Nigeria’s steady growth in artificial intelligence

AI which stands for artificial intelligence refers to systems or automated systems that perform tasks by mimicking human intelligence and can continually refine themselves based on the data they collect. 

AI manifests itself in a variety of ways. Christopher Strachey, later director of the University of Oxford’s Programming Research Group, wrote the first successful AI programme in 1951. Strachey’s checkers (draughts) programme ran on the Ferranti Mark I computer at Manchester University in England. 

This programme could play a complete game of checkers at a reasonable speed by the summer of 1952. AI is very much about the process and the ability to think faster and analyse the data than it is about any specific format or function. 

Although images of high-functioning, human-like robots taking over the world conjure up images of AI, the technology is not intended to replace humans. Its goal is to significantly improve human capabilities and contributions. As it relates to artificial intelligence, there are several types of learning. The most basic method is trial and error.

A simple computer programme for solving mate-in-one chess problems, for example, might try moves at random until a mate is found. The programme could then save the solution along with the position so that the next time the computer encountered the same position, it could retrieve the solution.  This simple rote learning of individual items and procedures is relatively simple to implement on a computer. Problem-solving, notably in artificial intelligence, can be defined as a systematic search through a set of possible actions to achieve a given objective or course of action. Problem-solving techniques are classified as either special purpose or general-purpose. A special-purpose method is tailored to a specific problem and frequently takes advantage of very specific features of the situation in which the problem is embedded.

A general-purpose method, on the other hand, is applicable to a wide range of problems. One general-purpose AI technique is means-end analysis, which is a step-by-step, or incremental, reduction of the gap between the current state and the final goal. 

AI programmes have solved a wide range of problems. Finding the winning move (or sequence of moves) in a board game, formulating discrete mathematics, and manipulating “virtual objects” in a computer-generated world are some examples. AI has evolved in lockstep with computer processing power, which appears to be the primary limiting factor. Early AI projects, such as chess and mathematical problem solving, are now considered trivial in comparison to visual pattern recognition, complex decision-making, and the use of natural language.

It is a driving force behind the Fourth Industrial Revolution and one of the most important technologies for business, the economy, and society. This necessitates a multifaceted approach and a holistic understanding of AI, encompassing technical, organisational, regulatory, societal, and philosophical aspects.

AI is arguably one of the most powerful technologies in the history of business, the economy, and society. While there are many different types of AI algorithms and AI systems, two that are commonly used in business are those that use predetermined, and possibly human-defined, rules to make predictions, recommendations, and decisions, and those that learn these “rules” (which are in general mathematical functions) from data.  For many years, organisations have used the former type of AI. 

The second type of AI system is based on “machine learning”. Machine learning, ML, is on the rise. It is the heart of the majority of today’s AI systems. ML is a field that combines computer science and mathematics. Science, math and statistics concentrate on the creation of algorithms capable of analysing data that is typically large in volume and complex in nature structure, in order to identify any patterns that may exist to be used to make predictions, decisions or recommendations.

Certain human functions, such as sensing and learning, can be performed by AI. Sensing: AI allows machines to perceive the outside world by analysing images, sounds, speech, text, and other data collected via IoT sensors or directly fed into the system.

Learning: AI allows machines to improve their performance over time by learning from past results, successes, and failures. AI has gradually crept into the mainstream. It now pervades people’s lives, tracking their movements, navigating them, and recommending content to watch, read, and listen to. Access to internet information is mediated by search engines, recommendation systems, and digital assistants.

Examples of common use cases in sales that use AI technologies for analysis, optimisation, and prediction include to predict optimal next actions for sales representatives (e.g. when to offer discounts), personalise customer journeys across multiple sales channels, predict demand to support pricing decisions and supply chain optimization. 

Similarly, in Human Resources, HR, multiple use cases use natural language processing technologies, including automating screening processes in recruitment and providing targeted appropriate training courses, providing analytic and predictive support to staff compliance monitoring e.g. spotting inappropriate behaviour such as bullying in internal emails.

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