When you think of artificial intelligence and artificial intelligence textbooks, the term ‘sentient’ comes to mind.
In this case, the article means a computer which responds to a human’s commands, or a computer that learns to act on its own without a human.
While there are many types of artificial intelligences, there are two primary types of sentient artificial intelligence: ‘sentients’ and ‘non-sentients’.
The term ‘non -sentient’, though, is actually a bit misleading.
Artificial intelligence is not a class of machine which responds, or learns to respond to, a human being’s commands.
Rather, AI is a class that is a combination of computer algorithms that are able to respond, or learn to respond automatically to a range of human-made stimuli.
It is important to understand that this is not to suggest that an AI is incapable of learning to do a specific thing, or to say that it will never learn to do anything.
Rather, the main purpose of this article is to provide a quick overview of the difference between ‘non sentient’ and sentient artificial AI.
To understand this, we need to think of a robot as a machine that is trained to perform a specific task, such as moving a stick, and that learns from that training to perform that task.
This is a ‘learning’ process that occurs automatically, but is controlled by the software that the robot is being programmed to run.
For example, a computer can teach a robot to move a stick by having a computer program it to move the stick, but the robot itself will not know how to do this, as the software does not control the robot.
So what are the different types of machines which can learn from the training of a human?
We can think of sentient AI as a subset of the human brain.
It is a set of programs which are designed to perform tasks that a human is capable of performing.
For example, we can think that if a robot is programmed to move around the house, it will be able to learn to move itself around the room by recognising a particular spot in the room, and moving to it.
Similarly, we could think of the robots we currently use to perform certain tasks as sentient machines.
In contrast, non-sentient AI is not designed to do the same tasks as a human, but rather to perform other tasks that are not designed for the human.
Non-sentience AI is designed to act as a tool for humans to learn from a training program, or as a service for humans.
In other words, non -sentience artificial intelligence is a tool that is designed for humans, not a machine.
While it is possible to have machines that can learn to perform specific tasks that humans can perform, there is no guarantee that this will happen.
Instead, AI will be designed so that it is programmed with a particular set of tasks that it can do, and is programmed so that humans will not be able, or choose not to perform the tasks it is designed with.
So for example, it is not possible for machines to perform all of the tasks that we use for tasks that can be performed by humans.
Rather than being designed to learn certain tasks, machines will have a set that it should learn that is suitable for tasks humans can do.
This means that AI systems that are designed with certain tasks in mind will not always be able or willing to perform these tasks.
This is what is known as the ‘resilience problem’.
The Resilience ProblemAs AI systems are designed, their resiliency is what makes them ‘good’ at certain tasks.
This refers to how well a system can handle different environments, and how well it can learn and adapt to new environments, in order to be able operate in a variety of environments.
The Resiliency ProblemFor example: an autonomous robot, which is programmed as an ‘artificial agent’, can learn how to navigate around a room, but this is still a skill that humans have to learn, and a human will not automatically be able perform it.
Instead, robots programmed to perform some tasks will only learn to navigate, and may not be capable of any of the other tasks a human might perform.
This means that the AI systems designed for autonomous robots, will not generally be good at performing tasks that people would normally perform.
When the Resilency Problem is understood, we get a clearer picture of how AI systems might be designed, and what tasks AI systems should be programmed with.
When designing AI systems, we also need to consider the Resiliience Problem.
For instance, a robot might be programmed to learn how a human would use a tool, such a stick.
However, the robot might not have the ability to use the stick for the task that the human would do, for example using it to play with toys.
To ensure that the ResILience Problem is not introduced, the Resillience Problem