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Plenty more for AI to deliver

This article is more than 12 months old

Artificial intelligence greatly helps humans today and throws up even greater possibilities for the future

Based on research gathered by MIT Technology Review magazine, Asia's business landscape is poised not only to benefit greatly from the rise of artificial intelligence (AI) but also play a major role in helping to define it.

While only a small percentage of respondents are currently investing in AI development in Asia, 25 per cent of firms have made investments at a global level and another 50 per cent are considering doing so.

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.

All these gave rise to a flurry of people claiming that the AI revolution is already upon us.

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.

Understanding the growth of AI capability is crucial for understanding the advances we have seen.

Full AI - that is to say, complete and autonomous sentience - involves the ability for a machine to mimic a human to the point of us being indistinguishable from them.

While it may be some time before we reach full AI, there will be many more practical applications of basic AI in the near term.

With basic AI, the processing system, embedded within the appliance (local) or connected to a network (cloud), 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 kind of ML-based AI will likely fall into two categories:

  • 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.
  • Exploration and development of new insights: AI can help accelerate the rate 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 and planets, ultimately pushing AI 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, which is the first step to it becoming practical and widespread.

Autonomous vehicle technology is one of the most publicised and one of the most needed applications of AI, in its ability to eliminate injuries or deaths in traffic accidents and improve availability and efficiency of transportation.

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.

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.

Learning from repetition, improving patterns and developing new processes is well within reach of current AI models, and will strengthen in the coming years as advances in AI - specifically machine learning and neural networking - continue.

Rather than being frightened by the perceived threat of AI, it would be wise to embrace the possibilities that AI offers.

Jason Bissell is general manager of Asia Pacific and Japan for data software company Talend, and Calvin Hoon is its regional vice-president for sales, Asia Pacific.