From chatbots for customer service to self-driving cars, AI is quickly becoming a major part of the world. Sentiment analysis, social media tracking, and media intelligence are all examples of AI-powered services which can assist marketers with their day-to-day responsibilities.

The media update team has compiled a list of AI-related terms that every marketer should be familiar with:

1. Algorithm 

A set of steps that runs on a computer program and is designed to solve a problem. These problems are solved by running through a number of processes, which all have their own specific rules.

Algorithms can be trained using machine learning, and are either programmed by humans or created and modified by machines during the machine learning process.

2. Artificial intelligence (AI)

A branch of computer science that focuses on creating intelligent systems that can perform and react like humans. AI systems are developed to perform tasks like visual perception, decision-making, speech recognition, and language translation.

3. Chatbot (or “bot”)

A computer program which can simulate conversation with human users, via a chat interface. Numerous brands have invested in chatbots on their social media accounts or websites to answer questions that consumers have about their products or services.

4. Data mining

The process of extracting information from extremely large sets of data, and establishing patterns within the data. It is a useful way to process Big Data, as it can be difficult to manually identify trends and find actionable insights within mounds of data.

5. Natural language processing

A component of machine learning that processes, sorts, and categorises various elements of text. While a human can inherently understand the meaning of sentences and paragraphs, a computer cannot really understand language. Using the mechanics of language, computers can, however, identify the context and grammatical use of words in sentences.


6. Image recognition (or “computer vision”)

This technology allows computers to identify patterns and recognise objects in images. It is used by some advert tracking companies to detect the logos of thousands of brands and businesses in newspapers and magazines. This technology allows the computer to understand what they ‘see’, which is why it is being used in self-driving cars, augmented reality, as well as the development of robots.

7. Machine learning

A process in which machines are fed data, and continually develop the algorithms they have been programmed with. The more data the machines are fed, the more accurately it can process similar information in future, and the more sophisticated the algorithms will become.

The training of machines by humans, where the algorithms learn from historical data submitted to it. The data that the machine learns from needs to be consistent and accurate so that learning can take place as efficiently as possible.

8. Neural networks

A method of processing vast amounts of data, which is modelled on the way the human brain interprets information. Neural networks consist of layers of nodes which receive data, extract the most relevant information, and send the data along to the next node.

Using neural networks allows computers to process complex information in a different way to using traditional algorithms. While algorithms will always follow the same steps every time data is processed, neural networks gather more connections, and become more complex, with each piece of data.

Deep Learning

9. Sentiment analysis

An AI process that studies opinions, attitudes, views, and emotions that people express in text. Once these have been analysed, the sentiment analysis can mark the text, sentence by sentence, as being positive, negative, or balanced. Analysing the sentiment of text – whether it is in the form of an email, newspaper article, or social media post – can reveal the attitude of its writer towards the topic they are addressing.

10. Turing Test

A test created by Alan Turing in 1951, designed to determine whether or not a computer could be classed as ‘intelligent’. It tests a machine’s ability to behave in a way that is inseparable from a human.

The test is conducted by having human judges chat to several people via a computer. Most of the people the judges will be speaking to are humans, but one will actually be a chatbot. The chatbot’s objective will be to convince the human judges that they are speaking to a real person. If it does this, it has passed the Turing Test.

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The development of AI is making machines smarter and more efficient. Despite this, humans still play an integral role in these processes. Read more in our article, The vital role of humans in machine learning.