AI artificial intelligence professional vocabulary collection
The field of artificial intelligence continues to expand, standing on the edge of the precipice of mainstream breakthroughs.
AI will be more involved in our day-to-day life in the near future. So, it is important to understand some basic AI terminologies. We will see AI in almost all technical gadgets in the near future.
Here’s some popular AI professional vocabulary collection to jumpstart your AI journey:
Artificial Intelligence (AI):
Artificial intelligence is an evolving modern technology that automates manual tasks, thus making machines in efficient manner. AI generates a sense of intelligence in machines, the same as human intelligence works. AI is an abbreviation used for Artificial intelligence. AI is a field of computer science, which works on data sampling and making decisions thus solving problems. AI is all about training and testing a machine to develop an intelligent power into the machine. Google images are one example of AI, where the model has been trained with similar images, when we upload an image it generates similar images as a result.
Machine Learning (ML):
Machine learning belongs to a branch of artificial intelligence. Machine learning acts as a means to solve artificial intelligence problems. It is a process by which AI algorithms use AI functions by applying rules to create results. Machine learning designs and analyzes AI algorithms that help a computer to “learn” the same as humans. In general, machine learning is an important collection of methods to realize artificial intelligence.
One of the examples of machine learning is image recognition. Give a machine learning system enough photos of cats, and it will eventually be able to spot a cat in a new picture without any prompt from a human operator. You can think of it as an artificial intelligence network that surpassed the original programming and first received a lot of data training.
Supervised learning is a training method/learning method in machine learning. When you train an AI model using supervised learning methods, you provide the machine with the correct answers in advance. Basically, AI knows the answer, it knows the question. This is the most commonly used training method because it produces the most data: it defines the pattern between questions and answers. If you want to know why or what happened, AI can look at the data and use supervised learning methods to determine connections.
Unsupervised Learning is an artificial intelligence networks algorithm. Unsupervised learning does not know the result prior to supervised learning, where the system is unaware whether the classification result is correct or not when learning. In the case of unsupervised learning, we will not give AI an answer. Instead of finding predefined patterns like “why people choose one brand over another”, we provide a machine with a bunch of data so that it can find the pattern it needs. Based on the input examples it finds the potential rules and then applies the classification. New cases can be applied after learning and testing the system.
The test was originally thought to be a way to determine whether humans might be fooled by dialogue, only in the text display, the confusion between human intelligence and artificial intelligence, and then it has become an abbreviation for any AI that can deceive people into believing they are watching to or interact with real people. The field of artificial intelligence research is not science fiction, although it is exciting and avant-garde.
The underlying model of artificial intelligence is the “neural network” . Applications such as pattern recognition, automatic control and advanced models such as deep learning are based on neural networks. Learning artificial intelligence must start from it. Neural networks try to imitate the human brain or, as we now understand, the process of simulating the human brain. Third, the development of neural networks can only be achieved with high-end processors in the past few years.
Essentially, it means a large number of layers. The neural network does not judge whether it is an image of a cat by looking at the image, but considers whether the various features of the image and the cat are the same, and assigns different importance to each image before making the final decision. . The end result is that the cat recognition engine is more accurate.
The concept of deep learning originated from the research of artificial neural networks, but it is not completely equal to traditional neural networks. Deep learning is an algorithm in machine learning based on characterization learning of data . The goal of representation learning is to find better representation methods and build better models to learn these representation methods from large-scale unlabeled data. You may teach your AI to understand cats, but once it understands that AI can apply this knowledge to different tasks. In-depth learning means that AI will not understand what is what, but will start to learn “why”.
Robotic Process Automation (RPA):
Robotic Process Automation (RPA) is an emerging business process automation technology. RPA allows configuration of computer software or robots to integrate and simulate human interaction in any system. The RPA system captures data using the user interface and uses it like humans. To perform a variety of repetitive tasks RPA interpret, trigger responses, and communicate with other systems. An RPA software robot never sleeps, makes zero mistakes, and its cost is much lower than an employee.
In short, RPA as a set of applications that “enable organizations to effectively automate tasks, simplify processes, increase employee productivity, and ultimately provide a satisfying customer experience.”
Natural Language Processing (NLP):
Natural language processing is a branch of artificial intelligence and linguistics. This field explores how to process large amounts of natural language data and train systems to use them in computer programs.
It requires advanced neural networks to parse human language. When artificial intelligence is trained to explain human communication, it is called natural language processing. This is very useful for chatbots and translation services, but it also represents AI assistants like Alexa and Siri.
Compared with humans, artificial intelligence is more like humans. We learn almost the same way. One way to teach a machine like a robot is to use reinforcement learning. This involves providing artificial intelligence with a goal that is not defined by specific metrics, such as telling it to “improve efficiency” or “find a solution.” Instead of finding a specific answer, the AI will run the plan and report the results, and then people will evaluate and evaluate it. AI accepts feedback and adjusts the next scene for better results.
Computer Vision is a branch in the field of artificial intelligence. Its goal is to recognize the picture content. Like, the picture is of a cat or a dog? The principle of human vision is as follows: start with the ingestion of raw signals, then do preliminary processing, and then abstract, and then further abstract.
Visual recognition is an important key in computer vision. Image classification, positioning and detection all come under computer vision. The latest developments in neural networks and deep learning have greatly promoted the development of these state-of-the-art visual recognition systems.
The process by which a model predicts which specific known group or groups a new input belongs to.
To give a specific example: In order to help keep the Gmail inbox clean and data safe, the ML model runs in the background and continuously classifies each email as spam or non-spam (if any questions arise in the process, Gmail will ask you to verify the email address of the unknown sender). In this example, “spam” is one group, and “non-spam” is another group. This classification is called “binary classification”.
Data science combines computer science/information technology, mathematics, machine learning, mathematics/statistics, software development, business, and traditional research methods. Its goal is to produce data products by extracting valuable parts from data. It combines theories and technologies in many fields, including Applied mathematics, statistics, pattern recognition, machine learning, data visualization, data warehousing, and high-performance computing. Data science uses a variety of relevant data to help non-professionals understand problems.”