WHAT IS ARTIFICIAL INTELLIGENCE?
Artificial intelligence (AI) is a broad field of computer science that focuses on creating intelligent machines that can accomplish activities that would normally need human intelligence.
WHAT ARE THE FOUR TYPES OF ARTIFICIAL INTELLIGENCE?
- *Reactive Machines
- *Limited Memory
- *Theory of Mind
- *Self-Awareness
- WHAT ARE EXAMPLES OF ARTIFICIAL INTELLIGENCE?
- *Siri, Alexa and other smart assistants
- *Self-driving cars
- *Robo-advisors
- *Conversational bots
- *Email spam filters
- *Netflix's recommendations
- CONCEPTS
How Does Artificial Intelligence Work?
AI Approaches and Concepts
- Every day, Artificial Intelligence makes more front-page news. Artificial Intelligence, or AI, is the technology that allows machines to learn from their mistakes and do activities that are similar to those performed by humans.Opinions on artificial intelligence's current and future applications, or, worse, consequences, swing drastically between utopian and dystopian. Our imaginations tend to float into Hollywood-produced waters, teeming with robot revolutions, autonomous cars, and a lack of comprehension of how AI actually works, if we don't have the necessary moorings.This is mostly owing to the fact that AI refers to a variety of technologies that enable robots to learn in a "intelligent" manner.We intend to shed light on these technologies and clarify what makes artificial intelligence, well, intelligent in the future series of blog entries.
- Norvig and Russell go on to explore four different approaches that have historically defined the field of AI:
- Thinking humanly
- Thinking rationally
- Acting humanly
- Acting rationally
The first two concepts are about thinking and thought processes, whereas the rest are about conduct. "All the skills needed for the Turing Test also allow an agent to operate rationally," Norvig and Russell write, focusing on rational agents that act to get the best outcome. (Russel and Norvig 4, for example.)"Algorithms enabled by limitations, exposed by representations that support models focused at loops that tie thought, perception, and action together," says Patrick Winston, the Ford professor of artificial intelligence and computer science at MIT.While these concepts may appear esoteric to the common person, they assist to focus the discipline as a branch of computer science and provide a roadmap for incorporating machine learning and other artificial intelligence subsets into machines and programmes.The Four Types of Artificial Intelligence
Reactive Machines
- 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.Reactive machines are designed to do only a restricted number of specialized tasks since they perceive the world directly. Deep Blue, an IBM chess-playing supercomputer that defeated international expert Gary Kasparov in a game in the 1990s, is a renowned example of a reactive machine. Deep Blue could only recognize the pieces on a chess board 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 plays, instead relying on its own neural network to assess current game developments, giving it an advantage over Deep Blue in a more complex game.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.
Limited Memory
- 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 in 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 machine learning in which a computer learns to generate better predictions through trial and error.
*Long Short Term Memory (LSTM) is a type of memory that uses previous information to predict the next item in a sequence. When making predictions, LTSMs prioritise more recent information and devalue data from the past, yet they still use it to draw inferences.
*Evolutionary Generative Adversarial Networks (E-GAN) is a type of adversarial network that evolves over time, developing to explore slightly different paths depending on previous experiences with each new decision. Throughout its evolutionary mutation cycle, this model is always looking for a better path and uses simulations and statistics, or chance, to anticipate outcomes.
Theory of Mind
Theory of Mind is just that. We have yet to develop the technological and scientific capabilities required to advance to the next level of artificial intelligence.
The idea is founded on the psychological notion that other living creatures have thoughts and feelings that influence one's own actions. This means that AI computers will be able to understand how humans, animals, and other machines feel and make decisions through self-reflection and determination, and will then use that information to make their own decisions. In order to create a two-way relationship between people and artificial intelligence, computers would have 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.
Self-awareness
The ultimate stage for AI to become self-aware will be to build Theory of Mind in 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 and emotional condition of others.
In artificial intelligence, self-awareness requires both human researchers to grasp the concept of consciousness and then learn how to replicate it so that it can be implemented into machines.
How is AI Used?
Artificial intelligence is frequently utilized to present individuals with customized recommendations based on their prior searches and purchases, as well as other online activity. In commerce, AI is critical for optimizing products, inventory planning, and logistics, among other things.
Artificial intelligence generally falls under two broad categories:
*Narrow AI, often known as "Weak AI," is a type of artificial intelligence that functions in a constrained setting and simulates human intellect. While narrow AI is frequently focused on executing a specific task very well, these machines operate under many more constraints and limits than even the most basic human intelligence.
*Artificial General Intelligence (AGI): AGI, often known as "Strong AI," is the type of artificial intelligence that we see in movies like West world's machines or Star Trek: The Next Generation's Data. AGI is a machine that has general intelligence and can use that intelligence to solve any problem, much like a person can.
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