The ability of small language models (SLMs) to achieve artificial general intelligence (AGI) can be demonstrated through their ability to mimic the human learning process, which generates a lot of data in the process of learning and adapting to new situations.

One example of a household robot is the cleaner robot, which includes products like Roomba and Lion's bot. These devices utilize simultaneous localization and mapping (SLAM) to create a map of the house and approximate its layout, which can then be used to optimize the cleaning process. The challenge with SLAM is that it generates a large amount of data, and it is unclear whether this data is truly necessary for efficient cleaning. To address this issue, we can evaluate robot

We may be several years away from achieving artificial general intelligence (AGI), and it is possible that we may never attain it unless we gain a deeper understanding of human cognition. While large language models (LLMs) have provided valuable insights, a more efficient learning process could be achieved by integrating a robust feedback and sensory system. By connecting LLMs to such systems via robots or other devices, the need for extensive training might be significantly

Day 1 - During the morning session of the IEEE RoboSoft conference, I had the opportunity to engage in interesting conversations with various players in the field of soft and hard robotics. One observation was the prevalence of arm robots, commonly referred to as cobots, which are designed to work alongside humans in industrial settings to perform tasks such as assembly and inspection.