By combining machine learning models that predict individual client behavior (take-up, prepayment, and risk) with mathematical optimization, Erste ...

Raia Hadsell, VP of Research at Google DeepMind, recently took the stage at AI Engineer Europe to discuss the evolving frontiers of artificial intelligence and its impact on the future of intelligence itself. With over 13 years dedicated to bridging academia and industry in AI, Hadsell, who also serves as a UK ambassador for AI

Researchers have discovered that artificial intelligence models can transmit hidden behavioral traits to one another through imperceptible signals, raising urgent questions about the safety of the multi-billion dollar synthetic data industry. Here is the uncomfortable reality now facing the AI industry: your model can catch bad habits from another model without anyone knowing it happened.

References Wu, Y., Chen, L., Yang, N. & Zongbao, S. Research progress of deep learning-based artificial intelligence technology in pest and disease detection and control. Agriculture 15 (19), 2077 (2025). Google Scholar  Ugwu, O. P. C. et al. Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach

Chief Technology Officer Terray Therapeutics Life Sciences/Biotech Executive Narbe Mardirossian serves as the chief technology officer at Terray Therapeutics, where he has architected the AI infrastructure powering the company’s drug discovery engine. By uniting large-scale experimentation with advanced machine learning, he led the development of EMMI (Experimentation Meets Machine Intelligence), an AI-native platform that evaluates

References Trung, N. H., Anh, N. T. & Liu, F. Multi-head deep Q-learning for continuous beamforming with selective MC-CDMA operation in V2X highway communications. Sci. Rep. 15, 29860 (2025). Nguyen, X. Secure beamforming design for MISO URLLC networks in IoT applications. Internet of Things 27, 101304 (2024). Wen, X. & Xu, F. Optimized beamforming for

References Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014) Yang, Z., et al.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1480–1489 (2016) Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. Bert:

Summary: The tiny zebra finch is a vocal learning champion, but its most shocking talent happens deep inside its gray matter. Researchers have discovered that when these birds grow new neurons, the cells don’t “politely” navigate around existing structures. Instead, they tunnel directly through mature brain tissue, squishing and shoving established cells aside to reach