Introduction

Machine intelligence represents a significant milestone in human innovation and after decades of research and two AI winters, artificial intelligence now dominates mainstream attention and is promising revolutionary changes in the way we work, create, and live our lives.

While the inception of AI as a field of study can be traced back to the 1950s, it's only in recent years that this technology has formed a significant component in our daily lives. Today, it manifests in various forms, from digital assistants like Siri and Alexa to recommendation engines on TikTok, Netflix, and Amazon, as well as new content generation tools like ChatGPT and DALL-E.

Despite its growing ubiquity, AI is often misunderstood and confused with data science, which is an intersecting field based on extracting insight from data and with its own set of use cases. As a comparison, a company might use the principles of data science to uncover new insights by analyzing customer interactions and website support tickets. This process involves aggregating and examining the data to identify common customer issues, peak times for customer support queries, or correlations between support ticket volume and specific product features. Using a data science methodology, the overall goal is to be as precise as possible at identifying patterns and trends that might help to inform the company’s decision-making.

Artificial intelligence, on the other hand, performs a different role that relies less on detective work and more on general intelligence. AI, for example, can be used by a company to power customer service chatbots on their website that mimic human interactions, answer simple customer questions, and refer more complex queries to human representatives. By applying artificial intelligence, the company’s overall goal is to automate key parts of the customer journey, enhance efficiency, and enable 24/7 customer service support without relying solely on human capital.

As highlighted in these two examples, data science focuses on extracting insights and knowledge from raw data, whereas artificial intelligence aims to simulate and embed human intelligence into machines. However, in many cases, AI systems will leverage insight derived from data science to enable machines to learn and make intelligent decisions. It’s important, therefore, to acknowledge the overlap between data science and AI, while also understanding that AI and data science remain two different approaches to solving complex problems.

Beyond data science, AI encompasses a variety of subfields and techniques including machine learning, deep learning, generative AI, natural language processing, cybernetics, and computer vision. Regardless of the methods used, artificial intelligence, at its core, returns to the overarching mission of creating systems capable of performing tasks that would normally require human intelligence. Such tasks include understanding human language, recognizing patterns, learning from experience, making informed decisions, and even displaying emotional intelligence.

Learning from experience and making informed decisions falls into the subfield of machine learning, which entails the use of statistical methods to create prediction models that improve their performance on a specific task through experience and exposure to data. An example of this can be seen in email spam filters, which learn to distinguish spam from regular emails more accurately over time.

Pattern recognition, meanwhile, forms the basis of many AI applications, from biometric identification systems that recognize fingerprints or retina patterns to recommendation systems that analyze our online shopping patterns to suggest products we are likely to buy.

Emotional intelligence in AI, while still in its nascent stage, aims to enable machines to recognize and respond to human emotions. It has potential applications in many areas such as mental health and customer service, where it’s possible for AI to assist in providing assistance, empathy, and emotional support to humans.

Understanding these different use cases and the breadth of AI helps underline the fact that AI isn’t one monolithic technology or technique but rather a collection of technologies and approaches that strive to emulate human intelligence. Acknowledging this diversity is crucial for appreciating the full spectrum of AI technology and recognizing the multitude of possible use cases.

The next essential insight for those keen on understanding AI further is the realization that AI is still only in its first stage of potential evolution. This first stage is known as narrow AI or weak AI, which describes systems designed to perform a narrow task, such as voice recognition or recommending relevant products. These systems excel at the specific tasks they were designed to undertake but lack the understanding or consciousness to freely apply their capabilities to other use cases.

At the same time, we are edging closer to the next stage of AI development, known as artificial general intelligence (AGI) or strong AI. AGI refers to a version of AI that possesses the ability to understand, learn, adapt, and implement its knowledge across a broad range of tasks at a comparable or superior level to that of a human being. To help grasp the concept of general AI, it’s useful to think of science fiction portrayals where AI entities, like Data from Star Trek or Ava from the movie Ex Machina, mingle with humans and exhibit cognitive abilities that are indistinguishable from ours. These AI entities are often shown to possess self-awareness, emotions, creativity, and the ability to understand and exhibit human-like behaviors, which are all hallmarks of general AI.

However, as with any powerful new technology, AGI raises a selection of ethical and privacy issues that must be navigated with care, as we will discuss in later chapters. We will also look further at the three stages of AI development and explore the potential ramifications of the final stage known as superintelligent AI. Beyond that, we will explore the major subfields of AI including machine learning, deep learning, natural language processing, generative AI, recommender systems, and computer vision. The final chapter will lay down a series of tips and insights for adopting AI in your job or organization.

For now, understand that your journey into AI has many potential paths and the field will continue to evolve as we edge closer to the next stage of machine intelligence.