Task 1-Cognitive science, artificial intelligence, prediction

Question 1

Article: Clinical versus Mechanical Prediction (Grove)

Answer 1

• Mechanical-prediction techniques were about 10% more accurate than clinical predictions
• Depending on the specific analysis, mechanical prediction substantially outperformed clinical prediction in 33% to 47% of studies examined
• Superiority for mechanical-prediction techniques was consistent, regardless of the judgement task, type of judges’ amounts of experience, or the types of data being combined
• Humans are susceptible to many errors in clinical judgment
 Include ignoring base rates, assigning nonoptimal weights to cues, failure to take into account regression toward the mean, and failure to properly assess covariation
 Heuristics can similarly reduce clinicians’ accuracy
 Clinicians often do not receive adequate feedback which gives them scant opportunity to change

Question 2

machine learning

Answer 2

• Machine learning – the ability of computers to learn without being explicitly programmed involves programming computers to learn from example data or past experience
• Environments with high degree of complexity are where machine learning is most useful
• AI developers now understand that it is easier to train a system by showing it examples of desired input-output behaviour than to program it manually by anticipating the desired response for all possible inputs

Question 3

prediction

Answer 3

Prediction – anticipating what will happen in the future -> huge decrease in costs of prediction
 Machine learning can be used to predict whether a bank customer will default on a loan
 Also be used to develop medical diagnosis using symptoms -> predicting presence of disease
• Prediction becomes more valuable when data is widely available and more accessible
• As data availability expands, prediction becomes increasingly possible in wide variety of tasks
• A vehicle could drive autonomously by predicting what a human driver would do in response to set of inputs (inputs from camera images for example)

Question 4

The Managerial challenge

Answer 4

1. Prediction is not the same as automation
2. The most valuable workforce skills involve judgement
   • The future’s most valuable skills will be those complementary to prediction (= judgement)
   • Judgement skills will be in greater demand if price of prediction falls due to advances in AI
3. Managing may require new set of talents and expertise

Question 5

Deep learning

Answer 5

learning method based on several layers between input and output, allows to learn overall relation in successive steps

Question 6

Intelligence analysis

Answer 6

• Intelligence analysis involves searching for, selecting, processing and interpreting data in order to gain an awareness of current situations and forecast potentially important future developments in areas of interest to decision-making stakeholders

Question 7

3 pillars of counselling

Answer 7

• Definition can be broken down into 3 pillars of counselling
a. Forming a professional relationship
b. Empowering
c. Accomplishing goals
• Act of counselling requires fulfilment of all 3 pillars

Question 8

fuzzy expert systems

Answer 8

• Fuzzy expert systems – expert systems that use fuzzy logic instead of Boolean logic

 Fuzzy logic = a method of reasoning that deals with approximate values rather than fixed and exact values and is useful for working with uncertainties during decision making
 Used to assist with the optimization of rules and membership classification
• Ex: in an AI-based mental health diagnostic expert system

Question 9

problems counselling and AI

Answer 9

• Therapeutic bonds with the AI?
• AI lacks warmth, empathy and compassion
• Cultural differences and expectations?
• Legal and ethical considerations (errors, legal responsibility…)
 advanced artificial intelligent agents may be capable of developing their own personal values and beliefs that inform decisions—which raises the question of whether those decisions will be consistent with those of their creators or the cultural context of use
 concerns about who should be legally responsible for the decisions and any mistakes made by AI systems
 moral and ethical considerations
 should not harm humanity
• Job loss in mental health care
 only improve and advance psychological practice and research, but have the potential to supplant mental health care professionals in core activities that require human intelligence and social interaction
 Economic impact
 Can be applied to any knowledge-based profession

Question 10

Current and Future Applications and Implications (Luxton) of ai in psychological practice

Answer 10

Virtual reality (VR) stimulation
\+Augmented reality
\+Therapeutic computer games 
\+EMR (Electronic medical record
\+The Super Clinician
\+Expert systems

Question 11

• The criteria for evaluating theories of mental representation (MR

Answer 11

1. Representational power – how much information a particular kind of representation can express
2. Computational power – mental representations are important for what they express but also for what you can do with them; computational power can be evaluated in terms of how it accounts for three important kinds of high-level thinking
    Problem-solving – a theory of MR should be able to explain how people can reason to accomplish their goals; should be able to explain (1) planning, (2) decision making, (3) explanation
    Planning requires a reasoner to figure out how to get from initial state to goal state
    Decision-making involves selecting best choice from different means of accomplishing goals
    Explanation requires figuring out why something has happened
3. Psychological plausibility – cognitive science has the goal of understanding human cognition, so theory must be concerned with how people think;
    Must explain the particular ways that humans carry out a task, not only how the task is possible computationally
    Must account for the quantitative results of psychological experiments concerning qualitative capacities
4. Neurological plausibility – must be consistent with neuroscientific experiments
5. Practical applicability – as there are many desirable practical results to which understanding the human mind can lead, theory must tell us about (1) education, (2) design, (3) intelligent systems; (4) mental illness

Question 12

machine learning algorithm

Answer 12

• Machine learning algorithms can be seen as searching through a large space of candidate programs (which have been guided by training experience) to find a program that optimizes the performance metric

Question 13

There are 3 major paradigms in machine learning

Answer 13

• There are 3 major paradigms in machine learning:
1. Supervised learning – learning a function that maps an input to an output based on example input-output pairs; most widely used machine learning method
 Parallels concept learning in human and animal psychology
 Exemplify function approximation – infers function from labeled training data consisting of a set of training examples; training data take the form of a collection of (x, y) pairs
 Inferred function can be used for mapping new elements – the goal is to produce a prediction y* in response to a query x*
 Requires learning algorithm to generalize from training data to unseen situations in a “reasonable” way
 Form predictions via learned mapping f(x) which produces an output y for each input x (probability distribution over y when given x); different forms of mapping f exist including (1) decision trees/forests, (2) logistic regression, (4) support vector machines, (5) neural networks, etc.

one high-impact area of progress in supervised learning involves deep networks

2. Unsupervised learning – a type of self-organized Hebbian learning that helps find previously unknown patterns in data set without pre-existing labels; known as self-organization and allows the modeling of probability densities of given inputs
3. Reinforcement learning – concerned with how software agents ought to take actions in an environment in order to maximize some notion of cumulative reward; intermediate between supervised and unsupervised learning
    Does not need labelled input and output pairs to be presented
    Does not need sub-optimal actions to be explicitly corrected
    Focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge)

Question 14

cognitive science

Answer 14

• Cognitive science is the scientific study of the human mind, a highly interdisciplinary field, combining ideas and methods from psychology, computer science, linguistics, philosophy, and neuroscience

Question 15

Marr

Answer 15

Marr describes 3 levels of analysis

1.Computational level – specifies the goals of a process, the suitability of the process, and the logic behind the manner in which it is executed; what does the system do?

2.Representational and algorithmic level – address how the process can be executed, present a representation of the inputs and outputs, the algorithms which transform input into output; what steps does the system go through?
 Cognitive psychologists frame their hypotheses at this level

3.Hardware implementation level (physical level) – addresses how algorithm and representation may be physically realized; in what ways are the steps the system goes through implemented?
 Neurobiologists describe the world at this level

Question 16

Singularity Principle

Answer 16

Singularity Principle: Denotes a point in which a function becomes infinite and Kurzweil says that this is the point beyond which technological evolution is so fast that humans cannot predict or understand what will happen.
•

Question 17

Turing Point

Answer 17

Turing Point: The point where machine intelligence exceeds human intelligence

Question 18

expert systems

Answer 18

Systems designed to incorporate knowledge and ability of a human expert in a domain these Systems can identify patterns, trends and meaning from complex data

Question 19

Moores law

Answer 19

says that the complexity in computer circuits doubles every two years

Question 20

Strong AI

Answer 20

creative conscious social, AI has intellectual abilities that are undistingusihabke from human

Question 21

weak AI

Answer 21

generell intelligence

Question 22

symbolic

Answer 22

traditional AI , rule based (1;=)

Question 23

sub symbolic

Answer 23

aspects of the body (movement, visualisation)are needed for higher intelligence
neural networks and connectionism

Question 24

Cognitive modelling

Answer 24

development of psychological concepts – translated to computer programs

Question 25

fuzzy logic

Answer 25

Probabilities (truth values 0-1) instead of clear cut categories (0 or 1)

Question 26

Boolean logic

Answer 26

values reduced to true or false (0 or 1

Question 27

Marovec‘s paradox:

Answer 27

Higher level thinking requires less computation than lower level (e.g. perception) – White collar jobs are more replaceable than blue collar jobs

Question 28

Singularity

Answer 28

Unforseeable point in future (when AI will be more intelligent then humans)

Question 29

Moore‘s Law

Answer 29

processor speeds and processing power for computers will double every two years, while prices go down

Question 30

MABA/MABA list

Answer 30

‘Men are better at, Machines are better at’

Question 31

Searle‘s Chinese Room:

Answer 31

A computer communicates with a woman by using only a book, without the woman realizing it.
BUT – the system does not understand what it is doing.

Strong AI: the system understands what it is doing (conscious, social)
Weak AI: System simulates ability to understand (general intelligence)

a computer performing an input output performance is not a strong AI

Question 32

Turing test

Answer 32

Turing test: PC must have a real time conversation

Undistinguishable whether it is a machine or a human

Question 33

Thagard: Computational Representational Understanding of Mind (CRUM)

Answer 33

Three way analogy of mind, brain and computer

Traditional computers are serial processors
The mind is a parallel processor

Question 34

Mind (cognitive theory)& machine (computational model)

Answer 34

Mind
(Cognitive Theory)
Machine
(Computational Model)
mental representational structures
(neuronal structures)
=
Data structures

Computational procedures operate on those structures
(neuronal firing)
=
algorithms

Question 35

CRUM Advantage

Answer 35

representations are computable & quantitative (wich helps making predictions)

Question 36

CRUM limits

Answer 36

ignores emotion, consciousness, the body, physical environment and social thought

Question 37

Crum analogy

Answer 37

representations= nodes /data structures

mental procedures,computations
=algorithms

thinking is like running a program

Question 38

prediction types

Answer 38

Timelines and outcome predictions with specific dates
Scenarios: conditional prediction: if A, then B
Plans: conditional: if someone follows a specific plan, a specific goal will be achieved
Issues and metastatements: includes all approaches (also impossibilities

Question 39

prediction methods

Answer 39

Causal models: physics and hard sciences
Non-causal models: without causality
Outside view: examples grouped together (trends)
Philosophical arguments: highlighting problems (even impossible ones)
Expert judgements: fill incomplete predictions
Non-expert judgement

Question 40

Assessment of AI predictions

Answer 40

Extracting falsifiable predicitons

Clarifying and revealing assumptions

Empirical evidence and the scientific method

The reliability of expert judgement

Scientific Method = Best

Question 41

mechanical judgement

Answer 41

Reproducible, statistical, objective

Question 42

clinical judgement

Answer 42

Unspecific, subjective

Question 43

ELIZA:

Answer 43

First psychotherapist Simulation

Question 44

Expert systems:

Answer 44

use fuzzy logic and pattern identification

Able to learn

Question 45

Virtual reality

Answer 45

Phobias, dementia, research

Question 46

Artificial Companion:

Answer 46

Autism

Question 47

Marr‘s Tri level hypothesis (T1

Answer 47

Computational level: what does the system do (e.g.: what problems does it solve or overcome) and similarly, why does it do these things

Algorithmic level (sometimes representational level): how does the system do what it does, specifically, what representations does it use and what processes does it employ to build and manipulate the representations

Implementational/physical level: how is the system physically realized (in the case of biological vision, what neural structures and neuronal activitiesimplement the visual system)

Question 48

Ai

Answer 48

system designed by humans , they act in the world by perceiving environment , interpret data, reason on knowledge and decide on action

Question 49

expert system

Answer 49

systems designed to incorporate knowledge and ability of humans expert - these systems can identify patterns , trends and meaning from complex data

Question 50

super clinician

Answer 50

His could make a super clinician by providing her with capabilities beyond humans (infra rot)

Question 51

Judgement

Answer 51

Judgment is the ability to make considered decisions and understand the impact different actions will have on outcomes in light of prediction.

New modes of machine learning might find ways to examine the relationships between actions and outcomes, and they use this info to improve predictions. It can help virtual assistants mimic human judgment, so that over time the feedback can turn some aspects of judgement into prediction problems.

Tasks where the outcome can be easily described and there’s limited need for human judgment are easier to automate.
In cases where decisions can be clearly defined with an algorithm, we can expect to see computers replace humans.

Question 52

The managerial challenge

Answer 52

As AI technology improves, predictions by machines will increasingly take the place of predictions by humans. To assess what role humans play, consider 3 interrelated insights:
I. Prediction is not the same as automation:
A task exists of data, prediction, judgment and action. Prediction is an input in automation,
machine learning involves just one component→prediction.
II. The most valuable workforce skills involve judgment:
Human workers have remained involved in prediction tasks, employers will want workers to augment the value of prediction→valuable skills will be those that are complementary to prediction. We can only speculate what aspects of judgement are apt to be most vital.
III. Managing may require a new set of talents and expertise:
As AI becomes better at prediction, managers’ prediction skills will become less valuable while their judgment skills become more valuable→their role will involve determining how best to apply AI.
The biggest challenges for managers and other employers will be:
- Shifting from prediction-based training to judgment-based training for employees.
- Determining the development of AI and timing the shift in training well not too early, not too
late.
- Assemble the most effective teams of humans who judge and AI machines that predict.

Question 53

a rule

Answer 53

if you want to pass you need to study

Question 54

Marr

Answer 54

However, the mind or brain cannot be understood if it is only studied at one level. Marr (1982) describes 3 levels of analysis:
I. Computational level: specifies the goals of a process, the appropriateness of the process, and the logic behind how it is implemented; what does the system do?
II. Representational and Algorithmic level: deal with how the process can be executed, give a representation of the inputs and outputs, the algorithms that turn inputs into outputs; what steps does the system go through?
o Cognitive psychologists formulate their hypotheses at this level
III. The hardware implementation level (physical level): addresses how algorithm and representation can be physically realized; in what way are the steps the system goes through
implemented?
o Neurobiologists describe the world at this level

Question 55

7 deadly sins

Answer 55

overestimation in short run/understeimatin in long run
Imagining magic
Performance vs. competence(generalisation of skills, computers are often very narrow skilled)
Suitcase words
Exponentials
Hollywood scenarios
Speed of deployment

Question 56

Areas in wich AI can improve

Answer 56

Psychopathology
medicine
Decision science could help the IC to improve its forecast accuracy by statistical or behavioral interventions.