mar 18, 2026
Human–AI co-learning with a metacognitive 'Cognitive Shadow' speeds decisions and reduces missed detections in simulated Arctic surveillance, while confidence estimates enable safe adjustable autonomy. The approach promises productivity gains and easier procurement adoption but remains to be validated in real-world operations.
medium quasi_experimental semantic_scholar
Human Autonomy Teaming and AI Metacognition in Maritime Threat Assessment · K. Schulze, A. Gallant, Tanya S Paul, Cindy Chamberland, Daniel Lafond, Sébastie... · Fetched Mar 18, 2026
In a live capture‑the‑flag contest, autonomous agents that self‑direct prompting and tool use beat most human teams, exposing human prompting and context specification as the key bottleneck to effective human–AI collaboration.
medium descriptive semantic_scholar
Understanding Human-AI Collaboration in Cybersecurity Competitions · Tingxuan Tang, N. Janis, Kalyn Asher Montague, Kevin Eykholt, Dhilung Kirat, You... · Fetched Mar 18, 2026
A Human–AI team can deliver near-expert online diagnoses while cutting required doctor involvement to roughly one-tenth; hierarchical reinforcement learning allocates human attention sparingly to preserve accuracy and lower labor per consultation.
medium descriptive semantic_scholar
Hierarchical Reinforcement Learning Based Human-AI Online Diagnosis · Jiaqi Liu, Xuehan Zhao, Xin Zhang, Zhiwen Yu, Bin Guo · Fetched Mar 18, 2026
AI partners match human partners on short virtual retail tasks, but whether they help depends on worker emotions; perceived service empathy drives how emotion translates into collaboration performance and interacts with partner type.
medium rct semantic_scholar
Adoption of AI partners in temporary tasks: exploring the effects of emotion on collaboration proficiency · Xiaodong Li, Jingwen Duan, Shanshan Zhang, Ai Ren · Fetched Mar 18, 2026
AI’s employee dividend is finite: moderate AI adoption raises firms’ employee-focused CSR, but once AI is deeply embedded managerial attention shifts toward systems and employee-oriented CSR falls. Industry automation risk accelerates the decline, while diverse leadership and employee ownership delay and soften it.
medium correlational semantic_scholar
Attention to Whom? AI Adoption and Corporate Social Responsibility Toward Human Employees · J. Lyu, Shi-Ye Wang, Chenhao Hu · Fetched Mar 18, 2026
AI can serve as a universal collaboration layer—using multilingual models, multimodal inputs and autonomous agents to mediate intent and execution—and thereby reduce coordination frictions, expand participation and raise productivity across distributed teams; however, the claim is currently theoretical and requires targeted empirical validation.
low theoretical semantic_scholar
AI as a universal collaboration layer: Eliminating language barriers in global teams · Raju Bandaru · Fetched Mar 18, 2026
Trust in AI is not an individual attitude but a socially negotiated, situational process embedded in team routines, with transparency, role clarity and feedback determining whether AI is used as an augmenting complement or an overlooked substitute. Poorly calibrated trust reshapes oversight, erodes informal coordination and shifts organizations toward metric-driven evaluations—so design, training and governance matter as much as algorithmic accuracy.
low descriptive semantic_scholar
AI in project teams: how trust calibration reconfigures team's collaboration and performance · Viraj Dawarka, M. Doargajudhur, Vincent Dutot · Fetched Mar 18, 2026
Treat AI as a partner, not an automaton: symbiarchic leadership sets out four practical leader behaviours and HR reforms that let firms realise AI productivity gains while maintaining oversight, trust and employee welfare; the framework is actionable but awaits empirical validation.
n/a theoretical semantic_scholar
Symbiarchic leadership: leading integrated human and AI cyber teams · Jonathan Passmore · Fetched Mar 18, 2026
AI is reconfiguring jobs more than it is eliminating them: stronger AI adoption correlates with major skill upgrades and a surge in hybrid human–AI roles, while job losses concentrate in Manufacturing and Retail and employment expands in Healthcare and IT Services. Robust reskilling programs and governance frameworks materially tilt outcomes toward complementarity and employment growth.
low review_meta semantic_scholar
AI-Driven Transformation of Labor Markets: Skill Shifts, Hybrid Employment, and Governance · Amit K Mogal, Trupti M Pagar, Arjun K Mahale · Fetched Mar 18, 2026
An AI-driven matching pilot in Nairobi improved job matches and raised reported short-term wages for participating youth by dynamically inferring skills and live demand. The findings are promising but preliminary: the pilot is observational and needs larger, randomized or better-controlled studies to establish causality.
low quasi_experimental semantic_scholar
AI-Driven Skill Mapping and Gig Economy Matching Algorithm for Youth Employment within Nairobi's Informal Sector · David Shiala Ongoma · Fetched Mar 18, 2026
Modern AI models — especially ensembles and deep neural networks — predict employee performance more accurately than traditional statistical methods across several public workplace datasets; gains generalize across companies and hinge on engagement, learning agility, tenure and workload signals, suggesting measurable upside for HR decision‑making if firms manage bias and privacy risks.
high descriptive semantic_scholar
Adoption of AI-Based HR Analytics and Its Impact on Firm Productivity, Employment Structure and Wage Dispersion: Evidence from Workforce Data · Richa Sharma, Dr. Neeraj Gupta · Fetched Mar 18, 2026
Firms often use AI as a pretext for cost-cutting driven by short-termist managers and incentive structures; unchecked, such layoffs can erode demand and trigger further automation. Policymakers should consider a phased shortening of the standard workweek—paired with conditional supports—to stabilize employment, sustain consumer purchasing power, and distribute productivity gains more equitably.
low commentary semantic_scholar
A Shorter Workweek as a Policy Response to AI-Driven Labor Displacement: Economic Stabilization in the Age of Automation · Jonathan H. Westover · Fetched Mar 18, 2026
AI is reshaping Albania’s job map rather than causing mass layoffs: routine service and administrative roles are most exposed to displacement, while other occupations gain from AI-enabled productivity; employers now prioritize digital literacy, basic data skills and stronger communication, leaving younger and less-educated workers most vulnerable.
medium correlational semantic_scholar
The AI Transition: Assessing Vulnerability and Structural Reform in Albania’s Labor Market · Bora Bimbari · Fetched Mar 18, 2026
Russia should replace passive AI monitoring with a state-led 'economic cybernetics' system—a National Data Management System running a dynamic intersectoral balance model—to actively steer digitalization for productivity and partial technological sovereignty. The approach promises coordinated gains but raises acute risks: centralized control can deepen social stratification, displace workers, and concentrate data-power absent strong safeguards.
low theoretical semantic_scholar
DIGITAL TRANSFORMATION OF THE RUSSIAN FEDERATION’S SOCIOECONOMIC INFRASTRUCTURE: FROM RISK ANALYSIS TO PROACTIVE MANAGEMENT BASED ON ECONOMIC CYBERNETICS · M. V. Buzmakova · Fetched Mar 18, 2026
A physics-informed offline RL routing system cuts mean voyage CO2 by about 10% and slashes extreme fuel-waste events ninefold in simulated Gulf of Mexico transits, while remaining robust to forecast uncertainty.
medium other arxiv
Physics-informed offline reinforcement learning eliminates catastrophic fuel waste in maritime routing · Aniruddha Bora, Julie Chalfant, Chryssostomos Chryssostomidis · Mar 18, 2026