AI and the human mind have long fascinated researchers trying to understand intelligence, creativity and unpredictable thinking. Early artificial intelligence pioneers questioned whether machines could reproduce the messy, irregular processes through which people generate ideas, make associations and sometimes learn from mistakes. A historical examination by Benjamin Lindquist, an assistant professor in MIT’s History Section, […]
Two cognitive scientists argue that modern AI behaves more like a brain than a database, and that its origin story explains why. We trace the psychology behind neural networks from Hebb and Rosenblatt’s perceptron to backpropagation, check the primary sources, show how research on human memory explains AI hallucinations, inconsistent answers and sycophancy, and set out practical rules for using these systems at work.
The University of Toledo published a profile on 26 August 2026 of Alex Bordovalos, a physics doctoral student building artificial intelligence tools to automate the slowest step in spectroscopic ellipsometry — turning raw polarisation spectra into thickness and optical constants. His advisor, Dr. Nik Podraza, calls it “a major bottleneck in industry and research”. This breakdown separates the university announcement from the peer-reviewed evidence behind it: what the technique actually measures and why its inverse problem needs a human, what Bordovalos and five co-authors published in the Journal of Applied Physics in September 2025, how two series of neural networks split the structural model from the parameter values, the 81-point maps and three amorphous silicon samples used to validate it, the half a million simulations behind the doctoral tool, and the simulation-to-reality gap that still has to be closed on real cadmium telluride cells at Toledo’s Wright Center. It closes with the procurement questions any buyer of automated measurement software should be asking.
On 21 August 2026 a University of Osaka and Universitas Diponegoro team published a navigation system in Device that lets a living cockroach read the ground it is walking on. A multilayer perceptron running on the insect’s own 2.3 gram backpack sorts terrain into flat, ascent, descent and hole at 92% offline accuracy, and the controller uses that call to decide whether to steer or to stay out of the way. The speed gain comes from suppressing commands during climbs rather than issuing better ones. This piece covers the hardware budget, the four terrain classes, the 20-insect swarm result, the companion internal-state paper, the welfare questions, and what a four-class model on a fractional-gram power budget teaches ordinary AI projects.
Convolutional Neural Networks have transformed computer vision by enabling machines to recognize, classify, and interpret images with remarkable accuracy. From facial recognition and autonomous vehicles to medical imaging, industrial quality inspection, satellite analysis, and smartphone photography, Convolutional Neural Networks have become one of the most influential technologies in modern artificial intelligence. Their ability to automatically […]
The concept of forgetting in AI language learning is transforming how researchers design artificial intelligence systems. Forgetting in AI language learning refers to the deliberate removal or suppression of certain learned patterns, memories, or associations during the training process. While this may seem counterintuitive, recent research demonstrates that strategic forgetting can significantly improve how AI […]
A practical guide to neuro-symbolic AI, covering what it is, how it works, why it matters, where it helps most, and what still limits it.
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