When Alan Turing postulated what machine intelligence could do, his question gradually evolved into a more practical and implementable form – from ‘can machines think?’ to ‘can machines do what we can do?’
Fast forward to present day, the combination of databases and technology has made machine learning more relevant in solving multiple business challenges. Rapidly digitised financial data offered a glimpse of what machine learning and AI can deliver, and the financial industry has become a hot bed of fervored talk on using AI to create new profit centres and reduce operational costs.
Yet businesses still grapple with the promises machine learning and AI, often wondering if expectations are inflated or if digging deeper and persisting could reap more impactful returns. I’d like to think that it is the latter.
With continual refinements in machine learning techniques, AI implementation should be more orchestrated in its machine’s content training strategies. Having a well-defined business metric is only the start. But to stop there and generalise machine learning to “letting the algorithm handle all the data” may not be the most effective strategy to achieve significant outcomes.
A cursory understanding of the building blocks of machine learning – supervised, unsupervised and reinforcement learning – may suffice from a business standpoint. But deeper appreciation for the statistical concepts that underpin these blocks hold the key to developing content for more robust machine training strategies.
Each business case has a unique mix of statistical concepts. Familiarity with knowing what backs up the machine’s thought process gives the business more options and to craft training strategies that support the machine’s learning, particular in the case of chatbots and natural language processing (NLP).
For most of us, the simplest way to train a chatbot is by pairing an answer to each question. You might go further and pay more attention to frequently asked questions. However, machines just like humans, need context to understand specific questions. Understanding the kinds of supervised learning techniques that can be employed, such as Bayesian theory, provides food for thought on how businesses can rethink content for machine training strategies.
Bayesian theory works backwards from the question that had been asked by guesstimating the context of why the consumer asked the question in the first place. It creates a common sense knowledge that a machine needs to have to answer the question in a relevant way. Just understanding this concept alone is powerful in helping businesses assess the need to build better content ontologies that could add more precision and efficiency in the learning strategies adopted by machines.
As the saying goes, there is always two sides to a coin. And in the case of AI, machine learning statistical techniques must be complemented by well-managed content training strategies to ensure robust implementation.