Patrick Schwerdtfeger is a business futurist specializing in technology trends including artificial intelligence and blockchain. The early use cases for AI include natural language processing (NLP), recommendation engines, object and image recognition, and autonomous driving vehicles. Patrick is the author of the award-winning book Anarchy, Inc.: Profiting in a Decentralized World with Artificial Intelligence and Blockchain (2018, Authority Publishing) and a regular speaker for Bloomberg TV. He has lectured at various academic institutions including Stanford and Purdue Universities. There are a variety of applications for NLP, also known as chatbot and socialbot technologies, and Patrick studies the proofs of concept (POCs) and pilot programs to understand emerging business models and anticipate where chatbot functionality will evolve in the future. Patrick’s focus on disruptive innovation and exponential technologies makes him an ideal futurist to help companies identify and capitalize on these trends. A few of the emerging use cases for socialbot/chatbot functionality are described briefly below. These will certainly be included in Patrick’s keynote presentation on this topic.
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Expert on Artificial Intelligence
Natural Language Processing (NLP) is one of the primary applications for machine learning technology. Three (3) primary use cases are emerging for NLP functionality, each with their own unique financial incentives and social implications. The first involves automated customer service calls, replacing today’s call centers and phone banks. There are over 20 million call center operators in the world today, and over 3 million here in the USA. Many companies have thousands (or even tens of thousands) of call center operators in the Philippines, India or Polland. Imagine the HR nightmare in hiring those individuals, training them to deliver consistent policies and instructions, and dealing with unexpected sick days throughout the team, not to mention all the salaries involved. The financial incentive to replace those workers with an automated chatbot or socialbot platform is enormous.
Even today, calls to your bank are met with voice-activated options like “check my balance” or “make a transfer.” Today, those options are often clumsy and frustrating to interact with, but the technology is improving at a rapid pace. The challenge is to allow the platform to add context from consecutive questions and comments, building understanding along the way. People refer to the Turing Test, where humans can no longer distinguish between a computer and a human when interacting, either by text or natural language. Chatbot technologies (communication via text) have already passed the Turing Test, but socialbot technologies (communication via natural language) have not, but we’re getting very close. Once that hurdle has been crossed, we will see the deployment of this technology very quickly.
It’s interesting to follow how different technology companies are building these capabilities. The essential ingredient for machine learning is data, and each company gets its data from different places. Apple gets their data from Siri. Google gets theirs from Google Home and Google Now. Amazon gets their data from Alexa. But Facebook had no direct access to data, allowing it to stay relevant in this area. Facebook recently launched an API for their Messenger app, allowing businesses to tap into their chatbot functionality to deliver automated customer service to their respective customers. Socialbot functionality will soon follow. This is a brilliant move, giving Facebook a source of data to learn from.
The second application of natural language processing is within companionship robots for seniors. Older people are often lonely and need help with basic tasks. Robots already exist to help with various chores, but the natural language part has been notably absent thus far. As soon as we pass the Turing Test, companionship robots will be developed to provide genuine emotional support for their human “clients.” The problem with this business model is that the financial incentives are minimal. These robots may replace a few salaries, or perhaps they’ll charge modest monthly subscription fees, but the profit potential seems relatively limited.
Compare that with the profit potential for companionship robots for single men. That’s a very different market, and new robotic sex dolls can be rented out by the hour. Already today, sex doll brothels exist is cities around the world, and the dolls are often more popular than the human women. This disturbing trend is dangerous but also inevitable. There are 30 million fewer women in China then men of the same age. In India, the female deficit is approximately 40 million. In other words, there are 70 million men (just in those two countries) who will never secure a female sex partner, and they will quickly turn to artificial alternatives to satisfy their human instincts.
With such enormous financial incentives, development will focus on these use cases. Not only are these dolls aesthetically beautiful and anatomically correct, but they will deliver natural language capabilities at the same time. Chatbot and socialbot functionality is scaling quickly, and they will become ubiquitous within three to five years, all driven by natural language processing and artificial intelligence. Businesses need to understand the current capabilities and possible business models to ensure they capitalize on the trends, rather than getting disrupted by it.
Patrick Schwerdtfeger is a keynote speaker who has spoken at business conferences in North America, South America, Europe, Africa, the Middle East and Asia.
