A new study has attempted to uncover the mysteries of the brain’s signal conversion process and the findings are interesting.
A materials breakthrough could lead to radically smaller phones that are more powerful and energy-efficient.
A novel algorithm enables robots to flexibly squish, bend, or stretch for tasks such as obstacle avoidance or item retrieval.
Researchers in Japan have discovered that manganese could help reduce reliance on iridium as a catalyst for hydrogen production.
Canadian researchers have found that blurring tomographic volumetric additive manufacturing lasers improve optical printouts.
A video depicting a Chinese humanoid robot factory reveals scores of robots at different stages of development, evoking surprise and concern.
Engineers at Stanford University have successfully combined AI and holographic imagery to develop the augmented reality headset of the future.
Researhcesr from EPFL and SIMIT have managed to develop a new lithium tantalate chip that could reduce costs and improve performance of PICs.
According to a statement by the firm, the study showed that AI models trained on hand images achieve comparable accuracy to those using facial images, with an average error of 4.1 and 4.7 years in predicting chronological age.
The AI model in the study was primarily trained by employing the Indian population dataset to ensure representation of diverse skin tones and address AI’s bias challenges, especially pertaining to ethnicity-specific considerations in age prediction.
By focusing on the Indian population, the study aimed to develop an AI model tailored to this demographic, mitigating biases and promoting fairer AI solutions. Additionally, the research’s market relevance in India’s growing skincare and AI sectors underscores the strategic importance of using an Indian dataset for this study.
The research could open doors for personalized brain therapies to target and treat the worst kinds of chronic pain.
