Category: Machine Learning

Zhe Zhao and colleagues at City University announced ResearchEVO, a new end-to-end framework that mimics the iterative process of scientific discovery, starting with experimentation and followed by theoretical explanation. The system uniquely combines performance-driven algorithmic advances and automated research paper generation to ensure factual accuracy and avoid fabricated citations. ResearchEVO examined the problem of quantum […]

Novel quantum residual neural networks overcome the limitations of existing models and provide a path to practical quantum machine learning. Amena Khatun and colleagues at the University of Melbourne demonstrate a hardware-efficient architecture that implements residual connectivity without relying on post-selection, an important advance in the field. The model achieves comparable accuracy of 99% for […]

A new methodology employing deep neural network models to empirically test the security of key encapsulation mechanisms (KEMs), hybrid structures, and cascading encryption schemes. Simon Calderon and colleagues at Linköping University have applied this deep learning framework not only to public-key cryptography methods such as ML-KEM, BIKE, and HQC, but also in combination with classical […]

A new approach called “soft quantum algorithms” addresses the long training times and limitations of quantum machine learning by directly training matrices while preserving unity through a proprietary regularization technique. Basil Kyriacou and colleagues avoided the need to decompose data into complex gate operations and achieved demonstrated speedups on a five-qubit classification task, completing training […]

Reinforcement learning (learning from observation and reward) is the method most similar to how humans (and animals) learn. Despite this similarity, it is also one of the most complex and tricky areas in modern machine learning. Quoting the famous words of Andy Karpathy. Reinforcement learning is scary. It just so happens that everything before was […]

AI Chemistry’s top keynote speakers note that artificial intelligence is rapidly transforming the industry from a traditionally experimental and time-consuming field into a data-driven innovation engine. bes AI’s chemistry keynote speakers argue to audiences at conferences, conventions, or corporate events that this change means not just incremental improvements, but a fundamental rethinking of how chemicals […]

Deep Jariwala, Peter Armstrong and Suzanne Armstrong Distinguished Fellow at the University of Pennsylvania, will be co-appointed as the UT-ORNL Governor’s Chair in January 2027 at the University of Tennessee, Knoxville, and Oak Ridge National Laboratory. Jariwala’s research focuses on new materials for microchips, with the aim of improving the energy efficiency of artificial intelligence. […]

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Albrecht, L. V., Tejeda-Munoz, N. & De Robertis, E. M. Cell biology of canonical Wnt signaling. Annu. Rev. Cell Dev. Biol. 37, 369–389 (2021). Mehta, S., Hingole, S. & Chaudhary, V. The emerging mechanisms of Wnt secretion and signaling in development. Front. Cell Dev. Biol. 9, 714746 (2021). Hayat, R., Manzoor, M. & Hussain, A. […]

Machine Learning as a Service Market The Machine Learning As A Service Market reached a valuation of 13.12 billion in 2025 and is anticipated to expand at a CAGR of 13.78% during the forecast period from 2026 to 2033, ultimately attaining an estimated value of 36.86 billion by 2033. Market growth is being driven by […]