Christopher Ahlberg writes:
@cahlberg Thrilled to see the @nytimes write up on the great research by the Insikt team at
@RecordedFuture Quote Tweet The New York Times @nytimes Jun 1
China is investing deeply in open-source intelligence from the Pentagon, think tanks and private companies to learn more about the capabilities of the U.S. military in the Pacific and beyond, according to a new report. https://nyti.ms/43iXyZQ ... '
I worked at the Pentagon, so interesting. During an earlier 'rewiring'. Crucial.
How the U.S. Can Rewire the Pentagon for a New Era, by Porter Smith and David Ulevitch
We are in the early stages of a generational defense cycle that requires unconventional thinking and tools. The invasion of Ukraine highlights this evolution: comparatively low-cost weapons, from drones to precision artillery, are leveraging cutting-edge networks, like SpaceX’s Starlink, to redefine battlefields. Meanwhile, a new variable — attritable tech — is emerging. The implications are upending how militaries fight.
Defense innovation has always sparked a helix of action and reaction, where faster evolution means greater success. In World War II, Turing’s codebreakers at Bletchley Park reacted to Nazi cryptographic improvements in a running duel that spanned years. The naval Enigma wasn’t broken once, but dozens of times.
We are in a similar moment today, although the rate and scale of change is accelerated. Consider the lightning speed at which today’s most advanced general computing projects evolve. OpenAI’s GPT-4, which was released a little more than two years after its predecessor, can now ace bar exams and convert sketches to functioning websites — all in a compressed upgrade window that overtook competitors seemingly overnight. As general computing platforms become applicable to a host of defense applications, from programming autonomous behavior to conducting live targeting analysis, sweeping advances in software and other technologies not originally designed for defense have unintentionally compounded computing’s impact on war.
Responding to the paradigm shift requires reengineering the Pentagon’s DNA for a new era. Given that the winner of the next big war will more closely resemble a distributed computer operating at scale — programmatically collecting, sharing, and acting upon data from relatively inexpensive and configurable endpoints, like drones — computing design and organizational principles honed in the tech industry can help provide guidance to meet the challenge.
Here are some ideas for how to do it.
1. Commoditizing hardware
As the Central Intelligence Agency’s Chief Technology Officer Nand Mulchandani pointed out in his essay “Software-Defined Warfare,” cloud computing (and modern application design, overall) ushered in an era where hardware components like servers, storage devices, and networking equipment have become interchangeable and standardized. The transition to treating servers as “cattle” instead of “pets” presaged a reduction in operational complexity, driving lower costs through commoditization. Businesses utilizing cloud resources don’t need to endure long hardware provisioning cycles just to test out new applications or features, or over-purchase hardware for peak workloads that might come just once a year. Businesses adopting cloud native design principles care relatively little about hardware at all — their workloads are packaged as application containers that can run on any virtual machine running Linux.
Modern militaries face a similar competitive landscape, but the consequences are defeat instead of bankruptcy.
For a glimpse of what’s to come, look no further than the Donbas. The Defense Advanced Research Projects Agency’s (DARPA) premonition of “Mosaic Warfare” — saturating an adversary with cheap complexity — has come to life. Modded quadcopters are pairing with artillery to serve as ubiquitous reconnaissance and attack assets at the lowest tactical levels. Sometimes, even drones themselves are the munition, as seen in individual tank strikes and the mass naval attack that heavily damaged the Admiral Makarov, the flagship of Russia’s Black Sea fleet. Drones, air defense systems, and missiles are democratizing lethality in much the same way offshoring, competition, and government subsidies relentlessly commoditized computing hardware and chips. This rapid shift in defense tech has economic implications; cheaper hardware can be deployed en masse.
Why, then, have hardware costs risen so dramatically at the Pentagon? The Air Force’s next-generation bomber, the B-21 Raider, is estimated to cost $700 million per aircraft — a 140x increase in today’s dollars over B-24 Liberators from World War II. Part of the problem is that the Department of Defense (DoD) doesn’t view most platforms as uniform endpoints in a larger network. For a substantial portion of the force, they should.
The future is modular ... '
(In another time, between some other wars, I was a nerd at the Pentagon. Even now with much smarter devices, we need more hard and software, even beyond the AI enablement.
from ACM OPINION
To Win the Next War, the Pentagon Needs Nerds By Wired, May 5, 2022
When Russia invaded Ukraine, the U.S. Department of Defense (DoD) turned to a team of machine learning (ML) and artificial intelligence (AI) experts to make sense of an avalanche of information about the conflict. These tech experts crafted code and ML algorithms, creating systems that are "especially valuable for synthesizing the complex logistics picture," said Deputy Secretary of Defense Kathleen Hicks in a recent interview.
Hicks says this helps prove a point that she and others have been making within the Pentagon for some time—that technology is fundamentally changing the nature of war, and the U.S. needs to adapt in order to maintain its edge. However, a 2019 report commissioned by the Defense Innovation Board, warns that software and its development has become a crucial strategic issue for the U.S. military. The board also notes that the DoD cannot typically compete with the salaries tech companies offer software developers. ....
There are those—inside and outside of the Pentagon—who worry that the U.S. is ill-equipped to adapt to the challenges presented by war in the future. ...
Long after I worked for NCSS at the Pentagon, but you could start to hear the same questions at the time, when we were predicting events using large scale visualization of plans and events in eastern Europe. Ultimately the same thing.
Pentagon Wants to Predict Events Before They Occur By IEEE Spectrum, October 15, 2021
What if by leveraging today's artificial intelligence (AI) to predict events several days in advance, countries could simply avoid warfare in the first place? That's the type of visionary thinking that is driving U.S. military commanders and senior defense policymakers toward the rapid adoption of AI-enabled situational awareness platforms.
But, it's tempting ask, "What could possibly go wrong?" Leveraging AI-enabled tools to make better decisions is one thing, but using them to predict adversarial actions in order to preempt them is an entirely different ballgame. In addition to raising philosophical questions about free will and inevitability, it is unclear whether any proactive actions taken in response to predicted adversarial behavior might be perceived by the other side as aggressive and end up catalyzing the war we sought to avoid in the first place.
A former employer of mine tests a by now classic approach. Note aiming at AI efforts here in particular.
The Pentagon Is Bolstering Its AI Systems—by Hacking Itself
By Wired, July 21, 2021
Intelligence as a way to outfox, outmaneuver, and dominate future adversaries. But the brittle nature of AI means that without due care, the technology could perhaps hand enemies a new way to attack.
The Joint Artificial Intelligence Center, created by the Pentagon to help the US military make use of AI, recently formed a unit to collect, vet, and distribute open source and industry machine learning models to groups across the Department of Defense. Part of that effort points to a key challenge with using AI for military ends. A machine learning "red team," known as the Test and Evaluation Group, will probe pretrained models for weaknesses. Another cybersecurity team examines AI code and data for hidden vulnerabilities.
Machine learning, the technique behind modern AI, represents a fundamentally different, often more powerful, way to write computer code. Instead of writing rules for a machine to follow, machine learning generates its own rules by learning from data. The trouble is, this learning process, along with artifacts or errors in the training data, can cause AI models to behave in strange or unpredictable ways.
"For some applications, machine learning software is just a bajillion times better than traditional software," says Gregory Allen, director of strategy and policy at the JAIC. But, he adds, machine learning "also breaks in different ways than traditional software."
From Wired View Full Article
I see that it is the 107th anniversary of computer Pioneer Grace Hopper's birth. I wrote about my brief encounter with her while I was working at the Pentagon, in an early post in this blog. It was an honor to be connected to an early pioneer in anything that became so important to technology. She has been honored by inclusion in Googles search page today with a cute animation. (Today only) Also by the declaration of an hour of code, made to encourage students to take up the science and art of coding. " ... Be a maker, a creator, an innovator. Get started now with an Hour of Code. ... ". I grate a bit at it being promoted in a celebrity based way, but it is still worth sharing with a student who may be interested in the craft.
