SharpLance

Mere notes Model: A mathematical representation of a problem, situation, phenomenon, or process Training: The process of using an algorithm to create a model from a set of data Training Set: A subset of our ground truth data that our model will learn from Algorithm: A procedure, or set of steps Machine learning (probabilistic approach) vs Rules-based system If the universe of possible outcomes is well delineated then stick with rules. Purpose of Ground Truth Data Ground truth data helps many learning systems to learn and evaluate their own performance by providing a gold standard on what the truth actually is. Source: Udacity AI for Business Leaders

Machine Learning is a branch that focuses on probabilistic reasoning.  Progress in artificial intelligence progressed due to advances in Machine Learning. Deep learning is a technique of applying Machine Learning with massive neural networks. Good source:  https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

Sebastain Thrun: Artificial Intelligence represents an opportunity for lessons learned to be applied on all systems and including systems not made yet. Business leaders often want to go a certain direction but available toolsets including education did not keep up. Anything repetitive would likely be done by AI within 20 years.  Erik Brynjolfsson. Director, MIT Initative on Digital Economy: Book: The Second Machine Age Three trends: Power Data Algorithms Machines with very narrow specialty. Machine learning - when to use and when not to use. Find the problem then find the solution. Identify the questions. Image recognition - machines are way better. Lots of churn under the surface so you have to be working on opportunities even though it looks like nothing is happening. Examples: A team looked flight patterns and was able to predict merger and acquisitions.  A team used logs from locomotives to identify improvements to be made. Finance showed massive improvements - 360,000 hou...