Artificial Intelligence and Machine Learning: Enhancing Human Effort with Intelligent Systems
Artificial intelligence has come a long way since scientists first wondered if machines could think. In the 20th century, the world became familiar with artificial intelligence (AI) as sci-fi robots who could think and act like humans. By the 1950s, British scientist and philosopher Alan Turing posed the question “Can machines think?” in his seminal work on computing machinery and intelligence, where he discussed creating machines that can think and make decisions the same way humans do (Reference 1). Although Turing’s ideas set the stage for future AI research, his ideas were ridiculed at the time. It took several decades and an immense amount of work from mathematicians and scientists to develop the field of artificial intelligence, which is formally defined as “the understanding that machines can interpret, mine, and learn from external data in a way that imitates human cognitive practices” (Reference 2). Even though scientists were becoming more accustomed to the idea of AI, data accessibility and expensive computing power hindered its growth. Only when these challenges were mitigated after several “AI winters” (with limited advances in the field) did the AI field experience exponential growth. There are now more than a dozen types of AI being advanced (Figure). Due to the accelerated popularity of AI in the 2010s, venture capital funding flooded into a large number of startups focused on machine learning (ML). This technology centers on continuously learning algorithms that make decisions or identify patterns. For example, the YouTube algorithm may recommend less relevant videos at first, but over time it learns to recommend better targeted videos based on the user’s previously watched videos. The three main types of ML are supervised, unsupervised, and reinforcement learning. Supervised learning refers to an algorithm finding the relationship between a set of input variables and known labeled output variable(s), so it can make predictions about new input data. Unsupervised learning refers to the task of intelligently identifying patterns and categories from unlabeled data and organizing it in a way that makes it easier to discover insights. Lastly, reinforcement learning refers to intelligent agents that take actions in a defined environment based on a certain set of reward functions. Deep learning, a subset of ML, had numerous ground-breaking advances throughout the 2010s. Similar to the connections between the nervous system cells in the brain, neural networks consist of several thousand to a million hidden nodes and connections. Each node acts as a mathematical function, which, when combined, can solve extremely complex problems like image classification, translation, and text generation.
Human lifestyle and productivity have drastically improved with the advances in artificial intelligence. Health care, for example, has seen immense AI adoption with robotic surgeries, vaccine development, genome sequencing, etc. (Reference 5). So far, the adoption in manufacturing and agriculture has been slow, but these industries have immense untapped AI possibilities (Reference 6). According to a recent article published by Deloitte, the manufacturing industry has high hopes for AI because the annual data generated in this industry is thought to be around 1,800 petabytes (Reference 7). This proliferation in data, if properly managed, essentially acts as a “fuel” that drives advanced analytical solutions that can be used for the following (Reference 8):
Ultimately, AI and advanced analytics can augment humans to help mitigate repetitive and sometimes even dangerous tasks while increasing focus on endeavors that drive high value. AI is not a far-fetched concept; it is already here, and it is having a substantial impact in a wide range of industries. Finance, national security, health care, criminal justice, transportation, and smart cities are examples of this. AI adoption has been steadily increasing.
