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What is AI?
What is Machine Learning?
- Supervise Learning… Input (A) to output (B) learning
What is Data?
- Need to determine what is A (Input – Features) and what is B (Output) on the inputs
- Acquiring data via manual labeling, observation, available downloads
- Once you start collecting data, start feeding it to a AI team for a feedback loop to start. Start this early.
- You can not assume value in data, because you have a lot of it. Becareful, have an AI with to asses
- Garbage in garbage out exists here too
The Terminilogy of AI
- AI(ML, et al (DL/NN, et al))…Data Science cuts across all of these.
What makes an AI company?
What machine learning can and cannot do
- Technical diligence: looking at the data, look at the input, and output A and B, and just thinking through if this is something AI can really do.
- Can NOT do market research and write an extended market report
- Complex interactions require lots of examples. the system would either remain vague in response, or return incoherent responses.
- What makes an ML problem easier
- Learning s “simple” concept <= 1 second of human thought…Cause we have to formulate? why?
- Lots of data available
- AI has a hard time of inferring intention of action in context A= hand gesture, B= stop, hitchhiker, left turn on a bike, hello, etc…
Deep Learning
- Deep Learning and Neural Network are used interchangeably
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