Avoid These Pitfalls When Investing In AI – Forbes

Artificial intelligence (AI) and machine learning (ML) are the latest buzzwords in technology, and companies of all sizes and industries are eager to jump on the opportunities they offer.

The powerhouse duo of AI and ML is now well within reach, thanks to the big data explosion and highly scalable cloud computing capacity. Together, AI and ML hold the promise of delivering insights that result in cost savings, increased efficiencies and productivity gains on a scale not possible with traditional processes.

At my company, Applause, we are using data science and machine learning to enhance various aspects of our software platform. Applause is a crowdsourced community of software testers and digital experts, and we have had strong success leveraging these new technologies to transform our tester selection process.

Our journey to integrate machine learning into our product portfolio has taught us a thing or two. Weve learned important lessons about avoiding bias, incorporating feedback loops, leveraging data and using progressive rollouts and popular tools.

Using machine learning models, we can now pinpoint the best candidates to deploy on any given testing project, for anything from an innovative user interface design to ensuring voice capabilities stand up in an omnichannel experience. By automating the process of matching optimal testers to specific projects, we deliver better value to our customers while removing some of the natural human bias that will inevitably sneak into the tester selection process.

Based on what we learned, here is a hands-on practitioners take on what to consider when starting to deploy AI and machine learning, along with some of the more common pitfalls to avoid in the spirit of keeping nascent projects on track.

Beware of human bias. Yes, AI and machine learning can help mitigate or eliminate human bias, but theyre not foolproof, especially if the software team doesnt keep the bias issue front and center when designing and training models. There have been more than a few high-profile AI failsthat have caused companies to take a step back and think about how bias is introduced into the data. These types of bias could cause consumers to distrust AI systems permanently, potentially impacting the technologys future uses.

To avoid these scenarios, developers should carefully consider the kinds of data used to train algorithms and where that data comes from. It is important to create specific rules about data selection to provide guidelines for developers.

In Applauses case, we refrained from having engineers pick the criteria and instead consulted with subject matter experts and solution leaders in areas like manual testing, test automation, usability and accessibility (among others) to ensure we were prioritizing the right skills that would deliver the greatest value to customers.

Create a feedback loop. Communication is key for AI and machine learning development, yet too many teams get stuck in silos and fail to integrate a consistent feedback loop into their processes. With a proper feedback loop, the team stays up to speed on all issues, including whats going right and what can be improved. Initially, you might start with a communications sequence limited to subject matter experts, but as machine learning models evolve, it is important to broaden the feedback loop to include other stakeholders in the process. Ultimately, you want the model to be able to collect feedback on its own in an automated fashion.

Avoid analysis paralysis. It happens to the best of us getting stuck on a machine model for too long with the quest for perfection that causes delays in the delivery cycle. AI and machine learning are all about iteration and continuous improvement, so the work is never really done. The more data fed to the hungry beast, the better the model will be.

At some point, however, you need to draw the line and get the product out the door. Weve scaled that hurdle at Applause by doing progressive rollouts. We get the model to an agreed-upon state and get it out the door, but we continue to iterate and improve the model as the process evolves so things are never at a standstill.

Fully leverage data. Dont let all the valuable data youre collecting go to waste. Our data science team at Applause is solely focused on taking the data we collect and turning it into action. Weve cultivated a strong partnership between that team and our business intelligence group, and the two work together to manage the mass amounts of data collected and turn it into something that will improve the machine models while delivering essential insights to the business.

Avoid an 'all or nothing' mindset. You dont just flip a switch on AI and machine learning and immediately take humans out of the picture. It takes time to build capable machine models that can become a 100% replacement for manual processes. Again, I go back to the idea of progressive rollouts: Create a minimally viable machine learning model that can be released and trained, and improve upon that model to make it more efficient over time.

Dont reinvent the wheel. Dont feel like you have to start from scratch to dive into machine learning and AI. There are plenty of off-the-shelf tools (Scikit Learn and TensorFlow, just to name a few) to fast-track deployment, and AWS, Azure and Google Cloud Platform (GCP) all have their own service-based offerings that can facilitate the process. These tools are successful because they work. Dont hesitate to take the help. Relying on a roll-your-own approach can dramatically slow down your timeline and increase your costs.

Just like any development effort, AI and machine learning will not be a fit for every application. In many cases, humans are still the more efficient and cost-effective option. While there is no one-size-fits-all development model, these basic tips should help companies steer clear of the pitfalls and navigate a successful path to AI-driven transformation.

Continue reading here:

Avoid These Pitfalls When Investing In AI - Forbes