Ask Professor Powell… your AI questions

Original article was published by Optimal Dynamics on Artificial Intelligence on Medium


Ask Professor Powell… your AI questions

I will be using this blog to summarize common questions about AI along with my answers. Feel free to pose your own questions here. Questions of general interest may be replicated here (possibly edited) along with my answer (also possibly edited).

Q: What is AI?

So many of the questions I get can be traced back to a misunderstanding of what is meant by AI. Everyone wants to jump on the AI bandwagon. In a nutshell, AI refers to any technology that makes a computer behave “intelligently,” which should mean something more than just adding up numbers. I provide an in-depth discussion of AI in my blog “What is AI” here.

Q: Can AI optimize my company?

This is like saying: Can tools build a house? Obviously, tools can build a house, but you need many tools to perform different functions. Similarly, the performance of a company depends on a variety of steps that depend on information and decisions. Some of the steps simply involve moving information from one place to another. Intelligence arises when we have to act on information to make decisions, where making a decision may involve judgments such as estimating the market response to a price, or how much inventory to order. Each step will require different AI tools, which can generally be organized into three broad classes:

  • Rules — Rules have to be specified by a human, and are generally limited to relatively simple settings (e.g. if eating red meat, order red wine).
  • Machine learning — This is the world of using data (sometimes big datasets) to fit mathematical models for classifying (is this email a customer order), inferring (what is the probability a customer will accept the price), and predicting (what will the market price be in three days). Machine learning turns information we know (such as the history of prices) to produce an estimate of something we don’t know (at least not now) such as the weather tomorrow. Machine learning needs a dataset to train the model (and this might be a big dataset). Neural networks is just one example of a machine learning tool. Machine learning is sometimes referred to (misleadingly) as “predictive analytics,” a term that tends to focus on prediction, ignoring classification and inference.
  • Decision optimization — Decisions represent something we control (inventory, prices, machine schedules, hiring decisions, what experiment to run next). Analytic tools for making decisions do not require a training dataset. Instead, they require performance metrics (cost, profit, service) and a model of the system.

If you want to improve the performance of your company, you have to break all the steps that involve processing data to identify places where you need to estimate something (this generally uses machine learning) or to make a decision (which involves some form of optimization).

Q: Why do so many AI systems fail?

The first reason why an AI system may fail is that the technology simply is not working very well. It is important to distinguish between AI as machine learning (estimating something) and AI as a decision-making tool.

  • AI as a machine learning tool — The medical community has long assumed that machine learning (typically using neural networks) would replace radiologists interpreting MRIs, but radiologists continue to outperform computers. We still seem to be a long way from driverless cars. A major problem with the neural networks that are so popular today is that these are systems that often have hundreds of thousands to many millions of parameters that need to be estimated. These systems require truly massive datasets, and yet may still not produce the right behavior (click here for additional discussion of this topic).
  • AI as a decision tool — An inventory system might place orders based on current inventory without considering forecasts of demands and inbound orders. Or, we may use a forecast, but do not properly account for the potential errors in the forecast. Decisions represent the highest form of AI, since good decisions have to stand on the shoulders of good information, which might also include good forecasts (or other estimates) from machine learning. A decision tool has to use an accurate model of the physical system being controlled

It is very important to understand how the output of an AI system (whether it be an estimate/forecast, or a decision) will be used within a company. Problems that can arise in the implementation of either type of AI system include:

  • A poor understanding of the data required by the AI system — This tends to arise particularly with decision systems. A truck dispatcher may know that the driver needs to get home, which he learned in a phone call. This is a common instance where a human knows something that the computer does not.
  • A poor understanding of how information is used by people to make decisions. It can be very hard to understand how a person makes a decision, which complicates the process of providing people with better information.

It is important to understand whether an AI system is being used to help a human, or if it is being allowed to run autonomously.