Artificial intelligence, much like human intelligence, is only as good as the education it receives. That's why training data -- the information used to "teach" deep learning algorithms how to perform complex tasks -- is key to AI success.But not just any training data will do; artificial intelligence requires well-annotated data in order to learn. Without annotation, even the largest data sets are of little use in training AI systems.
Annotation is the process of adding structured context to data. It identifies and distinguishes key concepts within your training data so AI can more easily understand what to look for when performing its tasks.
As an example, take a typical interaction between a sales representative and a prospective customer conducted over email. A human being could likely read that email chain and rather easily determine some key contextual elements, like who is the sales rep, who is the prospect, and was the sale successful?
If you want an AI system to learn from this conversation (perhaps to help identify the kinds of prospects that become customers, so you can enhance your sales qualification process), you'd first need to annotate the email string to identify which email address belonged to the salesperson, which belonged to the prospect, and -- most importantly -- whether the conversation led to a successful sale.
And that's just basic annotation. For truly sophisticated results, you'd associate the email chain with a specific opportunity in your customer relationship management solution (CRM), and, if the opportunity closed as a win, connect it to the ongoing revenue derived from that customer. You'd go even further and annotate which solutions the customer acquired and the usage metrics of those solutions. Looking backwards, the system would also annotate how the prospect first became a lead, how that lead was sourced, and how it was nurtured before the sales team made contact.
High-level annotation doesn't just give context, it gives structured, holistic context that allows an AI to consume data from multiple systems in multiple ways. It can learn to distinguish success from failure, and can predict and classify those states from data that many human observers might not notice or correlate.
With this level of annotation, our hypothetical AI algorithm could learn to predict the marketing methods and messages that create the best leads, which in turn generate the most profitable deals. And beyond analytics, the AI solution could learn to automate many of the soft, human-centric interactions that dominate the typical sales process.
At its most basic, all data needs to be annotated into at least two states -- success or failure, positive or negative, authentic or fabricated -- so that an AI algorithm can self-generate an "intelligence" for distinguishing between the two states. Once your AI can tell good from bad or true from false, it can accelerate those determinations, connect them with other actions, and automate formerly "human only" processes at computer speeds.
Without this annotation, almost every data set is incompatible with AI training. Thus, any AI system that doesn't include a tool or system to help you annotate your training data isn't really delivering an AI solution that can improve and progress over time.
Talla is building a customer support artificial intelligence solution -- and is developing an AI-optimized knowledge base to help you annotate the data needed to train and enhance the AI. If you're ready to start your journey to true customer support automation, contact Talla today.