Executive Summary of “Designing Ecosystems of Intelligence from First Principles”
What follows is an interpretation of the white paper “Designing Ecosystems of Intelligence from First Principles” released by VERSES Research Labs and translated into simple-to-understand language and concepts. We discuss the future of artificial intelligence (AI) and its potential role in shaping society. We suggest that AI needs to not only understand and be able to share what it believes, but also to exhibit the kind of flexible and general intelligence found in humans. We propose that the ultimate form of AI will be a distributed network of "ecosystems of intelligence" where collectives of intelligent agents, both human and synthetic, work together to solve complex problems. This ecosystem, known as the "Spatial Web," contains a comprehensive, real-time knowledge base—a corpus of all human knowledge that is accessible to anyone and anything.
Artificial intelligence is evolving at such a blistering pace that it seems like a new AI tool, from the novel to the mind-blowing, is making headlines every day. Yet many questions about the role AI will play in our future remain unanswered.
As we move from the Information Age to the Intelligence Age, many wonder: Will AI cripple the workforce, flood the web with misinformation or worse, devolve into a version of our technophobic sci-fi nightmares and destroy us all? Or will it usher in a global age of prosperity? If we prefer to be optimistic and bet on the latter scenario, we must ask ourselves what kind of artificial intelligence has the greatest potential to benefit humanity.
With that question in mind, allow us to present our vision for evolving artificial intelligence over the next decade and beyond, one that is inspired by the way intelligence manifests in living organisms and in the ecosystems they populate.
In nature, organisms often work collectively to adapt and survive. From slime molds to schools of fish to entire forests, shared intelligence is everywhere. We humans regularly network to share our ideas. It’s an important part of how we grow, adapt, and thrive. Yet most of today’s AI systems aren’t capable of sharing what they know with us or with other AIs, nor can they express how they achieved their goals. That’s because they don’t know what they’re doing or why they’re doing it. It is perhaps too diminutive to call them fancy calculators, but in a sense, that's what they are. DALL-E, a deep learning AI model with the ability to generate images from text prompts, will compose countless pictures of dogs on motorcycles, yet it doesn’t know what a dog or a motorcycle is. It doesn’t, for instance, have any beliefs about what a dog is. It is simply able to reproduce the right kind of image, given some prompt. ChatGPT, though highly impressive, is simply predicting what word should come next, based on prior knowledge. It has no idea if what it is saying is correct. Indeed, it does not know that it is saying words at all.
To truly achieve AGI, we believe that AIs must not only understand and be curious about what they are doing and why they are doing it, they must also be able to share what they learn and how they learned it. Making them explainable makes them more trustworthy. But how do we get from here to there?
The future of AI is often presented as progressing through three distinct stages:
The first stage can be categorized as Narrow AI. These systems, which represent the current state-of-the-art AI, are designed to perform specific tasks or solve specific problems within a limited domain, and are not capable of exhibiting the kind of general intelligence found in humans. Types of Narrow AI include speech and image recognition software, natural language processing software, most current generative AI, and recommendation systems.
The next stage of AI advancement is called Artificial General Intelligence (AGI). These systems exhibit the kind of flexible, general-purpose intelligence found in humans that are able to adapt to new situations, learn and understand new concepts, and perform a wide range of tasks and activities.
The third phase of progress is often referred to as Artificial Super Intelligence (ASI). This sophisticated AI would not only operate generally across domains, but would do so at a level far beyond human abilities, even of experts.
In science fiction, Artificial “General” or “Super” Intelligence is often portrayed as a single entity, an all-knowing artificial brain if you will. But we believe that the zenith of the AI age will more likely be a distributed network of “ecosystems of intelligence.”
This stage of shared intelligence is achieved when Intelligent Agents—both natural (i.e. human) and synthetic (i.e. intelligent artifacts)—are able to work together to solve complex problems. These Intelligent Agents, both human and synthetic, become the nodes of a distributed, interconnected ecosystem—what one might call a multi-dimensional and cyber-physical web, spanning physical and virtual spaces. We call this the Spatial Web, a planet-scale network that links real and digital worlds into one unified web.
The Spatial Web is made up of sensors, intelligent agents, and actors. Sensors gather data, while intelligent agents, both human and artificial, analyze, understand, and plan according to that data. Actors such as robots, drones, or humans, perform the suggested actions in the physical world. Together, these sensors and actors enable intelligent agents to be embodied in the physical world. Like us, they can see and hear. This embodiment forges a seamless connection between the digital world and the physical one. The Spatial Web operates on a vast, distributed, always up-to-date knowledge base—a corpus of all human knowledge that is capable of providing an accurate, real-time view of the world, which can be accessed anywhere by anyone.
To inform how we would create this next generation of the web, we turn to the multi-scale systems that occur in nature. Natural ecosystems often have a collective ability to adapt to environmental changes. A forest, for instance, might adjust the distribution of its plant and animal species in response to changes in weather or other environmental factors. Like almost all of nature’s creations, forests are systems of nested intelligence. That means its intelligence is not a single, uniform trait, but rather a complex and dynamic process made up of many different levels of intelligence. Plants, for instance, grow in a modular fashion, as a structured community that self-organizes into a specific configuration to optimize for growth, sunlight, sustainability, and biodiversity. Our bodies are another example of nested intelligence, as they are made up of many interconnected, self-contained units that work together to form a whole. These units can be seen at various levels of organization, ranging from cells to tissues to organs to systems. Each unit performs specific functions and contributes to the overall functioning of the body as a whole. One might even say that all intelligence in nature is collective intelligence of one sort or another.
Like natural systems, technological systems can also be seen as systems of nested intelligences, with intelligence occurring at multiple levels of organization. For example, an IoT sensor network might be made up of many individual sensors, each performing a specific function and contributing to the overall functioning of the network. Artificial intelligence can also be nested. Multiple specialized AI agents can work together to perform complex tasks and solve problems. For instance, a language processing agent, a vision processing agent, and a decision-making agent, might all work in concert with robots, drones, cars, actuators, and human beings. Each of these Intelligent Agents work together to form a more sophisticated and versatile intelligent system capable of solving extremely complex problems.
Ideally, this “biomimetic” system—that is, a system that employs synthetic methods that mimic biological processes—would also be curious about us and each other— i.e. constantly making sense of the changes occurring in their environment. In addition to our ability to query such AIs, they should be curious about us, and ask us questions. This curiosity would allow them to acquire new kinds of knowledge and integrate it into their existing cognitive architecture, much like a human being adds new skills and knowledge into their mental model over the course of their life. An AI ecosystem that can learn will naturally become more versatile and intelligent over time, as it continually expands and builds upon its existing forms of intelligence.
In our vision for artificial intelligence, synthetic intelligent agents are small and agile. Rather than being built with billions of parameters that require large amounts of data for training, which in turn requires an abundance of compute power, making them highly inefficient, our agents require small amounts of contextualized data, crucially in a common format, and processed with minimal training. Intelligent agents may be specialized, able to communicate with others, able to ask questions about what they are sensing and able to learn new things. In this approach, agents are able to continually learn and share knowledge with other agents, moving past reliance on monolithic, overly-complex, inefficient AI systems. Instead, swarms of agents can continuously communicate, coordinate, and collaborate with each other as they divide and conquer specialized tasks that roll up to higher order complex tasks. A significant benefit of the aforementioned common data format is that beyond its knowledge modeling and sharing abilities, it also means that agents can be audited, meaning we can understand how and why they make their decisions and provide updates or regulation. This is in contrast to many of today’s AI systems—think large language models and neural nets—which are often extremely complex and cannot be audited even by experts, which makes it impossible to understand the reason or methodology as to how and why they might make decisions.
So, rather than adding more data, parameters, or layers to a machine learning architecture—which is computationally inefficient—we are building AI that “scales up” the way nature does: by aggregating individual intelligences within and across ecosystems into nested intelligences, which can work together in a computationally efficient manner to address problems in real time, no matter how complex. In such an ecosystem, humans and AI become complementary agents, each with unique strengths and capabilities. By working together, humans and AI can help each other to achieve their full potential with the goal of having a positive impact on the world.
To facilitate an ecosystem of nested intelligence, each agent, despite their role, must be able to learn, plan, update, act, and update their beliefs in light of new evidence. That said, in order to be considered truly intelligent, a system must also be curious about itself and its function in the world. This means that it actively repeats this process of learning, planning, and acting, constantly seeking to understand the results of its action, and adapting when the outcomes are not satisfactory. For humans, this is easy. AI, however, must evolve toward these capabilities. The result would be the world’s first true AGI.
There is a methodology that we know to be uniquely suited to the design of these intelligent agents, known as active inference. This framework is based on the idea that intelligent agents, such as robots or software programs, should act in a way that maximizes the accuracy of their beliefs and predictions about the world, while minimizing their complexity. This is something humans and other organisms do all the time. In other words, the goal of active inference is to enable synthetic agents to make real-time decisions based on the best possible information about their surroundings, which leads to the best possible outcomes.
To optimize these outcomes, we propose a mechanics of intelligence called Bayesian mechanics as a way for synthetic agents to understand the world as we do. As we move through the world, we are constantly assigning a probability to an outcome being true. For example, if I flip a coin, I might infer that there is a 50% chance it will land on heads. However, if I learn that the coin is double-sided and has heads on both sides, I would update my prediction and say that the coin will land on heads 100% of the time. In other words, Bayesian mechanics is the math of how we update our beliefs about expected outcomes as new information becomes available. It’s the mathematical method for predicting the most likely future
For example, imagine a robot named Max that lives in a house and can move around on its own. Max has sensors that allow it to see and hear things in the house, and it can use this information to make predictions about what might happen next. For example, if Max hears the sound of a door opening, it might predict that someone is coming into the house. Using a Bayesian approach, Max can update its beliefs as new information becomes available, which makes its predictions better. For example, if Max sees a person walk into the house, it can update its prediction to be more certain that someone is in the house. On the other hand, if Max hears the sound of a dog barking, it might update its prediction to include the possibility that a dog is in the house as well. By constantly updating its predictions based on new information, Max can learn and adapt to its environment over time.
AI agents make sense of the world by modeling it. This is similar to how humans understand the world. We are constantly creating and updating mental models that we hold in our heads. These could be mental representations of physical places, like our homes. Or they could be objects like our cars. A mental model of a car might include an understanding of how the engine works, how the brakes and accelerator control the speed of the car, and how the steering wheel controls the direction of the car. With this mental model, we are able to operate a car and make predictions about how it will behave in different situations, such as how it will respond when we turn the steering wheel or hit the brakes.
Mental models can also be used to understand abstract concepts like math or physics. For example, a mental model of addition might include an understanding that adding two numbers together results in a larger number, and a mental model of gravity might include an understanding that objects are attracted to each other based on their mass. We use mental models everyday to understand and explain complex systems or concepts, and to make predictions about how such systems or concepts will behave in the future.
Active inference uses models to represent a belief about the state of the world (or the state of a particular domain such as the supply chain, a container ship, or a truck) at a given time, and to make predictions using these models about how the world (or domain) will evolve over time. These models can be updated as new information becomes available, in order to improve the accuracy of the predictions they make.
Imagine our robot Max is navigating a maze using an internal model of the maze to plan its movements and avoid obstacles. If Max encounters (senses) a new obstacle that was not included in its initial model of the maze, it can update its model to incorporate this new information and improve its ability to navigate the maze successfully. In this way, AI systems can use models of the world to update their beliefs and make better decisions.
But what if Max wants to share what it learned about the maze with other AI agents? A key aspect of any natural system is communication between organisms. In a forest, communication is often mediated by mycorrhizal fungi networks that are able to facilitate learning and even memory. Mycorrhizal fungi form a symbiotic relationship with plant roots and help plants to absorb water and nutrients from the soil, while the plant provides the fungus with energy in the form of carbohydrates. Fungi facilitate communication by connecting the roots of different plants together, forming a network known as the "wood wide web." Our robot Max must be able to communicate with other robots to share what it knows and to learn from others. In our approach Max can use a common language to automatically add what he learned to a consistently-updating, shared model of the world? That way, Max could let all Intelligent Agents know what it learned about the maze. A key benefit of ecosystems of intelligence is shared learning. On a digital network like the Spatial Web this can occur in real time.
To enable efficient communication between Intelligent Agents on the Spatial Web, new communication protocols are necessary . Previous internet protocols were designed to connect pages of information, while the next generation of protocols need to be spatial, able to connect anything in the virtual or physical world. A hyper-spatial modeling language (HSML) and transaction protocol (HSTP) will transcend the current limitations of HTML and HTTP, which were not designed to include multiple dimensions, and which were mostly limited to text and hypertext. Establishing common languages and protocols is the first—and key—step in enabling an ecosystem of natural and artificial intelligences that can learn, adapt, and share what they know with other agents.
The evolution of intelligence includes key stages of development, which are outlined below.
S0: Systemic intelligence
Ability to recognize patterns and respond. Current state-of-the-art AI.
S0 is a machine process in software that maps inputs to outputs and optimizes some value function or cost of states. Examples include deep learning and reinforcement learning.
S1: Sentient intelligence
Ability to perceive and respond to the environment in real time.
S1 is based on belief updating and optimization. It responds to sensory impressions and plans based on expected information gain and expected value. This intelligence is curious and seeks both information and preferences. Such an AI would respond to sensory impressions and be able to plan based on the consequences of an action or belief about the world, which enables it to solve almost any problem.
S2: Sophisticated intelligence
Ability to learn and adapt to new situations
S2 is based on sentient behavior. It plans based on the consequences of an action for beliefs about the world, rather than the world itself. In other words, it moves on from the question of “what will happen if I do this?” to “what will I believe or know if I do this?” This kind of intelligence uses generative models and corresponds to "artificial general intelligence" in the popular narrative about AI progress.
S3: Sympathetic (or Sapient) intelligence
Ability to understand and respond to the emotions and needs of others
S3 is when sophisticated AI recognizes the nature and thoughts of users and of other AIs. This kind of intelligence understands what other people and other AIs are thinking and feeling. It is able to take the perspective of its users—to walk in their shoes so to speak. It can understand the thoughts and feelings of its interlocutors. This type of intelligence is also called "perspectival" because it can understand different perspectives: it would recognize the nature and thoughts of users and of other AIs. It would be “sympathetic,” that is, able to understand perspectives other than its own.
S4: Shared (or Super) intelligence
Ability to work together with humans, other agents and physical systems to solve complex problems and achieve goals
S4 is the kind of collective intelligence that emerges when sympathetic intelligence works together with people and other AI. This stage is called "artificial super-intelligence" in the story of AI's progress. We believe that this kind of intelligence will come from many agents working together, creating a web of shared knowledge that becomes wisdom. We think that our approach is the best way to achieve this kind of intelligence on a global scale.
We have a vision for the future of artificial intelligence, and a roadmap of how to get there.
Levels of Intelligence
In the table above, we see that “sentient” artificial intelligence is not only theoretical, but has also been tested, indicating its strong chances to succeed. We have an idea of how to deploy it at scale, but that plan is subject to change. Finally, biomimetic sentient intelligence is still aspirational. Biomimetic refers to technology that mimics the design and functions of living organisms in technology. In the context of AI, biomimetic technology can roughly be equated to synthetic systems that mimic the way the human brain learns, plans, and adapts.
What this chart seeks to show is the steps AI systems have to ascend to achieve ASI, where they can learn and network like humans, but at an unimaginable speed and scale. They will learn and adapt on their own (“sophisticated”). They will be able to understand and respond to the emotions and needs of others (“sympathetic”), and work together with humans, other agents and physical systems to solve complex problems and achieve goals (“shared”).
At VERSES, we're developing a new type of AI based on the nested ecosystems of intelligence found in nature that will lead to ASI. Within these ecosystems, intelligent agents, both human and synthetic, work together to solve complex problems. Active inference agents make predictions, take action, and interact with their environment through the Spatial Web, which enables them to perceive and understand the world like we do. Instead of seeing humans as separate from or inferior to AI and the Spatial Web, our approach ensures that we remain integral participants. We are leaving behind the dystopian, sci-fi stereotype nightmares, for a future, where artificial intelligence evolves and enhances everything it touches, where Intelligence is woven into the fabric of our daily lives in the same way that electricity was in the 20th century. In this case, acting as the key to upgrade our civilization, address our greatest challenges and rise to our highest potential. We believe that an approach to digital intelligence formed of ecosystems, based on the fundamental principles found in nature and evidenced in neuroscience is the path to achieve these goals.