Quantum Security

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. ...

AI Creativity Now Matches Average Humans, But Lags Behind Top Innovators, Study Reveals A landmark study from the Université de Montréal, co-authored by AI pioneer Yoshua Bengio, reveals that leading AI models now match or exceed the average human’s creative abilities in linguistic tasks. Published in Scientific Reports, the research is the largest comparative study of its kind, testing large language models like GPT-4, Claude, and Gemini against 100,000 people. While models like GPT-4 have achieved a significant milestone by reaching average human creativity, the study underscores that the most creative individuals still significantly outperform the most advanced AI systems. ...

Explainable AI (XAI) Gains Traction Amidst Demand for Transparency The demand for transparency in artificial intelligence is driving significant growth in Explainable AI (XAI), a field dedicated to making AI decision-making understandable to humans. As AI systems become more integrated into critical industries, the need to demystify their often “black box” operations is paramount. This push is fueled by regulatory requirements, ethical considerations, and the necessity of building user trust. In high-stakes sectors like healthcare and finance, explaining an AI model’s reasoning is essential for accountability and compliance. Key trends propelling the XAI market include the development of inherently interpretable models and post-hoc techniques that clarify the logic of complex models, with market projections showing substantial growth. ...

AI and Human Collaboration Boosts Self-Driving Lab Performance by 150% Researchers at Argonne National Laboratory and the University of Chicago have developed a new shared control framework for self-driving laboratories, where human scientists and AI collaborate on the discovery process. This innovative approach has already yielded a 150% increase in mix conducting performance for MIECPs over previous methods. In this model, the AI advisor suggests potential actions, but the human researcher makes the final decision. The team’s future goal is to create a tighter feedback loop where the AI can learn directly from human choices, refining its own decision-making models to better mimic expert intuition. ...

Liquid AI Releases LFM2-VL: Open-Weight Vision-Language Models for On-Device AI Liquid AI has released LFM2-VL, a new family of open-weight vision-language foundation models created for low-latency, on-device deployment. The new models are designed to run efficiently on smartphones, laptops, wearables, and other embedded systems without needing to rely on cloud infrastructure. LFM2-VL is available in two versions: LFM2-VL-450M for highly resource-constrained devices and the more powerful LFM2-VL-1.6B. These models can process both text and images, offering up to twice the inference speed on a GPU compared to similar existing models. The models are available on Hugging Face under a license based on Apache 2.0, allowing for free academic and research use, as well as commercial use for smaller companies. ...