Chatbots — The New Face of Artificial Intelligence

As the data scientists keep pushing artificial intelligence closer and closer to general intelligence it’s worth reviewing what their progress has engendered thus far in terms of useful business applications. So with this post let’s examine the current buzz around chatbots, understand where they came from, and consider why they might be the web’s next marketing paradigm.

Why Chatbots, Why Now?

There are two main reasons:

  1. The democratization of the cloud’s supercomputing capabilities
  2. The proliferation of messaging platforms

Technology has become more open and innovation more decentralized than ever, in turn facilitating the free flow of information and rich media across the web in ways previously unimaginable.

Until recently due to the high cost of overcoming the infrastructure and processing limitations associated with scale, cloud computing for sophisticated data science applications had only been available to the enterprise. But now, in order to leverage the power of big data, established companies like IBM, Microsoft, and Google have opened up their APIs and adapted their innovation strategies to leverage developer networks. After years of resisting, the enterprise has finally embraced the cloud along swith its culture of online collaboration.

Running alongside this trend in the consumer space has been the emergence of instant messaging platforms as a significant market space in its own right (e.g. WeChat, Facebook Messenger, WhatsApp, Viber) as well as programmatic media buying encountering a variety of issues regarding scalability. Messaging platforms on the other hand present a different kind of marketing opportunity for brands, moving beyond the bidirectional paradigm of supply and demand as dictated by the present structure of real time bidding networks and ad exchanges. Evolving from profiling users to predicting behavior, brands are now developing the ability to enter into real time conversations with consumers at scale through leveraging technology. Might this be the reason why the world's largest social platform recently spun off its instant messaging service as a separate business?

To understand how this state of affairs came to be and what it means for your marketing strategy let’s explore the connection between language and data, as well as how this nexus could potentially drive innovation for the online marketing landscape for years to come.

Is Data Science Really Catching Up With Natural Language?

Sparing the reader any suspense the answer is “Yes.” But before delving deeper let’s review some definitions:

Formal language: A set of strings of symbols that may be constrained by rules that are specific to it.Wikipedia
Natural language: Any language that has evolved naturally in humans through use and repetition without conscious planning or premeditation.Wikipedia

Note the latter concept is a feature of the perpetually evolving natural world while the former is a closed reduction of the latter. Natural language processing (NLP) is just the scientific research discipline exploring the dynamics of how these two types of languages relate to each other. In order for computers to understand the natural language of humans they first have to reduce their semantics to a formal system of ones and zeros (data processing) as well as keep up with its ongoing evolution in the natural world.

If you’re in the mood to geek out and pursue a deeper understanding of how the machines will get around to achieving this, do some wikireading about neural networks and deep learning. Also check out WolframAlpha for some insight into the spectrum of human expression that said machines will someday reduce to data science.

Enter The Chatbots (Emergent Social Media)

Refocusing our analysis on a new marketing paradigm, the net-net to consider is that the widespread trend of datafication driven by cloud computing will inevitably accelerate concomitant software innovation in the areas of data processing and abstraction, which in turn will elevate real time natural language processing to science reality. Jumping to the next curve after search and social will undoubtedly entail some form of emergent value proposition as brands see the potential to achieve greater customer intimacy through engaging consumers by deploying chatbots to speak to them directly and in their native language via artificial intelligence.

Worth noting is that chatbots in their present form exist as primitive combinations of speech recognition and language databases. Check out OyeGennie for a good example of such. For the state of NLP art, check out Microsoft’s XiaoIce. Though by now you’ve likely already interacted with NLP through booking a meeting with someone’s artificially intelligent assistant. Was it Amy or Clara?

In closing, note that the bot landscape already spans across social media, instant messaging, and various other online communication platforms—i.e. Facebook, Twitter, Slack, WhatsApp, Hangouts, WeChat, Telegram, Skype, SMS, email, etc. It's undeniable; bot space's gonna be yuuuuuuge.

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