What is Artificial Intelligence?

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what is artificial intelligence?

 Artificial intelligence is the simulation of Human intelligence processes by machines, especially computer system. 



How does AI work?

 
As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI,such a machine learning.AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R and Java, are popular.

In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.

Why is artificial intelligence important?

AI is important because it can give enterprises insights into their operations that they may not have been aware of previously and because, in some cases, AI can perform tasks better than humans. Particularly when it comes to repetitive, detail-oriented tasks like analyzing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly and relatively few errors.

Advantages-
     
  • Good at detail-oriented jobs;
  • Reduced time for data-heavy tasks;
  • Delivers consistent results; and
  • AI-powered virtual agents are always available. 




 Disadvantages-

  • Expensive;
  • Requires deep technical expertise;
  • Limited supply of qualified workers to build AI tools;
  • Only knows what it's been shown; and
  • Lack of ability to generalize from one task to another.

What are the 4 types of artificial intelligence?


Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, explained in a 2016 article that AI can be categorized into four types, beginning with the task-specific intelligent systems in wide use today and progressing to sentient systems, which do not yet exist. The categories are as follows:

  • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
  • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
  • Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
  • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.

What are examples of AI technology and how is it used today?


AI is incorporated into a variety of different types of technology. Here are six examples:

  • Automation. When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger portions of enterprise jobs, enabling RPA's tactical bots to pass along intelligence from AI and respond to process changes.
  • Machine learning. This is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms:
  • Supervised learning. Data sets are labeled so that patterns can be detected and used to label new data sets.
  • Unsupervised learning. Data sets aren't labeled and are sorted according to similarities or differences.
  • Reinforcement learning. Data sets aren't labeled but, after performing an action or several actions, the AI system is given feedback.
The intelligence demonstrated by machines is known as Artificial Intelligence. Artificial Intelligence has grown to be very popular in today world. It is the simulation of natural intelligence in machine that are programmed to learn and mimic the action of humans.

Artificial Intelligence Definition-
 

:  An intelligence entity created by humans.

: capable of performing tasks intelligently without being explicitly instructed.

: capable of thinking and acting rationally and humanely.


3 type of Artificial Intelligence-

    * Artificial Narrow Intelligence (ANI)

    * Artificial General Intelligence (AGI)

    * Artificial Super Intelligence (ASI)

Artificial Intelligence Future-

Artificial intelligence is impacting the future of virtually every industry and every human being. Artificial intelligence has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future.


Risks of Artificial Intelligence
  • Automation-spurred job loss.
  • Privacy violations.
  • 'Deepfakes'
  • Algorithmic bias caused by bad data.
  • Socioeconomic inequality.
  • Market volatility.
  • Weapons automatization. 

What are positive and negative impacts of AI?
Image result for Positive and negative effects of artificial intelligence
But artificial intelligence as technology isn't morally good or bad – it just is. It's useful to think of AI as being like a toddler. It can learn, develop and improve its capabilities over time, but it isn't smart enough and doesn't have the emotional intelligence to know the context and impact of its decisions.

The late 19th and first half of the 20th centuries brought forth the foundational work that would give rise to the modern computer. In 1836, Cambridge University mathematician Charles Babbage and Augusta Ada Byron, Countess of Lovelace, invented the first design for a programmable machine.

1940s. Princeton mathematician John Von Neumann conceived the architecture for the stored-program computer -- the idea that a computer's program and the data it processes can be kept in the computer's memory. And Warren McCulloch and Walter Pitts laid the foundation for neural networks.

1950s. With the advent of modern computers, scientists could test their ideas about machine intelligence. One method for determining whether a computer has intelligence was devised by the British mathematician and World War II code-breaker Alan Turing.   

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