AI for Business Leaders Flashcards
4 main areas where AI is powerful
Scale - processing large amounts of data
Pattern - finding patterns and optimums
Grouping - data with similarities
Extracting features from unstructured data
4 main branches of AI
Machine Learning
Natural Language Processing
Machine Vision
Robotics
Name 5 skill sets that are hardest to automate:
Building relationships Empathy Critical thinking Creativity Storytelling
3 main reasons for on-going AI revolution:
Computing power up, and costs down
Storage capacity up, and costs down
Data transport costs down
‘Data that has no defined organizational structure’ =
Unstructured Data
Give 2 reasons why data is growing so fast:
- Dramatic cost reduction in devices (e.g. cameras, microphones, sensors)
- Massive user content generation (social media)
Name of Anil Kumble’s company?
Spektacom
Is often used by corporates
to be a focal point for innovation for the organization
Often is a dedicated space that can also be used to showcase ideas to the outside world
Can be both on-site (tend to have a business unit focus) or off-site (more disruptive focus)
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An Innovation Lab
Typically no fixed schedule
Typically sponsored by VC funds or corporates
Typically don’t provide upfront capital
Often have a specific industry or market focus
Example: Idealab
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An Incubator
Used to accelerate growth for a company that already exists
Typically a fixed time schedule (e.g. 3-months)
Benefits for the startup may include:
– Small seed investment
– Access to a mentor network
Examples include: Y Combinator, Techstars
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An Accelerator
Training a machine to identify patterns and/or predict outcomes
Often used to find relationships between variables
Contains many subsets of AI methodologies (Neural Network with Deep Learning, Evolutionary Algorithm etc.)
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Machine Learning
4 questions machine learning asks:
- How can we learn from data?
- Can we find new patterns?
- Can we predict outcomes better?
- Can we automate decision making at scale?
5 techniques used in machine learning:
- supervised learning
- unsupervised learning
- ensemble learning
- neural networks and deep learning
- reinforcement learning
Prediction of output results from specific input data
– e.g. predict apartment price based on size, location, etc.
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Supervised Learning
– No output results specified, only input data is given to the system
– Used to typically find insights or patterns from data
– e.g. McDonald’s can identify different customer groups based on their food preference
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Unsupervised Learning
– Allows a system to “learn” through trial and error by utilizing feedback generated from past actions
– e.g. OpenAI Multi agent-hide and seek
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Reinforcement Learning
Supervised learning, unsupervised learning, or reinforcement learning?
- optimization
- correlation analysis
- clustering
- classification
- learn objectives from real-life behavior
- regression
Supervised learning: classification, regression
Unsupervised learning: correlation analysis, clustering
Reinforcement learning: optimization, learn objectives from real-life behavior
