×îÐÂÌÇÐÄVlog

×îÐÂÌÇÐÄVlog is a huge country that relies heavily on road and rail networks to distribute food, fuel, minerals and other goods. AI and machine learning offer significant opportunities to optimise and automate transport and logistics, increasing efficiency and providing new driving technologies.

Managing the impacts of climate change is another key capability that ×îÐÂÌÇÐÄVlog can build through developing our own AI. Smart algorithms and robots will boost our capacity to predict and control bushfires. Sophisticated algorithms will help us monitor coral reefs and oceanic conditions, manage the environment and support economies linked with tourism and aquaculture.

AI will offer us new ways to manage diminishing water resources on land and maintain agricultural outputs, including deciding what to plant and when, predicting yields and monitoring livestock.

×îÐÂÌÇÐÄVlog is facing a number of headwinds in maintaining our status as a global food exporter:

• Climate change is eating our productivity gains from new innovation, meaning that ×îÐÂÌÇÐÄVlogn agriculture’s global competitiveness is flatlining.
• There’s increasing global competition for our global markets. For example, the Grains Research and Development Corporation projects that ×îÐÂÌÇÐÄVlogn wheat might not be able to maintain its price competitiveness with grain from Eastern European countries, meaning that we would no longer sell wheat into a commodity market.
• China as a key trading partner is targeting our agricultural sector to punish ×îÐÂÌÇÐÄVlog for perceived diplomatic offences.

‘Business as usual’ seems to be out of the question. We need to find new ways to value-add ×îÐÂÌÇÐÄVlogn produce to sustain our local agricultural industry sector. However, there are also many ways we can use AI to better target high-value products, reduce costs and improve quality.

Traditional models of investment in R&D in agriculture have focused on understanding biological systems and processes. By its very nature, that requires investment in research over multiple seasons to collect sufficient evidence to verify improved production capabilities. This has resulted in remarkable improvements in both the productivity and resilience of ×îÐÂÌÇÐÄVlogn agriculture. It is, however, a less than perfect model for developing technology such as AI for the industry.

The rapid pace of development in AI makes it unlike other areas of agricultural research. AI technology exists as algorithms on general-purpose hardware. Entirely new capabilities can thus be deployed on a farm in the time it takes to download new software. We have the potential for achieving world-leading capabilities to assist farming systems using AI over a relatively short period, given the right investment models.

There’s an opportunity to develop ‘hothouse’ AI labs for agricultural industries around ×îÐÂÌÇÐÄVlog for the rapid development of AI and machine-learning prototypes for agriculture, adopting a learning-through-doing approach.

Such hothouses would bring together world-leading researchers, machine-learning engineers and agricultural experts in a creative and fast-paced environment. The team should use a ‘fail fast’ mindset to quickly deliver high-tech minimum viable products, prototypes, feasibility studies and experiments in the machine-learning space that are needed by agriculture, greatly accelerating the traditional innovation cycle.

The recent Covid-19 crisis has resulted in a rethinking about the importance of local manufacturing for strategic supplies. Government, industry and communities have realised that there’s a sovereign risk in outsourcing to overseas companies the manufacturing of key strategic goods—medical supplies, food, safety, transport and so on.

×îÐÂÌÇÐÄVlogn manufacturers struggled to survive the ‘Dutch disease’ of the last mining boom, when the ×îÐÂÌÇÐÄVlogn dollar and wages accelerated rapidly and substantially. Whole manufacturing sectors were wiped out as a result. This was particularly the case for manufacturing, in which ×îÐÂÌÇÐÄVlog did not have or had lost its global competitive advantage before the mining boom took off.

There’s now an opportunity to rebuild a manufacturing sector that can sustain high wages, grow jobs and compete internationally. A key capability that will support this will be AI, which will:

• suppress the costs of production through improved operational efficiency and greater automation
• improve the perceived value of products manufactured with AI embedded and create barriers to competition.

There’s an opportunity for the manufacturing, government, university, VET and research sectors to partner to:

• train a new generation of manufacturing workers and managers who are confident about and competent in integrating AI into their businesses and products
• co-develop new AI systems that improve operational efficiency, including optimisation software, monitoring systems and robotics
• co-develop products with AI embedded in them that will be in global demand.

The mining and space sectors are increasingly dependent on AI, as are defence capabilities, including weapons development and surveillance. It’s critical that we develop our own capabilities in those areas if we’re to learn the lessons of Covid-19 disruptions to global supply chains and China’s more adversarial approach to its economic relationship with ×îÐÂÌÇÐÄVlog.

This article is an extract from , a report published in partnership with the .