tech enablers - AI/ML Flashcards
development stages of AI
- narrow artificial intelligence
- general artificial intelligence
- super artificial intelligence
supervised learning
- create training data (annotated by human)
- use AI algo to create model
- apply model to unseen data
microworkers (pre read)
- perform tasks that machines cannot
- provide data for machine learning algorithms that are the basis of AI by adding the human element
- educated individuals, no regulations, etc
- eg. Amazon’s Mechanical Turk platform
environmental sustainability?
high carbon emissions footprint to train an AI model
what is deep learning
- deep layers of neural network
- data: very important but hard to derive features
- representation learning: raw data -> feature learning -> model - perform actions
pre-trained models and transfer learning
- train from scratch
- fine tune a pre-trained model (transfer learning)
data centric vs model centric AI
- Labelling consistency is key : lack of consistency can deteriorate the outcome.
- Systematic improvement of data quality on a basic model is better than chasing the state-of-the-art models with low-quality data.
- With data centric view, there is significant room for improvement in problems with smaller datasets (<10k examples).
- When working with smaller datasets, tools and services to promote data quality are critical.
GAN
generative adversarial networks
- learn to mimic any distribution of data
generative AI
- textual prompt -> novel content
- powered by foundation model or AI models trained on vast quantity of unlabelled data at scale -> adapted to wide range of downstream tasks
- eg. chatgpt is trained using technique called reinforcement learning from human feedback (tune/teach model to human preferred response)
concerns of AI (2)
- difficult to distinguish real from fake
- replacing humans
Working with smart machines_ Insights on the future of work (pre reading)
ecosystems for supporting AI applications
1. technology-based ecosystem:
- platforms: exploration support/transaction support/automated decision platforms
- intelligent case management systems: workflow management/prioritisation/recommendations
- job role-based ecosystem
- new job specialisations/hybridisations
aspects of AI readiness index framework
- organisational readiness ( AI literacy/talent/governance management support)
- business value readiness (business use case)
- data readiness (data quality/reference data)
- infrastructure readiness (ML/Data)
AI biases
- learnt from training data and is amplified
- eg. healthcare, hiring, policing, gender, race
retail AI example
domino’s
obj: use AI for consistent quality and speedier delivery
tech:
- pizza checker (image analysis and ML)
- processing order via voice (NLP)
- autonomous delivery vehicle
results: invest further to fit all kitchens with pizza checker
transport AI example
Tesla - aiming for level 5 (full) autonomy
obj: minimise accidents and death on road
tech:
- IoT, sensors, camera (computer vision)
- cloud computing to analyse all driving data
- siri-style AI assistant (voice control NLP)
results: autopilot (2) cut rate of airbag deployment to 0.8 per million miles driven instead of 1.3
