Artificial intelligence explained: This article describes and explains the concepts and terminology behind what is today being termed as artificial intelligence. Further, it illustrates how these concepts relate to the field of project management, offering opportunity for better, more effective project planning and control.

What is artificial intelligence?

There are many definitions of artificial intelligence or AI. In fact, a Google search today returns 1.18 billion results. One of the funniest definitions I have run across is “AI is whatever hasn’t been done yet” — now there’s a vague and unhelpful answer!

One of the more useful definitions I have found is “AI is the ability of a computer program or a machine to think and learn. In general use, the term “artificial intelligence” means a machine which mimics human cognition.”

So, machines being able to think and learn seems to be the crux of AI.

The way humans think is through what is called cognition (stems from the Latin word for “know” or “recognise”). It is the scientific word for a thought process, the mental action of acquiring knowledge and understanding through thought and experience.

The way humans learn is through either observational or associative means. Observational learning is watching others behavior, such as watching your parent drive a car. You learn from watching which levers and switches they push as they drive along. Associative learning, on the other hand, is learning by establishing connections between events. You know you will hear thunder when you see a lightning strike.

Humans make decisions based on thought and learning. We make sound or good decisions based on observational reasoning as well as associative patterns. We also sometimes make bad decisions that we can learn from to make us smarter the next time around. So our thought process gets smarter the more we learn.

If a machine can acquire knowledge and understand or recognise it, then it too can start to make informed (and hopefully good) decisions for me. I believe AI is really about a machine being able to make an informed decision that is a sound one. AI is a decision support system (DSS) that helps me make a better decision faster than I could have otherwise made.

The problem with project planning today

One of the hardest challenges in project management is accurately forecasting future outcomes (project completion date, total cost) of very complicated and highly uncertain endeavours (projects) — we call this planning.

As an industry, we have developed some tried and trusted techniques such as Critical Path Method (CPM) to assist in modeling project outcomes. But these models are only as good as the inputs we feed into them. Any worthy planning tool today uses CPM as its underlying forecasting engine. As the planner, we are still left with the onerous task of knowing not only which activities to include in our plan, but worse, what should their durations, cost and even sequence be? CPM does little more than convert durations and sequences of durations into a series of dates. It doesn’t help one bit with:

• What scope should I focus on when building my plan?
• What activities should we include?
• What should our durations be?
• What is the true sequence and logic between our activities?
• What risks or opportunities can I expect to encounter?

If CPM were a complete solution, then we wouldn’t continue to experience project cost and schedule overruns. The problem isn’t CPM though. The problem is our inability to accurately model what we think will happen during project execution because:

• There are a huge number of variables (tasks and sequence)
• There are a huge number of uncertainties associated with those variables (duration or scope uncertainty)

Schedule risk analysis tools help tell us how bad our forecast may be, but they do nothing in terms of telling us what the inputs to our schedule should have been in the first place.

This is why I believe AI can massively help project planning. If AI can assist the planner by making suggestions that are sound, then the immense challenge described above starts to become surmountable. Added to that, if our planning tool can also start to make better suggestions by observational or associative learning, then we are headed down a seriously valuable and exciting path.

Artificial intelligence categories

If you thought the number of AI definitions was daunting, Google returns 754,000 results when searching for “Types of AI.” Sadly, very few of those results return a common set of type definitions.

I have found it most beneficial to categorize AI into the following three types:

1) Artificial Narrow Intelligence (ANI)

Artificial Narrow Intelligence (ANI), also referred to as “Weak” or “Applied AI,” is a type of AI that specialises in one area. Examples of this would be the IBM Deep Blue computer beating a chess master at a game of chess. The machine was programmed to be very good at one thing — playing chess. You might be surprised to learn that Apple’s Siri is also an example of ANI. She is programmed to respond to a limited set of questions but go beyond those questions and she cannot give an informed answer. The majority of today’s AI solutions are ANI-based. I believe, given the current state of AI-related technologies, ANI-based technologies are the most likely to support advancing the science of planning.

2) Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) is also referred to as “Strong” or “Human AI.” AGI refers to a computer that is as smart as a human across multiple domains. Computer science is nowhere close to achieving AGI yet.

3) Artificial Superintelligence (ASI)

Artificial Superintelligence (ASI) is “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.” This is an even greater leap than AGI, so let’s just move on.

Current approaches to artificial narrow intelligence

Today, our implementation of ANI-based AI can be loosely classified into two categories: expert systems and neural networks.

Expert (Knowledge-Based) Systems

Originally developed for use in the 1980’s, expert (or knowledge-based) systems (ES) came into their own as computing power got strong enough in the 1990s. An expert system is a programme running on a computer that uses a set of rules to answer a question (typically in the form of IF…THEN).

When asked a question, an ES will filter a set of data, based on rules, to establish a subset of what it believes is the answer. In general, the more rules that can be used to answer the question, the stronger the chance that a correct answer will be given. For example, if I wanted to determine a type of two-legged animal, simply querying “IF number of legs = 2” doesn’t narrow down our search enough to give us a useful answer since there are a large number of animals with two legs. Combine this with an additional set of questions relating to height, weight, habitat, pouch, etc., and we can quickly deduce a more reasonable answer.

An expert system is comprised of a knowledge base and an inference engine. For a project planning tool, the knowledge base would contain data pertaining to activities and their durations for different types of project. The inference engine is then responsible for trying to return a subset of this knowledge base back to the planner based on the question they may ask, such as “What activities should I include for my engineering scope of my hospital project?”