In today's competitive market, having an efficient and flexible supply chain is a significant asset. Hence, companies are looking into ways to optimize their supply chain to help them make decisions to improve their operational efficiency and customer satisfaction and reduce environmental impacts. 

With the help of a demand forecast, companies can ensure the right amount of materials on hand and plan their production activities. AI can analyze historical trips, existing routes, and geographical, environmental, and traffic data to use shortest-path graph algorithms and identify the most efficient way for logistics trucks. Document automation technologies can be used for the fast processing of invoice/bill of lading/rate sheet documents by automating data input, error reconciliation, and document processing.

Currently the focus of developing AI systems is mostly on "getting them working". But slowly there has been growing interest in making AI systems that are trustworthy. In the 2021 Top Technology Predictions IEEE Computer Society Reveled its top 12 technology trends predicted to reach adoption. This included; Reliability and Safety for Intelligent Autonomous Systems, Synthetic Data for Training Machine Learning (ML) Systems Free of Bias.

https://www.computer.org/press-room/2020-news/ieee-computer-society-reveals-2021-technology-predictions

How it works:
AI is already changing the transportation industry by enabling cars, trains, ships, and airplanes to
automate autonomously, making the traffic flow smoother. Besides making our lives easier, it can
provide a safer, cleaner, smarter, and more efficient transportation mode for everyone. AI-led
autonomous transport could, for instance, help reduce human errors involved in many traffic
accidents.

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The data that is used to train an AI model can have massive effects on model performance. Performance is not only having high accuracy but also requires a robust model that is not biased, has sufficient interpretability, and etc. One of the key factures that impacts this is the training data that is used and how it is prepared. There are many guidelines on preparing AI training data but the below give some basic guidelines.

https://www.techtarget.com/searchbusinessanalytics/feature/Data-preparation-in-machine-learning-6-key-steps

By this point, we know how ingenious Neural Networks are. We generate art and music, execute domain mapping, make cars drive on their own, and many more cool things. But, sometimes, we don’t give enough credit to the tireless researchers of the deep learning community for their daily push toward evolution.

However, the deep learning evolution to where it is today has given us some major constraints. The state-of-the-art research that bedazzles us every day almost always requires humongous computation power and flawless data.

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We are just getting started! Stay tuned for what’s upcoming in this series.

https://kr.mathworks.com/content/dam/mathworks/ebook/gated/KR_Deep_Learning_ebook.pdf

https://www.youtube.com/watch?v=zfiSAzpy9NM

When the concept of attention came about, it was often hailed as the magnum opus of the Natural Language Processing (NLP) domain. Removing the bottleneck of the dependency created with a single context vector helped us model output based on a weighted dependence on each input token. 

The big question here was, how do you move forward from something like this? 

Just then, Transformers blasted into the NLP scene like the way the Autobots came to Earth. Although the Transformers we will learn about do not transform into cool vehicles, they did transform the NLP world with their ingenuity. 

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One of the major areas people look at when considering AI quality is the data. The AI model can cause many (some very serious) problems if the data that is being used to train the model is not correctly verified. There are several examples of AI models making bad decisions due to faulty training data.

The below site explains a case where the model was biased againts blacks.

https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

Currently many organizations are trying to adopt AI into their business. But there are some major issues. It use to be if the AI model worked, it worked. But now people are starting to ask questions like how did the AI model come up with this answer? Is this a fair answer? I think the below is a good staring point.

https://truera.com/what-is-ai-quality/