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Machine-learning takes on ecommerce

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While machine learning is a relatively new concept for many, there are an increasing number of platforms and services that are adopting this construction of algorithms. ROBERT SCOTT, SafariNow CCO, talks about the relevance of supervised and unsupervised machine learning.

Given the massive increase in the amount of data that companies – of all sizes and across sectors – are generating, it is no surprise that data analytics and machine learning are fast becoming key components of every innovative company’s toolkit. For the uninitiated, machine learning refers to the way in which companies can now leverage computing power to find important patterns within their data – and then use these patterns to improve their service or product offering.

Because of the sheer volume and complexity of the data being created today, it is often far beyond the capacity of any human – no matter how analytically gifted – to find any relevant trends or insights within what has been tagged ‘Big Data’.

Notably, one of the big differences between machine learning and computer-assisted analysis (where humans are involved) is that the recent breakthroughs in machine learning enable computers to teach themselves how to solve problems. So previously, when humans were directing computers, they were limited to very direct questions and answers (for example, “what is my top selling item?”) and required the person using the machine to dictate which method to use to the solve the problem. Now, machine learning enables computers to find answers in ways that are unguided by human intervention.

Although it is a relatively new and novel concept for many, the technology has already been applied to platforms and services that we use daily. Take Google Search, for example. When we enter a search term, Google uses elements of machine learning to analyse our behaviour once the first results have been served up (i.e. did we need to type in the same search term again, or did we follow some of the top links provided?) and then refines and improves its service according to the data. Other examples include Google’s self-driving car, how Netflix suggests which movies you should try next, and how a dating site suggests which people are most likely to be a suitable match for you…

Unexpected Insights

As with most technological tools today, almost any company or sector can leverage machine learning to better serve their customers. The challenge for companies is to recognise where – and how – certain insights and trends can improve their product or service offering.

Within the travel sector, we have identified various areas in which machine learning can be applied in order to fine tune our offering and help travelers locate their dream destinations. One of the great benefits of this tool is that it often finds relationships between factors that are completely unexpected and unplanned.

Machine learning has led us to the insight, for example, that some accommodation providers have a preference for prioritising requests from customers who would like to stay with them in the next few days – whereas other providers would much rather prioritise requests far in advance (for the school holidays, for example). Often, it is these unexpected – or unplanned – insights that can be the most beneficial for customers.

Continual Improvement

As an online travel aggregator, there are in fact infinite possible use cases for machine learning – and we are at the tip of the iceberg in terms of harnessing its potential to improve our offering to consumers looking for the next adventure.

Looking ahead, machine learning will perhaps become a standard application within the travel and e-commerce environment. Companies that are open to innovative ways of finding insights in their data can ultimately serve their customers more efficiently – and even develop closer relationships with them in the long-term. The key for companies is to keep an open mind as to whether or not their long-held beliefs about what customers want is actually supported by the data.

By always remaining alert to new patterns and insights, companies can make adjustments – both big and small – to enhance their offering.

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Win Funko Fortnite in Vinyl

Gadget and Gammatek are giving away a set of three Funko Fortnite figurines to three readers.

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A Funko Pop figurine based on a character set is indicative of reaching the heights of pop culture. It is no surprise, then, that the world’s biggest online game, Fortnite, has its own line of Funko Pop figurines. The Funkos are modeled on the characters in game, including Drift, Ragnarok, Dark Vanguard, Volar, Tracera Ops, and Sparkle Specialist.

Now, local Funko distributor Gammatek has released the Fortnite figurines in South Africa. To celebrate, Gadget and Gammatek are giving away a set of three Funko Fortnite figurines to each of three readers. To enter, first follow Gadget and Gammatek on Twitter. Then click on your favourite Funko Pop on the next page and post the Tweet that appears.

You can put the tweet in your own words, but entries must have the competition’s hashtag (#FunkoFortnite), mention @GadgetZA and the link to this article (bit.ly/FPFortnite) to be considered valid.

Click here to see the Funko Fortnite characters and to select the one you want to tweet.

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CES: ThirdEye X2 mixed-reality glasses

The X2 mixed reality (MR) glasses, unveiled at CES last week, are the smallest mixed reality devices yet. They boast a 42-degree field of view, HD resolution, and run on the Android platform. The glasses are not connected to wires or tethered packs, and boast a built-in VisionEye Simultaneous Localization and Mapping (SLAM) system for accurate environment tracking. The UI allows the user to wear it while completing tasks indoors and outdoors.

Click through to read how the software makes these glasses a reality.

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Nick Cherukuri, founder of ThirdEye, said: “The goal of the X2 was to integrate SLAM into a small glasses form factor – that is the future of making MR Glasses mass produced.”

ThirdEye has also partnered with a major manufacturer, which will enable the X2 to be shipped in mass scale, which is currently a significant hurdle for many startups.

The glasses have built-in software like the ThirdEye App Suite, which provides a full MR software platform built into the units. The App Suite includes live audio and video streaming, AR data communication between remote users in the form of a “see what I see” application, and 3D scanning capabilities.  The glasses run on Android 8.0, creating a platform for a worldwide community of developers to submit AR, VR, and MR applications to the ThirdEye App Store. 

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