To put it simply: A world model is an artificial intelligence system that has the ability to predict future states given our actions. Through building an internal model or representation of a specified environment, an agent experiments actions to learn predicted outcomes, resulting in a data-driven understanding of long-term events. In other words: a world model can tell you what is likely to happen as a result of any event or action that you take.
These artificial world models mirror natural cognitive processes, utilising human observations and senses which are transformed to concrete understanding and decision-making. While that may seem a bit abstract, you can begin to think of the world model agent analogous to an experienced sportsperson. A Formula One driver must be agile in predicting and responding to the dynamic conditions of various circuits. Whether a first-time encounter or experienced perspective of the track, a driver is trained to anticipate the most efficient racing path, adjust speed, and navigate their competitors, while simultaneously assessing external factors, like weather and vehicle conditions. Like a seasoned professional, the world model agent has been trained to instinctively anticipate and predict, adjusting its decisions to navigate the complexities of the digital environment. The technical mechanics of this example is further expanded upon and supplemented with the scenario of a baseball batter in David Ha and Jürgen Schmidhuber’s discussion of world models in their 2018 paper World Models (https://arxiv.org/pdf/1803.10122) if you are interested in reading more.
With this, we can start to piece together how world models enable refined and adjunct human decision-making through predictive frameworks that understand mechanisms of action, simulate scenarios, anticipate outcomes, and suggest discerning insights.
World models have already proved promising in video generation, gaming, and virtual reality applications; Ergodic aims to apply world models’ proven utility to a new field–enterprise.
Enterprise World Models offer a novel perspective into the most complex interactions and dynamics of your business. Unlike existing analytical tools that are only able to offer isolated insights, a world model digitally simulates the environment in which your business operates, understanding how your business functions and reacts to changes.
You can imagine a world model as your enterprise’s “chessboard” – hear me out. Sixty-four squares and thirty-two pieces, a finite number of moves and strategies to enact to win a match. Your agent is a grandmaster, a masterful strategist with an expert understanding the chessboard environment: the dynamics of each move, strengths of each piece, confounding variables, predicted responses of their opponent. A world model transforms you into a master strategist, uncovering unknowns in your enterprise’s environment, simulating and predicting the outcomes of different actions, the potential impact of each variable, and planning how to navigate challenges.
To give you a more concrete example of how a world model could integrate and respond to ongoing changes, envision the last time you were in a hospital emergency. Perhaps you were there as a patient or a companion, sitting in the waiting area you probably began to notice: the layout of the room, the number of fellow people waiting painstakingly for care; how many individuals are in scrubs; the time it takes for a patient to go from filling out a registration clipboard to getting called back to see a physician; the frequency of severe arrivals that put the room in standstill. All of these observations contribute to a world model understanding the specific environment, individual variables, time, frequency, and other data to navigate issues; in this instance, challenges related to patient overload, staffing status, time to treatment, and triage levels.
Essentially, a world model has the ability to enable human control through observation, equipping you to make more informed, strategic decisions backed by data-driven insights. Through utilising a world model, an individual can test different strategies, learn potential long-term opportunities and risks, ultimately reducing uncertainty of complex decision-making.
As an asset to your business, world models amplify and support your judgement, offering clarity to decision-making and enabling accurate long-term planning.
Choosing a world model over conventional AI options has significant advantages, from expanding abilities for long-term planning to reasoned actionable insights. World models complexity, robustness, and adaptability are a paramount compared to other approaches, specifically expanding the following conventional limitations:
Expanded Systemic Intelligence:
A world model integrates structured and unstructured data from internal and external sources, being more data efficient and less error prone.
Dynamic Adaptation:
A world model continuously evolves and incorporates real-time data and events into the model. The world is continuously changing, so does your model.
Prescriptive Analysis:
World models don’t just generate insights based on historical trends and predescribed context, it goes beyond predicting how the environment behaves and providing informed insights.
World models combine and build upon contemporary advancements in artificial intelligence including: deep learning, reinforcement learning, and complex system modelling. Enterprise-specific world models, deliver a host of capabilities, a brief introduction follows:
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Each of these six capabilities illustrate how world models are able to understand, predict, and interact, intelligently navigating the enterprise environment.
1. Retail: Demand Planning Analyst
The retail industry is under the constant siege of emerging trends and shifting consumer demands. Traditional purchasing patterns are no longer sufficient to predict market demands and adjust supply accordingly.
A Demand Planning Analyst is developing inventory stocking strategies for a high street brand, across their Scottish locations, to implement ahead of the holiday shopping season. Their current strategy of conducting analysis on the historical purchasing data and patterns has failed to accurately predict future sales, falling short of offering any pathway to navigate the unpredictability of the upcoming months.
Using a world model, the Analyst can experiment with their strategies within the model. Gaining a more robust understanding of how various variables such as sales trends, economic conditions, and marketing strategies influence consumer behaviour, allows them to better tailor their inventory approach. Additionally, the model recommends a detailed optimised restocking schedule and campaign to minimise unsold inventory, assisting the Analyst in refining their management process and creating a well-informed holiday strategy.
2. Healthcare & Biotechnology: Health Care Official
Healthcare is ever-evolving; scientists, biotechnologists, and health care experts, are constantly seeking improvements to better address human health and healthcare management.
A Public Health Official focusing on perinatal epidemiology in the Greater London-area wants to identify areas of inequitable care and factors contributing to maternal mortality in the United Kingdom. To understand how to better provide care for pregnant individuals, the Official wants to analyse treatment protocols for all-risk type pregnancies and hospital visits.
With a world model, the Official can summarise patient medical history, geographic location, and treatment protocol to learn more about pregnancy risk variables, as well as monitor hospital response to pregnancy visits to understand contributing care factors of maternal mortality. Utilising a world model of an emergency department and labour and delivery ward can be simulated and analysed to identify deficiencies in care and improve outcomes for patients and babies.
3. Manufacturing: Manufacturing Operative
Manufacturing supply chains are susceptible to disruptions and constantly aim to minimise costs. Processes are often subject to equipment failures, quality control delays, and labour shortages, often making it difficult to anticipate challenges.
A Manufacturing Operative is overseeing the first implementation of 3D printing at their bespoke automotive company. The main objective of this first release is to reduce waste and cost in the production process.
Using a world model, the Operative can track data on raw material availability, equipment status, labour, and overall output. This comprehensive approach allows the Operative to understand how the introduction of new printers is affecting processing speeds, how workers are adapting to the new technology, and the overall production levels. Additionally, the model forecasts how these factors will evolve as the printing process becomes more integrated with the factory’s team in the following months.
Ergodic started with a straightforward idea: AI should work for people. Our industry-agnostic approach helps anyone manage, interpret, and act on their data with confidence.
Whether you resonate with one of the examples or individuals we outlined earlier, or need assistance navigating a different set of objectives, we understand you have a unique role and crave, just as unique, solutions. Our drive is to make every decision you make, backed with unwavering confidence.
Our mission at Ergodic is to build an AI that can answer any type of question about your data and equip you with the tools to tackle any task, across every industry. We want to be your favourite coworker—reliable and trustworthy–an ideal sounding board, offering a supportive avenue to experiment with your most creative ideas. Our goal is to collaborate with you, combining our expertise and enterprise world models to help accelerate your impact and achieve your goals.
If we’ve piqued your interest, want to book a demo, and you want to learn more about Enterprise World Models reach out at contact@ergodic.ai & follow our socials!
Looking for your next role and keen to learn more about our team? Check out our careers page!
Don’t hesitate to listen to, our very own, Andre Franca, PhD., on ODSC’s Ai X Podcast:
https://open.spotify.com/episode/6dkXV9GRIEgrk0O6kHeumf
