For decades, humanity envisioned and dreamed of a technology-enabled future. One with autonomous transportation, flying vehicles, a clean and safe environment and a healthy, extended life.
Today, more than 60 years after John McCarthy coined the term “artificial intelligence” to describe the science and engineering of making machines intelligent, and after surviving two so-called “AI winters,” recent advancements suggest that the once-distant future of our dreams is becoming a reality.
Our dreams, however, only scratched the surface of what we now believe will be possible throughout the coming years.
In recent years, AI bested its human counterpart in the most strategic of games, including Jeopardy and Go. Just recently, AI has acquired the skill to handle mis-information and incomplete information by winning against world-class poker players in a Texas’hold’em contest. Although Artificial General Intelligence (machines that compare to or surpass the human mind) still belongs in the distant future, researchers believe that machines are gradually approaching human levels when performing “simple” (tasks that are simple for humans, not machines) tasks, such as understanding naturally spoken language or evaluating unknown, new situations (in non predictable environments).
In fact, one of the most common applications of AI today is speech recognition. Personal virtual assistants like Alexa, Siri, Cortana and Google Assistant can understand speech and respond to it accordingly. The biggest breakthrough in speech recognition thus far has come from IBM, which has managed to reduce the error rate in conversational speech recognition to 5.5% (relative to the human error rate is of 5.1%).
Other existing AI applications include predictive technologies found in early self-driving cars and search engines. Companies such as Netflix and Pandora are also using AI to improve content recommendations.
If an AI-future is inevitable, then we must identify and study the paradigms of applying AI in the coming years.
It’s realistic to envision an AI-human hybrid that increases our mental skills and masters scientific challenges. This hybrid may also extend to combining our bodies with artificial devices that enable us to improve our physical or cognitive abilities.
Despite countless advancements, machines still lack the ability to process deep emotional intelligence. In response, much research is being conducted and time spent training AI to read a user’s emotional needs. Although these machines cannot fully understand emotion, businesses are now implementing cognitive technology tools in the form of bots and virtual agents to handle customer questions. These technologies can detect various emotions and develop customized responses to offer more empathetic feedback and support. We have finally reached a point where humans and machines can build engaging relationships, thus allowing businesses to provide more personalized services through AI.
This is the starting point for the three core paradigms that will shape the applications of AI technology in the coming years:
The internet enables us to connect, share and engage without time, location or other physical constraints. And now, bots are poised to change humankind’s favorite communication technology: messaging apps. However, while conversational interfaces allow us to engage chatbots, the technology still lacks the broad understanding of individual conversational context to create a meaningful and valuable interaction with the user.
However, Gartner predicts that conversational AI, when used properly with visual solutions and UX, will supersede today’s cloud and mobile-first paradigm by 2021.
Although conversational AI is primarily deployed in customer- or user-facing applications, I expect a much bigger use in bot-to-bot communication across business applications throughout the next 2 to 3 years. This type of bot will result in a true personal assistant.
Bot-to-bot communication will be the most used form of interaction involving bots.
It will extract the real value from these systems, providing access to information sets that couldn’t be provided by user-facing interactions, and even less by competitive services at scale in the past.
Mass individualization will change the way products are made. New offerings will automatically be created based on an individual’s current and future needs. Web applications will serve and produce specific items on the fly, via bot-to-bot communication and real-time customer engagement.
This trend will lead us to a world where machines are deeply integrated into our everyday lives.
How? Mass individualization is poised to take over today’s mode of mass standardization,and enable entrepreneurs and product managers to build offerings around each user’s personal context. This will include factors such as their behavior, attitude, goals and needs — all understood via conversational (message-based) applications, buying and browsing history, geography and much more.
Early progress in content and e-commerce will push the digitization of industries to new heights thanks to its ability to transfer complex processes and engagement models that usually require a large amount of service. High-cost, repetitive processes are poised for disruption, such as accounting and legal advice. AI will help professional augment their work, acomodate the customized needs of more customers and introduce entirely new business processes that increase efficiency and productivity.
By definition, technological convergence is the tendency that different technological systems will evolve towards performing similar tasks. New technologies take over to perform the same task but in a more advanced manner.
AI-enabled convergence means that AI-based technologies are embedded in all new systems which provide smart, context-aware and pro-active products and services that can engage with humans in a more natural and smarter way. These systems can either be based purely on software applications or on robotics that engage with us physically.
AI will be used together with enabling technologies such as Blockchain, a distributed but controlled network of billions of systems connecting and interacting with each other. Or IoT, which allows systems to collect, send and receive information about a product or device’s environment, condition and performance. It will also be used with other technologies like VR/AR, 3D printing, autonomous robotics, renewable energy sources and advanced genomics like Next Generation Sequencing (NGS).
The results will transform entire industries, our everyday lives and our socioeconomic existence.
These three core paradigms are going to shape the way we make, use and engage with machines in the next few years — and more advancements are expected soon.
These paradigms will guide society in handling our corporate, consumer and individual relationships with technology.
For now, we must seek new opportunities while remaining diligent in how we train AI. New systems will only be as responsible as they’re trained to be. As we continuously seek to develop intelligent AI, we hope that it will ultimately provide society with the tools that make everyday life easier, and the world operate a lot better.