You can also watch a longer interview with me where I discuss this topic:
Some people may consider what I write here fairly obvious. However, I believe that for others my description will offer a new perspective and help them understand how I see the big picture.
Simple description of intelligence
Intelligence is a tool that an intelligent agent uses to learn, adapt, solve problems, and achieve goals in a dynamic, complex, and uncertain environment. Intelligence achieves this by representing relevant parts of the environment in a simplified, abstracted mental model where searching for optimal solutions is faster and cheaper. Intelligence has fewer resources (atoms, computation cycles, energy, etc.) than the environment, so the intelligent brain must use resources in a smart way.
Evolution vs. intelligence
Like intelligence, evolution tries to find solutions or optimal ways to operate in a complex environment. However, instead of using a mental map or representation of the real world, evolution uses the real world – an environment that is extremely complex and has a nearly infinite number of parameters.
We can say that evolution is a “dumb” algorithm because it does not plan ahead and every solution has to be tested in the real world.
For example, we know that birds build different types of nests. Nests near the ground (where there are many predators) tend to have a protective dome structure, while nests in trees have an open cup shape. Birds do not use their intelligence to decide that when they build nests on the ground those nests should have a protective dome – instead, it is believed that it took generations before evolution found birds through natural selection who were suited to building ground nests with protective domes. Other birds are restricted to building nests in trees, because they haven’t evolved ground nest-building skills.
We can say that evolution “learns” by reusing and combining things that have already worked. However, its memory is limited – if it finds itself at a dead end, it has limited options to backtrack to a previous working solution.
Evolution is blind and has no sense of where it is, where it was, or where it is going.
Unlike evolution, intelligence searches for solutions in a simplified mental representation of the real world. The intelligent agent observes a portion of its environment and tries to create a simplified mental model of that environment. Since the agent operates with limited resources, it will model only those parts of the environment (internal, external, its own thinking, other agents, and so on) that are relevant for finding optimal solutions.
Since this representative model has fewer parameters than the real world and includes only relevant information, searching for solutions is faster and various non-evolutionary optimization strategies are made possible. These strategies include planning, forecasting, learning, abstracting, connecting things that are not close in time or space but may be related to one another, and more.
There can be higher or lower levels of intelligence and adaptability – depending on the abilities of the intelligent agent. At a certain low level, an agent doesn’t even need to be adaptive or intelligent to achieve goals, but can succeed simply by following a set of rules (discovered, for example, by evolution).
Properties and abilities that enable intelligence
Patterns are critical to the functioning of intelligence. A pattern is something that happens in a regular and repeated way (not random noise). Our current human approach to creating a mental model is to see the world as a hierarchy of spatial and temporal patterns which manifest as letters, words, songs, behavior, events, physical laws, and so on.
Intelligence depends on:
We can learn new patterns by transferring patterns from domain to domain, essentially problem solving by analogy. Learning by analogy means acquiring new knowledge about something (an object, an action, a problem, and so on) by transferring useful knowledge we already have about something similar (a similar object, action, problem, etc.).
For example, if someone learns how to use a hammer, this basic movement can later help them use an axe.
Attention: Attention is a phenomenon closely connected to the intelligent use of limited resources. Attention is a critical part of intelligence because intelligence models only those parts of reality that are relevant for a problem the agent is trying to solve. This is how I understand all levels of attention – mental, sensory, goal directed, etc.
Put simply, the intelligent agent uses previous experience and knowledge about the world to focus its attention on specific parts of the real world or specific parts of the model. The mind has limited processing and memory resources and cannot do or process everything at the same time.
People are always searching for new patterns, because finding patterns is what optimizes the results of intelligence and helps it achieve goals faster and more cheaply. We can say that new and useful patterns are as valuable as gold. However, pattern detection and generation cannot occur all at once or without preparation. Detecting and utilizing patterns (i.e. increasing intelligence) involves both gradual and guided learning.
Other mechanisms critical for understanding intelligence include:
IMPORTANT: The above properties and abilities are not sufficient for a human-level intelligence! This is why we have invented a framework for studying intelligence properties and abilities in a systematic manner – you can read more on this in our upcoming R&D roadmap for 2016.
Keep in mind this important principle: intelligence works in a simplified representation of the world where it searches for patterns that help it achieve optimal solutions. This model is much simpler than the real world and it also has a clearer structure. Intelligence can implement many optimizations that are impossible in real world investigations (planning, abstraction, and looking for correlations between events) and achieve efficient results. Intelligence always looks for more efficient, cheaper, and faster ways to use limited resources (mental, environmental, time resources, etc.).
Please let me know how you define or describe intelligence, if you think my description is too shallow or broad, where we can simplify it, and whether some additional details need to be added.
CEO, CTO & Founder
GoodAI, Keen Software House