 What is artificial intelligence, AI? Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. 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 as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. In general, AI systems work by ingesting large amounts of label 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 has fed examples of text chats can learn to produce life-like 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 improperly, AI tools often complete jobs quickly and with relatively few errors. This has helped fuel an explosion in efficiency and opened the door to entirely new business opportunities for some larger enterprises. Prior to the current wave of AI, it would have been hard to imagine using computer software to connect riders to taxis, but today Uber has become one of the largest companies in the world by doing just that. It utilizes sophisticated machine learning algorithms to predict when people are likely to need rides in certain areas, which helps proactively get drivers on the road before they're needed. As another example, Google has become one of the largest players for a range of online services by using machine learning to understand how people use their services and then improving them. In 2017, the company's CEO, Sundar Pichai, pronounced that Google would operate as an AI-first company. Today's largest and most successful enterprises have used AI to improve their operations and gain advantage on their competitors. What are the advantages and disadvantages of artificial intelligence? Artificial neural networks and deep learning artificial intelligence technologies are quickly evolving, primarily because AI processes large amounts of data much faster and makes predictions more accurately than humanly possible. Advantages are good at detail-oriented jobs, reduced time for data-heavy tasks, delivers consistent results, and AI-powered virtual agents are always available. Disadvantages are 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 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. 1. 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. 2. 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. 3. Machine Vision This technology gives a machine the ability to see. Machine vision captures and analyzes visual information using a camera, analog to digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision. 4. Natural Language Processing, NLP This is the processing of human language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and text of an email and decides if it's junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition. 5. Robotics This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. For example, robots are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings. 6. Self-Driving Cars Autonomous vehicles use a combination of computer vision, image recognition and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians. For more interesting videos, please subscribe our channel.