“Facebook shuts down robots after they invent their own language.” This headline was splashed across myriad news outlets just a few weeks ago. And although the story itself made the event seem like just a normal science experiment, this type of alarming tone in media reports is becoming the norm and is sowing seeds of doubt, fear, and uncertainty among consumers and even some businesses.
However, behind the vendor hype and the media fear mongering, there are real, bona fide reasons for organizations to invest in artificial intelligence (AI).
Humans can perform various expert tasks with relevant training and experience. For example, a research analyst trained for and with experience in market research, can predict future market size and growth with considerable accuracy. Using machine learning, a system can be trained to perform the same task. Yet, with their enormous computational power, such expert systems/machines can beat humans’ speed, accuracy, and efficiency in this and many other tasks. This is the reason why many organizations are investing heavily in developing and creating AI-enabled systems.
Have you ever encountered a situation where you’re talking to a customer service executive over chat, and wondered if you’re actually talking to a real human agent or a virtual agent/computer program?
I recently attended IPsoft’s Amelia 3.0 launch event. Amelia is an AI-powered virtual agent platform that uses advanced machine learning and deep learning techniques to get progressively better at performing tasks. In one of the more interesting demonstrations, Amelia went head-to-head with a real person in answering questions posed to it in natural language, by real-time processing of unstructured data from natural language documents such as Wikipedia pages. It was fascinating to see how Amelia could answer questions with considerable accuracy.
Such domain-specific expert systems that can simulate human-like capacities and even outperform human expertise in specific domains are called Narrow AI.
While most AI vendors typically focus on building Narrow AI systems for a specific purpose such as virtual agent capabilities, some large vendors such as IBM, under its Watson brand, offers multiple individual Narrow AI systems to cover a wide range of use cases. For example, it is being used at several top cancer hospitals in the U.S. to help with cancer research by speeding up DNA analysis in cancer patients. In the finance sector, DBS bank in Singapore uses Watson to ensure proper advice and experience for customers of its wealth management business. And in retail, an online travel company has created a Discovery Engine that uses Watson to take in and analyze data to better link additional offers and customize preferences for individual consumers.
True, or General, AI
Artificial intelligence with multiple and broader capabilities is called True, or General, AI. When it comes to developing General AI, which has the ability to generalize and apply learnings to unlimited new domains or unexpected situations – something that humans often do – I think we are just scratching the surface. Primary barriers to achieving General AI are our lack of understanding of everything happening inside human brain and the technical feasibility of creating a system as sophisticated, complex, and vast as the human brain. As per a survey of 352 researchers published in 2017, there is a 50 percent probability that General AI will happen by around the year 2060.
Current lay of the land – A world of opportunities
Despite the many evolutional, ethical, and developmental challenges researchers and technology developers continue to face in making artificial intelligence more capable and powerful, I believe that even existing AI technology presents unique opportunities for organizations. It enables them to improve the customer experience and operational efficiency, enhance employee productivity, cut costs, accelerate speed-to-market, and develop more sophisticated products.
To help its clients understand the AI technology market better, Everest Group is researching this field with a lens on global services. Although early in our research, one fascinating use case is how AI is automating decision making with complete audit trail in the heavily regulated financial services industry. The research will be published in October, 2017 as part of our research program, Service Optimization Technologies (SOT), that focuses on technologies that are disrupting the global services space.