It seems there are new announcements almost every day about the advancements of machines and their ability to ‘think’.  At the same time, research from Flamingo, a ‘conversational commerce’ company, has found that 77 per cent of Australian consumers are comfortable to very comfortable with the concept of using AI chatbots when interacting with organisations.

We have seen a machine master the complex game of Go, previously thought to be the most difficult challenge of artificial processing. We have witnessed vehicles operating autonomously, including a caravan of trucks crossing Europe with only a single operator to monitor systems. We have seen a proliferation of robotic counterparts and automated means for accomplishing a variety of tasks and all of this has given rise to a flurry of people claiming that the AI revolution is upon us.

However, while there is no doubt that there have been significant advancements in the field of AI, what we have seen is only a start on the path to what could be considered full AI.

Understanding the growth of AI capability is crucial for understanding the advances we have seen. Full AI, that is to say complete, autonomous sentience, involves the ability for a machine to mimic a human to the point that it would be indistinguishable from them (the so-called Turing test). This type of true AI is still a long way from reality; it requires immense computing power, the ability to teach a machine to interpret complex things like emotional responses, and how to generate both intuitive and emotional responses to situations. However, there will be many more practical applications of basic AI in the near term that hold the potential to greatly enhance our lives.

With basic AI, the processing system learns and interprets responses based on “experience.” That experience comes in the form of training through using data sets that simulate the situations we want the system to learn from. This is the confluence of Machine Learning (ML) and AI. The capability to teach machines to interpret data is the key underpinning technology that will enable more complex forms of AI that can be autonomous in their responses to input. It is this type of AI that is getting the most attention. In the next 10 years, the use of this type of ML-based AI will likely fall into two categories:

1. Improvement and automation of daily life: Managing household tasks, self-driving cars and trucks and the general automation of tasks that robots can perform significantly faster and more reliably than humans
2. Exploration and development of new trends and insights: Artificial intelligence can help accelerate the rote discovery and science happening worldwide every day. The use of AI to automate science and technology will drive our ability to discover new cures, technologies, tools, cells, planets, etc., ultimately pushing artificial intelligence itself to new heights.

There is no doubt about the commercial prospects for autonomous robotic systems in the commercial market for aspects such as online sales conversion, customer satisfaction and operational efficiency.   We see this application already being advanced to the point that it will become commercially viable; the first step to becoming practical and widespread. Simply put, if revenue can be made from it, it will become self-sustaining and thus continue to grow. For instance, the iRobot Roomba vacuum cleaner has succeeded as a solidly commercial application of autonomous technology. Autonomous vehicle technology is one of the most publicised and one of the most needed applications of AI. There are an estimated 4.4 million injured or killed in traffic accidents per year in the United States alone; autonomous vehicle technology could almost completely eliminate this and greatly improve availability and efficiency of transportation for everyone.

In addition to the automation of transportation and logistics, a wide variety of additional technologies that utilise autonomous processing techniques are being built. Currently, the artificial assistant or “chatbot” concept is one of the most popular. By creating the illusion of a fully sentient remote participant, it makes interaction with technology more approachable. There have been obvious failings of this technology (the unfiltered Microsoft chatbot, “Tay,” as a prime example), but the application of properly developed and managed artificial systems for interaction is an important step along the route to full AI. This is also am important application of AI as it will bring technology to those who previously could not engage with technology completely for any number of physical or mental reasons. By making technology simpler and more human to interact with, you remove some of the barriers to its use that cause difficulty for people with various impairments.

The use of AI for development and discovery is just now beginning to gain traction, but over the next decade this will become an area of significant investment and development. There are so many repetitive tasks involved in any scientific or research project that using robotic intelligence engines to manage and perfect the more complex and repetitive tasks would greatly increase the speed at which new breakthroughs could be uncovered.

There is also the tantalising possibility that as we increase the capability of our AI systems, they could actually perform research and discover new avenues to explore theories. While this is still a long way away, it could greatly accelerate the discoveries needed for many advancements that could improve and extend our lives.

Anthony Nantes, CEO of ASX-Listed fintech company, DirectMoney, has said, “A seismic shift in consumer expectations is happening globally, affecting every part of the finance industry. The winners over the coming years will be those companies that truly know how to effectively utilise ML and can meaningfully adapt AI to provide better outcomes for their customers. Chatbots are an effective first step in the direction of a brave new world of online relationships with customers, one that will dramatically alter the finance landscape in the years to come.”

As a result, it would be wise to embrace the possibilities that AI offers.

ABOUT STEVE SINGER//

Steve Singer is ANZ Lead at Talend.