In this video, you are going to see four different types of artificial intelligence. Let's get right into the video. Number 1. Self-awareness The final step for AI to become self-aware will be to establish the theory of mind and artificial intelligence, which will happen sometime in the future. This type of artificial intelligence is conscious on a human level, and is aware of its own presence in the world as well as the presence in emotional state of others. It would be able to deduce what others may require based on not only what they say to them, but also how they say it. In artificial intelligence, self-awareness requires both human researchers to grasp the premise of consciousness, and then learn how to replicate it so that it can be built into machines. Number 2. Mind body theory Theory of mind is exactly that, a theory. We haven't yet developed the technological and scientific capabilities required to advance artificial intelligence to the next level. The concept is founded on the psychological premise that other living things have thoughts and emotions that influence one's own behavior. This would mean that AI machines could understand how humans, animals, and other machines feel, and make decisions through self-reflection and determination, and then use that information to make their own decisions. In order to create a two-way relationship between people and artificial intelligence, computers would need to be able to grasp and interpret the idea of mind, the fluctuations of emotions in decision-making, and a slew of other psychological concepts in real time. Number 3. Reactive artificial intelligence A reactive machine is guided by the most fundamental AI principles, and, as the name suggests, is solely capable of perceiving and reacting to the world around it. Because a reactive machine lacks memory, it cannot depend on previous experiences to guide real-time decision-making. Reactive machines are designed to do only a restricted number of specialized tasks, since they perceive the world directly. However, intentionally reducing a reactive machine's worldview is not a cost-cutting technique. Instead, it means that this type of AI will be more trustworthy and reliable, it will respond consistently to the same stimuli. Deep Blue, an IBM chess-playing supercomputer that defeated international expert Gary Casparov in a game in the 1990s, is a renowned example of a reactive machine. Deep Blue could only recognize the pieces on a chessboard, and know how they move according to the rules of the game, as well as recognize each piece's current position, and choose the best logical move at the time. The machine was not looking for future potential plays from its opponent, or attempting to better place its own pieces. Every turn was treated as though it were its own world, distinct from any previous action. Google's AlphaGo is another example of a game-playing reactive machine. AlphaGo is also unable to predict future plans, instead relying on its own neural network to assess current game developments, giving it an advantage over deep blue and a more complex game. AlphaGo has also defeated world-class Go players, including Lee Seedall, the 2016 Go Champion. Reactive machine artificial intelligence can achieve a level of complexity and reliability when designed to accomplish recurring tasks, despite its limited scope and inability to be easily updated. Number 4. Limited Memory Artificial Intelligence When gathering information and assessing prospective options, artificial intelligence with limited memory can store previous data and predictions, essentially peering into the past for indications on what might happen tomorrow. Artificial intelligence with limited memory is more complicated, and has more possibilities than reactive machines. Memory problems when a team regularly educates a model in how to assess and use fresh data, or when an AI environment is constructed to allow models to be automatically taught and regenerated, AI is produced. Six steps must be followed when using restricted memory AI and machine learning. The machine learning model must be constructed, the model must be able to generate predictions, the model must be able to receive human or environmental feedback, that feedback must be saved as data, and these stages must be repeated in a cycle. There are three major machine learning methods that use artificial intelligence with limited memory. Reinforcement learning is a type of learning that uses positive reinforcement. Through repeated trial and error, it learns to make better predictions. Long-term memory is a type of memory that lasts for a long time, which makes use of previous data to forecast the next item in a series. When making predictions, LTSMs prioritize more recent information and devalue data from the past, yet they still use it to draw inferences. Evolutionary generative adversarial networks are a type of generative adversarial network that evolves over time. With each new decision, it evolves, allowing it to explore slightly modified courses depending on previous experiences. Throughout its evolutionary mutation cycle, this model is always looking for a better path and uses simulations and statistics or chance to anticipate outcomes. 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