What is Artificial Intelligence (AI) ? Definition From Soft Loft Technologies


Artificial Intelligence - Artificial Intelligence is the concept of giving the ability to learning to machines.

In Computer Science, Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans. Leading AI textbooks define the field as the study :  any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".


Artificial intelligence can be classified into three different types of systems:
●      Analytical
●      Human-inspired
●      Humanized artificial intelligence

Analytical AI - It has only characteristics consistent with cognitive intelligence; generating a cognitive representation of the world and using learning based on past experience to inform future decisions.

Examples - Made decisions on what type of music is given to Analytical AI, by learning from all other types of music.

Human-inspired AI - It has elements from cognitive and  emotional intelligence; understanding human emotions, in addition to cognitive elements, and considering them in their decision making.

Examples - Human-inspired AI decides to sing a song when the User(Human) is in stress by learning the emotions of the user.

Humanized AI - It shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), is able to be self-conscious and is self-aware in interactions.
Actually, Humanized AI is the combination of Analytical AI and Human-inspired AI.
Example - Sophia (robot)

Basically, It is a subset of AI.

Machine Learning learns from a sample of data and make predictions about what comes in the future.
Types of Machine Learning,
●     Supervised Learning
●     Unsupervised Learning
●     Reinforcement Learning

Supervised Learning - Supervised learning is the most popular paradigm for machine learning. It is the easiest to understand and the simplest to implement. Given data in the form of examples with labels, we can feed a learning algorithm these example-label pairs one by one, allowing the algorithm to predict the label for each example, and giving it feedback as to whether it predicted the right answer or not. Over time, the algorithm will learn to approximate the exact nature of the relationship between examples and their labels. When fully-trained, the supervised learning algorithm will be able to observe a new, never-before-seen example and predict a good label for it.

In the image, The Orange images is given to the machine-learning model and it responds whether the image has orange fruit or other.

We have to train or teach the model to identify a given image has Orange or not, by giving the more images of Orange and also we have to tell the machine learning model, that the given the image has Orange Fruits.

Unsupervised Learning - Unsupervised learning is very much the opposite of supervised learning. It features no labels. Instead, our algorithm would be fed a lot of data and given the tools to understand the properties of the data. From there, it can learn to group, cluster, and/or organize the data in a way such that a human (or another intelligent algorithm) can come in and make sense of the newly organized data.

In the above image, The Machine Learning The model learns itself What type of fruits present in the image.
Reinforcement Learning - Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training data set, it is bound to learn from its experience. 
Examples - One of the most common places to look at reinforcement learning is in learning to play games. Look at Google’s reinforcement learning application, Alpha Zero, and Alpha Go which learned to play the game Go.


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