Generative AI by Andrew Ng (coursera-DeepLearning.AI) - Note
Link to coursera: https://www.coursera.org/learn/generative-ai-for-everyone
AI
Supervised learning
Labeling the output
Performance depends on the amount of data
Unsupervised learning
Gnerative AI
Reinforcement learning
Large language models (LLM)
Using the supervised learning to generate the text (learning from the repetitive sentences)
LLM – > hundreds of billions of words – > that’s why the model can create a good performance
Language is quite repetitive – > that is why it can generate the text
LLM
A new way to find information
It can give you information
Sometimes, it hallucinate
Writing partner
Finding information through LLM (but better to double checks) – perhaps web search might be better
Generative AI
General technology (for now)
Useful for lots of things
Work well with unstructured data (non-tubular data, for example, table form)
Generating the image – > diffusion model by which labelling the text together with the image and repetitively do it step-by-step until getting the clear image – > by creating such model, that is why we can generate images based on text!
To improve the generative AI performance
Retrieval augmented generation (RAG)
Giving LLM access to external data sources
Have 3 steps
Finding the external data sources
Incorporate retrieval text into an updated prompt
Generate output from the new prompt with additional context
Fine-tune models
Adapt model to the specific task, like finance, law, or a medical issue
Give the LLM a modest number of inputs and outputs to adjust the model
Thus, the output might be more accurate and more precise
Pretrained models
Train model from scratch
Suitable for building specific application
It is better to start with general LLM first; otherwise, it will take a lot of resources - time and money
Choosing model
Model size
Depending on the parameters used to build model; more parameters mean complex model and take time to deploy
It is really depending on the tasks; less complex pick the smaller one, less parameters – it will execute faster
Evaluation of AI potential – for application
Technical feasibility
Can AI do it?
Cost (Business value)
Cost saving?
How much time is needed to get this task done?
Artificial General Intelligence
AI that can do any intellectual task that a human can
Human are multiskilled but AI is more specific (good at one particular task)
Job + tasks breakdown – > help to decide which task could be automated using generative AI
The economic potential of generative AI: The next productivity frontier
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