Semiconductors

January 2026 is shaping up to be a pivotal month in technology, with major strides in artificial intelligence, military tech adoption, and the quantum computing industry. This digest covers groundbreaking news, from a study showing Generative AI outperforming human creativity to the U.S. Marine Corps standardizing its official AI platform and a landmark $1.8 billion acquisition set to reshape the quantum landscape. Study Finds Generative AI More Creative Than the Average Human A recent study comparing over 100,000 humans to advanced AI systems like GPT-4 has found that generative AI can now outperform the average person on certain creativity tests. The research, led by the Université de Montréal, focused on divergent thinking and idea generation. While AI models demonstrated strong performance, the study also highlighted that the most creative humans, particularly those in the top 10%, still significantly outperform the best AI systems. The study utilized the Divergent Association Task (DAT) to measure the ability to generate semantically distant ideas. Researchers also found that AI’s creativity could be significantly influenced by adjusting parameters like ’temperature’ and through specific prompting techniques. ...

Global AI Spending to Reach $2.5 Trillion in 2026, Gartner Predicts According to a new forecast from Gartner, Inc., worldwide spending on artificial intelligence is projected to hit $2.52 trillion in 2026, a significant 44% increase from the previous year. The report identifies the build-out of AI foundations as a primary driver for this growth, with AI-optimized servers accounting for 17% of the total and AI infrastructure expected to contribute an additional $401 billion. ...

Cornell’s ME-AI Model Captures Human Intuition for Quantum Materials Discovery Researchers at Cornell University have introduced a novel machine-learning model, Materials Expert-Artificial Intelligence (ME-AI), designed to quantify and encapsulate the intuition of human experts to accelerate the discovery of new quantum materials. The model effectively “bottles” expert knowledge into descriptors that can accurately predict a material’s functional properties. In a proof-of-concept test, ME-AI was trained on expert-curated data to identify a specific desirable characteristic across 879 materials. The model not only reproduced the human expert’s intuition but also expanded upon it, proving its ability to generalize predictions to other compound sets. This research establishes a powerful model for future collaborations between materials scientists and AI experts to push the boundaries of scientific discovery. ...