The future of work pivots on AI

In the years since the term “digital business” first emerged, so has our understanding of digital maturity.  

In the first waves of digitization, it was enough to have a data warehouse or two, or even a data lake. Now, with data gushing out of every connected device, companies have access to entirely new categories of more meaningful data – unstructured data, IoT data, images, social data – which makes the challenge of finding needles in haystacks even more daunting. The wild success of Snowflake’s recent IPO is evidence enough, if it were needed, that solving this challenge can be hugely profitable. To address this issue, AI leaders in our recent study on the ROI of AI are spending on advanced AI technologies, such as machine learning, deep learning, computer vision and natural language processing. In contrast, non-leaders are more focused on basic AI technologies, such as data management, digital assistants and robotic process automation. Deep learning is proving incredibly valuable as AI adoption expands, as it provides businesses with the ability to find meaning in diverse sets of unstructured data. But among all the AI-related technologies currently being developed, natural language processing stands out as having perhaps the highest potential.

Over the last few years, the advances in voice recognition have been profound, whether to capture different accents and languages or to build capabilities into more devices. The most recent example of this is Generative Pre-trained Transformer 3 (GPT-3), released from the non-profit OpenAI research laboratory, established by Elon Musk. 

GPT-3 is described as an autoregressive language model that uses deep learning to produce human-like text. It has been trained on billions of words of text and, over time, has figured out the underlying rules of language and how to use them. The model generates written text so human-like that it has led to speculation on the impact of integrating it with agent coaching and real-time scripting software from companies such as ASAAP and then further integrating the result with the latest-gen “digital human” from UneeQ.

When a photo-realistic digital human can talk to a customer in the customer’s dialect and language, and look at them with the color eyes that they prefer (pre-selected during sign-up for the service) and solve the customer’s issue quickly and painlessly, then an entirely new threshold of performance will have been reached. Systems that can learn and become smarter through the collective intelligence of the network in the way that Tesla cars are all upgraded at once and Waze collects real-time data from all of its users to inform all of its users is a future that may appear science fiction, but it’s fast becoming science fact.

Deep learning is proving incredibly valuable as AI adoption expands, as it provides businesses with the ability to find meaning in diverse sets of unstructured data. But among all the AI-related technologies currently being developed, natural language processing stands out as having perhaps the highest potential.