This quarter, we will be looking at the interactions between artificial intelligence and digital marketing. Artificial Intelligence is shaping digital marketing in many essential ways. The prospects are even more exciting. It is, therefore, important that marketers understand the impact of artificial intelligence and explore its benefits to improve their campaigns and achieve their marketing (and overall business) goals.
What is AI?
Artificial Intelligence is an attempt to make computer systems able to perform tasks that ordinarily require human intelligence. According to SAS, AI makes it possible for “machines to learn from experience, adjust to new inputs, and perform human-like tasks.” Jeremy Achin, CEO of DataRobot, defines AI as “a computer system able to perform tasks that ordinarily require human intelligence… Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning and some of them are powered by very boring things like rules” AI seeks to equip computers and computer-controlled robots with cognitive functions that we associate with humans. Some of these cognitive functions include learning, problem-solving, reasoning, perception, and language. Intelligence consists of the ability to adapt to new circumstances. Investopedia defines it this way – “Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include learning, reasoning, and perception.”
Artificial Intelligence is a multidisciplinary discipline (drawing from computer science, information engineering, mathematics, psychology, linguistics, and philosophy) that became an academic discipline in 1955. There are divisions like robotics, machine learning, logic, and artificial neural networks. AI uses tools like search and mathematical optimization, artificial neural networks, statistics, probability, and economics.
AI is an evolving field of study. When machines become increasingly capable, some of the tasks that used to require intelligence no longer belong to the field of AI (as they now become routine and inherent computer function). Instead, they are classified as AI effect. Routine technologies like the Optical Character Recognition is no longer considered AI.
Discussions about AI evoke different reactions, including enthusiasm, disbelief, and concern. Some people raise ethical concerns regarding the morality of AI. Much of the concerns involve the fear of unemployment as machines develop a greater ability to carry out tasks previously associated with humans. The fear of job loss leads to huge trepidation among many people when they think about the prospects of artificial intelligence. However, on one side are those who approach all of these with disbelief. Can machines really develop the intelligence of humans? Many people also approach AI with enthusiasm, anticipating how all of these will play out and improve human life.
Types of AI
There are two basic types of Artificial Intelligence:
Narrow AI is also known as weak AI. According to Investopedia, Weak AI “embodies a system designed to carry out one particular job. Weak AI systems include video games such as the chess example from above and personal assistants such as Amazon’s Alexa and Apple’s Siri. You ask the assistant a question, it answers it for you.” Built In defines narrow AI as a “kind of artificial intelligence operates within a limited context and is a simulation of human intelligence. Narrow AI is often focused on performing a single task extremely well, and while these machines may seem intelligent, they are operating under far more constraints and limitations than even the most basic human intelligence.”
Applications of Narrow AI include (in addition to Alexa and Siri) Google Search, Self-driving cars, Image Recognition, and IBM’s Watson.
Artificial General Intelligence
Artificial General Intelligence is also known as strong AI. The goal here is to achieve general intelligence whereby a machine can apply its intelligence to solve any problem rather than a limitation to some specific tasks. According to Britannica, “The ultimate ambition of strong AI is to produce a machine whose overall intellectual ability is indistinguishable from that of a human being. As is described in the section early milestones in AI, this goal generated great interest in the 1950s and ’60s, but such optimism has given way to an appreciation of the extreme difficulties involved.”
There is still a lot of skepticism whether the goals of strong AI are achievable, with many people concluding that strong AI cannot even achieve the general intelligence of an ant.
The Britannica article identifies two other types of AI:
- Advanced AI: The goal of Advanced AI is to produce smart systems like medical diagnosis and stock-trading systems.
- Cognitive Simulation: Cognitive simulation aims to test theories about how the human mind works. It finds application in psychology and neuroscience.
Machine Learning and Deep Learning
Machine Learning is one of the algorithms used in artificial intelligence. Machine Learning “feeds a computer data and uses statistical techniques to help it “learn” how to get progressively better at a task, without having been specifically programmed for that task, eliminating the need for millions of lines of written code. Machine learning consists of both supervised learning (using labeled data sets) and unsupervised learning (using unlabeled data sets).”
According to Expert Systems, Machine Learning is “an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.” Machine Learning has been the most significant algorithm in the progress made in AI today.
Deep learning, on the other hand, is one of the techniques of machine learning. Investopedia defines deep learning as “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 artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.” Deep Learning helps to unravel relevant information from big data.
Applications of Artificial Intelligence
AI is shaping every industry. Its applications cut across various industries, and the future prospects are even more exciting. Some (among many) of those applications include:
- Healthcare: AI is being used in the healthcare industry to identify high-risk patients, improve diagnosis, and produce more efficient healthcare systems. AI is also growing in importance in the manufacture of drugs and dosage issues.
- Finance and Banking: AI is used in finance and banking to detect fraudulent activities. The neural networks can detect changes or claims that are outside of the norm. There are also applications of AI in financial trading.
- Automotive: AI is at the heart of self-driving cars and the increasing progress in the development of self-driving trucks.
- Manufacturing: AI helps manufacturers to retrieve relevant information from their large volumes of data. Manufacturers also use AI for predictive modeling.
- Government: Governments also use facial recognition systems for mass surveillance. There is also an increase in the use of traffic signal systems.
- Marketing: AI helps marketers deliver more personalized recommendations to shoppers or social media users. AI is at the heart of speech recognition systems, Google Suggest, and other search engine optimization, website design, content curation tools.
Over the next few weeks, we will consider how AI is changing the digital marketing industry ad how marketers can explore it to their advantage.