This proliferation of digitized information provides military leaders innumerable data mining opportunities to extract hidden patterns in a wide field of situations.
From complex information contained in this data, visualization tools and other data science methods facilitate smart decisions leaders, especially commanders and their staffs, in asking questions, developing solutions, and making decisions.
Today’s military has vast amounts of data, but it’s really not anything that is really truly AI-ready. In legacy systems we’re essentially playing the data as it lies, which gets complicated, because it’s messy, it’s dirty. You have certain challenges of data quality, data provenance, and data fidelity, and every one of those throws a curve ball.”
While the Pentagon needs solid data for lots of different purposes, not just AI, large amounts of good data are especially essential for machine learning. Fighting wars is only going to get more complex in the future.
Joint All-Domain Command & Control: This is a pilot project working towards what’s also called Multi-Domain C2, a vision of plugging all services, across all warfighting domains into a single seamless network. It’s a tremendous task to connect all the different and often incompatible technologies and organizations.
Autonomous Ground Reconnaissance & Surveillance: This involves adding analysis algorithms to more kinds of scout drones and even ground robots, so the tools can call commanders attention to potential threats and targets without someone having to watch every frame of video.
Operations Center Assistant: This project aims to streamline the flow of information through the force. It will start with using natural-language processing to sort through radio chatter, turning troops’ urgent verbal calls for airstrikes and artillery support into target data in seconds instead of minutes.
Sensor To Shooter: This will develop algorithms that can shrink the time to locate potential targets, prioritize them, and present them to a commander, who will decide what action to take. This is about making troops faster, more efficient, and more effective. Troops are still going to have to make the big decisions about weapons employment.”
Dynamic & Deliberate Targeting: The idea here is to take targets like for example, ones found by the Sensor To Shooter Tool and figure out which aircraft is best positioned to strike it with which weapons along which flight path, matching a driver with a route.
“The data’s there in all these cases, but what’s the quality? Who’s the owner of the data? “There’s a lot of data that exists in weapons systems” – from maintenance diagnostics to targeting data – “and unlocking that becomes harder than anybody expected.
Military leaders see huge opportunities to use AI to comb through that complexity to make operations more efficient, reduce collateral damage, and protect the troops.
Military leaders have demanded visualized access to massive amounts of data to enhance their decision making, which quickly morphed into an ambition project called Leader Dashboard.
Nearly a thousand unique data sources from its initial efforts such as training databases and equipment inventories emerged. This proliferation of data appeared limitless and provided a staggering potential to enhance decisionmaking with valuable real-time information.
Applications crossed multiple functions throughout military organizations such as logistics and risk assessments. But project developments soon revealed its data existed in different systems that didn’t talk to each other so good data was difficult to obtain and likely unreliable.
The military world is overflowing with data, which requires analyses brimming with subjective interpretations. Because many analyses support pre-existing beliefs with cognitive biases, military leaders should designate competent data antagonists .
Leaders should use analysts they trust to uncover compelling evidence to challenge analyses and become savvy with data science and other analytic tools themselves.
Leaders should treat pertinent data that is both high-quality and reliable as strategic assets and force multipliers and strive to become open-minded and adopt adaptive leadership practices and methodologies.
Must address full spectrum of threats and rapidly change tactics in response to new scenarios with available techniques, and procedures. It is key to use an approach that commits to decisions, but do not become permanently linked to them.
Data scientists first conduct exploratory analyses to search data for trends, correlations, and relationships between measurements. Then, they use description analytics to understand operational aspects of the data, such as data summarization with basic statistics deviations to calculate combat power of operational units.
Using complex statistical techniques together with machine learning and probability theory, predictive analytics are used to uncover relationships between data inputs and outcomes.
Data analytics enhance capability by using more data points instantaneously to transform asymmetries of data into useful information. Overcoming behavioural limitations and biases, data analytics allow military leaders to make quicker decisions with more valid, dependable, and transparent information, providing valuable input into military decisions.
Analysts exploit tools to unlock secrets hidden in a huge cache information, available today measured and stored in digital bits. As an integration of talent, tools, and techniques, data science is really an art of transforming data into actionable information needed for decisions. To obtain data and report information, the emerging tech capitalizes upon data cleaning, data monitoring, reporting, and visualization processes.
Previously, data collection and analyses were expensive, requiring paper-based reports. Now with advancements in sensor and satellite technologies, military leaders began to obtain real-time access to data remotely on almost any topic, providing better opportunities in obtaining clearer pictures of situations that were swiftly adjusted to updated circumstances tailored to specific missions.
Applying more complex techniques made up of modeling and simulation efforts, data scientists use prescriptive analytics to determine probabilities of potential outcomes based upon deliberate changes to inputs.
If available, qualitative techniques, such as decision theory and war gaming, further improves data understanding. To reduce technical barriers and make it easier to provide data-relevant information for its users in dispersed locations.
Computational support systems such as data warehouses, data mining, virtual teams, knowledge /optimisation and management systems augment these qualitative techniques.
Advances in data analytics have impacted military operations. Using updated networks with advanced number-crunching tools, analysts have identified significant bottom-line improvements impacting decision making capabilities for all of its activities.
While there will “minimum common denominator” standards for tagging metadata with various categories and labels, “you will have lots of flexibility for mission-specific tagging.” It’s a tremendous task, but one with equally tremendous potential benefits. “We are trying to fix all sorts of problems with data across the Department of Defense,” not just for AI.
“AI will likely become the driving force of change in how the department treats data. Technology is changing so fast that the painful data processes we endure today may well be transformed soon into something entirely more user-friendly.
But many challenges will remain well into the future. There’s no simple silver bullet solution. Some suggest rigorously imposing some kind of top-down standard for formatting and handling data, but the Defense Department has too many standards already, and they are inconsistently applied.
“There are a lot of people who want to just jump to data standards. But very weapons system that we have, and every piece of data that we have, conforms to some standard. There are over a thousand different standards related to data today. They’re just not all enforced.”
“It’s less a question of standards and more of policies and governance. We now have to think about data as a strategic asset in its own right. Now, a much better approach to drive interoperability is to start with a discussion of metadata standards that are as lightweight as possible, as well as a Modular Open Systems Architecture. Or put another way, we need to agree on the definition of ‘AI Ready’ when it comes to our weapon systems.”
The problem with Pentagon AI projects is that Leaders don’t know what they want.
Imagine you want a car with a sunroof. Do you buy a car that has a sunroof, or do you buy a car that doesn’t have one and pay a mechanic to take a blowtorch to it?
You really don’t want to reinvent any wheels by integrating new tools you don’t have to. All too often, “leaders believe they can just hand their product over, it goes into network infrastructure, and it’s magic and it works.” In reality, it takes a lot of time just to install the product.
Installation and integration aren’t the only problems. Leaders want to incorporate AI so it can pool data previously scattered across the bureaucracy, making it easier to analyze.
The military world is overflowing with data, which requires analyses brimming with subjective interpretations Because many a
A new workforce will have the ability to uncover compelling evidence to challenge analyses. Finally, they should use analysts they trust or, better yet, become savvy with data science and other analytic tools themselves. AI relies on the fact that, in any large set of data, clusters of data points emerge that correspond to things in the real world.
“Difficulties Using AI to Compare Different Information Domains “
Certain kinds of information collection systems might be unique to individual data sets. Resolving potential differences between what the essay calls “multiple interactions” might prove difficult.
Future AI will need to “analyze the integration challenges of different AI approaches—the requirements for delivering reliable outcomes from a range of disparate components reflecting the conventions of different information domains.
All this being said, the current and anticipated impact of fast-progressing AI continues to be revolutionary in many ways; it goes without saying that it is massively changing the combat landscape, bringing unprecedented and previously unknown advantages.
“Tech Infrastructure Adapt Facing New Threats”
AI is progressing quickly when it comes to consolidating and organizing data from otherwise separate sensors on larger platforms, such as an F-35 or future armored vehicle…. yet integrating some of these same technical elements has not reached dismounted infantry to the same extent.
For example, emerging algorithms can quickly distinguish the difference between someone extending a weapon or merely digging a hole -- or recognize enemy armored vehicles. The AI-empowered system could also quickly cue a combat analyst so they don’t spend time pouring over massive amounts of data.”
The concept here is not so much the specific systems as it is a need to employ solid engineering. “In some cases the better chance of victory will be due to faster adaptability. Creating intelligent systems that are able to self-adapt to Soldiers' needs and seamlessly adjust as Soldiers adapt to the changing situation promotes rapid co-evolution between Soldiers and autonomy.”
Teams are engineering the standards through which to create interfaces between nodes on a soldier or between groups of soldiers. For instance, some of these nodes could include laser designators, input from radio waves or data coming in from satellite imagery overhead.
“Computers are so much faster, and algorithms are now being advanced to “train at scale” to analyze a series of images and pinpoint vital moments of relevance.
““We build out algorithms we could run on some kind of soldier-worn system such as a small form factor computer, thermal imaging, daytime cameras or other data coming in quickly through satellites. When you network all of this together and bring in all the sensor data, machine learning can help give soldiers the accurate prompts. “The more we do this, the smarter the algorithms get.”
“Decisions Based on Intelligence at the Speed of Conflict”
Accessing information on the battlefield presents challenges. Every second matters during a critical mission, and you don’t want to waste time sorting through millions of documents looking for the right intelligence.
AI can help create a common operating picture for more reliable situational understanding. That means the system only pulls the most important information for your current situation, giving you accurate, legible intelligence faster.
To fully understand the situation, military leaders had to maintain a good grasp of the analyses used, else they could become prone to making poor decisions. For example, there could be incorrect coding, including data reporting bias, giving a false sense of reliability that led to unwarranted decisions.
Before the onset of data analytics to uncover information, military leaders used outdated guidelines to reduce complexity and to fill knowledge gaps, often based upon historical outcomes. Additionally compounding decision making today is the abundant quantity of data that masquerades as valuable information while more pertinent data remains misplaced and obscured.
Challenges facing military decisions clearly involve interdependences, uncertainties, and complexities such that military leaders need critical thinking to increase probability of desirable outcomes.
Since warfighting enterprise is a complex domain of human beings and their various personalities, quantifying big data alone to make decisions is often inadequate.
Instead, effective analyses require qualitative information to uncover insights into the human domain of warfare. To address these challenges, military leaders should demand answers to the following questions from their analysts:
1. Treat pertinent quality/reliable data as force multipliers assets
2. Adopt adaptive practices to address rapidly evolving threats
3. Commit to decisions, but do not become permanently linked to them.
4. Connectivity issues could leave your analysts in the dark
5. Even when the connection is clear, there’s too much available information.
6. Selected metrics chosen instead of the many other metrics
7. Reliability of the data used include human factors
8. Test risk factor assumptions before data is used
9. Calculate probability that the analysis was wrong
10. Determine potential risks if the analysis was wrong