Integrated Real-Time Data Boosts Content Delivery

sasha uritsky

Feb 12, 2016

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.

Interested in reading more? Check out our other blogs:

The Automation Is Coming

A close look at the history of humanity leaves us with no choice but to admit that the majority of jobs as we know them now will be transferred to automated systems. This is part of the technological and scientific progress our civilization is undertaking and it is irreversible.

Artificial intelligence became mainstream in 2016. For the first time artificial intelligence is not only available to big companies like Google, Amazon or Apple, but to the majority of businesses worldwide. Startups have started building products and services using artificial intelligence en masse.

The essence of artificial intelligence is massive, intuitive computing power: machines so smart that they can learn and become even smarter. The machines are becoming quicker and more nimble. They cover wider range of conversation topics. They now connect to robotic systems and online interactive systems. There is literally very little they cannot, or will not be able to, do as applied to industrial workforce.

With all the good that’s going to come with automation, we are suddenly faced with a new problem: the elimination of many low and middle class jobs. Many jobs that have already been severely impacted by computers (manufacturing, administrative support, retail, and transportation) will continue to diminish. In the nearest future routine-based jobs (telemarketing, sewing) and work that can be solved by smart algorithms (tax preparation, data entry keyers and insurance underwriters) are most likely to be eliminated.

What to do? It is fruitless to fight automation, we need to find ways to work with automation rather than against it.

The solution is to become more creative as species. Creativity is the natural advantage of humans over machines. Automation is about to change the course of the world, it’s going to be a great disruptor and impact the workforce like nothing we’ve seen before. We can sit around and gradually become obsolete, or accept the challenge and use the tool of creativity, which we are in unique possession of, to maintain our superiority.

WHAT IS AI TRAINING

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.