How Can Your Business Use Machine Learning for Augmented Reality?
Business + ManagementCareer InsightsFeatured Postaugmented realityposted by Elizabeth Wallace, ODSC October 24, 2019 Elizabeth Wallace, ODSC
Augmented Reality is a highly disruptive tech that could change the way your organization interacts with customers. As the demand for personalization increases and consumers take advantage of newer, more readily available tech, you could find yourself searching for programs that take advantage of this new paradigm.
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Augmented Reality is no longer in the realm of movies. It’s here now, and it has business applications. With the implementation of machine learning, businesses can take advantage of things like computer vision and artificial intelligence to apply AR principles in the real world. Here’s what’s happening currently.
Consumer Based: Visual Search and Visual Exploration
Search is dominated by text, but what do you do if your search item is primarily visual? Machine learning gives companies the chance to implement visual search. Customers can upload pictures to a website and “search” similar items. Machine learning enables computers to “see” the item and match related items across a company’s inventory or on the web directly.
Once customers find the item they want or need, they can also use AR to “try on” those items. If it’s a piece of clothing, they can get a feel for it on their body type. If it’s a piece of furniture or accessory, they can see it in their space directly. The entire process helps customers shop online more efficiently and more confidently.
Companies like Pinterest and Ikea are already implementing these types of AR programs, and soon it might be more common to see these programs than not in online commerce.
B2B Solutions: Estimates and Exploration
Augmented Reality goes beyond consumer-facing applications. In the B2B world, proposals and quotes have a high element of visualization. With the implementation of ML into the world of AR, we can finally take the data available to us and experience it, not just get a report.
For example, if you work in construction, you can use ML to examine data from things like topography, existing structures, new building codes, and architectural best practices to help developers visualize your proposal from the ground up.
This type of large visualization gives companies the ability to bring data to life without exporting containers or other tech integrations. AR could be opened on something as basic as an iPad or smartphone and experienced wherever the target party is.
Some of the potential features could include:
- digitized, explorable floor plans
- 3D representations of new structures
- analytics on interactions with the system for further refining.
Internal Solutions: Training and Certifications
Experiential training is expensive and sometimes dangerous. Textbooks or written materials may offer knowledge, but that’s only part of the picture. Businesses may soon be able to use the power of ML as the foundation for AR-based, experiential training.
ML could create personalized educational experiences designed to take advantage of an employee’s current knowledge, knowledge gaps, and best learning practices. Then, it can provide ongoing training that integrates well with the job itself.
The AR layer can provide experiences that mimic real-life conditions without harming either the student or the recipient – think surgeries or military operations. Computer intelligence processes the data from interactions for continual refinement of the educational experience.
What Machine Learning Brings to the Table
In all these situations, the purpose of ML is to bring context into the world of AR. We’re familiar with things like Snapchat filters and Pokemon Go. Still, the real benefit of ML in the world of business is using context to answer customer inquiries, quell fears and concerns, and field different scenarios.
The best news? Consumers and B2B clients don’t have to invest in any tech to take advantage of these technologies. Most of your customers already can participate in this type of tech. On your end, that leaves the adoption of ML designed to optimize the experience. And in return, you get valuable data, processed and ready to go.
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ML processes not only information required to deliver the AR experience on the front end, but the ability to process data coming in once users are integrated. It’s this bit of context that can provide the most valuable service for your organization.