Much of my current work is focused on leveraging AI to amplify intelligence collection and analysis. To say that it excels at this would be an understatement. However, the intelligence process remains profoundly human and intricate. It can break down in countless ways, often unrelated to the professionalism and capabilities of the intelligence professionals involved. Complex implementations of LLMs and other advanced models, coupled with supporting technical infrastructure, will eventually eliminate or reduce many high-risk failure points where humans and human dependent systems can become overwhelmed, make mistakes, or avoid responsibility.
Intelligence has long tapped into AI & ML's potential. But as AI evolves, it will move beyond basic signal/data analysis and similar narrow applications to embed itself in, or completely capture nearly all aspects of, the intelligence cycle. In some scenarios, it might even bypass human involvement altogether. I tasked GPT-4 with delving into this topic using multiple prompts, aiming to further its original insights and shed more light on the decision-maker’s role. I then stitched those results together to craft this piece.
I created the cover image for this post with Midjourney.
The 1941 bombing of Pearl Harbor stands as a stark testament to the perils of intelligence failures, leading to unexpected strategic upheavals. Despite having access to a plethora of intelligence inputs, the United States failed to connect the dots that might have signaled the imminent Japanese assault. Intelligence shortcomings often arise from an intricate web of human biases, communication breakdowns, overwhelming influx of information, and technological insufficiencies. In the contemporary tech-driven era, while AI already plays a role in intelligence sectors, this discussion presumes a broader deployment of Large Language Models (LLMs) tailored specifically for intelligence purposes, offering a level of autonomy and analysis hitherto unachieved. Such specialized LLMs can potentially revolutionize the way intelligence is processed and interpreted, fortifying against lapses similar to historical precedents.
Visualizing a scenario where a technologically advanced and dominant power, termed here as "Entity A", faces off against a lesser, perhaps non-state actor or insurgency on its periphery, termed "Entity B", the intelligence landscape is rife with intricacies. Entity A, leveraging its expansive resources, would be privy to a diverse range of intelligence sources, spanning satellite imagery, electronic interceptions, and human intelligence reports. Conversely, Entity B, constrained by its limited scale, would likely adopt asymmetric strategies, relying heavily on subterfuge, cunning, and the crucial element of surprise to orchestrate actions like border infiltration.
In this backdrop, AI, especially intelligence-centric LLMs with enhanced autonomy, can emerge as a game-changer in the following ways:
- Data Aggregation and Fusion: The sheer volume of data that modern intelligence agencies gather can be overwhelming. Traditional methods of analysis may not keep pace with the influx of information. AI, with its machine learning algorithms, can quickly aggregate, filter, and fuse data from disparate sources to create a comprehensive intelligence picture. This means that satellite imagery, electronic signals, and other sources can be combined to detect unusual patterns of activity that might indicate preparations for a border incursion by Entity B.
- Predictive Analysis: One of AI's strengths is its ability to identify patterns in vast datasets that might be invisible to the human eye. By analyzing past insurgent activities, tactics, and strategies, AI can help predict potential future actions of Entity B. This doesn't mean AI can predict the future with certainty, but it can highlight potential areas of concern based on patterns and trends.
- Reduction of Human Biases: Confirmation bias, among other cognitive biases, can plague intelligence analysis. Analysts might, inadvertently, give undue importance to information that confirms their pre-existing beliefs while ignoring contradictory signals. AI, being devoid of emotions and biases, evaluates data on its merit and consistency. By doing so, it can offer an objective analysis, potentially highlighting overlooked or underemphasized threats.
- Real-time Monitoring and Alerts: Given the potential for Entity B to quickly mobilize and change tactics, real-time monitoring is crucial. AI can continuously scan multiple data sources and, upon detecting anomalous activities or signals that match predefined threat indicators, can instantly alert human analysts or even initiate automated defensive measures.
- Simulations and War Gaming: AI can be used to simulate various scenarios of Entity B's actions based on the intelligence gathered. This "war gaming" can help Entity A test its responses and strategies, ensuring they are prepared for a range of eventualities.
- Exploiting Entity B's Use of Technology: As Entity B integrates technology into its operations, it inadvertently creates digital footprints. AI can exploit these by monitoring electronic communications, analyzing patterns in data traffic, and even detecting faint electronic emissions from devices. For instance, even if Entity B uses encrypted communications, the sheer volume or pattern of encrypted data can provide clues. Moreover, AI can be trained to identify potential vulnerabilities in Entity B's tech infrastructure, allowing Entity A to launch cyber operations, either to gather more intelligence or to disrupt Entity B's activities.
- Behavioral Pattern Recognition in Low-Tech Scenarios: If Entity B, anticipating AI-driven electronic surveillance, minimizes its use of technology and primarily relies on human-to-human communications, AI can still be invaluable. Advanced AI models can analyze patterns in movement, meeting locations, and other human-centric activities. For instance, satellite or drone imagery fed into AI can detect unusual congregation patterns of people, regular paths taken by messengers, or consistent nighttime activities. These patterns can be indicators of planning, training, or coordination, even if no electronic communication is involved.
- Natural Language Processing for Intercepted Communications: Even if Entity B avoids electronic means, there might still be instances of written or spoken communication that can be intercepted. AI-driven Natural Language Processing (NLP) can analyze these communications, translating them if needed, and detecting nuances, coded language, or specific terminologies that might hint at Entity B's intentions.
- Counterintelligence and Insider Threat Detection: AI can assist Entity A in identifying potential moles or double agents within its ranks. By analyzing behavioral patterns, communication anomalies, or irregular access to sensitive information, AI can flag potential internal threats. This becomes crucial, especially if Entity B tries to infiltrate Entity A's ranks to gain intelligence or sow discord.
- Adaptability and Evolutionary Learning: One of AI's strengths is its ability to continuously learn and adapt. As Entity B changes its tactics, whether it's adopting new technology or reverting to more traditional methods, AI can evolve its detection and analysis methodologies accordingly. This dynamic learning ensures that Entity A remains a step ahead, anticipating and countering Entity B's moves effectively.
In synthesizing the above, it's evident that AI offers multifaceted capabilities, from exploiting technological vulnerabilities of adversaries to detecting patterns in seemingly innocuous human behaviors. However, as AI becomes a cornerstone of intelligence operations, it's crucial for Entity A to balance technological prowess with human intuition. It's this blend of machine efficiency and human insight that will truly fortify Entity A against surprises and threats, irrespective of Entity B's tactics.
The Role of Decision-making and Complacency in Intelligence Failures
While the spotlight often shines on a lack of signals or the inability to decode them as the primary culprits behind intelligence failures, a deeper analysis reveals that the roots often lie in poor decision-making processes and organizational complacency. These two factors, intertwined and reinforcing, can severely undermine even the most technologically advanced intelligence apparatus.
- Human Decision-making Biases: Cognitive biases play a significant role in decision-making failures. Confirmation bias, for instance, can lead analysts to give undue weight to intelligence that aligns with their pre-existing beliefs, while overlooking contradictory signals. Similarly, the anchoring bias might cause decision-makers to overly rely on an initial piece of intelligence, making them resistant to updating their assessments based on new information.
- Organizational Inertia: Intelligence agencies, like any large organizations, can fall victim to inertia. Established protocols, resistance to change, or a “this is how we’ve always done it” mindset can stifle innovation and adaptability. This inertia can make agencies slow to respond to evolving threats or adopt new methodologies.
- Complacency from Past Successes: Past successes can breed a dangerous level of overconfidence. An agency that has successfully thwarted multiple threats might develop a sense of invincibility, leading to reduced vigilance. Such complacency can blind agencies to emerging threats.
- Fragmented Information Flow: Intelligence organizations often consist of multiple departments, each specializing in a particular form of intelligence. If there's inadequate communication or coordination among these departments, critical pieces of intelligence might remain siloed, preventing a holistic understanding of the threat landscape.
- Reluctance to Challenge Established Norms: In many hierarchical organizations, junior analysts or officers might hesitate to challenge the assessments of their seniors, even if they disagree or have contrary evidence. This culture can suppress critical dissenting voices that might otherwise provide valuable counterpoints.
AI and advanced LLMs, with their enhanced autonomy, can play a pivotal role in mitigating these challenges:
- Objective Analysis: LLMs can analyze vast amounts of data devoid of human emotions or biases, offering an objective evaluation. This can act as a counterbalance to human biases, ensuring that intelligence assessments are grounded in data rather than preconceived notions.
- Continuous Learning: Advanced AI models can learn from past mistakes, continuously updating their algorithms to avoid previous pitfalls. This dynamic adaptability can help counter organizational inertia and complacency.
- Facilitating Communication: AI-driven platforms can ensure seamless communication and data sharing among different departments, ensuring that all stakeholders have a comprehensive view of the intelligence landscape.
- Encouraging Diverse Inputs: AI models can be designed to give weight to diverse inputs, ensuring that a broad range of perspectives is considered in intelligence assessments.
In summary, while signals and their accurate interpretation are undoubtedly crucial, the human and organizational elements play an equally, if not more, significant role in intelligence successes and failures. Recognizing and addressing the pitfalls of decision-making processes and complacency, augmented by the capabilities of AI and specialized LLMs, can pave the way for a more resilient and effective intelligence framework.
Blogs of War generated this text in part with GPT-4, OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.