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HomeArtificial IntelligenceIntroduction to Artificial Intelligence

Introduction to Artificial Intelligence

Introduction to Artificial Intelligence

Artificial Intelligence can be described as machines that are primarily computers but act like humans. AI involves machines performing tasks like problem-solving, learning, speech recognition, and other similar tasks. If enough information is available, machines can perform and act as if they are human. Knowledge engineering plays an important role in artificial intelligence. Knowledge engineering can be implemented by establishing the relation between objects and their properties. Here are some techniques for Artificial Intelligence.

Top 4 Techniques in Artificial Intelligence

Artificial Intelligence is classified based on how the machine can use past experiences to predict future outcomes, memory, self-awareness, and predictive ability. Deep Blue was created by IBM. It is a chess software that can identify pieces on a board. However, it doesn’t have the memory necessary to predict future moves. While this system can be useful, it cannot adapt to different situations. Another type is an AI system that draws on past experiences, but has limited memory to help make decisions. A self-driving car’s decision making functions is an example of such an AI system. These observations assist in the decision making process. However, the data does not stay permanently since the observations are constantly changing. With technological advancements, machines might also be able to sense the world around them and infer what should be done. Such systems don’t exist.

Machine Learning

It is one application of AI where machines do not have to be programmed to perform specific tasks. Instead, they learn and improve automatically from past experience. Deep Learning is a subset that uses artificial neural network for predictive analysis. There are several machine learning algorithms such as Unsupervised Learning (Supervised Learning), Reinforcement Learning (Reinforcement Learning), and Supervised Learning (Unsupervised Learning). Unsupervised Learning is a method that does not use classified information and acts without guidance. Supervised Learning deduces a function using the training data. It consists of a set that includes an input object and the desired outcome. Machines can use reinforcement learning to make the most of the reward and find the best option.

NLP (Natural Language Processing).

It’s the interaction between computers and human languages where computers are programmed for natural language processing. Machine Learning is a trusted technology for Natural Language Processing that extracts meaning from human language. NLP captures audio from a human conversation. After the conversation is recorded, the machine then converts the audio to text. The machine then responds to people using the audio. Natural Language Processing applications can be found within IVR (Interactive Voice Response), applications that are used in call centres, translation applications such as Google Translate, word processors such Microsoft Word, to verify grammar accuracy in text. Natural Language Processing can be difficult due to the complexity of human languages. These rules are complex and hard for computers not to understand. NLP uses algorithms that recognize and abstract the rules in natural languages to convert unstructured data from human languages to a format that can be understood by computers.

Automation and Robotics

Automation helps automate monotonous repetitive tasks. It also increases productivity and makes it easier to obtain cost-effective and efficient results. Automation is used by many companies using machine learning, neural network, and graphs. By using CAPTCHA technology, such automation can prevent fraud from occurring online. Robotic process automation can be programmed to automate repetitive tasks that are high in volume and adaptable to different situations.

Machine Vision

Machines are capable of capturing visual information and then analyzing it. This is where cameras are used to capture the visible information. Analogue to Digital Conversion is used in order to convert the image into digital data. Finally, digital signal processing is used as a way to process the data. The data from the conversion is then fed to a machine. Machine vision is based on two key aspects: sensitivity, which refers to the machine’s ability detect weak impulses, and resolution, which refers to the distance the machine can distinguish objects. Machine vision can be used for signature identification, pattern recognition and medical image analysis.

Artificial Intelligence: Applications

These are just a few examples of the many uses for artificial intelligence. AI is used by the finance industry where personal data may be collected. This information can then later be used to give financial advice.

AI is being used in the education field, where the grading system may be automated and student performance can be evaluated.

AI is being used to aid in healthcare diagnosis. AI uses natural language processing to respond to questions and understands the question. Chatbots, which are computer programs, can be used to assist customers in scheduling appointments or billing.

Robotic Process Automation (AI) is used in Business to automate repetitive tasks that humans perform. Machine learning algorithms can be integrated with analytics in order to improve customer satisfaction.

AI is used to create Smart Home devices, security, surveillance, navigation and travel, as well as music and media streaming and video gaming.

Additional Resource:
https://www.w3schools.com/ai/
https://www.udemy.com/course/artificial-intelligence-az/
https://cloud.google.com/learn/what-is-artificial-intelligence

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