Duc Xuan Nguyen, PhD candidate, Deakin University and DELH

Machines and computing systems have proven their great capabilities in doing certain tasks such as executing repetitive operations in factories or performing numerical computations. However, human still possesses certain skills that machines cannot perform well. For example, human has a unique physical body which allows them to perform movements involving the whole body like running, dancing or dexterous movements such as writing, knitting. Human can also make common sense reasoning, such as inferring the context of a long conversation or understanding the situation in a movie scene. Therefore, tremendous efforts have been made to create systems to close the gap.

In recent years, artificial intelligence (AI) has become a hot topic, with many headlines on social media talking about its new potentials. While this can be exaggerated, I believe AI will greatly change our job landscape after the fourth industrial revolution[1][2]. In this blog post, I will give some brief introductions about AI and machine learning (ML), a subfield of AI, as well as showing some successful use cases of AI and its future effects.

What is Artificial Intelligence?

Artificial Intelligence, according to the pioneer scientist John McCarthy, is “the science and engineering of making intelligent machines, especially intelligent computer programs”. Basically, it is the intelligence expressed by machines, unlike the natural intelligence possessed by humans or animals. The goals of AI researches are to create systems that can learn, think and act like humans. Stuart J. Russell and Peter Norvig in their book, “Artificial Intelligence: A Modern Approach“, add an important point that the intelligent systems need to be rational, i.e. do the “right thing”, given what they know.

The field of AI is broad with multiple subfields, however, it can be classified into two types, narrow and general AI. The narrow AI focuses on solving specific problems which you may have seen on social media. Self-driving car technologies, virtual assistants such as Siri or Cortana, or chess algorithms fall into the narrow AI stream. In contrast, general AI, or artificial general intelligence (AGI), is the intelligence of a machine that possesses the capacity to understand or learn any intellectual task that a human being can. This form of AI is the ultimate goal of AI research but it is yet to be discovered.

What is Machine Learning?

What are the factors contributing to the successes of AI in recent years? Simply put, it is the huge computing resources allowing machine learning algorithms to learn useful patterns from large datasets. The term “Machine Learning”, often being used with AI, refers to a subfield of AI, which is the field of study that gives computers the ability to learn without being explicitly programmed (according to Arthur Samuel). A more engineering-related definition of ML, according to Tom M. Mitchell, that a computer program is said to learn from experience, or data, if its performance improves in some certain tasks, with respect to specific performance measures.

Artificial Intelligence versus Machine Learning


Applications of AI

With the latest research advances, AI has shown dominance in various applications:

Language Translation: Google’s translation system (Google Translate) supports more than 100 languages. Although the fluency and accuracy of the translated text are not 100% satisfied, the system’s performance has been greatly improved compared to the past.

Personal assistants: Everyone would be familiar with Siri on iPhones, or Google Now on Android phones. These assistants are powered by a natural language understanding engine as well as a speech recognition system, which are important research problems in the AI community.

Gaming: In 1997, IBM’s Deep Blue won against the then chess world champion Garry Kasparov. Nearly 20 years later, DeepMind’s AlphaGo won against Go champion Lee Sedol in a five-game match. Chess and Go are two of the most complex board games, and these wins mark important milestones in the history of AI research.

Self-driving cars: Tesla is definitely one of the best electric cars at this moment. It is currently semi-autonomous, however, it will likely be fully-autonomous in the near future. The AI algorithms behind its self-driving capability include advanced computer vision techniques, road condition prediction and path planning.

And the list goes on. AI algorithms are being applied in many systems, ranging from large-scale factories to small-scale devices like phones in your pockets.

Future of AI and humans

From the current achievements of AI, it is predicted to replace humans in certain roles, such as cashiers, drivers, or fast food preparers. However, interacting naturally with people, imitating the dexterity of fingers and limbs or thinking creatively are some of the abilities that current AI systems lack. In the book “AI Superpowers“, author Kai-Fu Lee proposes intuitive graphs about the risks of replacement for physical and cognitive labours.

Risk of Replacement: Physical Labour. Image courtesy: https://bit.ly/30erO8O
Risk of Replacement: Cognitive Labour. Image courtesy: https://bit.ly/30erO8O

In both graphs, the Y-axis moves from “asocial”[3] at the bottom to “social” at the top. For physical labour, the X-axis ranges from “low dexterity and structured environment” to “high dexterity and unstructured environment”. For cognitive labour, the X-axis is different, it ranges from “optimization-based” to “creativity- or strategy-based”.

There are four quadrants in each graph, the “Danger Zone” quadrant shows the jobs that are at high risk of replacement in the foreseeable future. On the other hand, the jobs in the “Safe Zone” quadrant are not going to be replaced anytime soon. The remaining two quadrants “Human Veneer” and “Slow Creep” depicts the jobs that can be partly automated, but the social interactive elements or highly creative nature makes them hard to be fully automated.


To conclude, AI systems will create both challenges and opportunities for humans. Although many jobs can be replaced, intelligent systems will allow us to spend our time on more important and complex tasks. This is also one of the key objectives of the ARC Industrial Transformation Research Hub for Digital Enhanced Living where we focus on the use of technology to improve the lives of all Australians, particularly those in the vulnerable age group. Similar to the past industrial revolutions, large-scale successes in AI will revolutionise our daily lives and greatly change our perception of intelligence. In my view, there’s exciting times ahead!


[1] https://sforce.co/2Cx6iEi
[2] en.wikipedia.org/wiki/Fourth_Industrial_Revolution
[3] Adjective – avoiding social interaction; inconsiderate of or hostile to others.


Written by:
Duc Xuan Nguyen
ARC Industrial Transformation Research Hub for Digital Enhanced Living PhD scholarship recipient
Applied Artificial Intelligence Institute (A2I2), Deakin University
NB: The author reserves the right to showcase/publish this blog piece elsewhere and/or in a different medium.

Editorial review by:
Dr Wei Luo, Chief Investigator
Kevin Hoon, Hub Manager