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Never have disruptive technologies like artificial intelligence (AI) and machine learning had such an impact on how your business interacts with your user community as it does now. And there is a reason for that; the accessibility of computing power and data sets have ushered in breakthroughs in our day-to-day lives. Say, for example, cashierless grocery stores, voice-activated devices responding to our commands, and lot more.

There is a great deal of concern over artificial intelligence: What does it means for our jobs? Whether robots will replace us in the workplace? Or are we heading towards robot wars? Every coin has its two sides: the positive side is that artificial intelligence can also be used for the greater good. In fact, according to Google, AI provides a unique way of approaching problems and can meaningfully improve people’s lives. But do you think it’s a silver bullet? Well, let’s find out. Down below, I would like to shed some light on specific pointers stating what problems you should address with AI?

1. Problems which you can prototype first: Being a tech-savvy geek, I would suggest that you prototype every AI application before commercializing it. This provides a better opportunity to test, iterate, and fail fast at a low cost and of course, in a safe environment. If you don’t prototype at first, chances are there that the product might conduct a limited amount of ability to make a meaningful impact.

2. Applications featuring bugs: There is always a room for improvement, with the help of AI featuring a feedback loop; one that highlights when it makes the wrong decision. However, that doesn’t mean you need to stop applying all your previous knowledge. After all, this must be continued just to ensure smarter, more accurate assumptions. Which also means you must start deploying AI even in such areas where the “nothings” make a significant negative impact on your customer experience or reputation.

Moreover, with your team becoming more sophisticated at deploying AI, you can expand your use cases to scenarios with higher risk.

3. Small specific issues, not the entire system: Professionals, especially those who have already been working with AI, would love to make valuable contributions to society and as big of an impact as possible. And maybe that’s the reason why it is crucial to make the most of AI when we have many deep-seated problems. For instance, things like personal transportation, healthcare, and energy conservation. You know that tackling a specific bunch of the issues is often more efficient and productive in the long run, which simply means that intelligence does not have to be solved at a system level. Breaking the effort into smaller, yet significant projects, also gives teams the ability to allocate their often-limited time and resources in a better way.

4. Transforming how we learn: Georgia Tech’s online M.S in Computer Science program recently came up with an AI teaching assistant. After initial teething problems, the robot started answering the students’ questions with 97 percent certainty. Unique research showed that the main reason for more and more students dropping out is the lack of support; the robot was designed for the same purpose, and it became a huge success.

We humans are way different when it comes to learning or understanding things. We tend to learn at different speeds and with different starting points. Now, do you think that our educational system can afford a tutor for every child? Probably not. And that’s when AI comes to the rescue! With the help of artificial intelligence, each one of us can learn in a more personalized way. Artificial tutors, made to look and sound as much like humans as possible, could take the lead in delivering personalized education.

5. Check for bias: There is a race of algorithms out there! Due to which new systems might end up making predictions based on racial disparities, especially in policy domains like criminal justice. So, it is pretty safe to say that the data used to train an algorithm may be biased, reflecting a history of discrimination. As a result, data scientists might unintentionally report misleading performance measures for their algorithms. This is a serious concern, no matter what the other benefits.

In a Nutshell

Of course, these services indeed come as a boon, but they do share a bunch of fair controversies as well. If you ask a time traveler, then he may warn you about the upcoming issues and encourage a similar approach. In addition to this, it is imperative to be mindful of whatever you are doing.