Artificial intelligence (AI) is an interdisciplinary science concerned with building smart machines capable of performing tasks that typically require human thought. The implications will change virtually every aspect of our world.
On an everyday level, AI figures out what ads to show you on the web, and powers those friendly chatbots that pop up when you visit an e-commerce website to answer your questions and provide customer service.
And AI-powered personal assistants in voice-activated smart home devices perform myriad tasks, from controlling our TVs and doorbells to answering trivia questions and helping us find our favorite songs.
What is Artificial Intelligence?
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.
Some of the activities computers with artificial intelligence are designed for a variety of include. Such as; Speech recognition, Learning, Planning, Perception, Reasoning, Analysis, Cloud, Computing, Knowledgebase, Problem Solving and Ability to manipulate and move objects.
Important to realize, Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Whereas, most AI examples that you hear about today are generally around you.
In general, from chess-playing computers to self-driving cars. Surprisingly, they rely heavily on deep learning and natural language processing. In particular, using these technologies, computers can be trained to accomplish specific tasks.
Who uses Artificial Intelligence?
Of course, this is one of the advantages of artificial intelligence. Where over 70,000 people are able to have a job that doesn’t involve mindless tasks. And that’s just the success it has had with one company.
Equally important, AI helps businesses grow, develop, and allocate new resources to hire more people. In fact, right now there are many companies out there following Google’s example and using Artificial Intelligence Software in their business plans.
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When did Artificial Intelligence begin?
Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism. Followed by disappointment and the loss of funding (known as an “AI winter“). And also, followed by new approaches, success, and renewed funding.
For most of its history, AI research has been divided into subfields that often fail to communicate with each other.
These sub-fields are based on technical considerations, such as;
- particular goals (e.g. “robotics” or “machine learning”)
- the use of particular tools (“logic” or artificial neural networks)
- or deep philosophical differences
- Subfields have also been based on social factors (particular institutions or the work of particular researchers).
As a matter of fact, the traditional problems (or goals) of AI research include factors such as;
- knowledge representation,
- natural language processing,
- and the ability to move and manipulate objects.
General intelligence is among the field’s long-term goals. Whereas, approaches include;
Many tools are used in AI, including versions such as;
- search and mathematical optimization,
- artificial neural networks,
- and methods based on statistics, probability and economics.
Borrowing from the management literature, Kaplan and Haenlein classify artificial intelligence into three different types of AI systems. Including, analytical, human-inspired, and humanized artificial intelligence.
Analytical AI has only characteristics consistent with cognitive intelligence. Especially, generating cognitive representation of the world and using learning based on past experience to inform future decisions.
In general, humanized AI shows characteristics of all types of competencies. Such as cognitive, emotional, and social intelligence. In particular, it is able to be self-conscious and is self-aware in interactions with others.
The AI field draws upon elements such as;
- computer science,
- information engineering,
- and many other fields.
How is Artificial Intelligence used?
A typical AI analyses its environment and takes actions that maximize its chance of success. In particular, an AI’s intended utility function (or goal) can be simple such as; if the AI wins a game of Go, or otherwise. And or even undertakes a complex task such as; doing mathematically similar actions to the ones succeeded in the past.
If the AI is programmed for “reinforcement learning“, goals can be implicitly induced by rewarding some types of behavior or punishing others. Alternatively, an evolutionary system can induce goals by using a “fitness function.”
As a matter of fact, in order to mutate and preferentially replicate high-scoring AI systems. Similarly to how animals evolved to innately desire certain goals such as finding food. Even though some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals.
Artificial Intelligence Evolution
AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute. A complex algorithm is often built on top of other, simpler, algorithms. Generally, a simple example of an algorithm is the following (optimal for the first player) recipe for play at tic-tac-toe. For instance, if;
- Someone has a “threat” (that is, two in a row), take the remaining square. Otherwise,
- a move “forks” to create two threats at once, play that move. Otherwise,
- take the center square if it is free. Otherwise,
- your opponent has played in a corner, take the opposite corner. Otherwise, take
- an empty corner if one exists. Otherwise, take
- any of the empty squares.
As can be seen, many AI algorithms are capable of learning from data. Whereas, they can enhance themselves by learning new heuristics, or can themselves write other algorithms. The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner.
The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. Such as;
- Reasoning and or Problem-solving
- Knowledge Representation
- Planning and Learning
- Natural Language Processing
- Perception, General and Social Intelligence
- Graphic Content Motion & Manipulation
In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines. In contrast to the natural intelligence displayed by humans and other animals.
Uniquely, computer science defines AI research as the study of “intelligent agents.” In regards to any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
More specifically, Kaplan and Haenlein define AI as “a system’s ability to correctly interpret external data and to learn from such data. In relation to using those achievable towards achieving specific goals and tasks. Especially through flexible adaptation”.
Colloquially, the term “Artificial Intelligence” is used to describe machines that mimic “cognitive” functions. In particular to that whereby humans associate with other human minds. Such as “learning” and “problem-solving“.
Here are Factors for the Artificial Intelligence Demand;
As technology improves, we as a race are becoming increasingly impatient. While typing emails, we are used to getting name suggestions before typing the whole name.
When using our smartphones, we prefer talking instead of typing (Siri, Cortana, Google Assistant anyone?). These are just a few variations of how AI is being used in our daily lives.
AI will be most useful in getting computers and robots to do stuff that’s possibly too dangerous for humans. Check out Boston Dynamics, a company owned by Google, that has used AI to make robots replicate animal movements and navigate weird terrains.
Think of a forest fire, and what we can potentially achieve by making a robot access areas that are not possible for humans (We’re still far away from that, but it’s still a great implication of AI)
This trillion-dollar industry continues to find applications in every new technology. There are so many rural areas with no access to doctors and basic diagnostic healthcare.
Imagine what could potentially happen if we’re able to teach computers to diagnose simple ailments in humans. Some work on this periphery is potentially being taken up by IBM Watson Health. It’s still in extremely nascent stages though!
The advantages of artificial intelligence don’t end there. Another benefit of it is that it’s convenient! One way AI makes life more convenient is through the elimination of repetitive tasks. A simple example of this is scheduling tasks and meetings.
Many staffers have noted that the incorporation of artificial intelligence in their office has allowed them to set aside extra time for tasks that they deemed more important. Higher convenience at work has also been correlated with increased productivity as well.
Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry. Research associated with artificial intelligence is highly technical and specialized.
The core problems of artificial intelligence include programming computers for certain traits such as:
- Ability to manipulate and move objects
Artificial Intelligence Machine Learning
Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties, and relations between all of them to implement knowledge engineering.
Machine learning is also a core part of AI. Learning without any kind of supervision requires the ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions.
Classification determines the category an object belongs to. Whereas, regression deals with obtaining a set of numerical input or output examples. Thereby discovering functions enabling the generation of suitable outputs from respective inputs.
Artificial Intelligence Perception
Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.
Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning, and mapping.
As machines become increasingly capable, tasks considered to require “intelligence” are often removed from the definition of AI.
Through a phenomenon that is known as the AI effect. A quip in Tesler’s Theorem says “AI is whatever hasn’t been done yet.” For instance, optical character recognition is frequently excluded from things considered to be AI. In particular, it has become a routine technology.
Modern machine capabilities generally classified as AI include successfully understanding human speech.
In addition to competing at the highest level in strategic game systems. Such as chess and Go, autonomously operating cars, and intelligent routing in content delivery networks and military simulations.
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