Author Archives: EzraSidran

Why the Pundits are Completely Wrong About AI

I have a lot of respect for Steve Wozniak – quite a bit less for Elon Musk 1)Though I have to admit losing $20 billion in a few months is impressive. – who both recently signed a letter calling for, “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4.” Woz is a true computer hardware pioneer; but he’s certainly not an AI expert and Elon, well, I’m not sure where his expertise lies, but it’s not AI.

When it comes to creating AI capable of commanding troops on a battlefield, I am probably one of the world’s top experts on the subject (it’s not a crowded field). I’ve been writing and studying ‘computational military reasoning’ for my entire professional career, it was the subject of my doctoral thesis, I’ve written AI for numerous computer wargames and I’ve been a Principal Investigator for DARPA (Defense Advanced Research Project Agency) on this very subject.

I am confident in stating that no humans have been injured or died as a result of my work in computational military reasoning. However, the most recent NHTSA data reports that there have been at least, “419 crashes [and]… 18 definite fatalities of autonomous self-driving vehicles (like Mr. Musk’s Teslas). So, clearly, in some circumstances AI can be dangerous. In all fairness, I should state that the reason the self-driving autonomous vehicles keep having fatal crashes isn’t technically the AI; it’s that the AI has imperfect information about the world in which it operates. The AI for self-driving vehicles gets that information from cameras and radar (LIDAR would be good, too). However, Telsa just removed the radar from it’s vehicles (“Elon Musk Overruled Tesla Engineers Who Said Removing Radar Would Be Problematic: Report,”) leaving the AI even more in the dark about the world in which it operates. So, is the AI at fault or corporate management? Maybe the problem isn’t AI.

Furthermore, most of what’s being sold to the public as AI are just some string manipulation parlor tricks tacked on to an internet search. ChatGPT-4, which is making all the headlines these days, was recently accurately described:

“Put simply, ChatGPT takes an initial prompt and determines – on an individual, word by word basis – what most often comes next based on the existing texts that it has scanned throughout the internet. In Wolfram’s words, “it’s just adding one word at a time” – but doing it so quickly that it seems as though a robot is writing an original, whole block of text.

Essentially, ChatGPT is a gigantic version of Google autocomplete.” – ​AI or BS? How to tell if a marketing tool really uses artificial intelligence

I recently asked ChatGPT for a quote from U. S. Grant about war and it responded:

Actually, it was W. T. Sherman who said, “War is hell.” But, ChatGPT has no real intelligence. How it erroneously linked Grant to the quote I have no idea. The greatest fear we should have of ChatGPT is incorrect citations in reference papers. The creators of ChatGPT have clearly traded accuracy for glitz and hype; it’s not even a good internet search engine, but it sure seems impressive!

There’s one more thing you should know. There are two kinds of machine learning: supervised and unsupervised. Probably >95% of machine learning programs are ‘supervised’; which means they are ‘trained’ on a data set. Whenever you see the words ‘training’ in reference to machine learning you know it’s supervised. Here’s an example of supervised machine learning: Netflix movie recommendations. Every time you select a movie on Netflix you are training their system on your likes and dislikes. It does a great job, doesn’t it? No, it does a terrible job. It once recommended Sound of Music to me because I watched Das Boot. Makes perfect sense. They both take place during WWII.

What I’m saying is that there is no ‘there’ there. There is no intelligence there. Somebody at Netflix (at one time I read they employed out of work screenwriters) tagged both Das Boot and Sound of Music with the same descriptor; presumably ‘WWII’ or ‘war movie’ and that was all that was necessary for Netflix to make a terrible suggestion.

I work in unsupervised machine learning. It doesn’t search the internet, or look for similar words in a big data base. It tries to make sense of the world in which it operates (a historic battlefield) and attempts to make optimal decisions for moving units based on math, geometry, trigonometry and boolean logic.

That’s AI. And it’s not dangerous. Autonomous self-driving cars? They’re dangerous.

References

References
1 Though I have to admit losing $20 billion in a few months is impressive.

Video Walk-through of the Army, Map & Scenario Editors

I‘ve just uploaded a video of a walk-through of the General Staff: Black Powder Army Editor, Map Editor and Scenario Editor. These applications are completed. We will be using Steam for distribution. While we are registered with Steam, and they have given us ‘our space’, we still have to build it out and make arrangements for download keys for early backers. We (why do I keep using ‘we’, it’s just me here) truly appreciate your patience.

I will be posting a gameplay video of General Staff: Black Powder next. As always, please feel free to contact me directly.

The Friction of War

The delay in the transmittal of orders from headquarters and staff is one example of the Friction of War. Note the calculated time for couriers to arrive displayed in the Subordinate Orders list on the left of the screen. The red lines are the routes that couriers from General HQ to Corps HQ to individual units will take. General Staff: Black Powder screen shot. Click to enlarge.

Carl von Clausewitz, in has seminal work, On War, (Book 1, Chapter 7) originated the phrase, “Friction of War”:

Carl von Clausewitz painted by Karl Wilhelm Wach. Credit Wikipedia.

“Friction is the only conception which, in a general way, corresponds to that which distinguishes real war from war on paper. The military machine, the army and all belonging to it, is in fact simple; and appears, on this account, easy to manage. But let us reflect that no part of it is in one piece, that it is composed entirely of individuals, each of which keeps up its own friction in all directions.”

I knew that if General Staff: Black Powder were to be an accurate simulation, and not just ‘war on paper’, that the friction of war would have to be calculated into the command / orders chain. One part of this – the distance the couriers will travel from one headquarters to the next to deliver their orders and the time it takes to travel this distance – can be calculated with reasonable certainty (I’m using the rate of 10.5 kilometers per hour for a horseman, I’m not an expert but this seemed reasonable, and it’s easy to change if somebody has a more accurate value).

Another example of friction of war is factored into the delaying of the arrival of orders is Leadership Value:

In this example, the Imperial couriers will travel over 4.3 kilometers, taking 24 minutes, to deliver their orders. Also, note the cost of the combined Leadership Values. Because Napoleon and Vandamme have very high Leadership Values little additional delay is added. General Staff: Black Powder screen shot. Click to enlarge.

You can specify at what time the order is to be executed (in this case 6:15), however you can not set a time earlier than when the couriers would arrive. This allows for coordination of attacks across units. General Staff: Black Powder screen shot. Click to enlarge.

The other value – and it is arbitrarily set – is the cost of ineptitude, incompetence, lack of motivation, and sloppy staff work. In the above scenario (Ligny) Napoleon’s Leadership is set at 93%:

The slider adjusts Napoleon’s Leadership Value which effects the delay in issuing orders. General Staff: Black Powder Army Editor screen shot. Click to enlarge.

I understand that Napoleon may have been feeling a bit under the weather during the Hundred Days Campaign. You can set his Leadership Value to anything you want in the Army Editor (above).

Major General George B. McClellan’s Leadership Value can be changed in the Army Editor. Click to enlarge.

Did I set McClellan’s Leadership Value too low? He was amazingly incompetent. Note below:

The combination of McClellan’s and Burnside’s extremely low Leadership Values adds an additional 29 minutes to the transmittal of orders. The blue lines trace the route that couriers would travel from McClellan’s headquarters to Burnside’s headquarters and then to each division and battery. General Staff: Black Powder screen shot. Click to enlarge.

The combination of McClellan’s and Ambrose Burnside’s Leadership Values results in almost a half hour delay in transmittal of the orders (remember after receipt of the orders, Burnside has to send couriers to his divisional and battery commanders, too and their Leadership Values effects the delay before their unit executes the order). After factoring the time it would take for a horseman to travel the distance between McClellan’s headquarters to Burnside’s headquarters (14 minutes) the earliest that a unit could be expected to respond to the original order from General Headquarters would be forty-one minutes later (and, in reality, a bit after that because of that unit’s Leadership Value).

The path of the couriers from McClellan’s headquarters, to Burnside’s Headquarters and then out to the divisions and batteries. General Staff: Black Powder screen shot. Click to enlarge.

I have spent some time at Antietam and studied it at length and this delay of about three-quarters of an hour between the time McClellan wanted to issue an order and the men of Burnside’s IX Corps moved out seems if anything, too optimistic of a timetable. In fact, as I write this, I think I need to increase the penalty for poor Leadership Value. McClellan and Burnside couldn’t possibly have got units moving in less than an hour.

As I have begun playtesting General Staff: Black Powder I found the delay between issuing orders and wanting to see something move now was a bit disconcerting. It shouldn’t have been. I’ve read enough military history to know that battlefield orders were often transmitted the night before and moving units around during the battle could be a risky proposition. Some armies, however, were less afflicted with these problems than others, and that I would attribute to ‘leadership value’ which also encompasses the army’s general staff.

If you don’t want to use General Staff: Black Powder as a simulation that inserts a calculated delay between orders and execution, and would rather just move units instantly, there is ‘Game Mode’:

The Select Mode screen in General Staff: Black Powder. The user chooses between ‘game’ and ‘simulation’ with differences in rules and unit icons. Click to enlarge.

Game Mode has the same maps but uses simpler icons and rules. I originally envisioned Game Mode as a way of introducing wargaming to a new generation (I wanted to write it for the XBox). Anyway, it’s included with General Staff: Black Powder.

Lastly, I know everybody is waiting for news about when can I get my hands on the game?!!?!!  My friend, Damien, wasn’t able to work on  finishing it using Unity so I’m finishing it up using MonoGame. As you can see I’m pretty far along and I think I will be playing the first ‘actual game’ (that is a simulation from start to finish) within the next couple of weeks; maybe sooner. After that, probably at least another month of fixing bugs, but then I’m hoping to set up a Beta download for all the early backers via Steam. We have a space on Steam but I haven’t even begun to build it out. Obviously, I’m just one guy, I’m working as fast as I can, but I think this is all good news. Also, I’m working on a video to show everything off.

As always, if you have any questions or comments, please feel free to contact me directly.

 

Schwerpunkt: Calculating the Optimal Point of Attack

MATE’s analysis of Blue (Union) position at Antietam. NB: Unable to outflank Red’s position, MATE has calculated the Schwerpunkt, or optimal point of attack on Red’s lines. Click to enlarge.

The holy grail of military science is an algorithm that calculates the optimal point of attack upon an enemy’s lines. In German, the word is Schwerpunkt and is commonly translated as “the point of maximum effort.” I have written extensively about Schwerpunkt previously in this blog, in academic papers and in my doctoral thesis.

MATE (Machine Analysis of Tactical Environments 2.0, the AI behind General Staff: Black Powder) is now able to calculate Schwerpunkt to a new, substantially greater, degree of accuracy. There are a number of reasons why this is now possible, but the primary cause must be the ability to analyze the battlefield in 3D and to accurately map where every unit on the map can project its force. Indeed, for many years now I have looked at the problem of computational military reasoning (AI for tactical situations) as a force projection problem.

Below, is a visual representation of the total force projection of all units at Gettysburg, Day 3 (July 3, 1863 0600 hours):

Visual representation of the total force projection (Range of Influence, or ROI) for all units at Gettysburg Day 3. Note: normalization and alpha values affect color output. Also, note how the terrain (woods, depressions, hills) shape the projection of force. Also, all projections are independent of unit facing. Click to enlarge.

If we ask MATE to determine the Schwerpunkt for the Confederates in the above situation, it responds with:

MATE’s selection (labeled OBJECTIVE) for Red Schwerpunkt. Click to enlarge.

And adds the following commentary (edited for brevity, the numbers are the Premise Statement ID#s. This is basically a logic trace of MATE’s thinking):

8|∴ The enemy does not need to capture more Victory Points.
9|∴ The enemy will be on the defensive.
...
22|The enemy's flanks are anchored.
23|[9] + [22] ∴ Frontal assault is the only remaining option.
...
25|COA: Battle Group #1 (Mixed) assigned objective Weak Point Calculated by ROI coords: 551,232
...
33|Red Battle Group #1 is opposed by Blue Battle Group #6
34|Red Battlegroup # 1's strength = 21,663
35|Blue Battlegroup # 6's strength = 13,635
36|Red Battlegroup # 1 has a numerical advantage of 8,028. Red has a 1.59 / 1 advantage over Blue Battle Group #6.
37|Distance to objective is 1,029.86 meters.
38|The maximum slope along the line of attack will be on an upward slope of 3.64%.
39|The attacking avenue of approach will be in enemy ROI for 541.18 meters.
40|The greatest enemy ROI along the avenue of approach is: 1,276.00 .
41|There is an unrestricted avenue of attack.

In other words, MATE has found a path to its objective that encounters the least amount of enemy projection of force. MATE would much prefer to flank the enemy position but it has calculated that this is impossible (#22, above).

ROI (Range of Influence) is calculated using values set up for each unit in the General Staff Army Editor and running a 3D Bresenham line algorithm to ensure that there is direct Line of Sight (LOS) to that point.

Screen shot of the General Staff Army Editor showing the interface for entering values for a typical artillery unit. Note that the accuracy curve is user editable (there are also default curves for various common weapons). Click to enlarge.

It is because every unit has an accuracy curve attached to it we can exactly map out the overlapping fields of fire (see above) and we can precisely calculate how long each attacking unit will be under fire and its intensity. That is how MATE chooses the optimal attack point: the path where its troops will be under the least amount of fire.

When MATE is presented with a tactical problem it first determines what it needs to do to win; is it on the offense or defense? On the offense, MATE will next check to find the enemy’s open flank and, if there is one, are there any crucial choke points on the flanking route? If MATE is unable to ‘fix and flank’ the enemy, and it has determined that it must be on the offensive, MATE then calculates Schwerpunkt (above). With this new Schwerpunkt algorithm the last big piece of the offensive AI puzzle is in place. Ironically, much of MATE’s defensive calculations involve first figuring out how to attack itself and then countering what it determines are its own optimal moves against itself (see this blog).

As always, please feel free to contact me directly with comments or questions.