3. Prediction Machines#
The current wave of advances in artificial intelligence doesn’t actually bring us intelligence but instead a critical component of intelligence: prediction. ~ Prediction Machines (4)
3.1. Pre-Reading#
Warden and Situnayake, 2019. TinyML, Chapter 3: Getting Up to Speed on Machine Learning
Objectives#
I can frame a problem in terms of Prediction, Judgement, Decision, Action, Feedback and effectively communicate it to senior leaders as a way of reducing risk.
Understand that AI is fundamentally prediction.
Understand that prediction combines with human judgement to result in a decision.
3.2. Context for Military Operations#
Our role as a military officer is to exercise judgment and make risk decisions in an uncertain and adversarial environment
In the updated principle of Mission Command outlined in AFPD 1, we are directed to:
Accept prudent risk. All military operations contain uncertain, complex, and ambiguous elements. Commanders must analyze risks in collaboration with their subordinates to balance the tension between protecting the force and accepting and managing risks that must be taken to accomplish the mission.
This is further articulated in Command and Control (C2) doctrine:
It is a given in future conflicts that the joint force will be conducting operations in a contested environment. We must be prepared to execute in a degraded C2 environment where clearly delineated and forward thinking commander’s intent will be a requirement. It is imperative senior leaders provide our commanders with conditions-based authorities delegated to the lowest capable and competent level, and empower command by negation to accept the appropriate level of risk, all while working toward moments of clear C2. ~ General Charles Q. Brown, AFPD-3-30
And from the current CSAF’s Integrating Risk and Readiness Campaign:
“Mission readiness is the cornerstone of this campaign. Mission command necessitates risk management at the tactical edge. Well-executed, risk-informed decision-making preserves and protects combat power without sacrificing mission success.” ~ Air Force Chief of Staff David Allvin
The question, then, is how do we make decisions that minimize risk in an uncertain environment?
3.3. Prediction Machines#
The following is derived from Prediction Machines: The Simple Economics of Artificial Intelligence by Agrawal, Gans, and Goldfarb, 2018.
A decision ingests data and experience, then uses prediction and judgement to lead to an action. This action results in an outcome, which can be fed back into future decision making.
Prediction takes information you have (data) and uses models to stochastically generate information you don’t have.
Judgement is the process of assigning value to different outcomes and selecting an action based on predictions.
Prediction#
AI only brings us a single, critical component of intelligence: prediction.
Prediction takes information you have (data) and uses models to stochastically generate information you don’t have.
Machine learning simply reframes algorithmic problems as probabilistic prediction problems.
The machine learning algorithm builds a model of the system based on the data we provide, through a process we call training. The model is a type of computer program. We run data through this model to make predictions, in a process called inference. ~ TinyML
Examples#
The Oakland A’s baseball team reframed roster creation as a prediction problem: which player will boost team performance the most? This made them have an unprecedented season, and these tactics are now used across MLB. Moneyball is a great movie about this.
The self-driving car problem has largely been recast to “predict what a human would do in this situation (and then do that).”
Now you come up with some!
Accuracy of prediction#
Machines passed the human benchmark for image classification (5% error) in 2015.
As dimensionality grows, humans’ ability to form accurate predictions diminishes.
Going from 85% to 90% accuracy means mistakes fall by one third. Going from 98% to 99.9% means mistakes fall by a factor of twenty. Better prediction leads to better outcomes. At some point, accuracy hits a threshold where the prediction can become transformational.
Prediction machines are valuable because:
they often offer better, faster, cheaper predictions than humans can
prediction is a key ingredient to decision-making under uncertainty
decision-making is ubiquitous throughout our economic and social lives
But
In contrast to machines, humans are sometimes extremely good at prediction with little data. This is largely because of our broad context of the world.
Judgement#
Judgement is the process of assigning value to different outcomes and selecting an action based on predictions.
Judgement takes predictions (or information provided by data) and evaluates them against subjective goals, tradeoffs, or contextual priorities to determine the best course of action.
Computers made arithmetic cheap. Internet made distribution, communication, and search cheap. AI has made prediction cheap.
A drop in price of one thing can increase the price of its complements. For example, cheaper coffee means more expensive cream and sugar.
As the price of prediction drops, the price of judgement will increase.
Having better prediction raises the value of judgement.
Only humans can express relative rewards of one action over another. The prediction may be handed to the machine but the human will still handle the judgment.
Objectives rarely have only a single dimension. Answering why you are doing something may be complicated.
Judging payoff requires time and effort. This slow decision-making has tradeoffs. Judgement, whether by deliberation or experimentation, is costly (especially in combat).
In some cases, the judgment may be that the reward function engineering is clear enough to hand off the decision to the machine based only on the prediction.
Prediction will likely shift to machine by default and human by exception.
To make the most of prediction machines you need to rethink reward functions throughout your organization to better align with your true goals. This task is not easy.
Humans are critical to decision making where the goals are subjective.
Contracting out judgement is risky because they do not have the full context to resolve uncertainty in a strategic dilemma; you should keep judgement-focused workers in house. (also, they are a big piece of your competitive advantage)
Judgement quality is hard to specify in a contract and difficult to monitor.
3.4. Risk#
A key task for a business leaders is to anticipate and manage various risks.
Risk exposure matters when a decision can change ones exposure to risk.
In the case of weather, an automated, decent prediction right now is preferable; the human only gets involved in extreme events. Error rate increases for extended forecasts, so decision maker must decide when to decide.
Risk can be managed with insurance, where you take actions to reduce the cost associated with bad outcomes. Or risk can be managed with protection, where you take actions to reduce the probability of a bad outcome.
When you use insurance to minimize waste, the waste is visible; by contrast, the waste may be difficult to see with protection.
Enhanced prediction increases returns by reducing the waste from unneeded insurance or protection.
Air Canada was able to predict how to fill underutilized capacity because it had:
Measurable waste
Wealth of data about the problem
A well-contained problem
Prediction machines will provide new and better methods for managing risk.
Uncertainty#
Perhaps the biggest weakness of prediction machines is the high confidence they can have in a wrong answer.
Uncertainty means you need judgement when the prediction turns out to be wrong, not just when it is correct. Uncertainty increases the cost of judging a payoff for a given decision.
3.5. Machine Learning Workflow#
As stated, the prediction component of our decision making will come from mathematical models. For this course we will focus on machine learning models, with particular emphasis on deep learning models.
TinyML Chapter 3 presents the following Deep Learning Workflow. Sub-bullets are added.
Decide on a goal
Collect a dataset
and understand it
Design a model architecture
data input pipeline
the model itself
outputs that meet the goal
Train the model
initial model evaluation
Convert the model
considering where you will be deploying it
Run inference
Evaluate and troubleshoot
on-hardware performance
collect data for feedback
This workflow is an adaption of the Engineering Method!
Here is a similar visual representation:
Warning
It is tempting to reach directly for the latest-and-greatest deep neural network. But for many problems that approach is too complex and will lead to worse performance at greater cost than a simpler model would!
Always consider a simple non-machine learning model first as a performance benchmark.
3.6. Conclusion#
Airmen execute mission command through centralized command, distributed control, and decentralized execution. ~ AFPD-1
This is the new way in which we will fight wars.
Discussion#
How can you adjust Boyd’s classic OODA Loop (Observe-Orient-Decide-Act) to fit the AI prediction paradigm?