AI has begun to impact nearly everything we do. The same technology that has made consumer internet search more personalised, connected, and ubiquitous is also starting to reveal itself in employee-facing search solutions, supporting enterprise search. Workers who depend on corporate search solutions often struggle to find relevant information in an ever-expanding pool of largely unstructured proprietary data. Companies can expect to see an increase in employee engagement, efficiency, and cost savings thanks to smarter search mechanisms, an embrace of open-source applications, and AI elevating virtually every aspect of data discovery.
Organisations need to maximise the value of RPA, or robotics process automation. RPA – is a software category that is driving seismic change across the international workplace. With both routine and non routine based-tasks being transformed, RPA is topping the corporate agenda. By getting the RPA strategy right, organisations will achieve even greater shareholder, customer and employee value — such as efficiency savings and increased productivity. Will we see a standard emerge in robotics process automation? What about seeing beyond the RPA hype? Consider RPA trends for 2019.
Machine learning can help brands develop more personalised conversations with their customers. It is more important than ever for brands to keep up a steady conversation with their customers. Those who become complacent with client communication could soon find foot-loose customers wandering in the direction of their competitors. As they say, out of sight, out of mind. That is why personalised conversations with their customers is vital. Machine learning can help make it happen.
Digital transformation and data protection; to some that may feel like a contradiction in terms, but in fact, the two can be interdependent. With digital transformation, there is a temptation. It’s the allure of just focusing on the deluge of information available and the potential for business advancement, if only one can successfully aggregate, interrogate and monetise it. Digital transformation and data protection, on the other hand, seem to be at odds. After-all, the principles of adding more control to data usage can feel like roadblocks on the path to becoming data driven.
Unsupervised models can essentially be trained on the knowledge that exists on the web that we could never as humans digest and read. There’s more information created in a single day than we could possibly absorb in a lifetime, but a machine can absolutely digest it, learn from it, understand it, and dynamically build knowledge of the world that we can then leverage.” And that’s what unsupervised deep learning means. Unsupervised deep learning is absolutely the future.