Think Big. Start Small.

According to Dan Olley, CTO of Elsevier an investment in machine learning 3 years ago would have been a waste of money but if you don’t invest now and wait 3 years you will never catch up. Fuelled by exponential amounts of data machine learning is driving innovations cross industry. The data needs to be ‘understood’ and placed in context; the sheer scale of data just can’t be managed by human interaction alone and that is where machine learning use cases are springing up.

The headlines are grabbed by futuristic sci-fi possibilities but we are finding our partners focussing on real-world business problems to create competitive advantage for their customers.

2 key drivers exist for the adoption of machine learning to address a specific business area –

  1. Reduction in cost OR
  2. Creation of new revenue streams

Often the creation of a new ‘cognitive’ application with our Cognitive Engine focusses on one or the other but they often go hand in hand. Cost is reduced through the replacement of semi-automatic or manual process and the customer senses an opportunity to create new revenue streams from the data-driven insight created.

With the Cognitive Engine the ‘data science is done’; meaning new #AI #ML #NLP models can be created in days. Our clever #algorithms get trained on your data in your own environment keeping your data secure. Trained machines are deployed and historical and real-time data sources directed at the machines who deliver industry leading accuracy rates out of the gate. Iterative improvements occur as the algorithms ‘self-learn’ and improve the ability of the newly ‘cognitive’ process.

Our ‘use cases’ are growing every week as partners apply our Cognitive Engine to real-world business scenarios cross industry. Once a new ‘cognitive’ model is deployed the project team sit down with key stakeholders within the customers’ business to review progress against the original objectives and to ascertain potential next steps. This may include adding new sources of data or creating new ‘cognitive’ models to focus on sub-areas of the original scope or perhaps new industry models. Gaining insight from the massive processing power of a machine learning model opens up new opportunity to do even more. The ‘learning’ process isn’t just about the machine; it’s about the human knowledge of a business informing the machine where to go next. Humans and machine work together to improve overall business operation and competitive advantage.

You can’t afford to wait; the practical application of ‘cognitive’ (AI, ML, NLP) technology is truly transformational. Gaining an appreciation of a ‘cognitive’ solution that is taught to understand the language in your data; to discover ‘insight’ in the data is an iterative process. If your competition is investing now; imagine where they will be in 3 years’ time if you have stood still?

About Chatterbox Labs

Chatterbox Labs power cognitive solutions for the world's leading technology and professional services companies. The Chatterbox Cognitive Engine has the data science baked in; providing the constituent components required to create disruptive cognitive products in days. At its core are market leading statistical machine learning classifiers that operate with speed, scale and precision. The Chatterbox Cognitive Engine can work with real–time short and long form data to embed cognitive intelligence into business processes and applications. Chatterbox Labs work with massive amounts of unstructured data, providing context and human like understanding to business information delivering efficiency, cost reduction and new revenue opportunities across industries. Chatterbox Labs only sell via partners; providing them with full source code on premise and private cloud delivering high performance, privacy and control.

For more information please visit our web site www.chatterbox.co or contact Andrew Watson, VP Strategic Alliances, Andrew@chatterbox.co