Robots are capable of performing 1 million tasks sequentially with high efficiency and precision. They can be programmed to carry out a wide range of tasks, such as assembly line operations, packaging, material handling, inspection, and even complex tasks like surgery or space exploration.Meta AI researchers introduced a method called Retrieval Augmented Generation (RAG) to address such knowledge-intensive tasks.

One key advantage of robots is their ability to work continuously without getting tired, which allows them to handle large volumes of tasks without compromising on productivity. They can maintain a consistent level of performance and accuracy throughout the entire sequence.

Additionally, robots can work at a faster pace compared to humans, reducing the time required to complete the sequence. This can greatly benefit industries that are dependent on time-sensitive operations, such as manufacturing or logistics.

Robots are also equipped with various sensors and advanced technologies that enable them to perceive and interact with their environment. This enables them to adapt to different task requirements and handle unexpected situations, further enhancing their capabilities in performing a million tasks sequentially.

Overall, robots have the potential to greatly enhance productivity and efficiency in industries by handling large numbers of tasks sequentially, meeting the demands of modern-day operations.




The complexity of the tasks will play a significant role in determining how efficiently and quickly a robot can complete them. Some tasks may require more time and resources than others, potentially impacting the overall duration of the sequence. Google currently has a robotic technology that can perform 150,000 task by using hybrid LLM .

Precision is done by instructions prompting to task based . Instructions can be sound or visuals depending on the task to perform .This enhances precision and accuracy, ultimately leading to improved task performance.

In robotics, GPT (Generative Pre-trained Transformer) models can be used for various computer vision tasks, including analyzing and processing images captured in the visible light spectrum. The visible light spectrum refers to the range of electromagnetic radiation that is visible to the human eye, typically ranging from approximately 400 to 700 nanometers in wavelength.


GPT models can be trained on large datasets of visible light images to learn patterns, features, and semantic information present in the images. Once trained, these models can be used for tasks such as image classification, object detection, image segmentation, and image generation.


By leveraging GPT models, robotics systems can analyze and interpret visual information captured in the visible light spectrum, enabling them to make informed decisions and perform tasks based on the visual input. This can be applied in various fields, including autonomous navigation, object recognition and manipulation, scene understanding, and more.


Overall, GPT models combined with computer vision techniques allow robots to perceive and understand the visible light spectrum, enabling them to interact with the environment and perform tasks based on visual cues. It can carry out task like construction, sealing of pipe leakages and more usage depending on the scope of tarrain.

AGI is in 2 stages

1. Pre-AGI refers to the period before the development of Artificial General Intelligence (AGI). During this stage, AI systems are typically designed to perform specific tasks or solve narrowly defined problems, such as image recognition or natural language processing. These systems are trained using machine learning techniques, but they lack the ability to generalize their knowledge and perform a wide range of tasks that humans can effortlessly handle.

2. Post-AGI refers to the stage when AGI has been successfully developed. AGI is often defined as an AI system that can perform any intellectual task that a human being can do. In this stage, AGI would possess human-level general intelligence and the ability to tackle a broad range of tasks, including those that require creativity, common sense reasoning, and adaptability. Post-AGI systems would surpass the capabilities of pre-AGI systems by being able to learn and transfer knowledge across domains, understand complex concepts, and exhibit human-like cognitive abilities. The development of AGI would mark a significant milestone in AI research and potentially have profound implications for various industries and society as a whole.

A multitasking AI agent is an artificial intelligence system that is capable of performing multiple tasks simultaneously. This agent can switch between different tasks, prioritize tasks based on urgency, and optimize its performance to complete tasks efficiently and effectively.

This type of agent is commonly used in various applications such as customer service chatbots, virtual assistants, and robotic process automation systems. By multitasking, the AI agent can improve productivity, save time, and enhance the overall user experience.

Agents performing various tasks simultaneously in multitasking AI will allow for increased efficiency and productivity. By utilizing multitasking capabilities, agents can handle multiple tasks at once, leading to faster completion times and more streamlined workflows. This can be especially beneficial in scenarios where real-time decision-making is required, as agents can process multiple inputs and outputs simultaneously to make informed decisions. Additionally, multitasking AI can help optimize resource allocation and workload distribution, contributing to overall operational effectiveness.

ASI is probably trillions or quadrillions of tasks
or skills which will use more computing power than ever . Nvidia, Intel and AMD are working to improve that domain

According to Nvidia in GTC 2024 AI is simulated intelligence from range of tasks before self learning and acquiring skills .AI is not just only about self-learning algorithms; it also involves simulating intelligence through a range of tasks to help machines acquire the skills needed to perform those tasks effectively. This can involve using large amounts of data to train algorithms and model human behavior, as well as using simulations to test and refine AI systems before they are deployed in real-world settings.


By simulating intelligence in this way, AI developers can ensure that their systems are robust, reliable, and capable of performing complex tasks in a variety of environments.