etailed explanation of the ten classic application fields of artificial intelligence
By: Sajjad Hussain
Tags: Technology, AI, Data Science, Deep Learning, Machine Learning

Detailed explanation of the ten classic application fields of artificial intelligence and its technical principles Sajjad Hussain Follow Jun 8 · 17 min read

This post will use the term “algorithm” to describe a single algorithm, model, or software that uses multiple algorithms in a highly simplified manner. In each category, discuss the type of data input, the algorithm as a black box, and the output one by one (for simplicity, even if the real algorithm is not a black box, consider it as a black box for the time being).

Because this is a high-level overview, I encourage you to delve into specific applications of interest and find out exactly how they apply to industries or business activities. There are also many resources available to learn the technical details and specific algorithms involved.

Predictive analysis

Prediction is synonymous with predictive analysis or predictive modeling. This is the process of judging the output data based on labeled and sometimes unlabeled input data. In machine learning and artificial intelligence, predictive analysis can be further subdivided into regression and classification.

The two subclasses that use labeled data (supervised) for prediction are discussed below.

Return

The process of inputting labeled data in the regression method, processing by the prediction model, and then generating values ​​from the continuous series (such as the stock market’s closing price).

Applications include customers’ full-cycle stock value and net profit, revenue and growth forecasts, price changes, credit default risk, and stock transaction calculations.

Classification

Classification refers to the process of inputting labeled data and processing the classification model to divide the input data into one or more categories

The spam filter is a standard case of binary classification application. E-mail is the input data processed by the classification model, and the output data is determined spam or non-spam. Non-spam refers specifically to those good emails that do not contain spam. Spam mail will be sent to the trash, and non-spam mail will be sent to the inbox.

If the third category “uncertain” is introduced, then the classifier can now divide the incoming mail into three categories. Because there are more than two categories, this is an example of multivariate classification. In this example, the email client may have a “suspected spam” folder for users to review each message and use this to train the classifier to better distinguish between spam and non-spam messages.

If you want to divide the input data into three or more categories, the algorithm can choose a single category for the input data or calculate the probability that the input data belongs to each category. In the latter case, you can use the category with the highest probability as the result of selection, or use the probabilities of all categories to process according to your own customized rules.

In this case, assume that a newly received email is determined to be 85% likely to be spam, 10% to be non-spam, and 5% to be uncertain. Because the probability of being spam is relatively high, it can be determined that the mail is spam, or the calculated probability can be used in other ways.

Finally, some algorithms can assign multiple tags to the same input. Here is an example related to image recognition. Assuming that the input data is an image of a red apple, the algorithm can assign multiple different labels such as red, apple, and fruit to the image. In this case, it is appropriate to assign all three categories to the image.

Applications include credit risk, loan approval and customer churn. Classification can be combined with the identification applications discussed later in this post.

Personalization and recommendation system

A recommendation system is a personalized form of recommendation based on existing information, and the results are very relevant to each user. It can be used to increase customer conversion rate, sales rate, satisfaction, and retention rate. In fact, Amazon increased its operating income by 35% by adding these engines, and 75% of Netflix viewing programs also came from such recommendations.

The recommendation system is a special information filtering system. Personalization can also be accomplished through user search, ranking, and scoring. The recommendation system completes recommendations (such as products, articles, music, and movies) based on input data such as commodities or users through recommendation model or engine processing

It is worth mentioning the “cold start problem” associated with the recommendation system. Cold start means that smart applications do not yet have enough information to make highly personalized and relevant recommendations for specific users or groups. For example, users have not yet generated information about their preferences, interests, or purchase history.

Another example is when merchandise (such as clothes, products, videos, songs) has just become available. There are several techniques that can help solve this problem, but due to space limitations, I will not discuss it in depth.

Recommendation system applications include recommended products, videos, music, songs, books, and TV shows (eg Amazon, Netflix, Spotify). In addition to recommendations, it also includes personalized content, including news, reports, emails, and targeted advertisements (such as Twitter).

Other examples include personalized medical plans, personalized images, and icons (e.g. YouTube, Netflix, Yelp), wine recommendations, personalized shopping (e.g. perfect jacket matching), fashion outfits (e.g. StitchFix), and full automation recommend.

Computer Vision

Computer vision is a broad field that includes pattern recognition involving visual information such as images and videos (another technique will be discussed in the next section). Computer vision takes photos, still video images, and a series of images (videos) as input, and processes the model to produce output

The output can be to identify, detect, and discover a certain target, feature, or activity. Vision-related applications imply a certain degree of automation, especially automated vision, which usually requires people to participate in the application (such as inspection). The term machine vision is used to describe similar or overlapping technologies in industrial applications, such as inspection, process control, measurement, and robotics.

There are many interesting and powerful applications of computer vision, and the application scenarios are also increasing rapidly. For example, you can use computer vision in the following scenarios:

Video analysis and content screening

Lip-reading

Command automated machines (e.g. cars and drones)

Video recognition and description

Video subtitles

Identify human interactions like hugs and handshake

Robot and its control system

Crowd density estimation

Number of people counted (e.g. queuing, infrastructure planning, retail)

Inspection and quality control

Retail customer walking path analysis and participation analysis

Unmanned aerial vehicles (UAV) are often referred to as drones. Through the application of computer vision, drones can perform inspections (such as oil pipelines, wireless signal towers), complete building and area searches, assist in making maps, and deliveries. Computer vision is now widely used in public security, security, and surveillance. Of course, such applications should also pay attention to ethics and protect people’s interests.

The last thing about computer vision is worth mentioning. Through the five senses of seeing, smelling, listening, touching, and smelling, human beings can perceive the environment and the world around them. The senses capture the information and then pass it to the nervous system for conversion. It also determines what action should be taken or what response should be made. Computer vision is an analogy for applying vision to specific artificial intelligence.

Pattern recognition

Pattern recognition involves inputting unstructured data, after model processing, and then detecting whether there is a certain pattern (detection), and then assigning a category (classification) to the identified pattern, or discovering the subject of the identified pattern (recognition)

Inputs to these applications can include images (including video — a series of still images), audio (such as speech, music, and sound) and text. Text can be further subdivided into electronic, handwritten, or printed (eg paper, check, license plate number) according to its characteristics.

The purpose of using images as input may be to detect targets, identify targets, discover targets, or all three. Face recognition is a good example. Train the model to detect faces in the image, classify the detected targets, and label them as humans. This is an example of target detection. The targets here are unrecognized faces.

“Detection” is used to refer to an object found that is different from the background. It also includes the measurement of the target position and the specific measurement around the margin frame of the detected target. Recognition refers to the process of classifying or labeling detected targets (in this case, faces). Recognition goes a step further and assigns an identity to the recognized face.

Biometric recognition technology such as face recognition can be used to automatically tag people in the picture. Another form of biometrics is identification based on fingerprints.

Other applications include:

Read the text in video and audio

Label and classify images

Car insurance based on images to assess the degree of car damage

Extract information from video and audio

Emotion recognition based on face and voice

Facial expression recognition

Audio recognition applications include:

Speech Recognition

Convert speech to text

Separate and identify speakers

Emotional intelligence analysis based on voice, real-time customer service and sales calls

Logging and deforestation sound detection

Defect detection (e.g. defects in the manufacturing process or parts failure)

Finally, the handwritten or printed text can be converted into electronic documents through optical character recognition (OCR) and handwritten character recognition. Documents can also be converted to speech, but this is considered more likely to be a generative application of artificial intelligence rather than a recognizable one. Generative applications will be discussed later in this post.

Clustering and anomaly detection

The clustering and anomaly detection are the two most common unsupervised machine learning techniques. They are also considered as pattern recognition technology.

Both processes take unlabeled data as input and undergo corresponding algorithm (clustering or anomaly detection) processing to complete the grouping in the clustering scenario or determine whether it belongs to an anomaly in the anomaly detection scenario. We first discuss clustering.

Clustering aggregates similar data from unlabeled data into groups. The specific number of groups is determined by the person who completes the clustering task (usually a data scientist). There is no absolutely correct or wrong number of groups, but for a specific application, the ideal number of groups can usually be determined by trial and error.

Because the data has no labels, the cluster must assign a certain meaning or label to each group in order to describe it clearly (for example, sports fanatics). The model is then used to assign new data to a group, thereby assuming the label or description of that group. You can think of this process as some form of predictive classification, that is, assign a class (by grouping labels) to each new data point.

Assigning new data points (such as customers) to clusters (market segments) will provide us with a better method for accurately positioning, personalizing, and strategically positioning products, and can use appropriate methods to identify each Marketing by customers in different markets.

Clustering applications include segmenting and focusing on markets and customers, 3D medical image analysis, product classification according to shopping habits, and social media analysis.

Anomaly detection is a technique used to detect patterns of abnormal data (highly unusual, deviation from conventional or deformed). Anomaly detection applications include audio-based defect and crack detection, network security, quality control (such as manufacturing defect detection), and computer and network system health (such as NASA defect and error detection).

In terms of network security anomaly detection applications, common threats include malware, ransomware, computer viruses, system and memory attacks, denial of service (DoS) attacks, phishing, unwanted program execution, credential theft, data transmission, and theft Wait. Needless to say, there are endless scenes of anomaly detection in this area.

Natural Language

Natural language is a very interesting and exciting field in the development and application of artificial intelligence, usually divided into three sub-fields: natural language processing (NLP), natural language generation (NLG) and natural language understanding (NLU). Let us discuss separately.

1. NLP

Natural language processing (NLP) inputs text, speech, or handwritten language, and outputs structured data after NLP algorithm processing. There are many potential NLP scenarios and outputs.

It is worth mentioning that sometimes NLP is also considered to be a superset of NLG and NLU, so artificial intelligence natural language applications can be considered as a form of NLP in general. Some people think it is a specific collection of natural language applications, and we are discussing part of it.

Specific tasks and technologies related to NLP include:

Quantification and target text analysis

Speech recognition (speech to text)

Topic model (such as topics and topics discussed in the document)

Text classification (eg TV series “Game of Thrones”)

Sentiment analysis (e.g. positive, negative, neutral)

Subject detection (e.g. people, location)

Name recognition (e.g. Grand Canyon, Miles Davis)

Semantic similarity analysis (e.g. similarity of overall meaning between different words and text)

Tag some voices (e.g. nouns, verbs)

Machine translation (e.g. English to French translation)

A specific NLP application involves company meeting recording, text conversion, and then provides a meeting summary, which includes analysis and meeting performance around different topics (https://www.chorus.ai).

Another application uses NLP to analyze recruitment interviews and give overall scores based on factors such as gender neutrality, intonation, and wording. It also provides optimization suggestions for improving the score and overall job description.

Other applications include:

Emotion-based news aggregation

Emotionally driven social media surveys and brand monitoring

Analysis of parent vaccine attention based on message board

Sentiment analysis of movie reviews and product reviews

Animal sound conversion

Many cloud service providers now provide some functions in this area through NLP services and API interfaces.

2. NLG

NLG enters the language in the form of structured data and is processed by the NLG algorithm to produce the corresponding language as output. This language output can be in the form of text or text converted to speech. Examples of structured input data can be statistical data on athletes in the game, advertising performance data, or company financial data.

Applications include:

Automatically generate text overview based on sentences and documents

Brief review (e.g. news and sports)

Story about pictures

Business analysis report summary

Recruiting staff to participate in hospital research

Patient hospital bills in natural language

Fantasy Football Draft Summary and Weekly Game Review

Property description and real estate market report

Press releases related to company revenue reports

3. NLU

Finally, NLU takes language as input (text, voice, or handwriting), and after processing by the NLU algorithm, it produces a language that can be understood as output. The resulting intelligible language can be used to take action, generate responses, answer questions, conduct conversations, etc.

The word “understanding” can be very esoteric and philosophical, and it will involve the concept of comprehension. It is important to note this. Comprehension refers to the ability not only to comprehend information (as opposed to rote memorization) but to integrate the understood information with existing knowledge and use it as a growing knowledge base.

The lack of language understanding and comprehension similar to humans is a major drawback of today’s natural language-based artificial intelligence applications. The root cause is that it is difficult for machines to acquire language understanding capabilities similar to humans. Remember the previous discussion about the status quo of artificial intelligence and the problems of artificial intelligence? This is a clear proof.

Without a full philosophical discussion, let us only use the term “understand” to express the algorithm (reiterate, greatly simplify) can do more work on the input language, not only to parse and perform simple tasks, such as text analysis. The problem to be solved by NLU is obviously much harder than NLP and NLG (general artificial intelligence problem), and NLU is the main basic component to realize general artificial intelligence (AGI).

The current NLU is improving day by day and has applications including personal virtual assistants, chatbots, customer success (support and service) agents, and sales agents. These applications usually include some form of handwritten content or voice dialogue, often around information gathering, question answering, or some assistive tools.

Specific application examples of personal assistants include Amazon’s Alexa, Apple’s Siri, Google’s Assistant, and Nuance’s Nina. Application examples of chat robots include lubricant experts, job interviews, student loan consultants, and commercial insurance experts. This is an area where artificial intelligence research is very active and has potential for development, and it deserves attention.

Time series and sequence-based data

In most cases, the data is collected in a sequence, so the sequence of the data is extremely important and determined by a specific index.

The most common data series index is time, and the data sorted by time is called time-series data. Stock price fluctuations, DNA sequences, IoT sensor data, and scientific phenomena such as wind direction are good examples of time series during daily trading hours.

Time series analysis and modeling can be used to learn, judge, and predict time-based events, including trends, seasonal changes, cycles, and noise.

For some specific applications, the sequence of letters and words is also valid sequence data. These sequences are labeled with different labels, such as n-grams, skip-grams, sentences, paragraphs, and even the language itself, where the language is speech, text, or Expressed electronically. In addition, audio and video are also sequence data.

Applications include:

Forecast (regression and classification)

abnormal detection

Predict the future exchange rate of currencies

Real-time tracking of health trends

market prediction

Weather forecast

Sequence-based recommendation

emotion analysis

DNA sequencing

Text generation

Sequence-to-sequence prediction (such as machine translation)

Information search, extraction, sorting, and scoring

Many powerful artificial intelligence applications revolve around searching, extracting, and sorting (scoring) information. This applies particularly to unstructured and semi-structured data, such as text documents, web pages, images, and videos.

This type of data (sometimes supplemented by structured data) can be used to extract information, provide search or optimization process recommendations, and rank or score entries by relevance, importance, or priority. Most of this group of technologies is related to personalization, because search results and other items can be arranged or sorted according to the size of the relevance for a certain user or group.

At present, many search tasks are provided by keyboard input or voice to search engines such as Google, which uses Google’s unique artificial intelligence search algorithm. E-commerce applications also use their own engines to search for products, and the search process can be driven by text, voice (voice), and visual input.

Text search includes Google search, Microsoft Bing, and distributed, transparent, and community-driven search.

Search applications based on sound and images include:

Clothes and Fashion Search

Song and artist search

Pinterest lens search

Image and video search

Font search

Video search is based on image content. Shopping apps have already adopted this approach. The user submits the photograph taken to the video search engine. Then use photos to generate similarity search results, such as clothes. Some image engines can also visually display similar other products and recommendations.

In addition to classification techniques, there are ranking and scoring techniques, including these applications:

Lead scoring

Information and document retrieval (eg web search)

machine translation

Pathogenic gene search and discovery

Determination of protein sequence structure

Reinforcement learning

Reinforcement learning (RL) is very different from the artificial intelligence techniques described so far (simply recall the aforementioned method of human learning). The basic idea is to have an agent acting in a virtual environment to obtain a positive return. Each action causes a change in the state of the environment, and each action is determined by a model called a strategy. The strategy attempts to determine the excellent actions to be taken in a given state.

Don’t worry if you don’t understand it for the time being; I will give an example, I hope it can be explained more clearly.

You can consider the game “Ms. Pac-Man” (Why Ms. Pac-Man?) as an example. Ms. Dou’s goal is to eat all the points on the screen, but the bigger goal is to get the most points out of the possible points. Why is it the real goal to score the most? Or why play this game?

First of all, the more points you get, the longer you can get a free life. The longer free life you can play, the longer you can accumulate more points. Secondly, if you can complete the game or create a world record, you can get the official “browning rights”, who doesn’t want it?

In this case, the score is the reward, Ms. Pacman is the agent, the environment is the screen, and the person (player) participating in it decides the strategy for taking action by manipulating the joystick of the game.

Of course, the environment is stateful. There is a common undetectable situation, that is, when Ms. Dou eats the dots and fruits on the screen, she must avoid the ghost that chases her, and there is an invincible situation, that is, when Ms. Dou eats invincible pills (I don’t know what it is called), she can eat the ghost and get a lot of extra points.

What determines the invincibility and non-invincibility is the change in the state of the environment and the ability of the agent in the environment.

It is worth mentioning that in the process of playing the game of “Ms. Pacman”, people are sometimes driven by the completion of on-screen goals, trying to get as many levels as possible instead of getting the most points. In this case, people will only use the invincible state to accelerate, eating as many unimpeded points as possible, and may not get the most points by eating ghosts.

Suppose you have a reinforcement learning application and the goal is to get the most points. In this case, the app will try to learn how to do this, that is, eat as many ghosts and fruits as possible.

One more thing to mention, scoring is a positive return. The death of a ghost is a negative reward. Over time, enhanced applications should try to maximize scores and minimize loss of life. Although this example is built in a game scene, we can use reinforcement learning in many other ways.

Applications include:

Beat the Go World Championship

Find excellent configuration of neural network

robot technology

Optimize drug dosage

Optimize traffic signal control

Optimize chemical reactions

Autopilot

Hybrid, automation and others

The final section of the actual application points out some applications, because they involve multiple combination techniques, or are not suitable for any category that has been discussed, so they are classified as mixed or miscellaneous.

Application examples include:

Self-driving cars and fleets and self-driving space shuttles

Real-time flight route prediction and air traffic optimization

Driverless racing

Warehouse logistics and picking automation

Dogs and humanoid robots

Ingenious hands of robots

Coral reef monitoring jellyfish robot

Hospital patient care automation

Disease outbreak prediction

Reduce cooling costs

Weather forecast

Automated meeting coordination

Predictive maintenance

Intelligent systems related to the Internet of Things

Another area of ​​real interest in artificial intelligence development is generative applications, which basically refers to artificial intelligence that can generate something for a given application from a specific type of input. Including the following examples:

Generate images from text

Generate images and image area descriptions

Generate images of galaxies and volcanoes

Generate images from sketches

Generate music according to the characteristics of the song

Generate diverse sounds and voices

Synthetic singing

Generate software code based on the design model

Generate video based on text

Other applications include style conversion (for example, ordinary image conversion to reproduce Van Gogh or Picasso-style “art”). There is also a technique called super-resolution imaging, which converts a two-dimensional image into a three-dimensional image by generating missing three-dimensional image data. Finally, automatic image coloring is another interesting artificial intelligence application