AI News, Artificial Intelligence/Definition
- On Sunday, September 30, 2018
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Over the past few years, you might have come across the term artificial intelligence and have imagined it to be a vivid personification of extraterrestrial beings or robots.
Over the course of this section, we will try to formulate a working definition, reasoning and articulating facts and preferences of various other authors and practitioners of the field.
In their book Artificial Intelligence: A Modern Approach, authors Russell and Norvig tried to establish a clear classification of the definition of the field into distinct categories based on working definitions from other authors commenting on AI.
Involves cognitive modeling - we have to determine how humans think in a literal sense (explain the inner workings of the human mind, which requires experimental inspection or psychological testing)
Problems: hard to encode informal knowledge into a formal logic system and theorem provers have limitations (if there's no solution to a given logical notation).
For (3.): Turing defined intelligent behavior as the ability to achieve human-level performance in all cognitive tasks, sufficient to fool a human person (Turing Test).
Searle believes that 'mind' emerges from brain functions, but is more than a 'computer program' - it requires intentionality, consciousness, subjectivity and mental causation.
One could go on and claim that these mental events are, in fact, physical events: My decision is neurons firing, but I'm not aware of this - I feel like I made a decision independently from the physical.
When I stand up after having made the decision (mental event), I'm not physically standing up, but my actions cause mental events in my and the bystanders minds - physical reality is an illusion.
Occurs whenever an organism or artificial intelligence is at some current state and does not know how to proceed in order to reach a desired goal state.
We dealt a lot with so called 'state space search' where the problem is to find a goal state or a path from some initial state to a goal state in the state space.
It is helpful to think of the search as building up a search tree - from any given node (state): what are my options to go next (towards the goal), eventually reaching the goal.
For search algorithms, open list usually means the set of nodes yet to be explored and closed list the set of nodes that have already been explored.
In order to direct the search towards the goal, the evaluation function must include some estimate of the cost to reach the closest goal state from a given state.
For CSPs, states in the search space are defined by the values of a set of variables, which can get assigned a value from a specific domain, and the goal test is a set of constraints that the variables must obey in order to make up a valid solution to the initial problem.
- On Monday, September 23, 2019
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