𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐢𝐬 𝐋𝐢𝐤𝐞 𝐚 𝐑𝐚𝐫𝐞 𝐃𝐢𝐬𝐞𝐚𝐬𝐞 / 𝐖𝐡𝐲 𝐀𝐈 𝐒𝐭𝐫𝐮𝐠𝐠𝐥𝐞𝐬 𝐰𝐢𝐭𝐡 𝐒𝐜𝐚𝐫𝐜𝐞 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧.
Artificial Intelligence thrives on data—the more it has, the better it gets. In medical diagnostics, AI excels at detecting common diseases like melanoma because it can learn from a vast pool of information. In Australia alone, about one-third of the population will experience melanoma at some point. That's over 10 million cases, providing ample data for AI to analyze and recognize patterns. This wealth of information helps AI improve diagnostic accuracy and assist doctors in catching the disease early.
But when it comes to rare diseases, AI hits a stumbling block. With so few cases, there's not enough data for the algorithms to learn from, making it tough for AI to identify these conditions accurately.
The situation is similar in the world of engineering. While AI can easily access and learn from legal texts or literature—since they're widely available—engineering data is a different story. It's scarce and highly protected, much like data on rare diseases. Companies guard their engineering designs and specifications closely because they're core intellectual property that gives them a competitive edge. Without access to this critical information, AI can't learn or operate effectively in complex hardware engineering tasks.
This lack of available data means AI can't make the same strides in engineering as it does in other fields. However, as technology advances and if ways are found to share data without compromising proprietary information, there's hope that AI could overcome these challenges. While it's a tough nut to crack today, the future may see AI playing a bigger role in engineering, complementing human expertise and pushing the boundaries of innovation.
