Unit 6.3 - artificial intelligence Flashcards
(14 cards)
What is artificial intelligence?
A branch of computer science dealing the the simulation of intelligent human behaviors by computers; it refers to a system’s ability to correctly interpret external data, to learn from it and to use those relevant learning to achieve specific tasks through flexible adaptation/adapt behavior based on experience. Its data model of past experiences help it plan & predict.
4 main characteristics of AI
- Perceive environment (through collection of data)
- Rules for using the data
- Ability to reason & detect patterns
- Update understanding & decide (learn and adapt)
Fundemental principle of how AI works
Training AI machine to recognize and remember patterns by feeding it with lots of examples, then feeding the machine new data and letting it decide what to do with it (independent of human interference).
What is Big Data?
Huge amounts of data that allow deep analysis of patterns and predictions of behaviors.
What is an expert system?
Systems that rely on humans to constantly update their data model and rules required to process the data in order to provide information to human experts by data analysis (which they perform by applying rules to the massive amounts of stored data in order to infer results)
They imitate the decision-making ability of human experts and are designed to solve complex problems based on set of information and rules.
Declarative programming
A type of programming where the intended outcome, along with the data model and rules are created but the precise steps to achieve the outcome are generated by the computer.
4 things required by expert system
- Knowledge base
- Rules base
- Inference engine - machine that processes rules & logic in order to arrive to a conclusion/result
- Interface to allow the user to enter querries and view results
Machine learning
The application of computer algorithm that improves automatically through experience. It’s used to discover patterns in data and make predictions based on those patterns; capable of updating their own data models when they encounter new models. It uses statistical techniques (e.g. calculating averages, probabilities & trends, measure errors, adjusting predictions based on that and finding correlations) to learn how to get progressively better at a task, without having been specifically coded for the purpose.
Deep learning
Type of machine learning that runs inputs through a biologically inspired neural network architecture which contains a number of hidden layers through which the data is processed, allowing the machine to go deep in its learning, making connections and weighing input for the best results.
5 real-world applications of AI
- Self-driving cars
- Smart assistants
- Interpreting medical images
- Fraud detection
- Optimization of harvests
3 types of artificial intelligence
- ANI - artificial narrow intelligence which specializes one area and solves one particular problem; machine learning
- AGI - artificial general intelligence which refers to a computer that is as smart as a human across the board; machine intelligence
- ASI - artificial super intelligence which is an intellect that is much smarter than the best human brains in almost every field; machine consciousness
What type of data does machine learning use to predict future outcomes?
Historical data
What type of data does machine learning work with?
All types of data - images, sounds, text, numbers, temperature readings and more!
Advantages vs disadvantages of expert systems
+ high level of accuracy & expertise, consistent results, faster response than human experts, can store large amounts of data & facts
- only as good as the data entered into the system, optimal use requires training, responses can be cold & lack humanity, may still make mistakes
