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Current Level of AI Training Progress

Navigating the complex world of artificial intelligence (AI) can be daunting, with breakthroughs occurring at a rapid pace. The current level of AI training has seen immense advancements, reshaping the very fabric of our society.

This article offers an in-depth guide to understanding these developments and their impacts on numerous industries. Let’s take this journey together to demystify the fascinating progression within AI technology!

Key Takeaways

  • The current level of AI training progress in Australia has seen remarkable advancements, with artificial intelligence systems achieving optimal performance, super-human performance, high-human performance, par-human performance, and sub-human performance.
  • AI training progress is evaluated through exams and competitions that test the capabilities of AI systems. These tests serve as benchmarks to assess technical proficiency and problem-solving abilities.
  • Notable achievements in AI training progress include IBM’s Deep Blue defeating world champion Garry Kasparov in chess in 1997 and Google’s AlphaGo defeating a world champion Go player in 2016.
  • The ultimate goal of AI research is to achieve human-level artificial general intelligence (AGI), which could revolutionize various industries. While an exact timeline is challenging to predict, experts believe human-level AGI could be achieved within the next few decades.

Levels of AI Training Progress

The levels of AI training progress include optimal performance, super-human performance, high-human performance, par-human performance, and sub-human performance.

Optimal Performance

The pinnacle of AI training progress is optimal performance, a stage where AI systems function at their absolute best. At this peak level, artificial intelligence can solve complex tasks efficiently and effectively, often surpassing human capabilities in specific domains.

We’ve witnessed glimpses of this high-level performance in areas such as chess or Go, games where AI has previously outmanoeuvred human champions. However, achieving optimal performance across broad fields remains a significant challenge for modern researchers.

In recent years in Australia, advancements have been made with deep learning applications showing considerable promise for the future development of artificial general intelligence (AGI). With continual improvements and breakthroughs in AI research down under, the journey towards optimal performance continues to advance rapidly.

Super-human Performance

Super-human performance in AI refers to the ability of artificial intelligence systems to outperform human capabilities in specific tasks. This level of AI training progress demonstrates how far we have come in developing intelligent machines that can exceed our own abilities.

For example, in the game of chess, super-human performance was achieved when IBM’s Deep Blue defeated world champion Garry Kasparov back in 1997. More recently, Google’s AlphaGo made headlines for beating professional Go player Lee Sedol.

These achievements showcase the immense potential of AI and its ability to surpass human limitations. The advancements made in machine learning and deep learning algorithms have allowed AI systems to analyze vast amounts of data quickly and make highly accurate predictions or decisions.

As a result, they can solve complex problems more efficiently than humans.

In Australia, there has been significant progress towards achieving super-human performance across various domains, including healthcare diagnostics, natural language processing, and autonomous vehicles.

High-human Performance

High-human performance in AI training refers to the level of intelligence achieved by artificial intelligence systems that surpasses the capabilities of an average human. This remarkable advancement has been made possible through breakthroughs in deep learning and machine learning algorithms.

With high-human performance, AI systems can perform complex tasks, such as natural language understanding and image recognition, with accuracy and efficiency comparable to or even better than human experts.

These advancements have significant implications for various industries, from healthcare to finance, where AI-powered solutions can provide valuable insights and support decision-making processes.

Par-human Performance

Par-human performance in AI training refers to the level of intelligence achieved by artificial intelligence systems that surpasses human capabilities in certain specific tasks or domains. While not yet reaching superhuman performance, par-human AI can outperform humans in narrow areas such as image recognition, natural language processing, and playing complex games like chess or Go.

These advancements have been made possible due to breakthroughs in deep learning algorithms and the availability of vast amounts of data for training AI models. With continued research and development, par-human AI has the potential to significantly impact industries ranging from healthcare to transportation by providing solutions that are faster, more accurate, and capable of handling complex tasks beyond human capacity.

Sub-human Performance

Sub-human performance in AI training refers to a level where the technology falls short of human intelligence. While advancements have been made, current AI systems still struggle with certain tasks that humans find relatively easy.

This includes understanding natural language, context, and making complex decisions based on nuanced information. Despite these limitations, ongoing research and development efforts are focused on improving AI capabilities to eventually reach human-level performance and beyond.

In Australia, experts are actively exploring innovative approaches to enhance AI training and unlock its full potential across various industries.

Proposed Tests of AI

Exams and competitions serve as proposed tests to assess the progress of AI in achieving optimal, super-human, high-human, par-human, and sub-human performance levels.


To evaluate the progress and capabilities of AI, various tests and exams have been proposed. These exams serve as benchmarks to assess the performance of AI systems in specific domains. For example, competitions like chess and Go have long been used as testing grounds for AI algorithms.

These games provide a clear objective with well-defined rules, making it easier to measure an AI’s performance against human competitors.

In addition to competitive events, researchers are constantly developing new evaluation metrics and datasets to further test the capabilities of AI systems. These tests aim to measure not only technical proficiency but also real-world problem-solving abilities.

By subjecting AI algorithms to rigorous testing, we can gauge their current state and identify areas that require further improvement.

As Australia continues its efforts in advancing artificial intelligence technology, these exams play a crucial role in determining the level of progress achieved so far. They help us understand how close we are to achieving human-level artificial general intelligence (AGI) while highlighting areas where more research is needed.


Competitions play a crucial role in assessing the progress of AI training. These competitions provide an opportunity for AI systems to showcase their capabilities and compete against each other in specific tasks or challenges.

They serve as a benchmark for measuring AI performance and identifying areas for improvement. By participating in competitions, researchers and developers can push the boundaries of AI technology, driving innovation and pushing towards higher levels of performance.

These competitions range from traditional games like chess and Go to more complex tasks such as natural language processing or computer vision. Through these tests, we gain valuable insights into the current state of AI advancements and how close we are to achieving human-level artificial general intelligence (AGI).

Past and Current Predictions

Past and current predictions in AI have shown significant progress, with notable achievements seen in games like Chess and Go.


Progress in AI training has been particularly evident in strategic games like chess. Over the years, artificial intelligence has achieved remarkable milestones and breakthroughs in this domain.

Notably, in 1997, IBM’s Deep Blue defeated world champion Garry Kasparov, marking a significant moment for AI development. Since then, AI algorithms have become even more sophisticated and powerful.

Today, AI-powered chess engines are capable of analyzing millions of possible moves within seconds, outperforming top human players with their super-human performance. This progress exemplifies the incredible advancements that have taken place in the field of artificial intelligence and sets the stage for further innovations to come.


In recent years, AI has made significant strides in the ancient game of Go. This complex strategy game was once considered a great challenge for AI, but advancements in machine learning and deep neural networks have allowed AI to surpass human grandmasters.

In 2016, Google’s DeepMind developed AlphaGo, an AI program that defeated the world champion Go player. This breakthrough showcased the remarkable progress in AI training and its ability to excel at intricate games like Go.

The continued development of AI in this area holds great promise for further advancements in artificial intelligence technology.

Human-Level Artificial General Intelligence (AGI)

Human-Level Artificial General Intelligence (AGI) is the ultimate goal of AI research. It refers to creating intelligent machines that can perform any intellectual task a human being can do.

AGI would possess not just specialized intelligence in specific domains, but also the ability to understand and learn new tasks and concepts like a human.

The development of AGI has been a topic of much discussion and speculation among experts. While it is challenging to predict an exact timeline, many believe that we could achieve human-level AGI within the next few decades.

This advancement in AI technology has the potential to revolutionize various industries, from healthcare and transportation to finance and entertainment.

Achieving human-level AGI would require significant breakthroughs in areas such as natural language processing, reasoning, problem-solving, and common-sense understanding. Researchers are constantly pushing boundaries through advancements in machine learning algorithms, deep neural networks, and computational power.

As progress continues at a rapid pace with advancements in AI training methodologies and increased availability of computing resources, it’s important for Australia to stay at the forefront of this evolving technology landscape.

Investment in research initiatives and collaborations between academia, industry, and government will play a crucial role in shaping the future of AI development in Australia.

(Source: Important Fact #5 & #10)


In conclusion, the current level of AI training progress in Australia is witnessing remarkable advancements and breakthroughs. With ongoing research and developments in artificial intelligence, we are moving towards achieving human-level artificial general intelligence (AGI) within the next few decades.

The future of AI holds immense potential to revolutionize various industries and further enhance the capabilities of AI tools. Stay tuned for more exciting updates on the evolving landscape of AI training progress in Australia.

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