Developers of AI applications face many obstacles, but the chief challenge is simply that these are different from traditional software development projects. 85% of businesses say they are looking to adopt AI but a similar percentage of data science projects never reach production. Too many organizations approach AI application development similarly to other software projects. Another issue is focusing on the machine learning model rather than the data set that will be used. Devang Sachdev of Snorkel AI suggests being data-focused instead, and reducing and optimizing models instead of continually expanding the number of parameters. Another issue is the manual process of developing training data, which is time-consuming and error-prone. Finally, we must consider a process of iteration over models and training data to ensure quality. Machine learning is an excellent tool but it requires a re-think in how a company approaches software development.
- Is it possible to create a truly unbiased AI?
- Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?
- How big can ML models get? Will today’s hundred-billion parameter model look small tomorrow or have we reached the limit?