Artificial intelligence allows machines to replicate the capabilities of the human mind. From the development of self-driving cars to the proliferation of smart assistants like Siri and Alexa, AI is a growing part of everyday life. As a result, many tech companies across various industries are investing in artificially intelligent technologies.

Artificial intelligence is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.

Less than a decade after helping the Allied Forces in World War 2

by breaking the Nazi encryption machine Enigma, Mathematician Alan Turing changed history within a second with a simple question: “Can machines think?” 

Turing’s 1950 paper “Computing Machinery and Intelligence”

and its subsequent, Turing Test established the fundamental goal and vision of the AI.   

At its core, AI is the branch of computer science that aims to answer Turing’s question in the affirmative. It is the endeavour to replicate or simulate human intelligence in machines. The expansive goal of AI has given rise to many questions and debates. So much so that no singular definition of the field is universally accepted.

The major limitation in defining AI as simply “building machines that are intelligent”

is that it doesn\’t actually means what AI is and what makes a machine intelligent. AI is an interdisciplinary science with multiple approaches, but advancements in Machine Learning and Deep Learning are creating a paradigm shift in virtually every sector of the tech or software industry.

In their book Artificial Intelligence: A Modern Approach, authors Stuart Russell and Peter Norvig approach the concept of AI by define their work around the theme of intelligent agents in machines. With this in mind, AI is “the study of agents that receive precepts from the environment and perform actions.”

• Thinking humanly: Mimicking thought based on the human mind and intelligence.
• Thinking rationally: Mimicking thought based on logical reasoning and explanations.
• Acting humanly: Acting in a manner that a human behaviour.
• Acting rationally: Acting in a manner that is meant to achieve a particular goal.

AI can be

divided into four categories, based on the type and complexity of the tasks a system is able to perform. For example, automated spam filtering falls into the most basic class of AI, while the far-off potential for machines that can perceive people’s thoughts and emotions is part of an entirely different AI subset.

• Reactive Machines: able to perceive and react to the world in front of it as it performs limited tasks
• Limited Memory: able to store past data and predictions to inform predictions of what may come next
• Theory of Mind: able to make decisions based on its perceptions of how others feel and make decisions
• Self-Awareness: able to operate with human-level consciousness and understand its own existence

AI has many uses —from boosting vaccine development to automating detection of potential fraud. 

AI private market activity saw a record-setting year in 2021, according to CB Insights, with global funding up 108 percent compared to 2020. Because of its fast-paced adoption, AI is making waves in a variety of industries.

Business Insider Intelligence’s 2022 report on AI in banking found more than half of financial services companies already use AI solutions for risk management and revenue generation. The application of AI in banking could lead to upwards of $400 billion in savings.

As for medicine, a 2021 World Health Organization report noted that while integrating AI into the healthcare field comes with challenges, the technology “holds great promise,” as it could lead to benefits like more informed health policy and improvements in the accuracy of diagnosing patients.

AI has also made its mark on entertainment. The global market for AI in media and entertainment is estimated to reach $99.48 billion by 2030, growing from a value of $10.87 billion in 2021, according to Grand View Research. That expansion includes AI uses like recognizing plagiarism and developing high-definition graphics.

The creation of a machine with human-level intelligence that can be

applied to any task is the Holy Grail for many AI researchers, but the quest for artificial general intelligence has been fraught with difficulty.

The search for a “universal algorithm for learning and acting in any environment,” as Russel and Norvig put it, isn’t new. In contrast to weak AI, strong AI represents a machine with a full set of cognitive abilities, but time hasn\’t eased the difficulty of achieving such a feat.

AGI has long been the muse of dystopian science fiction, in which super-intelligent robots overrun humanity, but experts agree it’s not something we need to worry about anytime soon.

Much of Narrow AI is

powered by breakthroughs in ML and deep learning. Understanding the difference between AI, ML and deep learning can be

confusing. Venture capitalist Frank Chen provides a good overview of how to distinguish between them, noting.

Simply put, ML 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. ML consists of both supervised learning (using labelled data sets) and unsupervised learning (using unlabeled data sets).  

Deep learning is a type of ML that runs inputs through a biologically-inspired neural network architecture. The neural networks contain a number of hidden layers through which the data is

processed, allowing the machine to go “deep” in its learning, making connections and weighting input for the best results.