week 6 - ai in business

Question 1

what is artificial intelligence?

Answer 1

the study + design systems that have the ability to perceive + evaluate the environment + take actions that maximises the changes of success.

Question 2

what are the definitions of intelligence?

Answer 2

• human performance (achieving results)
• rationality (doing the right thing)
• thought processes + reasoning (internal character)
• intelligent behaviour (external character)

Question 3

what are the different approaches in understanding ai?

Answer 3

• the pursuit of human-like intelligence (psych, observations, thought process + hypothesis).
• rationalist approach (combo of maths, stats, engineering, control theory + econ).

Question 4

what is meant by the turing test?

Answer 4

a test which can help determine whether a computer can pass as a human to understand its intelligence.
cares about the outcome regardless of whether its reliable or accurate.

Question 5

what does a computer need to do to pass the turing test (and are also the six disciplines that are ai)?

Answer 5

Natural language processing to communicate.
Knowledge representation to store what it knows or hears.
Automated reasoning to answer questions + draw conclusions.
Machine learning to adapt to new circumstances + detect patterns.
Computer vision + speech recognition to perceive the world.
Robotics to manipulate objects + move about.

Question 6

what is the cognititve model approach?

Answer 6

where ai simulates the human thought process.

Question 7

what are the 3 ways humans think?

Answer 7

• introspection (observing thoughts + emotions in action).
• psychological experiments (observing an action in person).
• brain imaging (observing a brain in action).

Question 8

how is the cognitive modelling approach used?

Answer 8

once it has a precise theory of the mind it becomes possible to express the theory as a computer program.

Question 9

what does cognitive science do in relation to the cognitive modelling approach?

Answer 9

it brings together computer models from ai + experimental techniques from psych to construct precise + testable theories of the human mind.

Question 10

what is meant by the “law of thought” approach?

Answer 10

it entails the ideas of thinking rationally through logic and applying that into ai algorithms.
describes how the human mind should work laying down the foundation for the field of logic.

Question 11

what does it mean by codifying right thinking?

Answer 11

logic provides structured patterns for reasoning, ensuring that info is arranged in a way that others can follow - creating rules that govern rational thought.

Question 12

what are the limitations of logic?

Answer 12

it always assumed that knowledge is 100% certain (in reality it deals with uncertainty).

Question 13

how can we reduce these limitations of logic in the “law of thought” approach?

Answer 13

the use of probability in ai can help fill this gap by allowing reasoning with uncertainty, modern ai uses probabilistic models instead of strict logical rules.

Question 14

is thinking rationally the only way to create intelligent behaviour?

Answer 14

no, rational thinking is only 1 way to create intelligent behaviour + needs other approaches to create enough to be successful.

Question 15

what is meant by the rational agent approach?

Answer 15

a development of a decision theory and ai planning that focuses on rational action not just rational thought.

Question 16

what is meant by an agent in ai?

Answer 16

anything that percieves its environment + takes action to achieve a goal. this could be a self driving car or chatbot.

Question 17

what is meant by a rational agent?

Answer 17

one that makes decisions to maximise its chances of success.

Question 18

what are the key characteristics of a rational agent?

Answer 18

1. autonomy (operates independantly without human intervention).
2. perception (observes environment through sensors).
3. goal oriented (makes decisions that align with a defined objective).
4. adaptability (can learn + adjust its actions based on new info).
5. persistence (can work over a long period of time without consistent monitoring).

Question 19

what are the different ways in which we can think rationally?

Answer 19

• deduce that a given action is best + then act on that conclusion (not always correct).
• ways that can’t be said to involve inference (eg recoiling from a hot stove is a reflex action that is used more successfully than a slower action taken after careful consideration).

Question 20

what is the conclusion made of the ways of thinking rationally?

Answer 20

that thinking rational is one way to act rational.

Question 21

what are the advantages of the rational agent approach?

Answer 21

• can be mathematically defined + well tested.
• recognises there are many possible strategies for making intelligent decisions.
• easier to analyse scientifically as it is based on mathematical models of decision making.

Question 22

what are the disciplines that contributed ideas, viewpoints + techniques of ai?

Answer 22

• philosophy
• maths
• economics
• neuroscience
• psychology
• linguistics
• computer engineering
• cybernetics + control theory

Question 23

is ai moving from automation to augmentation?

Answer 23

the relationship between humans + machines is shifting to a more collaborative + augmentative one. instead of viewing ai as a threat that replaces human workers it should be seen as a tool that enhances human abilities.

Question 24

what is meant by opacity?

Answer 24

ai is opaque meaning it is not always possible to expkain its outcomes.
the opposite of transparency, referring to when info is hidden or difficult to understand.

Question 25

what are the reasons for ai being opaque?

Answer 25

- complexity use of the algorithmic code - the use of multiple algorithmic components which are not all know or under the control of the designers. - intrinsic characteristics of learning algorithms that redefine parameters + relationships with each additional data point which makes it hard to explain, algorithms have to be tweaked to make their results understandable.

Question 26

how does algorithmic opacity affect value creation?

Answer 26

if ai is not explainable it is impossible to determine the value it generates for orgs.

Question 27

what is meant by explainability paradox?

Answer 27

it refers to the challenges in ai where making a model becomes more + accurate makes it less interpret-able so looks at the needs for human understandable explanations of their decisions + behaviours.

Question 28

how does this create dilemmas for designers and users of ai?

Answer 28

they need to balance the need for accuracy + performance with the need for transparency + accountability. it can be difficult to understand + interpret, even for experts, which can raise concerns about their reliability, fairness

Question 29

what is a benefit for prioritising complexity + accuracy of ai models?

Answer 29

highly complex ai models such as deep neural networks can achieve impressive levels of accuracy in tasks such as image recognition or natural language processing.

Question 30

can you discuss an example where privacy issues arose from prioritising complexity + accuracy in ai?

Answer 30

NHS + Deepmind partnership - wanted to help detect kidney failure in patients however: - data was shared without proper consent from patients which included sensitive info. - the nhs did not inform the patients on how their data would be used. - deepmind initially claimed the data would not be shared with google however in 2018, google took it over. this caused fears about the commercial use of data.

Question 31

how can an org assess the value creation of ai?

Answer 31

hard to determine ai's true value for an org due to its opacity + rigidity. ai algorithms need to be deeply analysed to see how it functions, without this it can lead to misinterpretations of its effectiveness. doesn't adapt to real world unpredictable conditions + forces the environment to fit into pre-defined logic.

Question 32

what is meant by generative ai?

Answer 32

ai models that generate new content based on learned patterns from existing data. they don't just recognise patterns, they produce creative outputs.

Question 33

what are some examples of generative ai?

Answer 33

- text generation (chatgpt). - image generation - music generation - video + animation - code generation

Question 34

how does gen ai work?

Answer 34

its based on deep learning + neural networks. they are trained on huge datasets + use statistical probabilities to generate new, coherent content.

Question 35

what do the key architectures of gen ai include?

Answer 35

- generative adversarial networks: two ai models compete against each other, one generates data and one evaluates it. - transformers: these use massive datasets to generate human-like texts + responses.

Question 36

what are the challenges + ethical concerns of gen ai?

Answer 36

- bias + ethical concerns: can generate biased or harmful content based on flawed training data. - copyright: who owns it? - misinfo + deepfakes: it can create realistic fake videos + news, leading to misinfo. lack of explainability: gen models are black boxes, making it difficult to understand how they make decisions.

Question 37

what is an example of the concerns + challenges of gen ai?

Answer 37

ai-generated deepfake videos can be used for both entertainment + fraud, raising concerns about digital trust.