We are surrounded by machines these days.
While some could listen to and respond to our commands, others could suggest our favorite food during midnight cravings.
Artificial Intelligence (AI) is what powers these machines.
For those who may be wondering, here is a quick definition of AI.
AI is machines’ simulation of human intelligence procedures, especially computer systems. In easy words, it is the technology behind the computers’ ability to perform and operate the actions that humans do.
The science of Artificial Intelligence is vast, as human intelligence is greater. Beginning from the creation of modern technology to attempting to land man on Mars, humans have come a long way. Each cognitive action requires a different comprehensive approach to encoding and decoding for the machines.
Every approach is categorized within three domains of AI, and they are:
- Data Science
- Computer Vision
- Natural Language Processing
This article will guide you through the domains in artificial intelligence and their possibilities in engineering terms and use cases of every classified approach.
- 1 What are the Domains of AI?
- 2 Natural Language Processing
- 3 Machine Learning
- 4 Deep Learning
- 5 Task Domains of AI
What are the Domains of AI?
Artificial Intelligence technology is hugely critical for organizations to extract value from data via automation, processes of optimization, or the production of actional insights. To make this possible, AI is classified into the following domains:
Data Science is all about the extraction of actionable insights from raw data. It involves the application of mathematical and statistical principles of data, mostly in formats of text, audio, or visuals.
There is a segment in AI known as Machine Learning, where a set of data-driven algorithms allows software applications to accurately predict outcomes without requiring explicit programming.
It is made possible by developing algorithms that can obtain input data and leverage statistics models to make predictions as an output while updating outputs as new data is made available.
You must have experienced machine learning in action in many ways. From shopping recommendations on Amazon to movie or show suggestions on Netflix, the backstage is about machine learning and data science.
Data science is a broader term that focuses on more than just algorithms and statistics but also empowers the data processing methodology as a whole.
The term Big Data is pretty common these days. It is called ‘Big’ for three reasons.
- Volume – The amount of data produced
- Variety – Types of data produced
- Velocity – Speed of data produced
Big data is all about data, which even includes massive sizes of data exceeding the capacity of old school software to process within a considerable time and value. John Mashey coined the term in the 1990s when he discovered that traditional systems could not process such enormous value and sizes of data.
Big Data generally includes data sets that are beyond the sizes to be able for the commonly used tools to capture, curate, manage, and process the data within an acceptable period.
Computer Vision is considered the hottest subfield of AI and ML (Machine Learning) as it involves many applications and potential. This technology aims to replicate the powerful capacities of human vision.
Computer vision enables a machine to recognize the presence of objects and their features, including shapes, colors, textures, sizes, and order, amongst other aspects of the image, to deliver a description as whole as possible.
In short, computer vision identifies visual aspects or symbols from an image of the object and learns the pattern to alert or predict the future object using its camera. The immense possibilities of this technology are already doing wonders in the healthcare and defense sectors.
Different techniques of computer vision include:
- Image Classification – This task helps classify the visual input from a predefined set of categories.
- Object Detection – This task helps find the instance of an object in an image and detects similar instances of objects in the real world.
- Classification & Localization – The task identifies the visual input and the image’s location.
- Instance Segmentation – The task after object detection, where the instance of objects is processed.
Applications of Computer Vision
- Facial Recognition – This is the capability of a system to recognize or verify a person from a visual form of data such as a photo or video source.
- Face Filters – When you click a selfie with that dog filter on Snapchat, the technology of computer vision identifies the nose, ears, and tongue in the image.
- Automobile – It is also one of those technologies behind those sensors enabling driverless cars to detect obstacles, pedestrians, vehicles, and more on the road to avoid accidents.
- Healthcare – Computer vision has been used by medical professionals to accurately classify several conditions and diseases to save patients’ lives by avoiding wrong and useless diagnoses.
Natural Language Processing
It is the segment of AI which helps computers understand, interpret and manipulate human language. It integrates disciplines, including computer science and computational linguistics, to bridge human communication and computer understanding.
NLP enables the computer to understand commands given in the human language in formats of voices or words and to provide output by processing the input.
Consider the computer as a friend of yours. If they could hear you, they could know the word spoken as they listen to it, and the brain could process the word to respond to it, making two-way communication possible.
The Process of NLP
The information is initially collected and understood to process the natural human language. Natural Language Understanding (NLU) uses algorithms to convert data in voice or written format from the user into a structured data model. It is not required to be an in-depth understanding of the input but should have a more extensive library of vocabulary.
The following subtopics of NLU will clear the concept.
- Entity Recognition – The extraction of the most critical data within the input, including name, place, location, date, numbers, and more.
- Intent Recognition – This is the most crucial process of NLU as it is the process of recognizing the user’s intention in the input and understanding the correct meaning.
And when the computer’s output of NLP is appropriate and is received by the user, it is called Natural Language Generation (NLG). It is the process of the software producing natural language output from structured data sets. It finds the most relevant output from its network, and after organizing it and correcting grammatical errors, it feeds the best output to the user.
Steps of NLG
- Content Determination – The first step of showing just the vital information or overview of the user’s speech.
- Data Interpretation – The second step is identifying the correct data and showing the result to the user in the input context.
- Sentence Aggregation – The next step of choosing the right words or expressions for an output sentence.
- Grammaticalization – The step where the sentence is cross-checked as per the grammatical patterns and ensures there are no grammatical mistakes.
- Language Implementation – The step is ensuring that the output data is per the user’s preferences and putting them into templates.
So, that is the whole process of how your homely Alexa recognizes your speech and understands it, then comes up with an answer after analyzing the most relevant responses to your question.
Applications of NLP
- Speech Recognition – It is when the system can give output by understanding and interpreting the user’s voice or speech as an input or command. Speech recognition is applied in devices like Google Assistant and Apple Siri.
- Sentimental Analysis – It is the process of detective negative and positive sentiments in the textual content. This applies to the content review policies on YouTube to identify violent and graphic content.
- Machine Translations – The process of translating one language in textual format to another language by machines. It is how Youtube captions and Google Translate works.
Machine learning (ML) is a form of artificial intelligence (AI) that allows software applications to achieve better accuracy at predicting results without being explicitly programmed. Machine learning algorithms use data based on history as input to forecast upcoming output values.
Supervised learning: Within this type of machine learning, data scientists provide algorithms with labeled data for training and define the variables they want the algorithm to assess to find correlations. Both the algorithm’s input and output have got specifications.
Under the umbrella of supervised machine learning comes Classification, Regression, and Forecasting.
Classification: The machine learning program should conclude values observed and verify which category of new observations belongs in classification tasks. For instance, when filtering emails as ‘spam’ or ‘not spam,’ the program should look at existing observational data and filter the emails accordingly.
Regression: To perform regression tasks, the machine learning program should estimate and comprehend the relationships between variables. Regression analysis focuses on one reliable variable and a series of other switching variables, making it specifically useful to predict.
Forecasting: Forecasting is the procedure of making predictions based on data from the past and present and is generally used to analyze trends.
Unsupervised learning: This form of machine learning involves algorithms that train on data that are not labeled. The algorithm scans through data sets, searching for any relevant connection. The data algorithms train on, and the predictions or recommendations they result in are already determined.
Within the umbrella of unsupervised learning comprise:
Clustering: Clustering is all about grouping similar data sets (on the basis of defined criteria). It’s resourceful to segment data into multiple groups and perform analysis on each data set to discover patterns.
Dimension reduction: Dimension reduction declines the number of variables considered to discover the necessary information.
Semi-Supervised Learning: Semi-Supervised learning is a form of Machine Learning algorithm between Supervised and Unsupervised machine learning. It represents the mixed ground of Supervised and Unsupervised learning algorithms and uses the fusion of labeled and unlabeled data sets during the training period.
Some segments where semi-supervised learning is used are:
Machine translation: Teaching algorithms to convert language based on less than a whole dictionary of words.
Fraud detection: Recognise fraud cases when you only have some positive examples.
Labeling data: These algorithms trained on small data sets could learn to apply data labels to larger sets automatically.
Reinforcement learning: Data scientists generally use reinforcement learning to make a machine learn to complete a multi-step procedure for which there are distinctly defined rules. Data scientists program an algorithm to achieve a task and provide it positive or negative cues as it works out how to get the task done. But for the significant part, the algorithm makes its own decisions about what steps to take along the way.
Reinforcement machine learning is used in areas including:
Robotics: It enables robots to learn to perform tasks in the physical world using this technique.
Video gameplay: Reinforcement learning can be used to make bots learn to play various video games.
Resource management: Provided with substantial resources and a brief goal, reinforcement learning could support enterprises plan out how to allot resources.
Deep learning is a form of machine learning and artificial intelligence (AI) that mimics how humans gain specific knowledge types. Deep learning is a vital component of data science, including statistics and predictive modeling.
- Feedforward neural network: Feedforward neural network is a very common neural network where the flow control happens from the layer of input and goes towards the layer of output. These types of networks only have single layers or just a hidden layer. Since the data shifts only in a direction, this network has no backpropagation technique. The total weights present in the input are fed into the input layer. These types of networks are used in the algorithm of facial recognition using computer vision.
- Radial basis function neural networks: This type of neural network usually has more than one layer, mostly two. In such a network, the relative distance from any point to the center is computed, and the same is sent towards the next layer. Radial basis networks are commonly used in systems designed for power restoration to back the power up in the shortest of time to prevent blackouts.
- Multilayer perceptron: This form of the network has above three layers and is used to categorize the data, which is non-linear. These types of networks are wholly connected with each node. These networks are highly used to recognize speech and secondary technologies of machine learning.
- Convolution neural network: The convolution neural network (CNN) is one of many variations of the multilayer perceptron. CNN can comprise more than a layer of convolution; since it comprises a convolution layer, the network is comparatively deeper with lesser parameters. CNN is highly effective in recognizing images and identifying various patterns of images.
- Modular neural network: This type of network is not a single but an amalgamation of various small neural networks. All the sub-networks make a sizeable neural network, and all operate independently to accomplish a common target. Such networks are very resourceful in breaking the small-large problem into smaller pieces and then solving them conveniently.
- Robotics– Robotics is a domain of AI that is created in Electrical Engineering, Mechanical Engineering, and Computer Science to design, construct, and apply to robots.
- Expert System – An expert system is a computer program that utilizes artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an enterprise with expertise and experience in a specific field. It is broadly used in several areas, including medical diagnosis, coding, games, accounting, etc. Expert systems are generally intended to complete human experts and not to replace them.
- Fuzzy Logic – Fuzzy Logic (FL) is a technique of reasoning that imitate human reasoning. The FL approach mimics how humans make decisions, which involves all intermediate possibilities among digital values of YES and NO.
The traditional logic block that a computer could comprehend takes accurate input and generates an actual output in the form of TRUE or FALSE, which is equal to a human’s YES or NO answer.
Fuzzy logic is used for commercial and practical purposes, including:
- It could control machines and consumer products.
- It might not give precise reasoning, but just acceptable reasoning.
- It helps deal with the unreliability in engineering.
Neural Network – A neural network is an AI method that makes computers learn how to process data in a way influenced by the human brain. It is a machine learning procedure known as deep learning that uses interconnected nodes or neurons in a structure that is layered, which resembles the brain of a human. The types of neural networks and their use cases are mentioned above in Deep Learning.
Cognitive Computing – is generally used to describe AI systems that simulate human thought. Human cognition consists of real-time analysis of the real-world environment, context, intent, and many secondary variables that inform the ability of a person to solve problems. This method can analyze emerging patterns, identify business opportunities, and care for crucial process-centric problems in real time.
Task Domains of AI
The tasks performed by Artificial Intelligence are classified under the domains of Formal, Mundane, and Expert tasks, just like the exact domains are classified for humans.
Humans tend to learn Mundane tasks, considered ordinary tasks since birth. The ability to have a perspective, speech, making use of language to communicate and locomotives are included within it. Humans later learn tasks within the formal and expert parts as they grow and achieve skills.
Mundane tasks are the easiest to learn for humans. Initially, the same was considered valid for AI until they realized they were wrong. Later the researchers planned to implement formal and expert tasks for AI as the system took more knowledge and complex algorithms to achieve that intelligence.
For AI, mundane tasks include the ability to have a perception, common sense, reasoning, and natural language processing.
This task set is performed when the machines are processed and powered by computer vision and data science. It helps them identify objects, voices, and words.
Natural language processing helps attain mundane tasks, including understanding languages to generate and translate outputs.
Robotics, another significant segment of Artificial intelligence, helps itself achieve the locomotive ability, that is, to move and do physical activities.
AI can perform formal tasks when they are processed through data science to attain mathematical and logical skills to do geometry, integration, differentiation, and more.
This is applicable for gaming AI where the users play against the computer. It also includes tasks requiring the ability to verify and prove theories.
Not all humans can manage the expert tasks, such as expertise in specific fields. Expert tasks for AI include attaining the ability and skill to do activities related to specific segments, such as engineering, manufacturing, and more. This is made possible to develop task-specific AI to meet industry standards like monitoring and fault prediction.
Different Domains of Artificial Intelligence Conclusion
Now that you know the different domains of AI you can better know which fields of AI to dive deep into. If you learned something from this article please let us know in the comments below!