Artificial Intelligence & Machine Learning .

What is artificial intelligence?

In computer science, the term artificial intelligence (AI) refers to any human-like intelligence exhibited by a computer, robot, or other machine. In popular usage, artificial intelligence refers to the ability of a computer or machine to mimic the capabilities of the human mind—learning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problems—and combining these and other capabilities to perform functions a human might perform, such as greeting a hotel guest or driving a car.

Application of AI

Artificial Intelligence has various applications in today's society. It is becoming essential for today's time because it can solve complex problems with an efficient way in multiple industries, such as Healthcare, entertainment, finance, education, etc. AI is making our daily life more comfortable and fast.

Following are some sectors which have the application of Artificial Intelligence:

Types of Artificial Intelligence:

AI type-1: Based on Capabilities

Narrow AI, also called as Weak AI, focuses on one narrow task and cannot perform beyond its limitations. It targets a single subset of cognitive abilities and advances in that spectrum. Narrow AI applications are becoming increasingly common in our day-to-day lives as machine learning and deep learning methods continue to develop. 

General AI, also known as strong AI, can understand and learn any intellectual task that a human being can. It allows a machine to apply knowledge and skills in different contexts. AI researchers have not been able to achieve strong AI so far. They would need to find a method to make machines conscious, programming a full cognitive ability set. General AI has received a $1 billion investment from Microsoft through OpenAI. 

Super AI surpasses human intelligence and can perform any task better than a human. The concept of artificial superintelligence sees AI evolved to be so akin to human sentiments and experiences that it doesn't merely understand them; it also evokes emotions, needs, beliefs, and desires of its own. Its existence is still hypothetical. Some of the critical characteristics of super AI include thinking, solving puzzles, making judgments, and decisions on its own.

Artificial Intelligence type-2: Based on functionality

Reactive machine is the primary form of artificial intelligence that does not store memories or use past experiences to determine future actions. It works only with present data. They perceive the world and react to it. Reactive machines are provided with specific tasks, and they don't have capabilities beyond those tasks.

Limited Memory AI trains from past data to make decisions. The memory of such systems is short-lived. They can use this past data for a specific period of time, but they cannot add it to a library of their experiences. This kind of technology is used in self-driving vehicles.

Theory of mind AI represents an advanced class of technology and exists only as a concept. Such a kind of AI requires a thorough understanding that the people and things within an environment can alter feelings and behaviors. It should understand people's emotions, sentiments, and thoughts. Even though many improvements are there in this field, this kind of AI is not fully complete yet.

Self-awareness AI only exists hypothetically. Such systems understand their internal traits, states, and conditions and perceive human emotions. These machines will be smarter than the human mind. This type of AI will not only be able to understand and evoke emotions in those it interacts with, but also have emotions, needs, and beliefs of its own.

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Some Machine Learning Methods

Machine learning algorithms are often categorized as supervised or unsupervised.

• Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
• In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
• Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
• Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

How Companies Use Artificial Intelligence & Machine Learning.

Apple

Apple, one of the world’s largest technology companies, selling consumer electronics such as iPhones and Apple Watches, as well as computer software and online services. Apple uses artificial intelligence and machine learning in products like the iPhone, where it enables the FaceID feature, or in products like the AirPods, Apple Watch, or HomePod smart speakers, where it enables the smart assistant Siri. Apple is also growing its service offering and is using AI to recommend songs on Apple Music, help you find your photo in the iCloud, or navigate to your next meeting using Maps.

Twitter

On a daily routine, we come across numerous controversies and many of them are created over twitter, why Twitter is catching so many eyes? Answer to this the outreach of the tweet to millions of people in just one tagging system with @ either in replies or comment section. One more aspect of machine learning technology-driven change is the addition of the algorithmically curates twitter timelines that evaluates each and every tweet in real time and score them according to internal scoring and produce them in a chronological driven manner. This allows maximum engagement on the displayed tweets done by the machine learning applications technology which studies the individual preferences and past data and publishes the algorithmically managed feeds which completely change the social media platform.

Facebook

One of the primary ways Facebook uses artificial intelligence and deep learning is to add structure to its unstructured data. They use DeepText, a text understanding engine, to automatically understand and interpret the content and emotional sentiment of the thousands of posts (in multiple languages) that its users publish every second. With DeepFace, the social media giant can automatically identify you in a photo that is shared on their platform. In fact, this technology is so good, it’s better at facial recognition than humans. The company also uses artificial intelligence to automatically catch and remove images that are posted on its site as revenge porn.

Pinterest

One who is Pinterest user knows very well that Pinterest has attracted a large number of people surfing for the preferences; it has great influence over the internet and social media system. In 2015 Pinterest acquired Kosei, a machine learning automated system of a commercial application that allows specific content searching and recommendations. This touches the business operations and spam moderation of Pinterest, this also allows additions for the users like email newsletter subscription, monetization of the advertising. This strengthens the ecosystem of the interest which is basically based on the curating existing online content and processing the effective advertisement in the basic priority.

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