Using Machine Learning for AI Automation in Software Development

AI-driven process improvement is an influential tool with a vast wealth of beneficial business-boosting advantages. But, higher concerns surrounding its reliability are a huge roadblock to mainstream adoption of AI automation technologies. Still, many enterprises don’t trust AI automation technologies to perform human tasks purely because, in reality, automation technologies can amplify productivity and add real-time value more effectively than human workers.

So, if the above doesn’t convince you, then perhaps you should familiarize yourself with the reasons why it’s hard to apply AI automation to business. First, there are plenty of “fraudulent” practices that are prevalent among AI implementation projects and it’s very difficult to verify whether they’re real or not. For instance, many projects have been canceled due to a single false positive or one faulty implementation. False negatives result from bad training, bad models, bad programming or even bad analytics.

There are many ways to avoid false negatives but the best approach is prevention. To prevent falsification in an AI project, you need to have defined thresholds for acceptance of artificial intelligence. Thresholds are typically measured in terms of accuracy and/or predictability. You may also want to have thresholds for different timescales. For instance, a system could be programmed to detect office politics within five days. This could be fine-tuned depending on the amount of political interference over time.

The threshold specification will help you make intelligent decisions about how to manage artificial intelligence. If you throw away data that’s not useful, you’ll throw away your confidence level in the system. It’s also important to build confidence in your own AI automation systems because false negatives can destroy your ability to fine tune the AI software. This will be compounded by bad experiences with AI project teams where false positives lead to high levels of dissatisfaction and turnover.

Beyond the threshold specification, you’ll want to make sure you can execute the artificial intelligence project on time. Many businesses have already run into this problem. They built an impressive artificial intelligence platform, trained their engineers and got all of their processes together, only to discover they didn’t have enough storage capacity. Running artificial intelligence across industries requires a lot more than hard data. You also have to make sure your systems are communicating well and are optimized for the industry you’re working in.

In the future, we’ll continue to see automation across industries. However, as we move into the future, we’ll also see more robust automated systems relying on humans to provide feedback. Humans are excellent at picking up on bad behaviors, inconsistencies and other things that are amiss in the automated environment. We’re also good at spotting patterns and anomalies. If you’re working in an industry that uses automated process or processes, there’s a good chance you’ve encountered false negatives.

The problem with using machine learning for AIs is that it can only work so well without human intervention. It’s a fact of life that the quality of an A.I., no matter how sophisticated, is only as good as the testing it gets. Testing is typically the single most expensive component of software development. When using software development for business, you’re not likely to be able to spend all your time just running a series of tests. You’ll need to devote a larger part of your time to the design and implementation of the tests as well.

For this reason, it’s crucial to utilize a combination of traditional testing along with A.I. machine learning in the production environment. Companies that can afford to invest more money into testing should consider using a combination of manual testing and A.I. machine learning to ensure the programs created are ready to go live.

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