There are tremendous number of potential military applications for AI, from the battlefield to the back office. The trick is not overwhelming the users with too many different specialized apps to figure out and not overwhelming the network with too much data moving back and forth.
Army has launched a new effort, dubbed Project Quarterback, to accelerate tank warfare by synchronizing battlefield data with the aid of artificial Intelligence.
The project aims for an AI assistant that can look out across the battlefield, taking in all the relevant data from drones, radar, ground robots, satellites, cameras mounted in soldier goggles, etc., and then output the best strategy for taking out adversaries with whatever weapons available.
Quarterback, in other words, would help commanders do two things better and faster, understand exactly what’s on the battlefield and then select the most appropriate strategy based on the assets available and other factors.
Just the first part of that challenge is huge. The amount of potentially usable battlefield data is rapidly expanding, and it takes a long time to synchronize it.
Building Battle Strategies
AI can a quickly potential lines of sight between different points of the battlefield. That’s why the AI Task Force is working on a “fields of fire” AI that uses the new IVAS targeting goggles to determine what area each soldier can cover with their weapons.
The AI tools would compile that data for the whole squad, platoon, company, or even battalion, giving commanders a map of exactly what approaches were defended and where any potential blind spots lie. Another potential application of this same technology would be to analyze potential fields of fire from suspected or confirmed enemy positions to identify the safest routes past them.
Ultimately, the Army is looking for a lot more than a data visualizer. They want AI to help with battle strategy. “How do you want to make decisions based on battlefield data? How do you want to select the most efficient way to engage a target, based on probability of hit, probability of kill? Do you have indirect fire assets available to you that you can request?
Do you have real assets that you can request? Can I you my wingman… or, does the computer then recommend, ‘Red One, our wingman should take that target instead of you for x, y reasons?’ That goes back to that concept of how you make a more informed decision, faster. And who is making that decision could be a tank commander or it could be a battalion commander.
“Sensor to shooter? It’s the network. The synthetic training environment? It’s the network. IVAS is the network. If there’s one thing that’s cross cutting everything we’re working on, it’s the network. The bandwidth requirements, the latency we can’t have, there’s a lot of technical hurdles to overcome with that.”
Data from Sensors
Shooting down drones, aiming tank guns, coordinating resupply and maintenance, planning artillery barrages, stitching different sensor feeds together into a single coherent picture, analyzing how terrain blocks units’ fields of fire and warning commanders where there are blind spots in their defenses are all uses that will be tested.
“All the vast array of current and future military sensors, aviation assets, electronic warfare assets, network assets, unmanned aerial, unmanned ground systems, next generation manned vehicles and dismounted soldiers will detect and geolocate an enemy on our battlefield.
AI Task Force is focused on intelligence support to operations, where we’re using offboard data” – that is, data gathered from a wide range of sensors, not just those onboard one aircraft or vehicle to look for targets.”
Using Data Effectively
We need an AI system to help identify that threat, aggregate data on the threat with other sensors and threat data, distribute it across our command and control systems and recommend to our commanders at echelon the best firing platform for the best effects, be it an F-35, an extended range cannon or a remote controlled vehicle.
The idea is to pull data from a host of sources; curate it by putting it into standard formats, throwing out bad data, and so on; and create a central repository — not only for the data but for a family of AI models trained on that data. Users across the Army could download those proven algorithms and apply them to their own purposes.
This means a lot of data moving back and forth over the Army network. Modernizing that network is a major Army priority and the service is developing major increases in satellite bandwidth for its upcoming update. But the AI Task Force is also looking to reduce those data demands wherever possible.
There are concerns about getting all of those systems to link up and share massive amounts of data. “The thing that keeps us up at night is the network. “It’s not problems within the network, it’s that we’re relying on the network for so much”
“There are some huge autonomy challenges, but one of the greatest challenges we’re going to have is the network. On the ground, when you have robots wanting to talk to other robots, wanting to talk to ground vehicles and you go behind the hill, you go behind the rock, you go down in the gully; you’re in a city and you go around the corner of the building.
The Army has had bad luck trying to institute large-scale data standards. Case in point: the Joint Tactical Radio System program spent billions in a fruitless attempt to buy a single radio to serve all of its communications needs.
Some time ago, military mandated the Commercial Mobile Device Implementation Plan — essentially an effort to lower its data-transfer costs by using commercial networks for unclassified data. But as the current debate over 5G networking shows, even commercial cellular providers are having trouble getting ahead of what they see as future demand.
“This is commercial technology that everyone uses and relies on and so we are trying to take some of that and pass full-motion video in some cases. This is a big technological challenge and everyone is going to say, ‘I’ve got a radio that will do it.’ Fine, as long you’re 100 feet apart and can see each other. So that’s going to continue to be our biggest challenge because we just haven’t fixed the physics yet.
Techniques are being developed to inspect networks and see why these networks might come up with a certain recognition or solution or something like that”
There’s important emerging research in fencing off neural networks and deep learning systems while they learn, including neural networks in robots, “How we can put this physical structure or constraints into these networks so that they learn within the confines of what we think is physically okay.”
Troops in the Loop
Commanders and soldiers will have to become more comfortable with robots and software that produce outputs via processes that can’t be easily explained, even by the programers that produced them.
You could feed the AI surveillance imagery of “a forested area” and ask it, “show me every tank you see in the image.” The AI would rapidly highlight the targets – far faster than a human imagery analyst could go through the same volume of raw intelligence – and pass them along to troops to take action.
Military networks have so many thousands of users and devices that just figuring out what is connected at any given time is a challenge, let alone sifting through the vast amounts of network traffic to spot an ongoing intrusion. For all its faults, AI is much better than humans at quickly sorting through such masses of data.
The way to develop that trust is the same way humans develop trust in one another, slowly and with a lot of practice. “Most humans are not as explainable as we like to think… If you demonstrate to a soldier that the tool or the system that you are trying to enable them with generally functions relatively well and adds some capability to them… they will grow trust very, very rapidly.”
But, when it comes to big decision aids, “that will be much harder. “You trust something because it works, not because you understand it. The way that you show it works is you run many, many, many tests, build a statistical analysis and build trust that way.
That’s true not only of deep learning systems but other systems as well that are sufficiently complex. You are not going to prove them correct. You will need to put them through a battery of tests and then convince yourself that they meet the bar.’
In the meantime, the Army will work with the network it has until more capability comes online at a price it can afford. You can’t just walk away from what you had because we invested a lot of money into the network. And so thickening, augmenting, improving the network with commercial solutions, and in short increments so you can capture the very best technology you possibly can.”
Consider self-driving cars as an everyday example. AI matches drivers with customers, but each individual user isn’t actually sending or receiving all that data. It boils down to “here I am, I want a ride to there” or “here I am, I’m willing to give someone a ride this far.” Likewise, a combat vehicle could transmit “here I am, I have this much ammunition, I have this much fuel. Please send supplies ASAP.”
You can extend this principle to even more complex functions like spotting targets or predicting breakdowns. If you put enough processing power and software on the frontline platforms themselves – a concept called “edge computing” – they can analyze complicated input from their own sensors and only send a summary report back over the network, rather than having to transmit raw data to some central supercomputer for analysis.
A vehicle could send, “I need X maintenance in Y hours,” for example, rather than every bit of data collected by the diagnostic sensors on its engine. Or a drone could send “I see tanks here, here, and here, and a missile launcher there” rather than transmit full-motion video of everything in view.
Now, not every present-day weapons system is smart enough to do the analysis by itself. Many systems don’t even have sensors to collect data, and installing them would be prohibitively expensive. “We can’t afford to retrofit all our Humvees from 30 years ago.
So one of the task force’s priorities will be to ensure that all the Army’s new weapons systems, from long-range missile launchers to targeting goggles, can collect and transmit the right kind of data on their own performance. That means embedding enough computing power to perform a lot of analysis at the edge, rather than having to transmit masses of data to a central location.
Big data gets a lot of attention and can put a lot of strain on networks. But, for many applications, we can just start little.”
1. Integration with existing processes
All the processes running in business are different from one another. So, the process that is automated should seamlessly integrate with the existing processes. The integration can happen at various levels of the automation process but it is essential for all the automation processes to work smoothly with the environment.
The automated processes should be consistent with other processes and their corresponding inputs and outputs.
3. Step by step approach
It is not necessary to perform the automation of all the necessary tasks at once. Depending on funds, resources and overall logic of the business processes, you can divide the job into several stages.
4. Process flexibility
Over a period of time, business processes tend to change. The automation solutions should be flexible enough to incorporate and reflect these changes.
The aim of automation is to make the business processes simpler and not more complicated. If after automation, the process requires a lot of human intervention, you must understand that something is wrong.
6. Training the staff
For efficient functioning of business processes, it is important for the staff to have a complete understanding of the workflow. The better they know a process, the better it will function.
7. Reduce business process automation operational costs
Running a business is costly . Automation can substantially help in reducing cost and increasing profitability. However, most of the people find it difficult to choose tasks for automation. Well, any task that that doesn’t require human thought process but just a series of steps that can be predicted using computer logic can be chosen for automation.
8. Complete conventional tasks
Paper processes can be completed using automation. These days, you can digitally sign documents, sign contracts and also easily manage organizational expenses using various tools available in the market making it easier to manage the flow of work.
9. Avoiding mistakes
Rectification errors cost businesses a ton of money every year. The greater the rate of error more is the cost for rectification. A small misconfiguration can expose vulnerabilities to attackers or disrupt business processes.
Automation can help you in improving efficiency and ensuring accuracy. You can take basic tasks out of human hands and engage their brain more into developing strategies and more important functions.
10. Deliver superior customer service
With major advancements in the field of technology, today, the way customer service is provided has changed a lot. Using artificial intelligence, you can create great customer service that will answer all of their questions directly and provide the team working at the back end with required information and resources.
Automated customer service can help you greatly reduce the response time. Time zone constraints do not affect automated customer service. Robots can help in responding to customer queries almost instantly, something which human support cannot guarantee. Thus customers no longer have to wait for hours just to get a response and virtual assistants will be able to predict what customers are looking for by tracking their actions and understanding their needs.