AI training is a critical part of conversational AI solutions, a part that makes AI software different from any kind of software previously created.
AI training is not coding.
Unlike all other existing software which is fully coded.
Let us consider a simple example:
We create chatbots for two companies, one company is selling shoes, another is selling cars. From the software standpoint it is one chatbot solution running as an online service accessed remotely or a program available locally. In both cases they are two identical instances of the same software (one instance for the shoes company, another for the cars company).
Yet, for the first company the chatbot is supposed to talk about flip-flops, summer shoes, high heels and so on. For the second company, however, the chatbot is not expected to know any of that. Instead, the chatbot should be able to support conversations about car brands, car models, should know how to tell Toyota Camry from Toyota Corolla, etc. This shoes and cars knowledge is not programmable. It is trainable. It is not coded, instead it is a part of language processing capability that AI solutions like chatbots have. And herein lies the major differentiation and advantage of the AI solutions compared to traditional software.
How to train AI?
There are several ways to do it. Sometimes AI system can train itself, improve its linguistic ability over time. It also can be trained by professional linguists. And in some cases, by the users. The latter is the desirable scenario because businesses know better than anybody else what they want their chatbot to talk about.
It is not easy, given the existing state of AI technology, and usually requires a high level of technical knowledge. You may have heard mentions of intents and entities in chatbot discussions. These are examples of linguistic elements AI training is currently based on.
Without proper understanding of what these linguistic elements are and how language acquisition process works in existing AI systems it is better to leave AI training to professional linguists.
WHAT IS AI TRAINING
How nmodes Intent API Improves Social Intelligence
Social media generates a vast amount of data. There are 500 million daily messages on Twitter alone. Still more data on Facebook, Google+, LinkedIn and other social networks. Some of this data is useful to businesses, in fact, it is extremely useful.
A business can use social data to generate actionable insights about customers, competitors and their company strategy. Social information empowers departments and teams, and when used correctly, creates a strong sustainable bond between businesses and their customers.
nmodes Intent API helps businesses to execute their social strategy efficiently. Here are the major elements of social strategy Intent API contributes to:
1. Listening. Intent API finds customer intent with any level of granularity. You might want to know who is looking to buy shoes in general, or looking to buy flip-flops in particular, or interested in buying only Nike footware, or interested in buying sneakers in New York region.
2. Sales and marketing. Intent API understands what stage in the purchase process your customer is in. Intent API tells if a customer is ready to buy, or is in the awareness stage, or considering the purchase but not ready yet, and so on.
3. Social intelligence. Intent API delivers meaningful intents and behavioral information on a large scale and for all verticals. Any insights and topics, as long as somebody is conversing on this topic, are available.
4. Teams and projects. Intent API channels information to the relevant departments within the company. Sales prospects should go to sales department, complaints to customer service, brand conversations to the marketers, and technical issues to tech support.
Integrated Real-Time Data Boosts Content Delivery
How to make content more relevant and appealing to the content consumer?
This is a problem that has been on the mind of content creators for some time now. In our age of information abundance it is not easy to stand out and make your voice heard. The competition for the consumer’s attention is escalating, and with the number of information sources ever increasing, it will only get tougher.
Traditionally, a content delivery does not change across the target audience. A commercial, or a blog, looks and is experienced in the same way by all viewers and readers. We are entrenched in this paradigm, and can hardly imagine it being otherwise.
It turns out, the advancement of new technologies capable of capturing individual intents in real time brings up new opportunities in creating personalized experiences within the framework of content delivery.
This is how content can become more relevant - by becoming more personalized.
In a rudimentary form, we are already familiar with this approach as seen in online advertising. Some web and social resources aim at personalizing their promotional campaigns based on whatever drops of behavioural patterns and interests they can squeeze out of our web searches. The problem, of course, is that the technologies used to power these campaigns understand human behaviour poorly and results, therefore, more often than not leave a great deal to be desired. To put it mildly.
nmodes has been working on semantic processing of intent for several years. We now can capture intent from unstructured data (human conversations) with accuracy of 99%. (Interestingly, many businesses do not require this level of accuracy, being satisfied with 90%-92%, but we know how to deliver it anyway).
We recently started to experiment with personalizing content by using available consumer intent.
We used Twitter because of its real-time appeal.
We started by publishing a story, dividing it into several episodes:
And we kept the constant stream of data flowing, concentrating on intent to dine in Paris:
We then merged the content of the story with consumer intent to dine in Paris as captured by our semantic software. Like this:
This merging approach shows promising results - the engagement rate jumped above 90%.
Overall we are only at the beginning of a tremendous journey. We know that other companies are beginning to experiment, and the opportunities from introducing artificial intelligence related technologies into content delivery are plentiful.
There is a long road ahead, and we've made a one small step. But it is a step in a very exciting direction.