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1 AI Terminology - Coggle Diagram

- - - - ML => Prioritization of Leads
      - DS => there are blips in the data whenever there's a holiday. Maybe more people will shop around the holidays because they're buying gifts or maybe fewer people will shop around the holidays because they're staying home rather than sometimes shopping from their work computers. In some countries, there may be time-of-day blips wherein countries that observe a siesta, there may be fewer shoppers online and so your sales may go down.
        They may then suggest that you should spend fewer advertising dollars during the period of siesta
  - - - ML => Automated Resume screening
      - DS => In case if none is moving to onsite interview then hypotheses are: 1. Phone interviewers are strict 2. Too relaxed criteria for phone interviews
  - - - ML => Visual Inspection (eg manufactured product is defective or not)
      - DS => whenever the humidity is too low and the kiln temperature is too hot that there are cracks in the mug or they may find out that because it's warmer in the afternoon that you need to adjust the humidity and temperature depending on the time of day. Based on the insights from your data science team you get suggestions for hypotheses and actions on how to change the operations and manufacturing line in order to improve the productivity of the line.
  - - - DS => Data science for crop analytics, where you can take data on the soil conditions, the weather conditions, the presence of different crops in the market and have data science teams make recommendations to what to plant, when to plant so as to improve use while maintaining the condition of the soil on your farm
      - ML => products that can go onto the farms, take a picture of weed and spray a weed killer in a very precise way just onto the weeds so that it gets rid of the weed but without having to spray an excessive amount of weed killers
  - - - DS => AB testing
      - ML => Customized Product Recommendation
- - - - how much data is needed to get to this desired level of performance, and do you have a way to get that much data
    - - Build a speech system that is 95 percent accurate
    - - Engineering timeline to try to figure out how long it will take and how many people it will take to build a system that you would like to have built.
  - - - Automating a few tasks or
        by squeezing more efficiency onto the system
    - - Driving more people to check out in your shopping cart or you may be building an AI system to help you launch a new product or a new line of business.