VR4HR encompasses cutting-edge technologies that ensures the assessment process now can delivers what it is supposed to, a TRUE reflection of what an employee faces in the workplace instead of a result based on assumptions. Through the utilization of Mixed Reality, Dark Data and Deep Learning, VR4HR now delivers what traditional assessment methods cannot…….
VR4HR measures how a candidate will perform under the exact same conditions that they would face in their workplace.
Mixed reality is a significant advancement of augmented reality (AR). In a “hybrid” environment, interactive virtual objects can be mapped to the physical environment, blending the real and the virtual. Whilst the core premise of both AR and MR is similar, the crucial difference is the underlying technology. Mixed Reality is headset-based, whereas AR is viewed through a flat-screen such as a smartphone or tablet. MR is also aware of the geometry of the environment around you – using it as the canvas for you to create immersive content that is defined by the space you are in.
Dark data are the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing). Similar to dark matter in physics, dark data often comprises most organizations’ universe of information assets. Thus, organizations often retain dark data for compliance purposes only. This is a goldmine of data that is often not used by HR departments.
The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without human involvement. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve. The amount of data we generate every day is staggering—currently estimated at 2.6 quintillion bytes—and it’s simply too much for humans to process manually.
Examples of how we use Deep Learning already in our daily lives :
Siri is an example of a virtual assistants from an online service providers that uses deep learning to help understand your speech and the language humans use when they interact with them.
In a similar way, deep learning algorithms can automatically translate between languages.
The way a driverless car understands the realities of the road and how to respond to them whether it’s a stop sign, a ball in the street or another vehicle is through deep learning algorithms.
Chatbots and service bots that provide customer service for a lot of companies are able to respond in an intelligent and helpful way to an increasing amount of auditory and text questions thanks to deep learning.
Deep learning is being used for facial recognition not only for security purposes but for tagging people on Facebook posts and we might be able to pay for items in a store just by using our faces in the near future.
Ever wonder how Amazon comes up with ideas for what you should buy next and those suggestions are exactly what you need but just never knew it before? That’s right, deep-learning algorithms at work.
