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Artificial Intelligence - Coggle Diagram
Artificial Intelligence
Impacts
Human factors
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Interpretability
The raw output from an AI is often hard to understand and needs to be interpreted and presented to users in a way they can understand.
AIs cannot exlain themselves, and AI researchers can't explain how an AI came to its outcome.
Sustainability
Weights an Biases is a company trying to avoid pointless training by saving trained ANNs on their website.
Retraining a natural language processing ANN like Google Translate can cost up to $3mil. Retraining it to add a few new slang words every year may not be financially sustainable.
Link
AIs saved 250,000 tonnes of CO2 in 12 months by optimising cargo ships.
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Training a single large natural language processing ANN can produce up to 284 tonnes of CO2 - five times the CO2 produced by an average car during its entire lifetime.
Link
Future proofing
ANN algorithms
The algorithms and computer science that ANNs are based on is well developed and will be effective for a long time.
Data
Trained ANNs may become out of date as the data that they were trained on becomes less relevant. They may need to be retired or re-trained periodically to remain useful.
Ethical issues
Human biases
AIs are trained on data that needs to be sorted and made usable. This is done by humans, who need to be careful about what they include and train the AI on.
Data biases
Crime prediction AIs in the United States were trained to predict where crimes would happen, to help send police patrols to the right place at the right time. Unfortunately, the AIs were trained on arrest data instead of conviction data, so they were racially biased because the data they were based on was racially biased.
Social impacts
Job losses
Automation will cause many people to lose their jobs and become not just unemployed, but unemployable e..g truckers and legal assists.
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