The Disruptive Potential of Artificial Intelligence Applications

by   |   January 16, 2014 6:00 am   |   0 Comments

Lars Hard of Expertmaker The Disruptive Potential of Artificial Intelligence Applications

Lars Hard of Expertmaker

If you are an information technology professional, you are probably aware of at least a few systems that gather data to create more intelligent systems and informed business decisions. But you may not even realize that artificial intelligence (AI) is increasingly becoming a part of your everyday life – not only in your business, but also in your life as a consumer.

AI is working across many different industries, but I have detailed three examples below of technologies that many people do not realize are already being enabled through artificial intelligence. These cases are also illustrated by examples of technology that you may be very familiar with, but unfamiliar with the data-gathering techniques and artificial intelligence technology behind these systems.

This is very important for business leaders across industries to take note of, as the opportunity to differentiate in a mobile and digital world will become increasingly crucial. Data-rich opportunities lie in the hands of those that innovate, and AI will open the door for new players to become industry leaders.

1. Vertical Search

Search engines are undoubtedly a staple in our everyday lives – for most of us, we rely on the search giant, Google, which provides us with tailored search results to many questions throughout the day. But many technology and data companies are realizing that the next generation of search lies in vertical, or topic-specific, search. Rather than solving large, more general problems, vertical search tackles more specific and precise queries. Vertical search is even beginning to emerge within industries such as travel, entertainment, fashion and more. This dynamic presents an opportunity for other companies to surpass Google in industry-specific verticals.

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As an example, Zite, a news recommendation smartphone app, offers the end-user recommendations of what to read based on preferences and uses artificial intelligence to learn the behaviors and preferences to create and continuously improve these recommendations. This creates intelligent search recommendations that are much more vertical and specific than what the end-user would experience with Google (or Google News, for that matter).

Truly intelligent vertical search engines utilize text and image classification coupled with other AI algorithms and big data analytics to gain detailed knowledge about both users and content in verticals or applications. It will be these companies can gain a competitive advantage over giants like Google.

2. Virtual Assistant Applications

It was not long ago that virtual assistants were thought of as something of the far-future. That was until Apple released Siri two years ago as part of iOS 5 and positioned it at the forefront of consumer AI technology. Since then, Siri and other competitors such as Google Now and Microsoft’s Cortana are beginning to become more intelligent as the data-gathering techniques on the back-end continue to advance. For example, Google Now presents its user with concert tickets once they enter the vicinity of the venue location on the day of the show.

But many end-users do not feel that today’s virtual assistants are delivering them completely relevant information. Common complaints for Siri in its current version include its lacking ease of use, and that it does not present you with truly relevant suggestions for your questions. This presents an opportunity for “next-generation” virtual assistants. Whether it be Apple, Google, Microsoft or another technology company – the opportunity to create smarter virtual assistants that deliver truly relevant information in real-time is there. As these technologies become increasingly relevant, users will continue to depend more and more on them to develop relationships with their devices, which will then in turn allow the devices to become more and more accurate over-time as they become more predictive of the users’ behaviors.

Retailers in particular have the opportunity to monopolize on next-generation virtual assistants, as they offer the opportunity to directly relate to shoppers and create truly loyal customers. The ideal virtual assistant of the future will help fill in the gaps that currently exist between a personalized experience that in-store shoppers are used to, and current, less helpful, online and mobile shopping applications. Some virtual assistants have already begun to use geo-targeting technologies to localize experience, but the next generation will focus on a direct interaction with the customer that will create a seamless customer experience – handling every step of the purchase cycle. We will continue to see more of these emerge as omni-channel retail becomes a larger part of our everyday lives, beginning with mobile virtual assistant apps.

3. Online Product Recommendations

But virtual assistants are not the only opportunity for retailers to utilize artificial intelligence to engage with customers and provide them reasons to become loyal. Online product recommendations are a potential area for retailers to utilize AI to present shoppers with long tail suggestions.

You are probably familiar with Amazon’s recommendations, a first-generation example of online product suggestions, as Amazon uses collaborative filtering. This method, however, is not capable of providing suggestions that present what the user actually wants. Collaborative filtering attempts to filter products driven by taste – for example, if you buy a pink shirt, it may then suggest you buy that same shirt in red.

Product advice powered by AI offer the next-generation of recommendations. By extracting data from multiple sources including your location and amount of time spent on the site, retailers will build the knowledge to predict their customers’ preferences and needs. Machine learning will begin to allow retailers to process this data and generate a deep knowledge of not only their products but their users, and even more importantly, their preferences and behaviors. Better recommendations can be built by classification of products and multiple recognition and data enhancement methods – laying the groundwork for retailers to establish meaningful relationships with their customers by recommending them truly relevant products.

As more apps and technology begin to incorporate AI, the more able they will become to predict behaviors of the end-user. You will begin to see more systems that learn your behaviors and are able to provide you with a much more personalized, seamless user-experience, as AI will continue to open up new doors to incorporate more abstract and difficult data sources.

In the past, only large enterprises had the resources to be able to invest in AI, as it required expensive software licensing as well as massive investments in both staff and time. However, advancements in AI technology have come a long way, and new AI platforms are more easily deployed, managed and cost-effective within the market. With these new exiting players have come new opportunities: this time particularly for small and mid-size businesses, which now have the chance to invest in AI technologies and compete with the larger enterprise players.

Lars Hård, the CTO and founder of Expertmaker, which offers a platform for companies to put AI in their products, such as virtual assistants, personalized advice and experience, and predictive medicine. He started the first games development company in Scandinavia, and has since pioneered large-scale use of AI. Lars is also a guest lecturer at Lund University in theoretical ecology and genetics.

Home page image of Genius, fresco painting by Josef Ferdinand Fromiller, via Wikipedia Commons. Used under Creative Commons license. 





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