AI Opportunities in 2023 - Lecture by Dr. Andrew Ng

AI as a general purpose tech...

• is useful for lots of different applications e.g. electricity is good for a lot of things
• AI collection: Supervised learning and Generative AI (in focus today) + Unsupervised learning and Reinforcement learning
• Supervised learning: Good for labelling things e.g. 

                    e-mail >> spam or not, ship-route >> fuel consumed, Ad & user-info >> will click

• Workflow of Supervised learning apps: e.g. restaurant reviews classification

                            Collect dataset >> label data >> train a model >> deploy >> run

• Last decade was the decade of large scale supervised learning. Small AI models could be built on not very powerful computers, which had good performance for certain small amount of data but with even large amount of data the performance would flatten out. With large AI models, however, the performance scales better and better with large data
• This decade is adding to it the excitement of Generative AI
• When we train a very large AI model on a lot of data, we get a LLM like ChatGPT
• RLHF and other techniques tune AI output to be more helpful, honest and harmless
• And at the heart of Generative AI is (Supervised learning) repeated prediction of next sub-part patterns given the data it has seen
• The power of LLMs as a developer (not programmer) tool: 
• With prompt based AI - the workflow is:

                    Specify prompt >> Deploy to cloud (e.g. build restaurant review system in few days)

• Opportunities: massive value will be created with Supervised learning and Generative AI together, by identifying and executing concrete use cases
• Supervised learning will double in size and Generative AI will much more than double
• for new start-ups and for large enterprises / companies
• Lensa was an indefensible use case as it did not add value; AirBnB or Uber are defensible because these create value 
• The work ahead is to find the many diverse, value adding and defensible use cases

• Refer to the "Potential AI projects space curve"
• Advertising, and web search are the only large money making domains, with millions of users
• As we go to the right of the curve, some example projects of interest may be:
• Food inspection: cheese spread evenly on a pizza
• Wheat harvesting: how tall is the wheat crop, at what height should it be chopped off
• Materials grading, cloth grading...
• Clearly industries other than advertising and web-search have a very long tail of $5 mn projects but with a very high cost of customisation
• So, AI community needs to continue building better tools to help aggregate such use cases and make it easy for end users to do the customisations at affordable costs

Instead of needing to worry about pictures of pizza AI community will create tools to enable the IT department of the pizza factory to train an AI system on their own pizzas. Thus, to realise the value of $5 mn by leveraging some low/no code tools for AI

Referring to the AI Stack...

• H/W semi-conductor layer at bottom is very capital intensive and very concentrated
• Infrastructure layer above the semi-conductor later is also highly capital intensive and very concentrated
• Developer tools layer is hyper-competitive, and only a few will be winners
  All the above said layers can be successful only if the
• All the above said layers can be successful only if the application layer on top is even more successful e.g. Amorai - app for romantic relationships coaching
• Recipe for building startups, "don't rush to solutions", has been inverted and now we can just do that while still keeping it cost effective

              Ideas >> Validate >> Get CEO >> Prototype (early users) >> Pre-seed Growth (MVP) >> Seed, Growth & Scale

• Concrete ideas can be validated or falsified efficiently
• Even highly profitable projects but low on ethics will / should be killed
• AGI is still decades away
• Other areas of interest may be predicting next pandemic, climate change predictions...