Category: Lexicon

Active learning is generally considered a subset of Machine Learning. It’s also sometimes referred to as a supervised form of machine learning. Here, the active learning system may interact with a human user to classify and label data for producing the required output. The ML algorithm chooses the next set of cases to classify from an unlabeled data set. The core idea behind active learner algorithms is to give the machine learning algorithm the freedom to select the data it wants to learn from. This helps improve its accuracy with fewer training labels. Applications of Active Learning Because it yields optimal performance with minimal labeled samples, Active Learning has broad practical applications in Artificial Intelligence. ML teams can save significant money by using it instead of standard Supervised Learning. Let’s investigate a few of them in more depth now. 1. Computer Vision A wide variety of techniques for analyzing and improving visual content are included under the umbrella of Computer Vision. Because there is so much unlabelled data available on the internet, Active Learning is commonly used in this industry. 2. Image Classification Image classification involves classifying the images under specific categories. Active Learning is used in the Cost-Effective Active Learning (CEAL) model for the classification of images, which proposes to automatically extract and pseudo-annotate unmarked samples rather than using the standard approach of only considering the most interesting, insightful, and representative samples. 3. Object Detection Object detection is a technique utilized in computer vision that isolates things of interest in a given image. Object detection is not the same as image classification. For example, during image classification, an entire image is assigned to a single category, such as “busy road,” without any evidence to suggest otherwise. 4. Natural Language Processing NLP, or natural language processing, is a subfield of AI that can understand spoken language in its most natural form (text in a majority of circumstances). This category encompasses a wide range of activities, such as text completion, emotion identification, etc. Due to its enormous success in Computer Vision, Active Learning has also become widely employed for many NLP tasks during the past decade. 5. Audio Processing Spoken language identification (crucial in multilingual operations), audio synthesis, audio completion, etc., are all significant automated operations that fall under the umbrella of “audio processing.” Active learning can reduce human effort in supervising acoustic and language model training.  Active Learning is effective across multiple AI paradigms, including picture segmentation, scene identification, and other sectors like speech processing and NLP.

It is a research field where Machine Learning meets computer security to enable the adoption of ML techniques in adversarial environments like malware detection, biometric recognition, and spam filtering.  In an adversarial attack, images undergo very minute and inconspicuous changes so that the ML model misclassifies the image. Such attacks are almost impossible to detect, making security systems extremely vulnerable to malicious impersonation. Such attacks can also exploit the control systems in automatic vehicles, causing them to make dangerous mistakes while in use. Applications of Adversarial Machine Learning The prime purpose of AML techniques is to prevent the intentional manipulation of inputs so that the model makes inaccurate predictions and decisions. AML techniques also improve the security and reliability of applications like fraud detection, medical diagnosis, and autonomous vehicle controls.  AML is used in: 1. Anomaly detection AML can detect unusual behavior in anomaly detection systems by analyzing real-time data from machines. By studying the data, AML algorithms can identify anomalies that signify an impending attack or system malfunction. 2. Malware detection In malware detection systems, AML identifies fresh and unfamiliar malware with the help of behavioral patterns in AML algorithms detected in real time. 3. Intrusion detection AML algorithms analyze real-time network traffic to spot out unusual behavioral patterns and anomalies on computer networks. With AML, intrusion detection systems can sense impending cyber attacks. 4. Adversarial detection By gathering and analyzing real-time inputs from ML models, AML algorithms can detect and prevent adversarial attacks and malicious attacks. Generally, real-time AML applications are becoming increasingly important in the AI industry, with applications in many fields. With such significant advances in AI, ML models are becoming more secure, reliable, and suitable for high-stake applications.

Data labeling is the process of identifying and labeling samples of data used in Machine Learning. Labeling is especially critical when it comes to supervised learning, where the input and output data are classified into categories to facilitate an AI model’s future learning. Data tagging, annotation, moderation, categorization, etc., are just some of the components of an information labeling workflow. Some common types of data labeling are: 1. In-house data labeling  The best quality labeling is achieved through in-house data labeling, often performed by data engineers and scientists employed by the company. It is of great importance in the insurance or healthcare industries, as accurate labeling plays a significant role. 2. Crowdsourcing Crowdsourcing utilizes a huge group of freelancers who have signed up for a crowdsourcing platform to collect annotated data. 3. Outsourcing Data annotation can be outsourced to a company or a person, creating a compromise between in-house data labeling and crowdsourcing. Individuals can be evaluated on their knowledge of the subject before the work is given, which is a major benefit of outsourcing. Applications of Data Labeling Labeling data is an essential part of developing ML models. In order to aid an AI model in learning and making reliable predictions, it is labeled manually with meaningful tags. 1. Computer Vision Labeled data is a crucial component of computer vision research, which aims to teach computers to “see” their surroundings. In computer vision, data labeling is done to add relevant information as tags or annotations to raw data. In compute vision, data labels are created as digital outlines around specific objects in an image. This helps the computer understand the different portions of the image for classification, which in turn forms the basis for data processing by the ML models. 2. Natural Language Processing Data labeling is used to train NLP models to predict the different attributes that enable the algorithm to understand spoken or written language. Data labeling in NLP can be in the form of labeling utterances, the intent, and entities that represent real-time objects such as people, organizations, locations, values, etc. 3. Audio Annotation  In audio annotation, labels are used to distinguish the different sounds in an audio dataset and tag them with specific keywords. This type of annotation is quite critical during the development of AI-assisted applications such as chatbots, virtual assistants, voice recognition systems, etc.

AIES or Artificial Intelligence Enabled Services refers to business processes or services that are integrated with AI technologies to improve their capabilities and enhance their efficiencies. AI-enabled services typically leverage AI/ML algorithms, natural language processing and computer vision techniques to automate routine business tasks or deliver personalized experiences. Some examples of AI-enabled services AI-enabled services and applications can be found across industry verticals, including healthcare, finance, retail, marketing etc. Some common examples of AI-enabled services include: Virtual assistants AI-enabled virtual assistants include applications that are now commonplace, such as Amazon’s Alexa or Apple’s Siri. These applications are capable of understanding natural language instructions and voice commands and help with simple tasks such as answering queries, offering recommendations, setting reminders, booking reservations, etc. Chatbots Businesses are enabling AI-powered chatbots to engage with customers in real-time conversations, responding to their queries, enabling personalized experiences and guiding them in their online transactions. Personalized recommendations AI/ML algorithms can handle and process huge volumes of data and deliver key insights on customer preferences, online behavior, browsing habits and history. This information can be used by businesses to deliver personalized products, services or content recommendations to their clients. Fraud detection Thanks to their ability to recognize patterns and detect anomalies, AI-powered systems can easily detect frauds or threats in real-time. Businesses can thus employ these systems to safeguard their business transactions and critical data from fraudulent activities. Predictive analytics AI algorithms are capable of processing historical data, analyzing patterns and predicting future outcomes. Hence, AI-enabled systems are excessively applied in predictive analysis to speculate customer behavior, and in tasks such as demand forecasting, predictive maintenance, etc. Language translation and sentiment analysis AI-powered language translation uses natural language processing (NLP) and machine learning techniques for translating content or conversations between languages. These techniques analyze patterns in multilingual data to enhance translation accuracy. Further, NLP techniques can also be applied to perform sentiment analysis on user-generated content to evaluate the tone or emotion expressed in comments, reviews or feedback. The term AIES as Artificial Intelligence Enabled Services was invented by Opporture for the betterment of the AI industry. So please seek written permission before using this term. Related terms Machine learning Model Natural language processing

Anomaly detection involves the technique of identifying abnormal deviations in system behavior from established patterns. Any event that deviates significantly from the ordinary norm is flagged as an anomaly by anomaly detection systems. By way of illustration, if the average value of a given feature is 100 and its standard deviation is 10, then a value of 200 should raise red flags in an anomaly detection system. Anomaly Detection in Al & Its Applications Anomaly detection has several practical uses in AI, including bolstering system stability and protection. Some of the many ways anomaly detection is used in the field of artificial intelligence are as follows: 1. Cybersecurity One usage of anomaly detection is in cybersecurity, where it can be used for data such as network traffic, user behavior, and system logs. Anomalies can be used to detect harmful behavior like cyber-attacks or system breaches since they reveal deviations from the norm. 2. Predictive Maintenance  Machine and equipment sensor data (such as temperature, vibration, and pressure) can be monitored and analyzed for anomalies as part of Predictive Maintenance. Identifying out-of-the-ordinary behavior allows for anticipating equipment failures to organize repairs in advance. 3. Fraud Detection  Anomaly detection may be employed to spot suspicious activity in financial transaction data like credit card purchases. Anomalies are a useful indicator of potentially fraudulent activities like unlawful activities or money laundering because of the patterns they reveal. 4. Medical Diagnosis  Anomaly detection has diagnostic applications in the medical field, including identifying outliers in medical data like electrocardiogram (ECG) signals, magnetic resonance imaging (MRI), and genomic information. Potential health hazards can be detected, and preventative measures can be taken if abnormalities are identified. 5. Manufacturing Quality Control Anomaly detection may be utilized in manufacturing quality control to look for outliers in product quality data, including dimensions, weights, and colors. Potential flaws in the product can be found and fixed before it goes out the door if anomalies are detected. In conclusion, anomaly detection is an effective method that sees widespread use in the artificial intelligence sector.

Artificial General Intelligence (AGI) is a theoretical form of AI. It can understand and apply knowledge to multiple tasks in a way that reflects human adaptability, intelligence, and versatility. Unlike Narrow AI, which specializes in specific tasks, AGI uses its intelligence to address diverse and unexpected challenges without requiring task-specific programming. In short, AGI is conceptualized as AI with the same cognitive abilities as humans in various fields. What Are the Applications of Artificial General Intelligence? Although AGI systems aren’t available, Narrow AI systems have made themselves indispensable in ways that exceed human abilities in many applications. Some of them include: IBM’s Watson IBM’s supercomputer, Watson, is well-known for its advanced abilities in AI technology integration and complex calculations. The healthcare field relies on Watson’s analysis and interpretation of medical data for disease diagnosis and the creation of treatment plans. The supercomputer also interprets complex scientific data like the Big Bang theory. Expert Systems Expert systems are AI applications that can make decisions like a human expert. They are used in medical diagnosis, forecasting finances, and making customer recommendations. In healthcare, expert systems recommend patient’s medications by analyzing their symptoms, medical history, and available data. Automated cars AGI enables automated cars to navigate roads without human assistance by utilizing sensors, algorithms, and cameras to recognize oncoming vehicles and other obstacles on the road. The cars are programmed to interpret sensor data, identify the correct navigational routes and road signage, adhere to traffic rules, change lanes, turn, or stop when needed. ROSS Intelligence ROSS is an AI-driven legal research tool that assists lawyers, and legal researchers in finding relevant case laws, legal precedents, and statutes. The tool analyzes billions of text documents to answer complex legal questions within seconds. It relies on NLP to interpret queries and deliver the most relevant legal information, thereby streamlining complex legal processes. AlphaGo A product of DeepMind, AlphaGo is an AI program designed to play the game Go. The program became popular worldwide by defeating the world champion, Lee Sedol. It is programmed with extensive human and computer training, Machine Learning, and tree search techniques. AlphaGo’s success demonstrated AI’s potential for pattern recognition, strategic thinking, and adaptation in solving complex problems. GPT-3 and GPT-4 GPT 3 and 4 are immensely popular for their capabilities in generating human-like text for content creation, conversation tools, and more. These AI tools can do everything from creating essays and poems and composing emails to code generation. They are trained on extensive text data to generate relevant responses similar to human-written text. FAQs 1. How is AGI different from AI? The main difference between Artificial General Intelligence and Artificial Intelligence lies in their scope and capabilities. AI is a specialized system designed for specific tasks. It demonstrates proficiency in domains such as image recognition and NLP and works on predefined algorithms. Contrariwise, AGI is a theoretical concept that aims to replicate human cognitive abilities, adaptability, and understanding to execute multiple tasks. Hence, it is not task-specific like AI but can address unforeseen and diverse challenges. 1. Is General AI available? General AI or Artificial General intelligence does not exist. The currently available AI is often called Narrow AI and is characterized by special systems excelling in specific tasks. However, this AI lacks the adaptability and understanding capabilities of General AI. Developing an AI system that closely replicates human cognitive abilities is yet to be achieved because human cognition and learning aren’t easy to replicate. 3. What are the current applications of AI? Currently, AI is used in: Voice assistants like Alexa and Siri Customer service chatbots Recommendation engines used by platforms like Netflix and Google to promote content. Business Intelligence and analytical tools used for data visualization, sentiment, and data analysis. Facial and image recognition tools Deep learning models 4. What is Artificial SuperIntelligence (ASI)? ASI is a software-based system. It possesses superior cognitive abilities surpassing human intelligence across general knowledge, creativity, social and problem-solving. It can also replicate. Currently, ASI is unavailable and remains in a hypothetical AI state. 5. Is it possible to create AGI? This is a topic of intense debate in the realm of AI. Although AGI exists only in theories and speculations, it remains a long-term goal for ardent AI researchers. Significant challenges hinder the possibility of AGI despite the advances in overall AI. Even Machine Learning and NLP remain Narrow or specialized AI formats. They lack the adaptable intelligence required to create AGI. Related Terms Image Recognition  Facial Recognition  Machine Learning  Natural Language Processing  Model  Training Data

Artificial Neural Network (ANN) is an algorithm that processes and analyzes data like the human brain. The term “Neural” refers to how the algorithm is inspired by our central nervous system and modeled to mimic human learning methods. Artificial Neural Networks is similarly structured like that of an animal brain. It consists of multiple layers of connected units interwoven with non-linear activation functions. These units are called Artificial Neurons. The ANNs are random algorithms. Hence, they may train various models every time they run with the same data, resulting in varying results. Applications of Artificial Neural Networks An Artificial Neural Network is an interconnected network of artificial nodes or neurons that process and transmit data like the human brain. ANN is indispensable in Artificial Intelligence, enabling machines to process data and make decisions based on it. ANN is used in: 1. Speech and image recognition ANN trains networks to recognize and categorize images and sounds using a larger audio and visual data dataset. 2. Fraud detection Using historical fraud data, networks learn to recognize fraudulence patterns, enabling more accurate and reliable fraud detection as more data is added. 3. Predictive modeling ANNs use historical data to train networks to identify trends and patterns and make near-accurate predictions about what will happen in the future or how people will behave. 4. Handwriting recognition ANN algorithms are trained to recognize handwritten characters such as alphabets and numbers. 5. Signature verification Artificial neural networks can be trained to identify if signatures are genuine or forged during the verification process. 6. Face recognition Using a person’s image, ANN compares the facial features with an existing database of faces to find a match that identifies the person. The pre-existing database is a collection of processed images that are used to train neural networks for face recognition. 7. Medical diagnosis In the medical field, ANNs help doctors diagnose diseases and understand complex clinical information across multiple medical applications like electronic signal analysis, radiology, detection of cancer cells, and analysis of scans and x-rays. The overall purpose of Artificial Neural Networks is to combine multiple ML algorithms to process large amounts of data. Even in the absence of specific instructions, these neural networks can learn to perform tasks by observing examples.

In Machine Learning, Attributes are data objects such as features, fields, and other variables. These attributes are predictors that influence results in predictive models. The attributes in descriptive models are bits and pieces of data evaluated for groupings and connections. Examples of AI Attributes 1. Object Recognition Real-time objects are identified using attributes such as color, shape, size, texture, etc. By analyzing such attributes, the algorithms can respond promptly and avoid collisions. For example, object recognition algorithms in automatic, self-driving cars can analyze and identify pedestrians, vehicles, and obstacles on the road. 2. Fraud detection Fraud detection algorithms on online payment systems use attributes like location, transaction amount, and frequency to spot suspicious transactions. 3. Voice recognition To recognize and respond to voice commands, voice recognition algorithms detect attributes like tone, pitch, and accent. These attributes are especially used in voice-activated devices and virtual assistants. 4. Sentiment analysis Sentiment analysis can aid in better decision-making regarding products and services. Sentiment analysis algorithms use attributes such as tone, language, vocabulary, etc., to categorize positive and negative sentiments. These algorithms are used in customer service and social media monitoring apps. 5. Facial recognition Security and access control systems operate with facial recognition algorithms that identify faces through specific facial features, expressions, and emotions. Such attributes enable these systems to recognize faces and remember people.