40 Must-Know Artificial Intelligence Terms

AI Robotics
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Science Fiction Reality

 

No longer primarily associated with science fiction, Artificial Intelligence is a field which has been increasingly attracting attention for a variety of reasons. Existing applications of AI have already cemented our trust in its power and potential (and cost-effectiveness!), but there is still uncertainty as to the exact ways and degrees to which it will impact various industrial sectors as well as our everyday life. Moreover, pondering the promise of AI is FUN! Which is undoubtedly a factor in its elevation to “hot topic” and “buzzword” status in telecoms.

One thing is for sure: AI, Machine Learning and Robotics are already a reality and we will be seeing much more of their creative and practical applications in days to come. One unique trait of this sphere is its potential to grow in impact and omnipresence exponentially, thanks to the nature of its premise – to continuously learn, adapt and predict!

Seeing as there is no escaping the ubiquity of AI, here is a blog post which aims to serve as a quick guide to terms and acronyms related to the field of Artificial Intelligence. It is the first installment in a series of articles, dedicated to collecting, classifying and demystifying topical technology and telecoms buzzwords and terminologies.

  1. Theory of AI
  2. Chatbots
  3. Machine Learning
  4. Robotics

Theory

 

Theory of Artificial Intelligence

 

  • Algorithm: In general, an algorithm is a set of rules or specific mathematical steps that are used to solve a problem. In AI, algorithms are used in machine learning to make predictions from the data sets they analyse. For example, social media platforms like Facebook use algorithms to generate personalised newsfeeds based on the user’s past behavior.[1]
  • AI (Artificial Intelligence) / Machine Intelligence / Computational Intelligence: The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.[2] The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to learn(the acquisition of information and rules for using the information[3]), to reason(to usе the rules to reach approximate or definite conclusions), discover meaning, generalize, self-correct.[2]
  • IA (Intelligence Augmentation) / Augmented Intelligence: Аn alternative conceptualization of artificial intelligence that focuses on AI’s assistive role, emphasizing the fact that it is designed to enhance human intelligence rather than replace it.[4]
  • Reasoned VS Intuitive AI
    • Artificial Intuition: A theoretical concept which refers to the capacity of an artificial object or software to function with the factor of consciousness known as intuition; a machine-based system that has some capacity to function analogously to human intuition.[5]  New advances in artificial intelligence (AI) technologies propel the artificial intuition niche as one of the most important trends in the industry, according to tech experts. Artificial intuition and cognition through algorithms have become part of machine intelligence (MI).[6]
  • GOFAI VS Embodied AI
    • GOFAI (Good Old Fashioned Artificial Intelligence): AI divides roughly into two schools of thought: GOFAI (Good Old Fashioned Artificial Intelligence) and New AI. GOFAI mostly involves methods now classified as machine learning, characterized by formalism and statistical analysis. This is also known as conventional AI, symbolic AI, logical AI or neat AI.[7] GOFAI tried to reduce human thought to the manipulation of symbols, such as language and maths, which could be made comprehensible to computers.[8]
    • Embodied Cognition: Related to the theory that a machine may need a human-like body to think and speak as well as a human being. The embodied approach to AI has been given several names by different schools of researchers, including: Nouvelle AI (Brooks’ term), Situated AI, Behavior based AI and Embodied cognitive science. [9]
  • Narrow VS Strong AI
    • ANI (Artificial Narrow Intelligence) – Weak AI: Narrow AI, also known as Weak AI,  refers to a computer’s ability to perform a single task extremely well. The rapidly growing field of bots serves as an excellent example of ANI at work.[10]
    • AGI (Artificial General Intelligence) – Strong AI: General AI, also known as Strong AI or True AI, is an artificial intelligence construct that has mental capabilities and functions that mimic the human brain.[11] Its goal is to develop artificial intelligence to the point where the machine’s intellectual capability is functionally equal to a human’s.[12]
    • ASI (Artificial Super Intelligence) – Super AI: According to University of Oxford scholar and AI expert Nick Bostrom, when AI becomes much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills, we’ve achieved Artificial Super Intelligence.[13]
      Today, most experts would agree that ANI is still maturing, AGI is at least a couple decades away from being perfected, and ASI is even farther off.[10]
      Narrow AI is the only form of Artificial Intelligence that humanity has achieved so far.[13]
  • SI (Synthetic Intelligence): An alternative term for Artificial Intelligence, proposed by John Haugeland in 1985. SI recognizes that although the capacity for software to reason may be manufactured, it is nonetheless real intelligence and not just an imitation of how human beings acquire and apply knowledge and skill.[14]
  • Computer Vision: The ability for a machine to acquire, process, analyse and understand digital images and feedback relevant information e.g. identifying a fruit and conveying information about its properties. It seeks to perform the same tasks that the human visual system can, making it part of the AI field.[15]
    • Facial Recognition (Face Recognition): A biometric method of identifying an individual by comparing live capture or digital image data with the stored record for that person.[16]
    • Sentiment(al) Analysis (Opinion Mining or Emotion AI): Refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. It aims to aims to determine the attitude of a speaker, writer, or other subject with respect to some topic or the overall contextual polarity or emotional reaction to a document, interaction, or event.[17]
  • Existential Risk (of humans): – The study of the threat to humans and humanity which artificial intelligence (and other technological advances) can potentially pose. Concerned with the extent to which AI can surpass human intelligence and become difficult to control.[1]
  • Turing Test: the Turing Test is a method for determining whether or not a computer is capable of thinking like a human. According to this kind of test, a computer is deemed to have artificial intelligence if it can mimic human responses under specific conditions. In Turing’s test, if the human being conducting the test is unable to consistently determine whether an answer has been given by a computer or by another human being, then the computer is considered to have “passed” the test.[18]

 

Chatbots

 

  • Chat Ops: ChatOps is the use of chat clients, chatbots and real-time communication tools to facilitate how software development and operation tasks are communicated and executed.[19]
  • HCI (Human Computer Interaction): An interdisciplinary field focused on the interactions between human users and computer systems, including the user interface and the underlying processes which produce the interactions.[20]
  • Chatterbot (Chatbot): A Chatbot is an artificial intelligence program that helps users interact with a computer via a chat interface. It uses machine learning and natural language processing technologies to mimic human conversation and responds to a user as a conversational partner. Chatbots are mostly used in customer service and marketing sectors through messaging apps and social media platforms for instant messaging and conversation with the users.[1]
  • Cognitive Chatbots: Self-learning intelligent agents that are driven by data, powered by artificial intelligence, and personalized through natural-language conversations with users.[21] Cognitive chatbots will allow much more natural conversations with humans, deepening the ability to persuade humans, but also increasing the value they create for humans.[22]
  • Cognitive Computing: Cognitive computing refers to computing that simulates the thought processes of humans. Cognitive computing utilizes self-learning or deep-learning algorithms backed by natural language processing, artificial intelligence and extensive data resources (“Big Data”) to operate in a manner similar to the way the human brain thinks and works when attempting to solve problems.[23]
  • Artificial Personality: A collection of characteristics, tendencies, behavioral quirks and backstory related to some AI simulation of a human, such as a chatbot or a digital assistant. Another implementation of artificial personalities is in robotics. Personality allows a robot or a software system to interact with people emotionally as well as on a logical level.[24]
  • Digital Assistant Intelligent Assistant / Virtual Assistant / Virtual Agent: An application program that can understand natural language and complete electronic tasks for the end user. Today’s digital assistants are programmed with artificial intelligence, machine learning and voice recognition technology.[25]
    • IVAs (Intelligent Virtual Assistants): An engineered entity residing in software that interfaces with humans in a human way. This technology incorporates elements of interactive voice response and other modern artificial intelligence projects to deliver full-fledged “virtual identities” that converse with users.[26]
    • NLP (Natural Language Processing): A field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora.[27]
    • NLU (Natural Language Understanding): Deals with machine reading comprehension. There is considerable commercial interest in the field because of its application to news-gathering, text categorization, voice-activation, archiving, and large-scale content-analysis.[27]NLP allows developers to organize and structure knowledge and perform various tasks, including translation, speech recognition, and topic segmentation, named entity recognition, automatic summarization, and sentiment analysis and relationship extraction.[28]
    • IVR (Interactive Voice Response Systems): An IVR is an automated telephony system that interacts with callers, gathers information and routes calls to the appropriate recipient.[29] In the customer contact space, AI applies higher-level “thinking” to traditional IVR. This automated solution empowers natural speech recognition throughout the call and an intelligent brain on the back end to better interpret and even predict user responses. As a result, customer intentions are anticipated more effectively and directly, bringing greater satisfaction and less likelihood of zeroing out to speak to a live agent.[30]
  • Conversational AI (Dialogue Management): The ability to conduct a natural conversation with a user. The idea behind conversational AI is to have a computer program respond to the user as close as possible to a real conversation.[31]
  • Polite Cell Phone: A mobile telephone with built-in intelligence so that it adapts to its current environment and behaves appropriately (acquires a sense, over time, of when the phone should respond to incoming calls and when it should remain quiet).[32]

 

Machine Learning

 

  • Machine Learning: A type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range.[33]
  • Supervised Learning: A type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions. The training dataset includes input data and response values(i.e. both the input and desired output are provided). From it, the supervised learning algorithm seeks to build a model that can make predictions of the response values for a new dataset.[34]
  • Unsupervised Learning: A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labelled responses.[35] In contrast to Supervised Learning, the output is not provided.
  • Deep Learning (Deep Neural Networking / Advanced Machine Learning): An artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in AI that has networks which are capable of learning unsupervised from data that is unstructured or unlabelled.[36]
  • Deep Learning Agents: Any autonomous or semi-autonomous AI-driven system that uses deep learning to perform and improve at its tasks. Systems (agents) that use deep learning include chatbots, self-driving cars, expert systems, facial recognition programs and robots.[37]
  • Embedded Deep Learning: Implies the use of computer systems or devices that serve a specific function within a larger device. The prospect for embedded deep learning revolves around devices that are specifically designed for deep learning technology.[38]
  • ANNs (Artificial Neural Networks): A set of algorithms, modelled loosely after the human brain, that is designed to recognize patterns. The algorithms interpret sensory data through a kind of machine perception, labelling or clustering raw input.[39]
  • Pattern Recognition: – Pattern recognition is a branch of machine learning that deals with recognizing visual and sound patterns in data. It extracts and classifies data based on a priori knowledge and statistical information.[1]
  • (Automatic) Image Tagging: Describes identifying objects, categories, and even human gestures, and organising this image data so it might be used in ongoing machine learning algorithms.[15]

 

Robotics

 

  • Robotics: The field of computer science and engineering concerned with creating robots, devices that can move and react to sensory input. Robotics is one branch artificial intelligence.[40]
  • Software Robot: Аn AI (artificial intelligence) system that runs on a host device rather than existing as a standalone machine.[41] Software Robots are increasingly used to perform a variety of repetitive tasks traditionally executed by humans. These next-generation robots automate business processes, and they are fast, accurate, and highly scalable at a known cost.[42]
  • RPA (Robotic Process Automation): An emerging form of clerical process automation technology based on the notion of software robots or Artificial Intelligence (AI) workers.[43]
  • Robot Economy: A scenario in which most of the labor required to sustain human life is automated.[44]

 

References:
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[2] Copeland, B.J. Encyclopædia Britannica. (2017, January 12). artificial intelligence (AI).
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[44] Rouse, Margaret. TechTarget, SearchCIO.com. (2017, May). Robot economy [Web log post].
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