AI image generators have become remarkably proficient in a very short period, capable of creating faces that are considered to be more realistic than the real thing.

However, a new study points to a way that we can improve our AI-face detection capabilities.

Researchers from the UK tested the face-assessing capabilities of a group of 664 volunteers, consisting of super-recognizers (who have shown a high level of skill for comparing and recognizing real faces in previous studies), and people with typical face-recognition abilities.

As AI and brain technology advance, scientists warn understanding of consciousness and awareness is lagging behind, raising ethical questions.

Voracious demand from the generative AI revolution has already chewed up power grids, copper supplies, and data centers

A landmark study suggests that this daily habit may reshape the way your mind activates.

In a Nature Communications study, researchers from China have developed an error-aware probabilistic update (EaPU) method that aligns memristor hardware’s noisy updates with neural network training, slashing energy use by nearly six orders of magnitude versus GPUs while boosting accuracy on vision tasks. The study validates EaPU on 180 nm memristor arrays and large-scale simulations.

Analog in-memory computing with memristors promises to overcome digital chips’ energy bottlenecks by performing matrix operations via physical laws. Memristors are devices that combine memory and processing like brain synapses.

Inference on these systems works well, as shown by IBM and Stanford chips. But training deep neural networks hits a snag: “writing” errors when setting memristor weights.