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Summary Task 1 - Outsourced Intelligence
- Instelling
- Maastricht University (UM)
Summary of Task 1 in Man and Machine
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OUTSOURCED INTELLIGENCEWHAT TO EXPECT FROM ARTIFICIAL INTELLIGENCE (AGRAWAL)
Cognitive science – analogy between mind & program, try to combine these 2 & use
human thinking processes to make computers work, try to make processes that
resemble neurons in the brain
CRUM (computational representational understanding of the mind) – mind has
mental representations & these are analogous to data structures in a computer
Want to explore thinking in humans & try to compare it with AI
Human thinking processes are not entirely understood – makes it difficult to apply
it to AI
AI can make sth that has been expensive abundant & cheap
Main task: prediction – to take information you have & generate new information
Anticipating what will happen in the future
The anatomy of a task
Actions are shaped by underlying conditions & resolution of uncertainty to
lead to outcomes
Use judgement & prediction
AI has reduced cost of prediction & made data more valuable
Judgement – ability to make considered decisions
Understanding the impact different actions will have on outcomes in light of
predictions
Tasks with less need for human judgement – more easily automated
Understanding the level of judgement necessary to pursue actions
Ways in which AI may affect workplace
Where whole decisions can be clearly defined with algorithm AI
Takes longer in areas where judgement can’t be easily described
MACHINE LEARNING
Most useful in environments with high degree of complexity
Costs of machine learning-based predictions dramatically reduced in recent years
Example: identifying an apple – machine references info from past images of apples to
predict whether an unidentified new image contains an apple
EMPLOYING PREDICTION MACHINES
Machines that can generate reliable predictions & rely on those predictions to
determine what to do next
, In some contexts: role of human judgement will become limited (e.g., business related
language translation)
In other contexts: increased value for human-led judgement tasks (e.g., automated
replies to email proposed BUT human has to judge which response is most
appropriate)
Medicine – treatment & care will still rely on human judgement
THE MANAGERIAL CHALLENGE
As AI improves, predictions by machines will increasingly take place of prediction by
humans
3 insights
Prediction is not the same as automation
Automation – tasks are made up of data (collection), prediction, judgement,
action
Machine learning – only involves prediction
The most valuable workforce skills involve judgement
Judgement is complementary to prediction
if price of prediction falls due to AI advancement, judgment will be in higher
demand
Managing may require a new set of talents & expertise
Role will increasingly involve determining how best to apply AI
KEY CHALLENGES
Shifting training of employees from focus on prediction-related skills to judgement-
related skills
Assessing rate & direction of adoption of AI technologies to properly time workforce
training
Developing management processes that build most effective teams of judgement-
focused humans & prediction-focused AI agents
ARTIFICIAL INTELLIGENCE IN PSYCHOLOGICAL PRACTICE: CURRENT & FUTURE APPLICATIONS &
IMPLICATIONS (LUXTON)
WHAT IS AI?
Technology designed to perform activities that normally require human intelligence
Important branches
Machine learning – ability of computers to learn without being explicitly programmed
Artificial neural networks – mathematical, computational, technological models that
mimic logic & learning functions of neurons in the brain
Natural language processing – how computers process human natural languages
Turing test – method for judging intelligence of machines
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