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The field Artificial Intelligence is really starting to hit it’s stride. Our developers were sitting around the office today thinking about some of the older AI app – Siri, Cortana, and so on.

Obviously, both Virtual Personal Assistants or VPAs as they are often called, are still around and going strong. But all three of the big VPAs (the two previously mentioned and Google Now) today are far more advanced than what they were even 5 years ago.

Even more noticeable, we now have AI devices like Echo that connect to the Internet of Things to bring your whole world online. As we move inexorably down the path of increasingly more complex machines, AI will become one of the world’s biggest markets – ever.

But with all of the noise and excitement stirred but up techies and engineers, it can be hard to see the potential business opportunities presented by AI-based apps. Of course, we won’t know the true extent of how AI can help app and web-based businesses for sometime.

That being said, there are immediate benefits that we can point. So without further ado, here are some ways entrepreneurs can use AI for apps to make money and grow their business.

Natural Language Processing

NLP is what coders and programmers call a series of algorithms that help computers better understand natural languages. This is different from earlier attempts called “rules-based” systems in that it attempts to understand language contextually as opposed to just grammar or keywords.

Why do you ask? Well, it helps an AI based app to understand language as it is spoken normally. That way, a user can search for something by voice or just type normally, and an AI app will be able to better understand the user, returning more accurate and precise rules.

So what are the business applications here? Well, very generally speaking, improved services you can offer to your customers. At SDI, we’ve used NLP tools to create better search engines for our apps.

Our algorithms are better able to understand how a person would normally speak or search; using this contextual tool, AI apps for Android and iOS can return better results, more refined results, and more intelligent results.

For instance, let’s say you own a mobile app that lets users track down upcoming music events at local venues. Say a user searches ‘Find cheap bars with a good 60’s Classic Rock band.’ With an older rules-based system, an app would return items for each keyword in that phrase:

    • Cheap;

    • Bars;

    • good;

    • 60s;

    • Classic;

    • Rock;

    • Even band!

While this can be helpful in terms of covering any possible meaning of your question (and this can still be an effective tool!), it can also return an overwhelming amount of frankly useless results.

With NLP and Machine learning tools our developers use to build apps, a user gets the results they want – bars with great music and affordable drinks. So, clearly, if your app has a search function (as most do) you will need to have machine learning NLP tools.

Sentiment Analysis and More

In case you haven’t yet figured it out, Machine Learning’s clearest and biggest impact is in the field of search analysis. They all largely fall into the broad category of NLP, but there are some clear distinctions between these various algorithms.

1. Sentiment Analysis

One of the ways that our developers improve the search results an app produces. Sentiment Analysis is a Machine learning tool that will search for opinion-based results, as opposed to numeric or rules-based.

If we return to our earlier example of music, SA will examine all references to band online from other people – reviewers, fans, venues, critics, and so on. What are people saying in the reviews?

Are they good? Crap? Best thing since sliced bread? This is opposed to looking at the band from a more numeric based perspective – it has 3,000 3 star ratings, 2,000 4 stars, and so on.

From a business perspective, this is useful to offer results more in keeping with what user is actually seeking. Better products = more users & higher engagement = $$$$.

2. Support Vector Machines and Anomaly Detection

SVM helps an app reduce some queries to binary categories – it is a bar or it is not a bar. This helps to narrow a field of search results to something more manageable. This is especially effective when it comes to images, for those with Design lovers with AI apps for iOS.

Anomaly Detection works in concert with SVM to reduce the level of errors in search results. While SVM narrows the field, Anomaly Detection graphs search results and determines the outliers. If these outliers fall far out of the expected parameters, they are probably errors, and the algorithm will remove them.

3. Reinforcement Learning

This is the meat and potatoes for Machine Learning. When you use Cortana or Siri, RL techniques study how you speak or type, what you commonly search online, what you like from a search result and more. All of this is then used to change the algorithm to better fit your needs; that’s right, an AI app is literally evolving every time you use it.

One great business application we’ve seen, is with a recent app we built. Since this app is in final development phases, we can’t provide exact details. But suffice to say, it centers around helping users stay trendy.

But as anyone will tell you, trendy can be a matter of choice. For instance, a hipster does not typically enjoy the same sense of design as say, a juggalo. So this app learns what each user finds cool and hip.

Thus when a hipster searches for cool local bands, they will get directed to Mumford and Sons venues, and the ICP lovers of the world can enjoy…well whatever juggalos enjoy.

Build your Own AI App for iOS or Android Today

If you have an app or an app idea that you think can benefit from AI and Machine Learning tools, contact us today. Our developers know the best ways to leverage machine learning into a successful app – and our marketers ensure it makes the money you need!

Call 408.802.2883 / 408.621.8481 now for a free consultation and quote.

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