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AI and What It Means for the Rail Industry The Advantages and Limitations of AI in Rail Matthew Dick, P.E., Chief of Strategy & Development, ENSCO, Inc., Pueblo, CO Serkan Sandikcioglu, AI ML Business Area Lead, ENSCO, Inc., Charlottesville, VA
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elephants weren’t part of the training data. Thus, broad and exhaustive training data is imperative for optimal performance. One of the leading AI companies, OpenAI, developed ChatGPT, a Large Language Model (LLM) specializing in generating written text from prompts. Prompts are the request to the AI tool that triggers it to create an output. For example, giving ChatGPT the prompt “Please generate a checklist of safety inspection items that should be inspected for on a hi-rail vehicle”, will result in ChatGPT generating an impressive, but maybe not 100% accurate hi-rail safety inspection checklist. Prompt Engineering refers to best practice of effective prompts. Apart from ChatGPT, other LLMs like Microsoft’s Azure, Google’s Bard, and Meta’s Llama, alongside text-to-image and text-to-video AI tools such as Midjourney, DALL-E, Stable Diffusion, and Sora, have gained popularity for their impressive outputs.
Why is AI Big Now? Machine learning, neural networks, and algorithms have all been in existence for decades. Why is AI now seemingly skyrocketing, with new companies and tools emerging each month? Several culminating factors are at play, enabling today’s expanding AI. Firstly, there are astronomically large training datasets. The amount of written text, images, and videos available on the internet has exploded over the past few decades, with social media being one of the largest drivers of this growth. All the AI tools mentioned above utilize vast amounts of data obtained from the internet via public sources and licensed use. The second factor is the openly shared ML techniques. An interesting aspect of the growth of machine learning is its roots in academic research, where openly sharing research results is the norm through published papers and open-source software. This has dramatically accelerated the evolution of
rtificial Intelligence (AI) has recently experienced a significant leap forward. AI, a broad term encompassing software tasked with performing various functions, may either replicate tasks historically done by humans or pioneer new territories. Machine Learning (ML) is closely intertwined with AI, employing software and mathematical techniques to process analytical data, with “algorithms” being a primary function of ML. This article will delve into what AI entails and its implications for the rail industry. What are Algorithms? Machine Learning, existing for decades, manifests in various forms, with neural networks currently at the forefront. Operating akin to human brains, neural networks necessitate training data with defined inputs and outputs. For instance, a dataset featuring animal photographs labeled as “cat,” “dog,” “goat,” etc., serves as training data. Both human children and neural networks learn by associating images with their labels. Once trained, the model identifies the type of animal in unseen pictures. However, there are a few important items to keep in mind. Firstly, neural networks achieve higher accuracy with extensive training data. Secondly, they struggle when tasked beyond their training data parameters. For example, if presented with a photograph of an elephant, the algorithm would struggle as 6 Railway Track & Structures // March 2024
Figure 1. Example AI generated image using Midjourney v6.
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