Articles tagged with oracles

1. LLMs and intelligence analysis: tools not oracles

   Richard Heuer in the classic “Psychology of Intelligence Analysis” made these points:

   • Human minds are poorly “wired” to deal with uncertainty.
   • Simply learning about our biases doesn’t in fact improve outcomes.
   • Instead, we need tools and techniques for structuring information, challenging assumptions and exploring alternative interpretations.

   Therefore, he said, we should encourage products that “clearly delineate assumptions, chains of inference and specify degree and source of uncertainty.”

   This is a problem for AI tech! An LLM is a big ball of assumptions and uncertainty. Naive use of LLM tech will only degrade our analyses.

   If you hoped that the AI would replace human analysts by slurping up OSINT at scale and spitting out actionable analyses on demand, I’m sorry but that won’t work. We must think of AI technology as providing “tools, not oracles”.

   Instead, we can take advantage of the incredible (and rapidly improving) LLM technology to build tools. The actionable insights will always come from human analysis. The AI tools can supercharge our ability to open our minds, help us to explore possibilities and structure our knowledge to provide guide ropes for creativity.

2. Who makes the decisions?

   When the internet first came along, it immediately transformed intelligence collection and analysis. Suddenly, there was a new source of material, unfiltered through governments and newspapers. Over the years the flood gates opened wider and wider, until the amount of material became unmanageable by traditional analysis techniques.

   Big data is a loaded term. It means something different to a Google or Facebook who analyse their customers in a strictly limited domain, trying to prove click attribution or ranking search results. Statistical methods are effective ways of doing this.

   In intelligence, there’s a hope that we can use AI to make sense of the deluge of information. But we must be careful. Our best AIs today will give you a facsimile of a person from 2024, but past performance is not indicative of future results. After all, an AI trained on data up to and including the 18th century will give you a facsimile of an 18th century person, but won’t tell you anything about what happens in the 19th.

   Perhaps the biggest problem we face is a poverty of imagination. We have fallen into narrative-based thinking, modelling adversaries and allies as predictable economically-driven automatons, even as we’re hit by events that we failed to predict, or often were told were impossible — everything from Trump and Brexit to Ukraine and Gaza. There’s a real danger that a naive approach to integrating AI decision support will weaken our ability to understand reality by constructing a fantasy cocoon of statistically based reasoning about the world.

   All is not lost. We can take advantage of AI to help us with the flood of source material, but not simply by training models on the data and expecting those models to spit out predictions. Instead, we must look to the work done on frameworks intended to make sense of human behaviour for human analysts, frameworks which are used today to great effect in the field and in training.

   At Deep Drama, one approach we use is to use LLMs to automate the generation of Confrontation Analysis (Drama Theory) models from intelligence sources. With those models as a base, other LLMs can provide insights, simulations and decision support tied to a causal chain of reasoning from the model, not from the blackbox AI.

   The goal of conflict analysis must be to understand our adversaries. In a complex, multi-polar world, this is a daunting task, and we can and should use AI to help. However, real help does not come as predictions from a blackbox. At Deep Drama we are building “AI intelligence analysts” that can sit alongside decision makers, keeping track of the complexity and providing guard rails for decisions as they are made.

3. Against prediction

   Everybody wants someone to tell them what's going to happen. But whatever consciousness is, it's fundamentally unpredictable. As long as you're in the human domain, telling the future is out.

   There's a story Drama Theorists use to illustrate this. A husband and wife are playing chess. The husband makes a move, "checkmate". The wife objects, wait, there must be something I can do. No, says the husband, it's checkmate -- there are no possible moves. The wife reaches out and smashes the board into the air, pieces flying, "how about this for a move?".

   Okay, so life isn't a game, there aren't rules. So what then? If analysis isn't predictive, what is it? Analysis is the art of understanding the present moment as fully as possible. Choosing models for compressing reality to aid decision making. The actions that make up the future will come from unpredictable human creativity. A wider understanding of the present moment gives our creativity the best grounding from which to imagine the future.

   AI can't predict the future, but it can help us model the present.

4. Decision making needs explainable AI

   Decision making is being transformed by AI technology. LLMs are a new capability, allowing us to quantitatively model human behaviour like never before. However, using LLMs to drive a statistical approach to decision making will only end in disillusionment.

   Those who invent a technology often are not best placed to take advantage of that technology. The current advances in AI were born from statistics, and it's a natural impulse to use them for “statistical” approaches. For example, one might try to use the ability to mine vastly larger datasets to predict human behaviour from analysis of past behaviour. This statistical black box approach is wrong. Human behaviour is inherently unpredictable. Decision making is about choices, not narratives.

   Instead, we can use LLMs to supercharge our modelling of human behaviour. The models are explainable because they are using frameworks intended for human use. LLMs can help humans to understand, while the explainable model is always there as a ground-truth. We can also use generative AI to make exploratory sallies into possible near-futures. All of this gives decision makers better understanding of their choices and those of the counter-parties.

   In decision making, there is no black box that will output a perfect strategy. Better choices come from a more complete understanding of the situation. We must be careful never to fool ourselves with statistically derived far-reaching narratives. In the end, our opponents are creative humans, not trendlines on a graph.

5. Interface to human language

   One approach to making use of LLMs is to see it as an oracle: we can say "solve this problem for me", or at least "give me a choice of solutions". As all the answers to our problems can theoretically be generated, we can go ahead and retire all the human theorists and engineers. Science is solved.

   This approach assumes that creative problem solving is a matter of rearranging existing knowledge. Or, if more knowledge is needed, that knowledge acquisition is a process of mechanically recording the universe. Both are mistaken. We don't know the algorithms for creative problem solving or knowledge acquisition. While the "LLM as oracle" approach will certainly produce advancements in knowledge retrieval, it won't create new knowledge.

   Another approach, one that we're following at Deep Drama, is to use the LLM primarily as an interface for human language. The potential of "LLM as language API" is greater than it seems. There is the obvious path of building more powerful User Interfaces far beyond chatbots. But there is also the potential to expand the scope of our software, beyond solving transactional problems to encompass as-yet-untapped social frameworks. Our software has blind spots. Large areas of human knowledge and experience have been overlooked by engineers because of the messiness of their interfaces.

   As an example, I used Deep Drama's LLM-powered Source tool to generate this Drama Theoretic model of a random news article:

   	Don	Joe	Nip	Uni	dilemmas
   Donald Trump
   Block the deal immediately if winning the 2024 election	✓
   Joe Biden
   Express opposition to the merger	✓	✓		✓	Don T wrt Joe Uni T wrt Joe
   United Steelworkers labour union
   Oppose the merger		⨉	✓	✓	Joe P wrt Uni Nip T wrt Uni
   Nippon Steel
   Acquire US Steel in a non-hostile deal worth $14.1 billion	⨉	⨉	✓	⨉	Don P wrt Nip Joe P wrt Nip Uni P wrt Nip
   Introduce new technology and capital to US Steel		⨉	✓	⨉	Joe P wrt Nip Uni P wrt Nip

   The model's format is explainable and programmable. By using the LLM as an interface to language, Deep Drama can then use this model as the basis for further analysis and interaction, both with LLMs and with traditional interfaces. Deep Drama keeps the knowledge, and the opportunity for creativity, in the hands of the human users.