Part 1: Introduction to AI
Artificial Intelligence (AI) is everywhere. This article is just one more in a virtual flood of postings, TED talks, videos, and forum discussions about this topic in recent years, and that’s just what’s on the web. Odds are it has also crept into or been responsible for your favorite TV shows, not to mention its central presence in movies such as The Matrix, Ex Machina, or yes, A.I. Artificial Intelligence.
Part of the problem with talking about AI, as shown by the examples above, is there are still differences in what may be meant by AI or any of its components. AI often implies autonomy, which carries with it self-awareness and ability to act without any direction, but that isn’t where we’re at right now. Even without autonomy, AI is still a powerful and flexible tool to analyze data and make decisions.
This article is the first in a series exploring the effect of AI and related technologies on the world around us. The discussion will be punctuated throughout with examples of AI’s ongoing impact on the construction industry.
Artificial intelligence: A definition
For the purposes of these articles, I define artificial intelligence as “software agents that can perceive their environment and take actions that maximize their chances of success at some goal.” Artificial intelligence also mimics cognitive functions that humans associate with human minds, such as learning and problem-solving. A good example of artificial intelligence that fits this definition is DeepMind, developed by Google, and its AlphaGo program, which has triumphed over many of the world’s leading Go players, starting with Lee Sedol in 2016.
Another distinguishing factor when discussing AI systems is their conceptual breadth. Most fictional autonomous AI examples of general AI in that they can act as a human would in any situation. implementation of AI is entirely “weak” or narrow AI. Narrow AI is tuned to specific datasets and problems. The AlphaGo program is a good example of a narrow AI. These narrow systems have AI cognitive abilities (learning, decision-making, maximizing success), but can only apply these abilities to specific problems. General AI is still theoretical, but a subject of ongoing research globally.
Machine learning
Central to artificial intelligence is its ability to learn and be trained, in contrast with most software that executes set responses based on its programming. Through learning, the algorithms that power AI become modified as new information and relationships are acquired.
Learning is usually done by presenting the AI with datasets, often generated by human crowd input (for example, “tag your Google photos”) or collected by sensors or other digital equipment, and then allowing the AI algorithms to use those datasets in combination with rules established by the programmers. As relationships between data points are found through continuous review, the intelligence of the AI grows and its performance at its established task (for example, “identify photos with cats”) becomes optimized.
Another form of learning that has taken precedence in recent years is known as deep learning. Deep learning exceeds the capacity of the task-specific machine learning described above, by focusing on the relationships in a brain that are formed during learning. Deep learning systems are based on artificial neural networks (ANN) that mimic brain structure and neural function. The algorithms that make up the ANNs subdivide the task of learning by each specializing on a specific feature area of the presented dataset to learn. Connections between concepts are established by ANN “neuron” algorithms, giving this method of learning greater speed and flexibility than task-specific machine learning with its focus on problem and goal. Instead, the individual neuron algorithms and their small specializations can be recalled, and relationships reestablished as new information is gained by the AI as a whole.
So, to recap, in this article we have established starting points for:
- A definition of artificial intelligence.
- The importance of machine learning to artificial intelligence.
- Methods of machine learning.
Next month, we will discuss AI and the future of work.