AI Key Terms and Concepts Notes

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

Supervised, Unsupervised, Semi-supervised, and Reinforcement Learning

https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/ Source: Udacity AI for Business Leaders Supervised Learning - A category of machine learning which relies on the proper values, or labels , being present for the output data in the ground truth dataset, which the model can “learn” from during its training process. Labels - Values of the output variable/column/piece of data you are interested in Unsupervised Learning - A category of machine learning which uses the underlying characteristics of data itself, rather than already-supplied labels for outputs, to inform the model’s training process. Often uses segmenting or clustering algorithms to determine the output values. Reinforcement Learning - A category of machine learning which uses a series of simulations/cycles to reward optimal behaviors to learn policies for decision making in complex scenarios such as autonomous driving.

PowerPC 750 on James Webb Telescope

Another PowerPC usage was spotted, this time it is on the James Webb Telescope. The reliable and radiation chip was known to be used on the Orion spacecraft. Most of you know the variant called G3 on Apple PowerPC products. https://www.talospace.com/2022/01/another-powerpc-in-space.html?m=1 Here is an interesting link with many more examples of the PowerPC usage: https://forums.macrumors.com/threads/a-fun-thread-on-the-powerpcs-longevity-durability-and-performance-for-ongoing-science-work.2170348/

Data and Differences Between AI, ML, and DL

Data: Volume - the amount of data that is being produced over any given unit of time Variety - The level of deviation within your data, which can have both positive and negative effects depending on what it is you’re hoping to achieve Velocity - A term referring to how quickly new data is produced. Velocity can also allude to the concept of drift, or, how quickly data underlying a model can change over time Veracity - The accuracy of data that is being collected, a trait which can be affected by faulty inputs, poor organization, or a variety of other factors Value - A holistic measure based on all other underlying characteristics of data and rooted in how likely the data is to help you reach your desired end state Differences between AI, ML, and DL? NVIDIA: https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/ Source: Udacity AI for Business Leaders

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