Evaluación sensorial

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 Detection of credit card fraud

When a complete specification of tasks and environment exists, the operating environment of the system becomes a closed world consisting only of task-relevant information.

Narrow AI systems have – in a sense – a tunnel vision view on the environment, with static fixation points. A complete specification of task-relevant information can be derived from a complete operating specification without much effort. As a result, the attention of the system can be manually implemented at design and implementation time (as seen in the example above), with the concrete implementation being that the system processes particular information coming from particular types of physical or artificial sensors, while ignoring others known to be irrelevant – all dictated by the operating specification and pre-defined tasks. This results in an enormous reduction in the complexity and amount of information that the system needs to deal with, in contrast to constantly perceiving through all possible sensory channels in the target environment. Importantly, the frequency of which the environment needs to be sampled by the system (rate of incoming sensory information), and time constraints involved with the target tasks, may also be derived from the specification in the same fashion.

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 Completely ignoring modalities (in a general sense, i.e. data streams) that are available yet irrelevant to the task as specified.

 Filtering data for characteristics that are known, at design time, to be task relevant.

 Sampling the environment at appropriate frequencies (typically the minimum frequency that still allows for acceptable performance).

 Making decisions to act at predetermined frequencies that fit the task as specified.

A combination of these methods could allow narrow AI systems to effectively filter incoming information to deal with information overload, as well as being alert to predefined interrupts. As the task and environment are known, operational boundaries are also known to some extent, including boundaries with regards to how much information the system will be exposed to. A fixed type of attention based on the methods described above, along with proper allocation of hardware resources, would be sufficient for most narrow AI systems.

The previous section discussed examples of narrow AI tasks. In contrast, in AGI systems the luxury of knowing these things beforehand is out of question – by design and requirement. To illustrate, the following is an example of an AGI-level task in a real-world environment:

Let us imagine an exploration robot that can be deployed, without special preparation, into virtually any environment, and move between them without serious problems. The various environments the robot may encounter can vary significantly in dynamics and complexity; they can be highly invariable like the surface of Mars or the Sahara Desert and dynamic like the Amazon jungle and the vast depths of the ocean. We assume the robot is equipped with a number of actuators and sensors and is designed to physically withstand the ambient environmental conditions of these environments.

It has some general pre-programmed knowledge, but is not given mission-specific knowledge prior to deployment, only high-level goals related to exploration, and neither it nor its creators know beforehand which environment(s) may be chosen or how they may change after deployment. For the purposes of this example, missions are assumed to be time constrained but otherwise open-ended. The robot has the goal of exploration, which translates into learning about the environment, through observation and action.

Immediately upon deployment, the robot thus finds itself in unfamiliar situations in which it has little or no knowledge of how to operate. Abilities of adaption and reactiveness are critical requirements as the environment may contain numerous threats which must be handled in light of the robot's persistent goal of survival. Specific actuators may function better than others in certain environments, for example when moving around or manipulating objects, and this must be learned by the robot as quickly as possible.

Resource management is a core problem, as the robot's resources are limited.

Resources include energy, processing capacity, and time: Time is not only a resource in terms of the fixed mission duration, but at lower levels as well since certain situations, especially ones involving threats, have inherent deadlines on action. The resource management scheme must be highly dynamic as unexpected events that require action (or inaction) can occur at any time. (Thórisson & Helgason 2012, p. 4)

This example represents a case where the benefits of having a detailed operational specification at design time are not available. The goals of the AI system’s design are expressed at a high level of abstraction, precluding such a specification. Here the methods for reducing information and complexity

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for narrow AI systems, discussed above, do not help. For the exploration robot to accomplish its high-level goals, any of its sensory information may be relevant. At the same time, its resources are limited;

giving equal treatment to all information is not practically possible. Goals specified at a high level of abstraction are not unique to this example; they are a unifying feature of all AGI systems.

Such systems must learn to accomplish their own (high-level) goals by relating them to their sensory experience as collected in complex, real-world environments. Already several references to “real-world”

environments have been made. Some clarification is in order to disambiguate this concept. Researchers have built upon the work of Russell & Norvig (2003) in classifying environments for AI agents. The following discusses each of the environmental properties proposed by them in the case of the target and real-world environments.

1) Fully observable / Partially observable

This property is not critical to what is considered a real-world environment, but does raise an important issue. It is undesirable to limit the focus to the three-dimensional environments that people live their lives in and sense in a very particular way, a result of the biological sensory system of humans. Such environments can be abstracted to environments where the agent/human must perform proactive, goal-directed sensing, meaning that not all aspects of the environment are observable simultaneously at any given time. If particular aspects of the environment are not observable, reorienting sensors (as allowed for by the mobility of the system) can make other aspects of the environment observable.

However, in the process of making new things observable the scope of what was observable before may change. Additionally, a partially observable environment does not imply that the environment is fully observable if all possible agent positions and sensor orientations were somehow simultaneously possible, as there may be aspects of the environment that are relevant to the agent but can never be observed directly.

Environments where all information is visible at any time would be called “fully observable” by Russell &

Norvig. But this definition becomes less clear when we consider systems that perform active sensing where the system decides what senses to sample, and at what temporal frequency. One reason active sensing may be desirable is that real world environments contain such enormous amounts of information, that while in theory a system could observe the entire environment, practical issues such as available resources would make this completely impossible, as perception – even of just a small aspect of the environment – may demand significant processing resources. Consider also that time may be so fine-grained in the operating environments that no system will attempt to, or be able to, sense it at the lowest theoretical level of temporal granularity, inevitably causing it to miss some information. This is not to say that such extremely fine-grained temporal processing would be useful for the system, but rather to point out that any practical system is virtually guaranteed to miss some high-speed events that occur in the environment.

In a practical sense, our conclusion from all of this can only be that an AGI system must be expected to operate in partially-observable environments and that fully-observable environments are likely to be exceptions.

2) Deterministic / Stochastic

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In a deterministic environment, as defined by Russell & Norvig, any changes to the state of the environment are dictated only by the current state of the environment and the actions of the system.

This implies that no other entities can make changes to affect the environment, and also that the behavior of the environment is fully predictable to the system.

In stochastic environments, there is uncertainty and unpredictability with regards to future states of the environment and many different outcomes are possible. An AGI system will in all but the most trivial cases be dealing with stochastic environments because, whether the environment is truly stochastic in nature or not, there will be causal chains not immediately accessible or obvious to the AGI system that affect it.

Some aspects of the environment may be truly stochastic while others appear stochastic to the system because it does not have necessary knowledge to predict their behavior. Based on this, an AGI system must be expected to operate in stochastic environments.

3) Static / Dynamic

Static environments are not governed by the passage of time. When dealing with such environments, the system can take an arbitrary amount of time to decide the next action; the environment will not change meanwhile. This is clearly not the case for real world environments, where changes are driven by the clock of the environment regardless of the actions of the system. The present focus on real-world environments dictates that an AGI system must be expected to operate in dynamic environments.

4) Discrete / Continuous

Discrete environments offer a finite number of perceptions and actions that can be taken by the system.

A chessboard is a good example of a discrete environment, where there are limited ways to change and perceive the environment. Environments that do not have discrete actions and perceptions are called continuous; typically, this involves real valued action parameters and sensory information. Hence, we must assume continuous environments for AGI systems, while noting that continuous aspects can be approximated with fine-grained discrete functionality.

5) Single agent / Multi-agent

Choosing between these properties is not necessary for AGI systems. Many conceivable operating scenarios involve some type of interaction with other intelligent entities (e.g. humans) while there are perfectly valid and challenging scenarios that are of the single agent variety (e.g. space exploration).

The conclusion from the above analysis is that the types of environments that must be targeted for AGI systems are four (4):

 Partially observable

 Stochastic

 Dynamic

 Continuous

From this, an attempt can be made to define more formally the types of environment that AGI systems target.

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A real-world environment is a partially observable, stochastic, dynamic and continuous environment that is governed by its own temporal rhythm and contains vast amounts of continuously changing information.

As AGI systems are by definition unable to use the kind of techniques previously described for narrow AI systems, which rely on design-time domain-dependent knowledge, a fundamentally different approach must be adopted that involves making complex resource management decisions at run-time rather than design-time and gradually learning to adapt such decisions to actual tasks and environments that the system is faced with. Implementing such attention mechanisms is thus a key research problem that must be solved in order to realize practical AGI systems operating in real-world environments.