The essential AI glossary for marketers (2023)

The field of AI - which blends elements of computer science, cognitive science, psychology, game theory and a number of other disciplines - comes with a huge variety of technical terms that can be tricky for outsiders to understand. This guide can help get you started as you learn to speak the language of AI.

There’s been a lot of recent chatter about artificial intelligence (AI) lately and how it could either deliver a work-free paradise or escape our control and quickly escalate into a nightmare (the likes of which have been captured in countless Hollywood blockbusters like 2001: A Space Odyssey and the Terminator franchise). Regardless of where you happen to fall on the utopia-dystopia spectrum, one thing is by now abundantly clear: AI is here to stay – and it seems almost certain to transform civilization to a degree that many can scarcely imagine.

That being the case, it’s important for all of us – including marketers, whose industry is already feeling the effects of the AI revolution – to have at least a basic understanding of what AI is and how it works. That starts with understanding some of the languages that’s spoken in this strange technological territory.

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Here are some critical AI terms that you need to know [we will be updating this glossary on a regular basis, so we recommend checking in on it routinely]:


A/B testing: A form of randomized experimentation wherein two variants of a particular model, A and B, are tested by a human subject to determine which of them performs better than the other.

Algorithm: A set of instructions or rules used - often by a computer - to solve a set of problems, execute calculations or process data.

AlphaGo: An AI model developed by DeepMind and designed specifically to play the ancient Chinese board game Go. In 2015, AlphaGo became the first AI model to defeat a professional human Go player (Chinese-born Fan Hui). It beat Lee Sedol, a then-professional Go player from South Korea, the following year. Sedol retired from playing professional Go in 2019, telling the South Korean media outlet Yonhap News Agency that AI specializing in the game of Go “is an entity that cannot be defeated.”

Artificial general intelligence (AGI, also sometimes referred to as Strong AI): An AI program with an intellectual ability that’s comparable to that of an average adult human. AGI, in other words, would hypothetically (we have yet to build one) be able to solve problems across a vast range of categories, just as a human brain can.

Artificial narrow intelligence (ANI, also sometimes referred to as Weak AI): An AI program built to perform a single, narrow function, such as playing chess or responding to customer service questions. All of the AI programs that have been developed to date fall into the category of ANI.

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Artificial neural network (ANN): A synthetic system, roughly modeled on the architecture of organic brains, comprised of layers of artificial neurons.

Artificial superintelligence (ASI): First postulated by Oxford philosopher Nick Bostrum, “Superintelligence” is a theoretical intellect – artificial or organic – which is more advanced than that of humans. An ASI could have only a slightly higher IQ score than the average human being, or it could be vastly, unfathomably more intelligent, comparable to the difference in cognitive ability between an ant and Nobel Laureate Roger Penrose.

Association rule learning: A method of unsupervised and rule-based machine learning aimed at identifying commonalities or associations between variables in a dataset.

Automatic speech recognition (ASR – also known as computer speech recognition, speech-to-text or simply speech recognition): A machine’s capability to recognize human speech and then convert it into text. The iPhone dictation feature, for example, uses ASR.


Backpropagation: The process by which a neural network informs itself that it has made a predictive error, and subsequently corrects that error. The word “backpropagation” means roughly responding to flawed information by sending new information back in the direction of the source of the error. Sometimes colloquially referred to simply as “backprop” or “BP.”

Bayes’ theorem: Named after the 18th-century statistician Thomas Bayes, this theorem is a mathematical formula that can be used to determine what’s known as “conditional probability” – that is, the likelihood of a particular outcome based on one’s prior knowledge of a previous result that occurred in similar conditions.

Black box: A metaphor that’s invoked to describe a system whose inner workings are hidden and ultimately mysterious to the system’s creator (or creators). AI is sometimes described as a “black box” because models will often behave and evolve in ways that even the system’s programmers cannot fully understand or predict.

Central processing unit (CPU): The most important component of a digital computer. The CPU – sometimes referred to as the “brain” or the “control center” of a computer – is the locus of every digital computing system’s memory, arithmetic capabilities (adding, subtracting, multiplying and dividing), and the orchestrator of its operating system. The CPU of modern computers is built upon a microprocessor.

Chatbot: An AI-based computer program that leverages natural language processing (NLP) to field customer service questions in automated verbal or text-based responses that simulate human speech.

ChatGPT: An AI-powered chatbot launched by San Francisco-based startup OpenAI in November of 2022. ChatGPT uses NLP to simulate human conversation. According to OpenAI’s website, ChatGPT can “answer follow-up questions, admit its mistakes, challenge incorrect premises and reject inappropriate requests.”

Computer vision: A branch of AI that’s concerned with enabling machines to understand and respond to information derived from visual inputs - such as images and video - in a manner similar to that of the visual system in the human brain.

Convolutional neural network (CNN): A subset of artificial neural networks, commonly used in machine visual processing, which can enable an AI model to differentiate and analyze various components within an image.

Cooperative inverse reinforcement learning (CIRL): Coined by AI researchers Stuart Russell, Pieter Abbeel and others, cooperative inverse reinforcement learning (CIRL) is a hypothetical methodology for solving the so-called alignment problem, in which an AI model is designed to carry out an objective function that’s valuable to humans without knowing from the outset what that objective function is. Rather, the machine’s ability to perform the given task is enhanced through “behaviors such as active teaching, active learning and communicative actions that are more effective in achieving value alignment,” according to the paper from Russell and colleagues which first defined CIRL.

Dall-E 2: A deep learning model developed by OpenAI and released in 2022 which generates images based on the input of text-based natural language prompts. Its predecessor is Dall-E. The name of both models is a play on both the name of the title character of the Pixar film Wall-E and the surname of the 20th-century surrealist painter Salvador Dalí.

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The Dartmouth Summer Research Project on Artificial Intelligence: A conference - colloquially referred to as the Dartmouth Workshop - which began in mid-1956 at Dartmouth College and is widely considered to be the event that gave birth to AI as a field of research. The conference was organized by Marvin Minsky, John McCarthy, Nathaniel Rochester and Claude Shannon.

Deep Blue: An AI program developed by IBM, the sole purpose of which is to play chess. In 1997, it made history by becoming the first intelligent machine to beat chess master Gary Kasparov in a chess match.

Deep learning (also known as deep reinforcement learning): An extension of machine learning based on the premise that machine learning models can be made more intelligent if they’re provided with vast quantities of data. Deep learning requires neural networks of at least three layers; the more layers it’s equipped with, the better its performance will be.

Deepfake: An AI-generated piece of media depicting a real person (or a real person’s voice, in the case of a deepfake audio clip). Deepfakes can be difficult to detect and are often made and spread around the internet in an effort to tarnish someone's reputation or spread some kind of misinformation.

DeepMind: An artificial intelligence research laboratory based in London and founded in 2010 by Demis Hassabis, Shane Legg and Mustafa Suleyman. The company was acquired by Google in 2014 and is now a wholly-owned subsidiary under Alphabet Inc.(Google’s parent company). DeepMind describes itself on its website as “a team of scientists, engineers, ethicists and more, committed to solving intelligence, to advance science and benefit humanity.”

Decision tree: An imagistic illustration of the process of arriving at a decision, wherein each “branch” represents a particular course of action. Decision trees start at a “root node” (which consists of all the relevant data that’s being analyzed), branch off into “internal nodes (also known as “decision nodes”) and then terminate in “leaf nodes” (also known as “terminal nodes,” which represent all the possible outcomes of a given decision-making process).

Here’s a simple example of a decision tree rooted in the question of whether or not you should go outside to play soccer:

The essential AI glossary for marketers (1)

Entropy: In the context of machine learning, “entropy” refers to the degree of randomness, disorder and unpredictability within a dataset that’s being processed by a machine learning system. More broadly, the concept of entropy is commonly associated with the second law of thermodynamics, which essentially holds that the degree of disorder or randomness within a system will never decrease over time – it can only remain constant or increase.

Embodied agent (or “interface agent”): An entity which interacts and is reciprocally responsive with its environment through sensory-motor functions. A human being, a dog, a Boston Dynamics robot and a virtual avatar of a human are all examples of embodied agents.

Foom: An onomatopoeic word that’s supposed to represent the sound of an explosion, “foom” is used to describe a hypothetical scenario in which AI suddenly and irrevocably enters the realm of superintelligence and escapes human control.

Game Theory: A mathematical formula, first postulated by mathematician John von Neumann and economist Oskar Morgenstern in 1944, relating to the dynamic interaction between two or more rational agents seeking their own gains within a parameterized (rule-governed) framework. Game Theory defines a broad set of games, including zero-sum and nonzero-sum.

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Generative adversarial network (GAN): A machine learning methodology wherein two neural networks compete with one another in a zero-sum game – that is, one network’s loss translates to the other’s gain, and vice versa. Both networks are provided with a dataset, and one network called the “generator” is essentially tasked with tricking the other – the “discriminator” – into believing that the new information that’s being generated is part of the original dataset. For example, the generator might generate a new image of a human face, based on many images of real human faces, at which point the discriminator will try to determine whether or not the new image is real or manufactured. This contest will continue until the generator succeeds at tricking the discriminator with the majority (more than 50 percent) of its original output. GANs were invented in 2014 by American computer scientist Ian Goodfellow, who has henceforth been dubbed “The GANfather.”

GPT-3: Generative Pre-Trained Transformer 3 (GPT-3) is an open-source large language model developed by OpenAI and released in 2020. The model is the framework for the viral chatbot ChatGPT and is able to generate text responses in natural language based on text-based prompts.

Hallucination: In an AI context, the term “hallucination” refers to any kind of output from an AI model which is seemingly inconsistent with its training data. A hallucinating AI-powered chatbot, for example, might confidently and falsely insist that there are around 5.7tn stars in the Milky Way galaxy, even though it was not trained using any astronomical data.

Human-in-the-loop (HITL): A methodology deployed in some machine learning models wherein at least one human programmer provides feedback to the model (during testing or training) to improve the model’s performance. Ideally, HITL results in a positive feedback loop that enhances the intelligence of both machines and humans.

Hyperparameter: An overarching, predominant parameter, established by a human programmer, which determines the parameters that an AI model will establish and hone by itself during its training process.

Inverse reinforcement learning (IRL): First described in a paper by AI researchers Stuart Russell and Andrew Ng, inverse reinforcement learning (IRL) is a methodology which, to put it simply, seeks to design machines that are capable of determining an agent’s (usually a human’s) goals and rewards by analyzing its behavior. An example that’s commonly invoked to describe the benefits of IRL is autonomous vehicles (AVs): Rather than trying to train an AV with every conceivable situation that it might encounter on the road - which is virtually impossible, given the fact that such possibilities are combinatorially explosive arnd therefore limitless - IRL could be leveraged to generate a comprehensive dataset of human driving behaviors and then instruct the algorithm to infer the correct course of action through a given scenario by following patterns that it detects from within that dataset.

The King Midas problem: Alluding to the Greek myth of King Midas - who had the power to turn everything he touched into solid gold, including his food and his daughter - this problem poses a crucial question related to AI research and Stuart Russel’s famous “alignment problem”: How can we be sure that an intelligent machine’s objective function is actually one which will serve the long-term best interests of human beings? In a famous thought experiment first posed by the philosopher Nick Bostrom, for example, we can imagine an AI system whose sole purpose is to create paperclips. At some point the AI decides to eliminate human beings, because they could potentially interfere with its mission, and also because it figures that the atoms in their bodies would be put to better use as raw materials for the manufacture of more paperclips. Eventually, it expands and embarks on a mindless mission to turn the entire cosmos into paperclips. The point is that we as human beings don’t always have a firm grasp on what it is that we ultimately want - and very often what we think we want ends up being bad for us.

Machine learning: A subdiscipline of artificial intelligence that, using statistical formulas and data, enables computers to progressively improve their ability to carry out a particular task or set of tasks. Crucially, a computer leveraging machine learning does not need to be explicitly programmed to improve its performance in a particular manner – rather, it’s given access to data and is designed to “teach” itself. The results are often surprising to their human creators.

Machine translation (MT): An automated process that leverages AI to translate text or speech from one language into another.

Meta-learning: In the context of AI, “meta-learning” (also sometimes described as “learning to learn”) refers to a model’s capacity to improve its ability to learn over time. Humans are also meta-learners, because we’re able to deploy a variety of strategies - such as watching and emulating others - which can gradually turn us into more effective learners, and which can improve our overall ability to navigate through the world.

Microprocessor: A CPU for digital computing systems contained within a single integrated circuit (also known as a microchip, hence the prefix in the word “microprocessor”) or a small grouping of integrated circuits. Intel introduced the world's first microprocessor, dubbed the 4004, in 1971.

Midjourney: A research lab that launched a text-to-image AI model by the same name in open beta in 2022.

Model drift: The tendency for the performance of an AI model to degrade over time as its external environment changes, thereby also causing the relationship between input and output variables to change.

Moore’s Law: A principle, based on an observation usually attributed to former Intel CEO Gordon Moore, which holds that the number of transistors that can be contained within an integrated circuit (i.e., a microchip) doubles roughly every two years.

Natural language processing (NLP): A branch of artificial intelligence – that also blends elements of linguistics and computer science – aimed at enabling computers to understand verbal and written language in a manner that imitates the human brain’s language-processing capability.

OpenAI: A non-profit AI research lab founded in 2015 by Sam Altman, Elon Musk and others. As its name suggests, the original foundational goal of OpenAI was to collaborate with other organizations in the field of AI and to open-source its research. In 2019, the organization launched a “capped profit” subsidiary called OpenAI Limited Partnership (OpenAI LP). (Musk has lamented this decision on Twitter.)

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Parameter: A variable within the process of training an AI model which can be adjusted by the model in order to hone its ability to produce a particular output using a given dataset.

Pattern recognition: An automated process whereby a computer is able to identify patterns within a set of data.

Prior probability (also sometimes referred to simply as a prior): A term used in the field of Bayesian statistics to refer to the assigned likelihood of an event before (prior to) additional (posterior) information necessitates the revision of that likelihood.

Reinforcement learning (RL): The process of teaching machine learning models to make optimal decisions within a dynamic environment. When using RL, a programmer will often present a machine learning model with a game-like situation in which one outcome is preferable to others. The machine then proceeds to experiment with different strategies and the programmer will “reinforce” the desired behavior with rewards and discourage other behaviors through penalties.


Reinforcement learning from human feedback (RLHF), or reinforcement learning from human preferences: Like traditional reinforcement learning (RL), Reinforcement learning from human feedback (RLHF) aims to fine-tune the performance (or the “policy”) of a machine learning model by rewarding (i.e., reinforcing) certain actions. Unlike RL, however, RLHF involves human beings in the process of choosing which of the model’s actions should be rewarded. OpenAI’s ChatGPT, for example, was trained using RHLF.

Self-supervised learning: A branch of machine learning wherein an AI model is provided with unlabeled data and is allowed to label the data according to its own pattern recognition capabilities. A self-supervised algorithm will then use those initial labels as it continues to interpret subsequent iterations of data input.

Semi-supervised learning: A branch of machine learning which, as the name suggests, blends elements of both supervised learning and unsupervised learning. Semi-supervised learning is based on the input of some labeled data and a higher quantity of unlabeled data, the goal being to teach an algorithm to categorize the latter into predetermined categories based on the former, and also to allow the algorithm to identify new patterns across the dataset. It is widely considered to be a kind of bridge, connecting the benefits of supervised learning with those of unsupervised learning.

Supervised learning: A branch of machine learning based on the input of clearly labeled data and aimed at training algorithms to recognize patterns and accurately label new data.

Stochastic: A mathematical term referring to a system’s tendency to produce results that are unpredictable. (Roughly synonymous with “probabilistic,” “indeterminable” and “random.”) Many AI algorithms are programmed to incorporate some degree of randomness into their learning processes and are therefore described as stochastic. The results of a deterministic system, in contrast, can reliably be predicted beforehand.

TensorFlow: An open-source platform, developed by Google, designed for the management of machine learning and AI systems.

Turing test: A blinded experiment – invented by and named after 20th-century mathematician Alan Turing – where a human subject interacts with an artificially intelligent machine and asks it a series of questions. If the human interlocutor is unable to say definitively whether the responses are being generated by a human or an AI, the latter has passed the Turing Test.

Uncanny valley: A theoretical concept, first postulated by roboticist Masahiro Mori in 1970, which refers to an eerie, uncanny quality that will be perceived by a human being interacting with an artificial entity that closely (though imperfectly) resembles another human.

Unsupervised learning: A branch of machine learning which is based upon the input of unlabeled data. In contrast to supervised learning, unsupervised learning allows an algorithm to create its own rules for identifying patterns and categorizing data.

Value alignment problem: Coined by computer scientist Stuart Russel, the phrase “value alignment problem” – or simply “alignment problem” – refers to the difficulties that come with ensuring that intelligent machines share the same values and goals as their human programmers. This problem has spawned a subfield of AI and machine learning called “alignment research.”

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How AI can be useful for marketers? ›

AI marketing can help you deliver personalized messages to customers at appropriate points in the consumer lifecycle. It can also help digital marketers identify at-risk customers and target them with information that will get them to re-engage with the brand.

What was the advice that Andrew gives to large companies on how to incorporate AI into their company? ›

In his last point, Mr. NG gives advice on how to incorporate AI talent to the organization. Given that AI profiles are scarce, he recommends to create a unified AI unit across the organization, so different BUs do not fight for these scarce resources.

How should we define AI answers? ›

Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment.

Under which of the 4 AI classes the AI senses read and write is classified? ›

Expert-Verified Answer. The artificial intelligence " think" is classified under theory of mind.

What is an example of using AI in marketing? ›

Starbucks is one example of a brand using its loyalty card and mobile app to collect and analyze customer data. They announced plans for personalization back in 2016. Since then, they've built quite the app experience. It records purchases, including where they are made and at what time of day.

How to use AI in digital marketing? ›

Digital marketers are using AI to identify and market to consumers with similar preferences. The more merchant data the machine learning software receives, the better its predictions. Pet wellness online retailer Finn is casting a wider net to reach new customers without overspending.

What are two 2 possible benefits to businesses of implementing artificially intelligent systems in today's world? ›

save time and money by automating and optimising routine processes and tasks. increase productivity and operational efficiencies. make faster business decisions based on outputs from cognitive technologies. avoid mistakes and 'human error', provided that AI systems are set up properly.

How AI will power the future of successful content marketing? ›

The Impact of AI on Content Marketing Strategy

For example, AI can help content marketers to better understand their target audience by analyzing data on customer demographics, interests, and online behaviors. This information can be used to create more targeted and effective content marketing campaigns.

What are the four approaches to AI? ›

There are four types of artificial intelligence approaches based on how machines behave - reactive machines, limited memory, theory of mind, and self-awareness.

What is the most advanced AI? ›

GPT-3 was released in 2020 and is the largest and most powerful AI model to date. It has 175 billion parameters, which is more than ten times larger than its predecessor, GPT-2.

What are the 5 senses of AI? ›

Sensory AI is learning through sensory inputs: information from the five human senses, vision, hearing, smell, taste, and touch.

What are the 5 main groups of AI? ›

We can broadly group the fields of application of AI into 5 main macro-areas:
  • Text AI.
  • Visual AI.
  • Interactive AI.
  • Analytic AI.
  • Functional AI.

Are there 4 basic AI concepts? ›

For those of us who like to look under the hood, there are four foundational elements to understand: categorization, classification, machine learning, and collaborative filtering.

What are three real life example of AI? ›

Apple's Siri, Google Now, Amazon's Alexa, and Microsoft's Cortana are one of the main examples of AI in everyday life. These digital assistants help users perform various tasks, from checking their schedules and searching for something on the web, to sending commands to another app.

What are the disadvantages of AI in marketing? ›

4 Cons of AI in Marketing
  • Machines can't replace human connection. While AI can personalize marketing campaigns using up-to-date data, this doesn't remove human connections' importance. ...
  • AI predictions and analyses can sometimes be wrong. ...
  • They require huge sets of data and human intervention. ...
  • AI lacks human creativity.
Mar 6, 2023

How is AI used in marketing analytics? ›

How is AI used in marketing analytics? A. AI in marketing analytics helps analyze vast data, identify patterns, and forecast trends to optimize campaigns, improve targeting, personalize content, and enhance customer engagement, ultimately increasing ROI.

How many marketers use AI? ›

More than 80% of industry experts integrate some form of AI technology into their online marketing activities.

How do you integrate AI into a business? ›

  1. Get Familiar With AI. ...
  2. Identify the Problems You Want AI to Solve. ...
  3. Prioritize Concrete Value. ...
  4. Acknowledge the Internal Capability Gap. ...
  5. Bring In Experts and Set Up a Pilot Project. ...
  6. Form a Taskforce to Integrate Data. ...
  7. Start Small. ...
  8. Include Storage As Part of Your AI Plan.

How small businesses can benefit from AI? ›

Small businesses can use the power of AI to create personalized marketing campaigns, collecting and analyzing such customer data as behavior, preferences, and demographics to create marketing messages that resonate with customers as individuals—just as a streaming TV service can use AI algorithms to offer personalized ...

What is the positive impact of AI on business? ›

Artificial intelligence can help: Automate routine tasks, freeing up employees to focus on more creative and strategic work. Make better decisions by providing accurate and timely data analysis. Improve the customer experience by personalizing interactions and providing more tailored recommendations and support.

What is the biggest advantage of artificial intelligence? ›

One of the biggest advantages of Artificial Intelligence is that it can significantly reduce errors and increase accuracy and precision. The decisions taken by AI in every step is decided by information previously gathered and a certain set of algorithms. When programmed properly, these errors can be reduced to null.

Why AI is so important in the industry today? ›

Agility and competitive advantage

Thanks to machine learning and deep learning, AI applications can learn from data and results in near real time, analyzing new information from many sources and adapting accordingly, with a level of accuracy that's invaluable to business. (product recommendations are a prime example.)

What are the advantages and disadvantages of AI in business? ›

The advantages range from streamlining, saving time, eliminating biases, and automating repetitive tasks, just to name a few. The disadvantages are things like costly implementation, potential human job loss, and lack of emotion and creativity.

How AI is changing the marketing industry? ›

Right now in the marketing world, AI works as machine learning. Imagine if a machine can watch two paid search advertisements perform in real time and funnel more budget towards the ad that performs better. Well, it can. This makes plenty of sense, but that lack of human insight can make a real difference.

How AI is shaping the future of marketing? ›

AI is revolutionizing how marketers approach digital campaigns, from leveraging predictive analytics in order to improve customer segmentation to using machine learning algorithms to optimize ad targeting.

How will AI impact marketing in 2023? ›

AI has already been widely used in many fields such as medicine, finance and manufacturing. However, by 2023, it is also expected to have a fundamental impact on marketing, providing more efficient and personalized ways to create visual content and better understand the target audience."

What are the 3 main components of AI? ›

To understand some of the deeper concepts, such as data mining, natural language processing, and driving software, you need to know the three basic AI concepts: machine learning, deep learning, and neural networks.

What are the six stages of AI? ›

Origin of AI
  • Stage 1- Rule Bases System. ...
  • Stage 2- Context-awareness and Retention. ...
  • Stage 3- Domain-specific aptitude. ...
  • Stage 4- Reasoning systems. ...
  • Stage 5- Artificial General Intelligence. ...
  • Stage 6- Artificial Super Intelligence(ASI) ...
  • Stage 7- Singularity and excellency.
Feb 28, 2021

What are three 3 main categories of AI algorithms? ›

There are three major categories of AI algorithms: supervised learning, unsupervised learning, and reinforcement learning. The key differences between these algorithms are in how they're trained, and how they function.

What are the 6 key AI principles? ›

Microsoft outlines six key principles for responsible AI: accountability, inclusiveness, reliability and safety, fairness, transparency, and privacy and security. These principles are essential to creating responsible and trustworthy AI as it moves into more mainstream products and services.

Who is the father of AI? ›

and Why is he called the 'Father of AI'? In a world where machines are rapidly becoming smarter than their creators, one man stands out as the driving force behind this revolution: Geoffrey Hinton. Born in Wimbledon in 1947, Hinton was destined for greatness, hailing from a family of celebrated scientists.

What are the top 3 technology direction of AI? ›

In this article, I will explain three major directions of artificial intelligence technology, that are speech recognition, computer vision, and natural language processing.

What is the smartest AI ever? ›

GPT-3 (OpenAI)

GPT-3 is said to be one of the most advanced language models ever made, trained on terabytes of data containing 175 billion parameters, compared to Turing NLG by Microsoft has 17 billion parameters.

What is the most intelligent AI to talk? ›

Engati is the best no-code development AI chatbot platform that enables you to converse intelligently with your customers over WhatsApp, Instagram, Facebook Messenger, Slack, MS Teams, and 10+ other messaging applications.

What company is leading the AI race? ›

Microsoft is leading the AI race, as per analysts. Microsoft's CFO said on a call with analysts that the company is expecting revenue growth at Azure. It was earlier reported that Samsung might replace Google as default search engine on its devices.

What are the original 7 aspects of AI? ›

The original seven aspects of AI, named by McCarthy and others at the Dartmouth Conference in 1955, include automatic computers, programming AI to use language, hypothetical neuron nets to be used to form concepts, measuring problem complexity, self-improvement, abstractions, and randomness and creativity.

What is Big 5 in AI? ›

Given the success of existing companies with new epochs, the most obvious place to start when thinking about the impact of AI is with the big five: Apple, Amazon, Facebook, Google, and Microsoft.

What is the 5th wave of AI? ›

The 5th wave of computing

AI allows for machine learning. IoT allows for complex monitoring of home activity. 5G allows for a faster phone connection.

What are the 8 foundations of AI? ›

✒️ Algorithm, incompleteness theorem, computable, tractability, NP completeness, Non deterministic polynomial and probability.

Which programming language is used for AI? ›

Python is widely used for artificial intelligence, with packages for several applications including General AI, Machine Learning, Natural Language Processing and Neural Networks. The application of AI to develop programs that do human-like jobs and portray human skills is Machine Learning.

What type of AI is Alexa? ›

Conversational AI systems are computers that people can interact with simply by having a conversation. With conversational AI, voice-enabled devices like Amazon Echo are enabling the sort of magical interactions we've dreamed of for decades.

How is AI helping us the leading approach to AI right now? ›

How is AI helping us? The leading approach to AI right now is machine learning, in which programs are trained to pick out and respond to patterns in large amounts of data, such as identifying a face in an image or choosing a winning move in the board game Go.

What are 2 ethical considerations that revolve around AI? ›

But there are many ethical challenges: Lack of transparency of AI tools: AI decisions are not always intelligible to humans. AI is not neutral: AI-based decisions are susceptible to inaccuracies, discriminatory outcomes, embedded or inserted bias. Surveillance practices for data gathering and privacy of court users.

Who was the first to put forward the concept of AI in 1950 while proposing a machine intelligence test model? ›

Armed with a formalism that defined computing machines, Turing honed-in on what it means for a machine to think. In the article “Computing Machinery and Intelligence” published in 1950, he proposed his infamous test of machine intelligence, now known as the Turing test.

How AI can be used to improve the business strategy? ›

Artificial intelligence (AI) has become essential for businesses to streamline operations and improve overall efficiency. AI-powered tools can help companies automate time-consuming tasks, gain insights from vast data and make informed decisions.

What is the greatest contribution of AI in this modern world? ›

The greatest contribution that artificial intelligence could make to humanity might be in health care. According to the consultancy firm Frost & Sullivan, AI has the potential to improve medical treatment outcomes by 30-40 per cent and reduce costs by as much as 50 per cent.

What role does AI play in decision making and problem solving? ›

AI models and algorithms are the workhorses of decision-making. They are designed to systematically extract useful information from data. Such information is then used to provide predictions, forecasts or interpretations that support decision-making.

What are some risks or potential dangers of AI? ›

Some of the biggest risks today include things like consumer privacy, biased programming, danger to humans, and unclear legal regulation.

How will artificial intelligence change the future? ›

The productivity of artificial intelligence may boost our workplaces, which will benefit people by enabling them to do more work. As the future of AI replaces tedious or dangerous tasks, the human workforce is liberated to focus on tasks for which they are more equipped, such as those requiring creativity and empathy.

Who is the godfather of AI? ›

Geoffrey Hinton is known as the godfather of artificial intelligence.

Can a human fail the Turing test? ›

Can a Human Fail the Turing Test? Yes. Although a Turing test is based on knowledge and intelligence, it is also about evaluating how responses are given and whether the answers are interpreted to be sneaky.

What are the 4 AI business strategies? ›

Four Steps to an AI Strategy for Your Business
  • Start with the right problems.
  • Define the business outcomes.
  • Collect and organize your data.
  • Choose the right technology.

What business problems can AI solve? ›

Now, let's take a closer look at the problems that AI helps to solve in companies:
  • Customer support.
  • Data analysis.
  • Demand forecasting.
  • Fraud.
  • Image and video recognition.
  • Predicting customer behavior.
  • Productivity.
Sep 30, 2022

What is one of four principles of responsible AI? ›

Focusing on those four foundations of responsible AI — empathy, fairness, transparency, and accountability — will not only benefit customers, it will differentiate any organization from its competitors and help generate a significant financial return.


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