This is the"meta-learning" with few-shot. LLM can "learn" on whatever you manage to cram into the context window with "prompt LLMs"
Artificial Intelligence (AI) generally and Generative AI technologies like GPT-4 , specifically, are expected to have a significant impact on the future workforce, with some occupations being more exposed to AI than others.
Shopify's AR/VR Product Lead Russ Maschmeyer has demonstrated an interesting and cool prototype service that would allow its users to come up with ideas for real-life wallpapers, preview AI-generated results in AR, and instantly purchase the final product with the exact design a person would want.
Data scientists are getting automated faster than the rest of the engineers
Imagine that you are either * The Data Scientist, who is going to implement the solution, will require technical feasibility and complexity assessment like is the right data available? how inference will be integrated with the business process? and is the acceptance criteria achievable with the given data set and constraints?
For business-facing (B2B) AI products, it’s often difficult to get the data necessary to build a prototype because a lot of highly specialized data is locked up within the companies that produce it. There are a couple of general ways in which AI teams can get around this problem:
A list of key questions that will help asses business and technical feasibility of a machine learning solution Lets take an example of a machine learning and deep learning based forecasting solution. Imagine that you are the Machine learning engineer and want to asses a forecasting use case from two