Tag Archives: industry transformation

machine learning

New frontiers in digital disruption

The building blocks of digital technology consists of information theory (which codifies content into binary 0/1 format) and transistors (essentially on/off switches). They were both invented during the hey-day of American research and scientific development company Bell Labs in the decade following WWII. Subsequently, each new and improved wave of digitisation has caused upheaval as it visits particular markets and occupations. However, from the perspective of the whole production and consumption system, progress has been relatively slow and staggered compared to what we are likely to see in the future.

In the 1950s, computers at even the most advanced tech locations in the US comprised two-storey buildings and only performed highly specialised and limited functions. It was not until the 1980s – when smaller mainframes became cheap and fast enough to replace routine operations – that digital technology effectively eliminated the labour market for clerical workers.

Automation, robots and digitally guided technologies started making inroads into manufacturing around this time. Although satellites have been used since the 1960s to provide market intelligence for producers (giving US farmers advice on what and how many crops their competitors were growing, for example), it took until the 2010s for satellite-aided location services to become ubiquitous and part of consumers’ daily lives.

More and more, digital disruption is being triggered by innovative software, such as travel search engines and language translation services, rather than hardware. Since software can be shifted into large-scale production much faster than hardware, this accelerates the pace of disruption.

One form of software that is playing an increasingly important role is a form of artificial intelligence called ‘machine learning’. Computers are governed by algorithms comprised of many rules that dictate “if X, then do Y”. These rules are usually set by the programmer(s) that wrote the algorithm code. But things are different in the case of machine learning algorithms. Such an algorithm can ‘learn’ from data by altering its own parameters, progressively improving its ability to determine patterns or predict future trends in the data (analogous to the way our brains learn from past experience).

For example, machine learning algorithms have been used for the past two decades in spam filters. When we label emails as spam, we are generating a labelled dataset that can be used to train a machine learning algorithm to recognise the properties of emails that are usually associated with spam. The trained algorithm can then remove such emails automatically.

Machine learning has even begun transforming the oldest of professions, such as medicine and the law, hitherto considered the preserve of nuanced interpretation and experiential knowhow. Law has long resisted automation from computers and digital analytics, in part because of the non-routine nature of contracts and litigation. However, this is now changing as machine learning methods have partially automated tasks by detecting patterns and inferring rules from data.

eDiscovery is one such digital tool used to assist lawyers’ search through emails and piles of office documents to find evidence needed to clinch a case (looking for the proverbial needle in a haystack). Machine learning can disrupt the eDiscovery process by efficiently bringing together similar documents based on their contents and metadata. Brainspace provides lawyers an eDiscovery tool that increases the efficiency and accuracy of finding information pertinent to a court case. Alternatively, ROSS, a machine learning law tool, can provide answers to legal research questions, posed using natural language, and can monitor recent legal developments that are relevant to a particular case.

In medicine, machine learning algorithms are increasingly being used to help perform radiological diagnoses. They can be trained to classify medical scans as normal or diseased, or to quantify the size of diseased areas. In the area of brain cancer, Microsoft’s InnerEye research project has been investigating the use of an image analysis tool to measure the size of brain tumours.

As these machine learning methods save lawyers’ and medicos’ time, we will see their labour productivity rise along with a major shift in content of their work, and perhaps a reduction in the demand for lawyers and medicos. Handled sensibly by governments, this reduced demand will release workers for other occupations in for example, the creative, scientific and caring industries.

Professor Beth Webster

Pro Vice-Chancellor of Swinburne University (Research Policy and Impact) and Director, Centre for Transformative Innovation

Co-authored by:

machine learning

Dr Stephen Petrie, Data Scientist, Centre for Transformative Innovation 

machine learning

Mitchell Adams, Research Centre Manager, Centre for Transformative Innovation

Read next: Dr Bronwyn Evans, CEO of Standards Australia, traces the rise of blockchain technology and defines the framework needed to build trust in blockchain systems. 

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More Thought Leaders: Click here to go back to the Thought Leadership Series homepage, or start reading the Women in STEM Thought Leadership Series here.

autonomous vehicles

Driverless cars disrupting industries and lifestyle

On a recent visit to the USA, I came across several professors and entrepreneurs who held the view that autonomous vehicles would be “an invention with greater significance than the original invention of the automobile”.  

Seeing many of the world’s earliest automobiles in person, at the enlightening Petersen Automotive Museum in Los Angeles, I saw how their design was derived from either a bicycle dispensing with the rider, or a buggy dispensing with the horse.

Autonomous vehicles can be a lot more than just dispensing with the driver. They provide an opportunity for radical rethinking of design and usage.

Massive changes are set to occur in the automobile industry, with many people already choosing to buy rides instead of cars. The continuation of this trend will see today’s car manufacturers and dealerships, rental car companies, taxi companies, ride-sharing companies, bus companies, pickup and delivery services, intercity transportation entities and other transportation services morph into fresh entities with new business models.

Rides will be significantly cheaper than today’s taxis and Ubers, because the major cost – the driver – will be eliminated. For many, it may be financially unattractive to own a car.

Significant lifestyle changes will also be possible. Commuting will no longer be about driving, but focused instead upon working, studying, socialising, entertaining, sleeping, dining and business meetings. Perhaps some rides will be free, funded by face-to-face selling and marketing.  

Long distance commuting will have less of a lifestyle impact, but rural and regional transportation will become more integrated. Travelling between meetings will be quicker and more efficient. The elderly and disabled will be more mobile, with no fears of driving on busy roads and no parking problems.

Think about your current daily activities and how driverless cars will change them! You’ll choose what type of car you need, when you need it, and you’ll travel efficiently. New patterns of life, leisure, work and commuting will emerge. 

With major growth predicted in our cities over the next few decades, pollution-free autonomous vehicles will be a relief in terms of congestion and amenity.

What happens in our cities when all cars become driverless? Roads will carry up to 3-6 times more traffic. Tailgating may be encouraged for less drag, heightened fuel efficiency and maximum utilisation of road real estate. Speed limits will increase, as will lane channelling during peak hours. Cars will no longer need to park on streets meaning defacto clearways, 24/7.  Extra lanes could be added to freeways by making existing lanes narrower. Traffic lights may become superfluous. Cars will reroute depending upon congestion.

Most importantly, roads will be safer, helping to eliminate most of the 34,000 accidents in Australia today at an annual health cost of $16 billion. There will be no guardrails needed if autonomous vehicles are accident free. No acoustic barriers required if all cars are electric. No more driving offences, meaning no fines, no points, fewer police. Drink and drug driving will be eliminated, as will driver distraction from mobile phones. If autonomous cars can see and sense better than humans, and drive without distraction, then pedestrians may be safer as well.

If every car is driverless, we can totally rethink our infrastructure. But the transition won’t come without challenges. How will older cars, driver assisted and driverless cars all coexist in the short to medium term? Will older cars have their own lanes, roads, circuit tracks or specific hours of use? Will they be tolled more to discourage people from driving cars?

For the evolution to autonomous vehicles, digital technology and disruption processes have been converging, resulting in precision GPS, 3D mapping, odometry, deep learning, computer vision, ultrasonic sensors, LiDAR, radar, driver assist options, smartphones, ride sharing and much more new tech. 

The driverless car transition will take several decades with a step-by-step approach. Australia has the opportunity to become a global leader in several fields including design, technology, infrastructure, specialist systems and fitout. There are vast opportunities for innovation and technology for associated spin-off and support industries.

Hollywood’s driverless cars such Herbie (‘The Love Bug’ in 1969) and K.I.T.T. (David Hasselhoff’s ‘Knight Rider’ in 1980) no longer seem like far-fetched dreams. Soon we can turn these dreams into reality for new lifestyles, improved amenity and new industries for Australia.

Simon Maxwell

Managing Director, Information Gateways

Read next: Heather Catchpole, Managing Director of Refraction Media, explains why digital disruption will create your next career.

Spread the word: Help Australia become digital savvy nation! Share this piece on digital disruptors using the social media buttons below.

More Thought Leaders: Click here to go back to the Thought Leadership Series homepage, or start reading the Women in STEM Thought Leadership Series here.