Artificial Intelligence Flashcards
What was one of the first AI projects?
- Organised by John McCarthy, the Dartmouth workshop in 1956 studied 10 people for a duration of 2 months.
- It gave rise to the idea that every aspect of learning or any other feature of intelligence could be stimulated by a machine.
Why was AI challenged in its early days?
It was believed that a machine would never develop the capabilities to reason logically, problem solve or play games - that these features were reserved for humans.
Give an example of when the challenges associated with AI were overcome.
- Arthur Samuel created program which learned to play draughts (checkers) better than he could, disproving idea that computers can only do what they are told to do.
- Showed that computers could actually be intelligent.
What was one the weaknesses associated with automatic translation in its initial development?
- Poor translations deriving from the automatic translators (e.g. in the Cold War - a time when US didn’t have many Russian speakers so drove need for translation) caused funding to dry up.
- These initial automatic translators gave illusion of intelligence but didn’t really know anything about the subject, they were just manipulating syntax.
Give an example of an early chatbot.
- Eliza created in 1964-66.
- Early computer program
- Supposed to be a phycologist, offering intelligent advice and support.
- Not a reality, illusion of intelligence broken as picks up mere words and phrases.
- However, still considered intelligent as signified potential of computers.
What does combinatorial explosion refer to?
- Need for immense computer power to effectively deal with large number of combinations.
- This is prevalent through the game of chess where there can be multiple combinations of different moves.
What is a knowledge-based system?
- Emerged in 1969-1979.
- Generally a computer program that reasons and uses a knowledge base to solve complex problems.
What are some problems associated with knowledge-based systems?
- Difficult for computer to gain the knowledge.
- The creation of multiple expert systems also creates problems as unable to share knowledge.
- No way of making a generally smart computer
What is Artificial Intelligence?
The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
BUT is it really the goal to create a computer which acts as a human? More interest in creating something which can perform differently?
What is one of the main reasons why AI is taken more seriously now?
- More data exists than ever before (trillions of words and billions of images on web) therefore, more data demands more computing power which demands better algorithms which demand more investment which all allows AI to function more effectively and successfully.
Give an example which shows that AI has improved.
Google translate now provides much more accurate and reliable results that the automatic translators used during the Cold War.
Give examples the emerging AI of today which challenges the morality and ethics of AI.
- Always been a debate about ethics and morality behind creating AI.
- Development of self-driving cars. (E.g. Google has developed Waymo by collecting data of driving around streets)
- How safe do they need to be?
- They actually highlight how most crashes are caused by human error. E.g. self-driving shuttle bus crashed on 1st day because the human wouldn’t stop even though the bus did.
- Disrupting the job market in numerous sectors such as military, pharmacy and farming.
- Research suggests that some AI programs exhibit racial and gender biases, reinforcing them.
What are some other aims of AI?
- Developing a more global brain.
- Building a computer which can pass the Turing Test.
- Work cohesively with humans to produce a collective/combined intelligence.
What does supervised learning refer to?
The machine learning task of inferring a function from labeled training data. (I.e. a teacher is required in supervised learning/human provides some data)
What does unsupervised learning refer to?
- Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. (I.e. not specified what data is more important/machine learns from patterns etc.)
- Can help us gain knowledge by organising data and examining the patterns that emerge.
What does machine learning refer to?
- Another branch of AI.
- Allows software applications to become more accurate in predicting outcomes without being explicitly programmed.
- The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range.
What is generative design?
- Mimics nature’s evolutionary approach to design.
- Designers or engineers input design goals into generative design software, along with parameters such as materials, manufacturing methods, and cost constraints.
- Software explores all the possible permutations of a solution, quickly generating design alternatives.
- It tests and learns from each iteration what works and what doesn’t.
What are some benefits of generative design?
- Brings designs that would otherwise never have been considered to light
- Provides many designs that all fit the criteria
- Creator and software working co-operatively
- Example of optimisation
What is reinforcement learning?
- Branch of AI that allows machines & software agents to automatically determine the ideal behaviour within a specific context, in order to maximize its performance.
- Can produce novel (potentially surprising, worrying, disturbing) behaviour.
- E.g. Chatbot Tay released by Microsoft was taken offline 16 hours later for tweeting racist and sexually explicit messages. However, Tay was designed to learn from its users so internet trolls deliberately taught it offensive behaviour i.e. taking advantage.
