Artificial Intelligence(A.I)

In this article, we shall be explaining briefly about artificial intelligence, A.I, and other terms related to it. How the knowledge of A.I has in recent years, contributed to the development of computer technology in general.

The Relationship between A.I, artificial intelligence, deep learning, D.L and Machine Learning, M.L their types and examples and other related matters.

What is Artificial Intelligence?

Artificial intelligence (AI) is a broad field of computer science aimed at producing intelligent computers that can accomplish activities that would normally need human – like intelligence.

Examples of Artificial Intelligence

There are a lot of examples of A.I we can see around us today, but in this article we will be mentioning the most prominent and popular ones, which we are all familiar to.

  1. Self-Driving Cars (i.e. Tesla).
  2. YouTube or Netflix video recommendations.
  3. Alexa, Siri, and other Web assistants.
  4. Conversational Bots.
  5. Email Spam Filters.
  6. Drones.
  7. Robo-Advisors.

The above examples mentioned are just but few amongst the hundreds of examples of A.I softwares and hardwares.

Types of A.I

There are basically four types of Artificial Intelligence, A.I, we shall mention them and also explain them briefly in this article. You can read more on A.I here.

1. Self Awareness

In artificial intelligence, self-awareness requires both human scientists to grasp the concept of consciousness and afterwards discover how and when to reproduce it so as to be implemented into machines.

The ultimate stage for AI at becoming self-aware would be to build Theory of Mind in artificial intelligence, which will happen some day in the future.

This type of artificial intelligence is conscious on a human level and therefore is aware of its own presence in the environment and also the existence and mental condition of others.

This would be able to deduce what others may require based on not always what they say to them, but also how they say it.


2. Theory of Mind

The notion is founded on the behavioural principle that other living beings have feelings and thoughts that influence one’s own actions.

It would imply that AI computers might understand how people, animals, and other machines react and decide things via consciousness and determination, and then use knowledge to form their own conclusions.

In order to create a two-way interaction between people and artificial intelligence, computers would need to be able to understand and interpret the idea of “mind,” the swings of emotions in decision making, and a slew of other scientific concepts in real time.

Theory of Mind is exactly that: a theory. We haven’t yet developed the technology and intellectual capabilities required to advance A.I to the next level.

3. Reactive Machines

Because a reactive machine lacks memory, it cannot depend on previous experiences to guide relevant decision.

A reactive machine is guided by much more fundamental AI principles or, as the title suggests, is solely responsible of seeing and reacting to the environment around it.

Reactive machines are made to do only a restricted number of specialized tasks since they see the world immediately.

However, purposefully confining a reactive machine’s viewpoint isn’t a cost-cutting tactic; rather, it implies that this form of AI will become more trustworthy and dependable — it will respond to changes in just the same manner each time.

Deep Blue, an IBM game(chess) computer that defeated world expert Gary Kasparov in a game in the 1990s, is a renowned example of a reactive machine.

Deep Blue could only recognize the parts on a chess board and understand how they move according to the rules of the game, as well as recognize each piece’s current location and choose the best logical move at the time.

The machine was not looking for future possible plays from its adversary or attempting to better place its own pieces. Every turn was treated as though it were its own world, distinct from any previous action.

4. Limited Memory

While collecting information and assessing prospective options, artificial intelligence with limited memory can store prior data and predictions, basically peering into the past for indications on what could happen tomorrow.

Artificial intelligence with limited memory is more complicated and has more potential than reactive robots.

Memory problems Whenever a team regularly educates a model about how to assess and use fresh data, and when an AI environment is constructed to allow models to be autonomously taught and regenerated, AI is produced.
Six steps need to be taken when using restricted memory AI in machine learning: The machine learning model should be constructed, the model have to be able to generate predictions, the model must be ready to accept human or environmental feedback, that feedback must be saved as data, and these stages must be repeated in a cycle.

There may be 3 primary machine learning methods that use artificial intelligence with limited memory:

  1. Evolutionary Generative Adversarial Networks(E-GAN).
  2. Long Short Term Memory(LSTM).
  3. Reinforcement Learning(RL).

How Does A.I Work?

Artificial intelligence (AI) is a computer system that can do activities that would normally need human intellect… Most of these artificial intelligence algorithms are based on machine learning, while others are based on deep learning and yet others are based on mundane activities like regulations.

Deep Learning and Machine Learning

Deep learning is a sort of machine learning that processes data using a neural network design inspired by biological architecture. The data is processed via a number of layers in the neural networks, that allows the machine to go “deep” in its learning, creating connections and weighing data for the best outcomes.

It might be difficult to identify the difference between artificial intelligence, machine learning, and deep learning.

Artificial intelligence (AI) is a collection of classification algorithms and intelligence that attempts to emulate human intellect. One of these is machine learning, and deep learning is one of the machine learning approaches.