What is AI Artificial Intelligence? - A Brief Introduction
Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.
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| AI Artificial Intelligence |
Artificial Intelligence is a branch of computer science focused on building computers and machines that can simulate intelligent behavior.
Artificial Intelligence systems are able to perform tasks traditionally associated with human intelligence, such as visual perception, speech recognition, decision-making, and translating languages.
Algorithms
An algorithm is a series of mathematical instructions created for a machine to follow. Think of it as simple step-by-step instructions: do A, then B, then C. In AI, programmers create algorithms that tell a computer to look at data, identify a problem, and learn from its attempts to solve the problem.
How Artificial Intelligence works
AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies, as well as the following major subfields:
Machine Learning
What happens if you want to teach a computer to do a task, but you’re not entirely sure how to do it yourself? Or the problem is so complex that it’s impossible for you to encode all the rules and knowledge upfront?
Machine learning is the field of computer science that enables computers to learn without being explicitly programmed and builds on top of computational statistics and data mining.
Machine learning is one of many algorithms used in AI. The machine learning field is concerned with designing programs that learn to make predictions from data, alone, without requiring assistance from a programmer. These algorithms are used in applications such as music recommendations, spam filtering, and fraud detection.
A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
Deep Learning:
Deep learning is built on neural networks, a kind of machine learning model structured in a way that resembles neurons in a human brain. In a neural network, artificial neurons are arranged in interconnected layers.
There is an input layer to receive data from the outside world, and there is an output layer which dictates how the system will respond to the information. Between these two layers, there are additional “hidden” layers of neurons, which process data by putting a numerical weight on the information they receive from the preceding layer, and passing this information to the next layer in the network.
A neural network can solve very complex problems because of the huge quantity of neurons working together. Deep learning gets its name from “deep” neural networks, with dozens or even hundreds of hidden layers. These networks are powering the AI revolution with state-of-the-art object detection, machine translation, and audio synthesis.
Natural Language Processing
Natural language processing is how we get computers to understand, process, and manipulate human language. To achieve this, a computer needs to be able to “understand” a huge amount of information—from grammar rules and syntax, to different colloquialisms and accents. In a speech recognition system, for instance, human voice input becomes audio data, which then gets converted to text data, a difficult process in itself. This text data can then be used in an “intelligent” system for various applications such as translators, or controlling devices like TVs.
Computer Vision
Computer vision is aimed at helping computers identify and process images in the same way humans do. Just as we learn to distinguish between the faces of different people, computer vision aims to teach machines to recognize different objects that it “sees” through a camera. It does this by looking at individual pixels, identifying different colors, and converting them to a numerical value, then looking for patterns so that it can identify groups of similarly colored pixels and textures. This helps it identify different objects.
Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech – and then speak coherently in response.
Additionally, several technologies enable and support AI:
Graphical processing units are key to AI because they provide the heavy compute power that’s required for iterative processing. Training neural networks requires big data plus compute power.
The Internet of Things generates massive amounts of data from connected devices, most of it unanalyzed. Automating models with AI will allow us to use more of it.
Advanced algorithms are being developed and combined in new ways to analyze more data faster and at multiple levels. This intelligent processing is key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.
APIs, or application processing interfaces, are portable packages of code that make it possible to add AI functionality to existing products and software packages. They can add image recognition capabilities to home security systems and Q&A capabilities that describe data, create captions and headlines, or call out interesting patterns and insights in data.
In summary, the goal of AI is to provide software that can reason on input and explain on output. AI will provide human-like interactions with software and offer decision support for specific tasks, but it’s not a replacement for humans – and won’t be anytime soon.
Artificial Intelligence vs Artificial General Intelligence:
Due to a resurgence in popularity and hype, the term “artificial intelligence” has been misused to describe almost kind of computerized analysis or automation, regardless of whether the technology can be described as “intelligent”. If you define “intelligence” as “human-level intelligence”, then by that definition we don’t have artificial intelligence today.
AI is like teenage sex: everyone talks about it, nobody knows how to do it, everyone thinks everyone else is doing it & so claims to do it— Mariya (@thinkmariya) April 5, 2017
To avoid confusion with the more general term AI, experts prefer to use the term Artificial General Intelligence (AGI) to refer to human-level intelligence capable of abstracting concepts from limited experience and transferring knowledge between domains. AGI is also referred to as “Strong AI” to differentiate against “Weak AI” or “Narrow AI”, which are systems designed for a specific task whose capabilities are not easily transferrable to others.
All of the AI systems we have today are “Weak AI”, including impressive achievements like Deep Blue, which beat the world champion in chess in 1997, and AlphaGo, which did the same for the game of Go in 2016. These narrowly intelligent programs defeat humans in a specific task, but unlike human world champions are not capable of also driving cars or creating art. Solving those other tasks requires other narrow programs to be built.
While many novel techniques have emerged recently to built “Narrow AI”, most experts in the industry agree that we are far from achieving AGI or human-level intelligence in our machines. The path towards AGI is also unclear. Many of the approaches which work well for solving narrow problems do not generalize well to abstract reasoning, concept formulation, and strategic planning – capabilities that even human toddlers exhibt that our computers cannot.
Why is artificial intelligence important?
AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.
AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.
AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.
AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.
AI achieves incredible accuracy though deep neural networks – which was previously impossible. For example, your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.
AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.
How Artificial Intelligence Is Being Used
Every industry has a high demand for AI capabilities – especially question answering systems that can be used for legal assistance, patent searches, risk notification and medical research. Other uses of AI include:
Health Care
AI applications can provide personalized medicine and X-ray readings. Personal health care assistants can act as life coaches, reminding you to take your pills, exercise or eat healthier.
Retail
AI provides virtual shopping capabilities that offer personalized recommendations and discuss purchase options with the consumer. Stock management and site layout technologies will also be improved with AI.
Manufacturing
AI can analyze factory IoT data as it streams from connected equipment to forecast expected load and demand using recurrent networks, a specific type of deep learning network used with sequence data.
Sports
AI is used to capture images of game play and provide coaches with reports on how to better organize the game, including optimizing field positions and strategy.
Bank and Financial System
Banks are using AI technology to handle numerous activities in the bank. They handle work like financial operations, Money investing in stocks, Managing different properties and much more. Using AI to handle this activity beat a human in trading challenges. Using AI in the bank helps the bank to handle their customer and give them a quick solution.
Industrial Design
Today in most of the big manufacturing company AI are mostly used in the production unit. They are used as a robot who give a different shape to an object, who displace object from one place to another, they are used as a convey belt and much more.
If they are used in management system also. They are used to keep the records of the employee. They are used to extract correct data for decision making of the company. Using AI in the big industry help them to complete their task in time and helps business to get proper leads generation.
AI role in Gaming Zone
Computer and TV games got more development and updates in their fields. There was a time when “Super Mario” was considered as the best game. But nowadays there are different gaming bots are introduced and you don’t have to weight for other to play with yours. Bot are developed who will play with you.
WTF is AI Artificial Intelligence? - A Gentle Introduction