Generative AI Tabletop Fails
Generative ai tabletop fails will happen more and more.
Introduction
With the advent of generative AI and machine learning, it’s now possible to create adversaries that can learn from past experiences. These tabletop fails will help you understand the dangers of playing against an unpredictable opponent.
Generative AI is just a fancy way of saying ‘AI that can make decisions, plans and action on its own.’
So, what exactly is Generative AI?
Generative AI is just a fancy way of saying ‘AI that can make decisions, plans and action on its own.’ In other words, it’s an algorithm that simulates human intelligence in computer systems. You know how some video games have NPCs who act like real people? That’s generative AI! It’s also used in movies to create realistic looking characters like WALL-E (2008) or Jar Jar Binks (1999).
In tabletop exercises and wargames for cyber defense training purposes, generative AI can be used to disrupt the exercise by creating simulations based on past experiences with similar scenarios so that participants learn from their mistakes rather than repeating them again and again.
There are two types of AI – generative or reactive.
There are two types of AI – generative and reactive. Generative AI is capable of making decisions on its own, while reactive AI responds to what happens in the environment around it. These systems can be used for different purposes:
- Generative systems could disrupt tabletop exercises and wargames for cyber, reputation, brand communications and national/critical infrastructure defense by creating a simulated opponent that learns from past experiences (like humans do).
- Reactive systems can help train employees on how to deal with situations they may encounter in real life by giving them access to virtual environments where they can practice their skills without putting themselves at risk or wasting resources like time or money on live simulations
Generative AI can make decisions on its own, while reactive is more akin to a vending machine.
Generative AI can make decisions on its own, while reactive is more akin to a vending machine.
Generative AI is like a human. It can make decisions and act accordingly, while reactive AI is more like a vending machine that has no choice but to respond to what happens in its environment around it. This makes generative AI harder to predict than reactive (but still very unpredictable).
Generative AI will create simulated opponents that can learn from past experiences and adapt their strategies accordingly, which means they’re even harder to predict than regular generative algorithms!
Reactive AI will respond to what happens in the environment around it.
Reactive AI will respond to what happens in the environment around it. This can be used to automate repetitive tasks, make decisions and learn from past experiences. You can also use reactive AI to make plans for future actions.
Generative AI is much more unpredictable due to the random nature of its decisions and behavior.
Generative AI is much more unpredictable due to the random nature of its decisions and behavior. It can learn from past experiences and use that knowledge to disrupt tabletop exercises, wargames, and financial stress tests.
For example, say you’re running a simulated war game with a Generative AI opponent. You might want to see how well your unit tactics work against an enemy that acts like humans do–meaning they move around randomly and don’t always follow their plans perfectly. But if you ask a human player for help with this kind of experiment (or “sandbox”), they’ll probably try not to make things too easy on themselves because it doesn’t feel realistic enough for them! On top of that problem being solved by using a Generative AI opponent who doesn’t care about realism at all…there’s also another: Your sandbox could end up being too easy because someone else did all the work beforehand!
Generative AI could be used to disrupt tabletop exercises and wargames for cyber, reputation, brand communications and national and critical infrastructure defense by creating a simulated opponent that can learn from past experiences.
Generative AI could be used to disrupt tabletop exercises and wargames for cyber, reputation, brand communications and national and critical infrastructure defense by creating a simulated opponent that can learn from past experiences. The danger is that we’re not just creating an adversary but also teaching them how to do better next time.
AI is learning from its mistakes and successes, but it’s also learning from the mistakes and successes of other AI. And those other AIs are themselves learning from their own experiences with mistakes and successes–and so on down the chain. In this way, each new iteration of machine learning becomes more intelligent than its predecessors as it’s fed more information about how previous iterations performed in certain situations or tasks. This can lead us down a dark path where we give up control over what our systems are doing because we don’t understand how they work anymore (or even if they’re working at all).
Human involvement in most tabletop exercises will be replaced by artificial intelligence
As you can see, there are many reasons why AI cannot replace human involvement in tabletop exercises. Human intelligence is still the most valuable resource on the planet.
AI has made great strides in recent years and will continue to do so in the future, but it will always be limited by its programming and hardware. AI can’t replace human intuition, creativity or judgment because those things aren’t programmed into its system; they come from actual people who have been taught how to think by other people before them–and so on back through history until we reach our ancestors who first learned how to use tools like fire sticks or bows and arrows as weapons against prey animals such as deer or rabbits (or maybe even lions!).
Even if we were able to create an artificial intelligence with all these capabilities built-in from day one (which isn’t possible yet), there would still need to be someone around afterwards who could maintain that system over time while ensuring its continued functionality without needing any further upgrades due either software bugs/glitches affecting performance output accuracy levels etcetera…
Conclusion
While generative AI is still in its infancy, it has the potential to disrupt tabletop exercises and wargames for cyber, reputation, brand communications and national and critical infrastructure defense by creating a simulated opponent that can learn from past experiences. The danger is that we’re not just creating an adversary but also teaching them how to do better next time.
