Don’t underestimate the power of a yes-or-no question. Some of the toughest computing problems boil down to thousands of tiny yes-or-no decisions. Finding the best combination of answers could be the key to anything from nailing a new drug to keeping a city of commuters on the move. For example, take this scenario:Don’t underestimate the power of a yes-or-no question. Some of the toughest computing problems boil down to thousands of tiny yes-or-no decisions. Finding the best combination of answers could be the key to anything from nailing a new drug to keeping a city of commuters on the move. For example, take this scenario:[#item_full_content]

An international research team involving the University of Bayreuth has, for the first time, analyzed the “inner workings” of AI language models when predicting political voting decisions. To do so, the researchers examined six national elections and AI-based election forecasts and developed a new method for making more precise predictions. They presented their findings at the International Conference on Machine Learning (ICML 2026) in Seoul, South Korea.An international research team involving the University of Bayreuth has, for the first time, analyzed the “inner workings” of AI language models when predicting political voting decisions. To do so, the researchers examined six national elections and AI-based election forecasts and developed a new method for making more precise predictions. They presented their findings at the International Conference on Machine Learning (ICML 2026) in Seoul, South Korea.[#item_full_content]

Artificial intelligence isn’t just capable of translating between existing languages—it can also create entirely new ones.Artificial intelligence isn’t just capable of translating between existing languages—it can also create entirely new ones.[#item_full_content]

The era of building “personalized AI” by training AI models on individual or corporate documents and data is beginning. However, while such customization can improve task performance, it can also weaken a model’s existing safety safeguards. KAIST researchers have developed a core AI technology that preserves customized performance while further strengthening safety.The era of building “personalized AI” by training AI models on individual or corporate documents and data is beginning. However, while such customization can improve task performance, it can also weaken a model’s existing safety safeguards. KAIST researchers have developed a core AI technology that preserves customized performance while further strengthening safety.[#item_full_content]

When can we trust the results we get from AI, and when is learning impossible? Researchers have shown that there are some problems that even the most powerful AI cannot reliably solve, no matter how much data it is given.When can we trust the results we get from AI, and when is learning impossible? Researchers have shown that there are some problems that even the most powerful AI cannot reliably solve, no matter how much data it is given.[#item_full_content]

Quantum computers lack useful functionality without the right algorithms to facilitate their operation. Currently, there are few simple, standardized operations, known as “primitives,” in the quantum toolkit that can help deliver the unique quantum behaviors required for such computers to achieve results beyond classical systems.Quantum computers lack useful functionality without the right algorithms to facilitate their operation. Currently, there are few simple, standardized operations, known as “primitives,” in the quantum toolkit that can help deliver the unique quantum behaviors required for such computers to achieve results beyond classical systems.[#item_full_content]

Neural networks, a fascinating technology inspired by the human brain, form the basis of artificial intelligence. These networks consist of layers of interconnected nodes, or artificial neurons, that learn patterns from data and make predictions. For example, large language models generate text by predicting the next word or phrase based on the words that came before it.Neural networks, a fascinating technology inspired by the human brain, form the basis of artificial intelligence. These networks consist of layers of interconnected nodes, or artificial neurons, that learn patterns from data and make predictions. For example, large language models generate text by predicting the next word or phrase based on the words that came before it.[#item_full_content]

Korean researchers have developed a hierarchical AI technology that autonomously plans even complex, long-horizon tasks. The development of this hierarchical task-planning AI technology, which reduces hallucinations and doubles the success rate, is expected to help robots and agents carry out long-term missions.Korean researchers have developed a hierarchical AI technology that autonomously plans even complex, long-horizon tasks. The development of this hierarchical task-planning AI technology, which reduces hallucinations and doubles the success rate, is expected to help robots and agents carry out long-term missions.[#item_full_content]

Training artificial intelligence to enforce even seemingly straightforward rules—like balls and strikes in Major League Baseball (MLB)—is a messy, dynamic process that takes time and careful evaluation of the technology in the wild, according to new Cornell research.Training artificial intelligence to enforce even seemingly straightforward rules—like balls and strikes in Major League Baseball (MLB)—is a messy, dynamic process that takes time and careful evaluation of the technology in the wild, according to new Cornell research.[#item_full_content]

Semiconductors are central to modern technology. They are used in computer chips, solar cells, sensors, LEDs and communication devices. Before researchers make new semiconductor materials in the lab, they often test them first using quantum mechanical simulations. One of the main tools for this work is density functional theory, or DFT, a computer-modeling method that skips tracking every single electron and instead uses their overall cloud density to quickly calculate a material’s atomic structure and energy.Semiconductors are central to modern technology. They are used in computer chips, solar cells, sensors, LEDs and communication devices. Before researchers make new semiconductor materials in the lab, they often test them first using quantum mechanical simulations. One of the main tools for this work is density functional theory, or DFT, a computer-modeling method that skips tracking every single electron and instead uses their overall cloud density to quickly calculate a material’s atomic structure and energy.[#item_full_content]