AI mimics human decision-making and problem-solving with computers and other gadgets. The categories of learning in AI include "narrow," "general," and "super." These categories—doing a limited range of tasks, generally thinking like humans, and performing above human capability—demonstrate AI's abilities as they develop. 

"It's going to be interesting to see how society deals with AI, but it will be cool." - Colin Angle.

Robots need more than learning. We must remove boundaries between humans, robots, and the four main types of artificial intelligence. It includes reactive machinery, limited memory, theory of mind, and self-awareness.

Reactive machines

Reactive machines are memoryless, task-focused AI systems that consistently produce the same result in response to the same input. Most machine learning models are reactive, responding to consumer input such as search or purchase history with personalised recommendations. In most cases, reactive AI is trustworthy and effective in technologies like self-driving automobiles. It must be given the correct data to forecast what will happen accurately. In contrast, humans can recall and learn. Therefore most of their behaviours are not reactive. 

Google's AlphaGo, which has defeated the best human Go players, is similarly unable to foresee every possible outcome. It uses a neural network to analyse game progress, making it a more advanced analysis method than Deep Blue. While these techniques help AI systems perform better in certain games, they need to be more flexible to be used in other contexts. The limited scope of these artificial minds makes it easy to trick them into thinking something different than what they have been programmed to.

Limited memory

The next stage in the growth of AI is limited memory. This algorithm mimics how neurons in our brains communicate, which means it gets smarter as it receives more data to train on. Memory limitations Unlike reactive robots, AI can look back in time and track specific objects or situations. These observations are then put into the AI to take action based on past and present facts. 

However, due to limited memory, this data is not preserved in the AI's memory as an experience to learn, as humans may gain meaning from their achievements and failures. As it is taught on additional data, the AI improves over time. These observations are incorporated into the preprogrammed representations of the world in self-driving cars, including lane markers, traffic signals, and other critical aspects such as road curves. They are used when the car decides whether to change lanes to prevent cutting off another driver or getting hit by another vehicle.

Theory of mind

Machines in the following, more advanced class form representations not only of the world but also of other agents or things. It is critical to how humans establish communities since it allows us to interact socially. However, working together is difficult and impossible at best unless we comprehend each other's goals and intentions and consider what others know about me or the surroundings.

Self-awareness

The final stage of AI development is to create systems capable of forming representations of themselves. Ultimately, AI researchers will need not just to understand consciousness but also to construct robots with it. Conscious beings are aware of themselves, are aware of their internal states, and can foresee the feelings of others. "I'm hungry," for example, becomes "I know I'm hungry" or "I want to eat lasagna because it's my favourite food." Since we know so little about human brain intelligence and how memory, learning, and decision-making work, self-aware AI still needs to be revised.

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