Category: Machine Learning

Malone, T. W. & Bernstein, M. S. (eds) Handbook of Collective Intelligence (MIT Press, 2022). Flack, J., Ipeirotis, P., Malone, T. W., Mulgan, G. & Page, S. E. Editorial to the inaugural issue of Collective Intelligence. Collect. Intell. 1, 26339137221114179 (2022). Article  Google Scholar  Yang, X.-S., Cui, Z., Xiao, R., Gandomi, A. H. & Karamanoglu, […]

A series on reinforcement learning (RL) following Sutton and Barth’s famous book “Reinforcement Learning” [1]. In my last post, I completed a detailed analysis of the first part of the book mentioned above, which introduces the basic solution techniques that form the basis of many RL techniques. these are: Dynamic programming (DP), Monte Carlo method […]

In April 2017, Deputy Secretary of Defense Bob Work established the Algorithmic Warfare Cross-Functional Team. project mavenan initiative aimed at integrating artificial intelligence (AI) and machine learning into military intelligence and combat operations. 2017 was a significant turning point for AI technology, defined as the transition period from early development to large-scale integration. Project Maven […]

Chinese AI darling DeepSeek is back with a new openweight large-scale language model that promises performance comparable to the best proprietary LLMs in the US. Perhaps more importantly, it claims to significantly reduce inference costs and expands support for Huawei’s Ascend family of AI accelerators. Announced Friday, DeepSeek V4 is available for download from popular […]

Researchers at the University of California, Berkeley and City of Hope have developed a machine learning platform to identify individuals susceptible to breast cancer based on the mechanical properties of single cells. This photo shows the MechanoAge platform. [Adam Lau/Berkeley Engineering] A study led by City of Hope and UC Berkeley researchers found that the […]

Komar, AA & Hatzoglou, M. Exploring internal ribosome entry sites as therapeutic targets. front. Oncor. 5233 (2015). Article Google Scholar Martinand-Mari, C., Lebleu, B. & Robbins, I. Oligonucleotide-based strategies to inhibit human hepatitis C virus. oligonucleotide 13539–548 (2003). Article Google Scholar Nulf, CJ & Corey, D. Intracellular inhibition of hepatitis C virus (HCV) internal ribosome […]

Gain faster insights at a fraction of the cost Since introducing RTM, Coinbase has improved its ability to mitigate fraud by ensuring its risk models operate on the most up-to-date transaction data. Latency was reduced to sub-second freshness, achieving 150 ms for aggregated stateless streaming features and 250 ms for aggregated stateful streaming features. Consistency […]

Federated learning (FL) is no longer a research curiosity. This is a practical response to the severe constraint that most valuable data cannot be moved. Regulatory boundaries, data sovereignty rules, and organizational risk tolerance routinely prevent centralized aggregation. On the other hand, sheer data gravity makes even permitted transfers slow, expensive, and vulnerable at scale. […]

Deep learning in the machine vision market According to a newly released report by QY Research, the global deep learning in machine vision market in 2026 will provide comprehensive, data-driven analysis aimed at enhancing business decisions and unlocking high-growth opportunities across industries. This research provides deep insights into market dynamics, competitive environment, and future growth […]

Carson Easterling plans to specialize in control theory when he enters graduate school at Auburn University this fall, but he needed more preparation. Applied Statistics and Machine Learning (ELEC 5970 6970 600) is an interdisciplinary course in the Department of Electrical and Computer Engineering that introduces students to basic and advanced machine learning tools through […]