Generally AI Episode 4: Sold Out!

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GPUs and Parallel Computing

GPUs (Graphical Processing Units) are in high demand due to their ability to perform general-purpose computing.
Cuda is a parallel computing platform and programming model that enables GPUs to perform general-purpose computing.
Parallel computing involves breaking down problems into smaller tasks that can be solved simultaneously by multiple processes communicating over shared memory.
CUDA, developed by NVIDIA, is commonly used for tasks like deep learning, matrix multiplication, and image processing.
The number of cores on a GPU, memory bandwidth, and data transfer efficiency impact overall speedup.
Machine learning engineers optimize code for efficient GPU utilization, considering factors like core usage, data movement, and memory allocation.

The shortage of GPUs is due to supply chain disruptions, increased demand for gaming and AI applications, and the ongoing shortage of peppers used to make Sriracha hot sauce.
The limited supply has led to price increases and scalping, with some cards being sold for well above their retail price.
Some cheaper gaming cards now have built-in protection to prevent cryptocurrency mining, limiting their use to gamers.
The demand for GPUs is expected to remain high due to the increasing popularity of AI and the need for parallel processing capabilities.

Jay Forrester invented the beer distribution game (beer game) in the 1960s to model supply chains as dynamic systems.
The beer game is a four-step supply chain board game where players take on the roles of a brewer, distributor, wholesaler, and retailer.
Demand for beer fluctuates randomly, and there is a two-week lag between placing an order and receiving it.
The bullwhip effect occurs when small fluctuations in demand at the retailer end turn into larger fluctuations in orders as you go upstream in the supply chain.
The optimal strategy for playing the beer game is called the base stock policy, which involves ordering an amount of beer to bring the inventory position to a fixed value.
Reinforcement learning can be used to train an AI model to play the beer game optimally, even when other players are humans who are not following the optimal policy.

AI in supply chains can be beneficial if it learns from past experiences, but it can also lead to unintended consequences like the "paperclip maximization" scenario.
Predicting supply and demand can be challenging, especially when local patterns differ from global patterns.
The optimal strategy for the beer game is surprisingly simple: keep track of inventory, backorders, and orders.
Supply chains can be compared to control algorithms, where predicting demand is difficult due to factors like anxious customers and changing trends.

Fidget Spinners exemplify the challenge of predicting demand, as they quickly became popular and then just as quickly became obsolete.
AI language models like ChatGPT can make mistakes, such as suggesting a vegetarian menu that includes chicken.
Microsoft discontinued the Microsoft Sculpt keyboard, which the speaker highly recommends and is now struggling to find a suitable replacement.
Podcast recommendations often rely on word-of-mouth rather than recommendation engines.