Artificial intelligence (AI) is becoming ever more prevalent in our lives. It’s no longer confined to certain industries or research institutions; AI is now for everyone.

It’s hard to dodge the deluge of AI content being produced, and harder yet to make sense of the many terms being thrown around. But we can’t have conversations about AI without understanding the concepts behind it.

We’ve compiled a glossary of terms we think everyone should know, if they want to keep up.

Algorithm

An algorithm is a set of instructions given to a computer to solve a problem or to perform calculations that transform data into useful information.

Alignment problem

The alignment problem refers to the discrepancy between our intended objectives for an AI system and the output it produces. A misaligned system can be advanced in performance, yet behave in a way that’s against human values. We saw an example of this in 2015 when an image-recognition algorithm used by Google Photos was found auto-tagging pictures of black people as “gorillas”.

Artificial General Intelligence (AGI)

Artificial general intelligence refers to a hypothetical point in the future where AI is expected to match (or surpass) the cognitive capabilities of humans. Most AI experts agree this will happen, but disagree on specific details such as when it will happen, and whether or not it will result in AI systems that are fully autonomous.

Artificial Neural Network (ANN)

Artificial neural networks are computer algorithms used within a branch of AI called deep learning. They’re made up of layers of interconnected nodes in a way that mimics the neural circuitry of the human brain.

Big data

Big data refers to datasets that are much more massive and complex than traditional data. These datasets, which greatly exceed the storage capacity of household computers, have helped current AI models perform with high levels of accuracy.

Big data can be characterised by four Vs: “volume” refers to the overall amount of data, “velocity” refers to how quickly the data grow, “veracity” refers to how complex the data are, and “variety” refers to the different formats the data come in.

Chinese Room

The Chinese Room thought experiment was first proposed by American philosopher John Searle in 1980. It argues a computer program, no matter how seemingly intelligent in its design, will never be conscious and will remain unable to truly understand its behaviour as a human does.

This concept often comes up in conversations about AI tools such as ChatGPT, which seem to exhibit the traits of a self-aware entity – but are actually just presenting outputs based on predictions made by the underlying model.

Deep learning

Deep learning is a category within the machine-learning branch of AI. Deep-learning systems use advanced neural networks and can process large amounts of complex data to achieve higher accuracy.

These systems perform well on relatively complex tasks and can even exhibit human-like intelligent behaviour.

Diffusion model

A diffusion model is an AI model that learns by adding random “noise” to a set of training data before removing it, and then assessing the differences. The objective is to learn about the underlying patterns or relationships in data that are not immediately obvious.

These models are designed to self-correct as they encounter new data and are therefore particularly useful in situations where there is uncertainty, or if the problem is very complex.

Explainable AI

Explainable AI is an emerging, interdisciplinary field concerned with creating methods that will increase users’ trust in the processes of AI systems.

Due to the inherent complexity of certain AI models, their internal workings are often opaque, and we can’t say with certainty why they produce the outputs they do. Explainable AI aims to make these “black box” systems more transparent.

Generative AI

These are AI systems that generate new content – including text, image, audio and video content – in response to prompts. Popular examples include ChatGPT, DALL-E 2 and Midjourney.

Labelling

Data labelling is the process through which data points are categorised to help an AI model make sense of the data. This involves identifying data structures (such as image, text, audio or video) and adding labels (such as tags and classes) to the data.

Humans do the labelling before machine learning begins. The labelled data are split into distinct datasets for training, validation and testing.

The training set is fed to the system for learning. The validation set is used to verify whether the model is performing as expected and when parameter tuning and training can stop. The testing set is used to evaluate the finished model’s performance.

Large Language Model (LLM)

Large language models (LLM) are trained on massive quantities of unlabelled text. They analyse data, learn the patterns between words and can produce human-like responses. Some examples of AI systems that use large language models are OpenAI’s GPT series and Google’s BERT and LaMDA series.

Machine learning

Machine learning is a branch of AI that involves training AI systems to be able to analyse data, learn patterns and make predictions without specific human instruction.

Natural language processing (NLP)

While large language models are a specific type of AI model used for language-related tasks, natural language processing is the broader AI field that focuses on machines’ ability to learn, understand and produce human language.

Parameters

Parameters are the settings used to tune machine-learning models. You can think of them as the programmed weights and biases a model uses when making a prediction or performing a task.

Since parameters determine how the model will process and analyse data, they also determine how it will perform. An example of a parameter is the number of neurons in a given layer of the neural network. Increasing the number of neurons will allow the neural network to tackle more complex tasks – but the trade-off will be higher computation time and costs.

Responsible AI

The responsible AI movement advocates for developing and deploying AI systems in a human-centred way.

One aspect of this is to embed AI systems with rules that will have them adhere to ethical principles. This would (ideally) prevent them from producing outputs that are biased, discriminatory or could otherwise lead to harmful outcomes.

Sentiment analysis

Sentiment analysis is a technique in natural language processing used to identify and interpret the emotions behind a text. It captures implicit information such as, for example, the author’s tone and the extent of positive or negative expression.

Supervised learning

Supervised learning is a machine-learning approach in which labelled data are used to train an algorithm to make predictions. The algorithm learns to match the labelled input data to the correct output. After learning from a large number of examples, it can continue to make predictions when presented with new data.

Training data

Training data are the (usually labelled) data used to teach AI systems how to make predictions. The accuracy and representativeness of training data have a major impact on a model’s effectiveness.

Transformer

A transformer is a type of deep-learning model used primarily in natural language processing tasks.

The transformer is designed to process sequential data, such as natural language text, and figure out how the different parts relate to one another. This can be compared to how a person reading a sentence pays attention to the order of the words to understand the meaning of the sentence as a whole.

One example is the generative pre-trained transformer (GPT), which the ChatGPT chatbot runs on. The GPT model uses a transformer to learn from a large corpus of unlabelled text.

Turing Test

The Turing test is a machine intelligence concept first introduced by computer scientist Alan Turing in 1950.

It’s framed as a way to determine whether a computer can exhibit human intelligence. In the test, computer and human outputs are compared by a human evaluator. If the outputs are deemed indistinguishable, the computer has passed the test.

Google’s LaMDA and OpenAI’s ChatGPT have been reported to have passed the Turing test – although critics say the results reveal the limitations of using the test to compare computer and human intelligence.

Unsupervised learning

Unsupervised learning is a machine-learning approach in which algorithms are trained on unlabelled data. Without human intervention, the system explores patterns in the data, with the goal of discovering unidentified patterns that could be used for further analysis.

Samar Fatima, Research Fellow Enterprise AI and Data Analytics Hub, RMIT University and Kok-Leong Ong, Director, Enterprise AI and Data Analytics Hub, RMIT University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

As more than half of Australian office workers report using generative artificial intelligence (AI) for work, we’re starting to see this technology affect every part of society, from banking and finance through to weather forecasting, health and medicine.

Many people are now using AI tools like ChatGPT, Claude or Gemini to get advice, find information or summarise longer passages of text. But our recent research demonstrates how generative AI can be used for much more than this, returning results in different formats.

On the one hand, AI tools are neutral – they can be used for good or ill depending on one’s intent.

However, the models powering such tools can also suffer from biases based on how they were developed. AI tools, especially image generators, are also power hungry, ratcheting up the world’s energy usage.

And there are unresolved copyright claims surrounding AI-generated outputs, given the content used to train some of the models isn’t owned by the organisations developing the AI.

But ultimately, there’s no escaping generative AI. Learning more about what these tools can do will improve your digital literacy and help you understand their full impact, from benign to problematic.

1. Imagining what lies beyond the frame

Adobe’s recently developed “generative expand” tool allows users to expand the canvas of their photos and have Photoshop “imagine” what is happening beyond the frame. Nine News infamously experimented with this tool for a broadcast featuring Victorian politician Georgie Purcell.

Here’s a video that shows how that tool works:

But it can also be used more innocently to extend the borders of a landscape or still-life image, for example. You might do this when trying to edit a square Instagram photo to fit a 4x6 inch photo frame.

2. Visualising the past or the future

Photography was only invented within the past 200 years, and camera-equipped smartphones within the last 25.

That leaves us with plenty of things that existed before cameras were common, yet we might want to visualise them. This could be for educational purposes, entertainment or self-reflection.

One example is the writings of historical figures, like architect Robert Russell, who conducted the first survey of what is now Melbourne in 1836. He wrote at the time:

The soil is in this country superior to any in the colony, we have a good grazing land, and a fine supply of water: a fine harbour, a Town on which much capital (I am afraid to say how much) has been expended, enterprising settlers and flocks and herds increasing in all directions, a climate well fitted for Englishmen, and events hastening forward the necessity for some scheme of extended emigration from which we shall soon feel the benefit.

We can feed this text from Russell’s letters into a text-to-image generator and see what the area may have looked like.

Conversely, we might want to look ahead and see if AI can help us visualise what is to come.

For example, a probe is currently heading to a never-before-seen metal asteroid, 16 Psyche. It’s projected to reach the asteroid in 2029. We can feed an AI tool a description from NASA to get a rough sense of what the asteroid might look like.

NASA currently works with artists to illustrate concepts we can’t see, but artists could also draw on AI to help create these renderings.

3. Brainstorming how to visualise difficult concepts

Where we might have once turned to Google Images or Pinterest boards for visual inspiration, AI can also help with suggestions on how to show difficult-to-visualise subject matter.

Take the Mariana Trench, for example. As one of the deepest places on Earth, few people have ever seen it firsthand. It’s also pitch black and artificial light wouldn’t allow you to see very far.

But ask AI for suggestions on how to visualise this spot and it provides a number of ideas, including taking a more familiar landmark, such as the Burj Khalifa, the world’s tallest structure, and placing a scaled model next to the trench to better allow audiences to appreciate its depth.

Or creating a layered illustration that shows the flora and fauna that live at each of the ocean’s five zones above the trench.

4. Visualising data

Depending on the tool, you can prompt AI with numbers, not just text.

For example, you might upload a spreadsheet to ChatGPT 4 and ask it to visualise the results. Or, if the data is already publicly available (such as Earth’s population over time), you might ask a chatbot to visualise it without even having to supply a spreadsheet.

It’s a great way to speed up such tasks, as long as you keep in mind AI can “hallucinate”, or make things up, so you need to double check the accuracy of the results.

5. Creating simple moving images

You can create a simple yet effective animation by uploading a photo to an AI tool like Runway and giving it an animation command, such as zooming in, zooming out or tracking from left to right. That’s what I’ve done with this historical photo preserved by the State Library of Western Australia.

Another way you can experiment with video is using Runway’s text-to-video feature to describe the scene you want to see and let it make a video for you. I used this description to create the following video:

Tracking shot from left to right of the snowy mountains of Nagano, Japan. Clouds hang low around the mountains and they are about 50m away.

6. Generating a colour palette or simple graphics

Maybe you’re creating a logo for your small business or helping a friend with the design of an event invitation. In these cases, having a consistent colour palette can help unify your design.

You can ask generative AI services like Midjourney or Gemini to create a colour palette for you based on the event or its vibe.

If you’re designing a website or poster and need some icons to represent certain parts of the message, you can turn to AI to generate them for you. This is true for both browser-based generators like Adobe Firefly, as well as desktop apps with built-in AI, like Adobe Illustrator.

Next time you’re interacting with a generative AI chatbot, ask it what it’s capable of. In addition to these six use cases, you might be surprised to know that generative AI can also write code, translate content, make music and describe images. This can be handy for writing alt-text descriptions and making the web more accessible for those with vision impairments.

T.J. Thomson, Senior Lecturer in Visual Communication & Digital Media, RMIT University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Have you ever wondered if your partner really loves you? Well apparently so have thousands of TikTok users, who are testing their theories for the world to see.

In the past year we’ve seen a rise in TikTok trends that purport to “test” a partner’s loyalty or the strength of a couple’s relationship. These tests vary in severity, from telling your partner you saw a cool bird and hoping they respond with equal enthusiasm, to asking an influencer to flirt with them to see if they’ll cheat.

If this sound stressful or potentially problematic to you, that’s because it is.

What are these tests?

Generally, TikTok relationship tests involve the tester acting a certain way in front of their partner, while making a video, and posting the results on TikTok for others to judge.

Several different tests have trended this year. One of the most popular is the orange peel “theory” (we use the word very lightly here), in which the tester tells their partner they feel like eating an orange.

Some partners simply acknowledge the comment, while others actually go and get an orange. But the “winners” are those who first retrieve the orange and then happily peel or cut it up for their significant other.

In another popular test, women TikTokers ask their partners to “name a woman”. If they respond with any name other than the tester’s, the woman pretends to act shocked, surprised or angry.

Why do we watch?

So why do millions of people watch and comment on these videos? There are two things we can point to here.

The first is the close resemblance these videos have to reality television, which research has shown we watch, in part, to feel better about ourselves.

Research has also found people are more likely to like, share and comment on stories when they feel a sense of moral outrage. It then comes with a pleasurable payoff once the source of the outrage seemingly gets what they deserve.

One emotion at play here is “schadenfreude”, which refers to taking pleasure from another’s pain. Whether we agree someone’s boyfriend deserves to be called out, or that the original poster “had it coming” with the response, we tend to feel satisfied by being part of the mob response.

Unfortunately, this motivates a hostile online environment. In such spaces, where we see others piling on the shame, we’re also more likely to do so ourselves.

Are the ‘tests’ legitimate in any way?

It’s safe to assume relationship TikTok tests aren’t even close to what would be recommended by relationship science. Currently in psychology research there is no single diagnostic test that can give us exact information about the validity or longevity of someone’s relationship.

Such psychological assessment is complex and can require multiple types of assessment. Moreover, these assessments are often done in various contexts and with feedback from multiple others. Even well-designed personality tests can give questionable, useless or misleading information when applied at an individual level.

Relationships benefit from all parties taking the time to understand one another – not from rushing to diagnose and assign labels.

How might the tests be harmful?

Sometimes we treat our partner as an object: a means to the end of our own desires, with no recognition of their desires.

These TikTok video are often driven by a desire for personal fame, reassurance about the security of one’s relationship, or wanting to feel like the “superior” partner. Of course this can backfire, especially if the person being unwittingly “tested” has their response shared online against their will.

People in relationships are interdependent. This means they influence each other and can choose to meet or deny each other’s needs.

When our needs aren’t met, communication is necessary to understand what matters to each person, and why a particular behaviour or pattern of behaviour is hurtful. It also requires a shared understanding of how to move forward together.

Working through what happened, taking time to recognise each other’s needs and agreeing on the shared values underpinning a conflict are important for repairing a relationship. Using a shame-based video to prove a point suggests the absence of healthy communication.

Beyond that, people tend to seek out information that confirms what they already think. This is called “confirmation bias”. If someone feels insecure about their partnership, they may subconsciously seek out information that confirms their insecurities, or interpret new information in this way.

Research has found if you act in a way that’s untrustworthy – such as setting up your partner to fail in a TikTok “test” – you are more likely to create insecurity in your relationship.

The line between humour and harm

Of course, some people posting these videos are just doing so to be playful or funny. Their partner may not mind, or may even be in on the gag. As long as a behaviour is an expression of a couple’s shared values, is done with consent (especially in terms of uploading the video online) and makes both parties feel respected, who are we to judge?

At the same time, many TikTok relationship tests presented as “jokes” will often portray women as being stereotypically jealous, needy or manipulative. And like all media that stereotype men and women, such content can further feed sexist attitudes and harm our relationships.

Edith Jennifer Hill, Associate Lecturer, Learning & Teaching Innovation, Flinders University and Lydia Woodyatt, Professor of Psychology, Flinders University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

If you’re a fan of all things space, you’ve doubtless heard about the “devil comet”, which has been captivating keen-eyed observers in the northern hemisphere for the past few weeks. Now it’s our turn, as comet 12P/Pons–Brooks is creeping into view for the southern hemisphere.

Before you get too excited, let me quash your hopes. Comet Pons–Brooks is visible to the naked eye, but only if you know where to look. It will look like a fuzzy glowing patch, but nonetheless promises some amazing photo opportunities for the coming weeks.

Even better, it may serve as a celestial warm-up act for an even more special comet later in the year.

Here’s everything you need to know about Pons–Brooks, and how to get the best view.

Why do people call it the ‘devil comet’?

Named after two astronomers who independently discovered it in the 19th century, Comet 12P/Pons–Brooks (its full, official name) was last visible in 1954.

It takes around 71 years to orbit the Sun, making the comet’s visits to the inner Solar System a rare treat for us here on Earth.

At its heart (its nucleus), Pons–Brooks is a dirty snowball around 34 kilometres in diameter. As the comet came swinging back towards us in its orbit, astronomers spotted it back in 2020. At that time, the comet was almost 1.8 billion kilometres from the Sun, and lay dormant.

As the comet kept falling inwards toward the Sun, its surface temperature began to rise, making it “active”. Exposed ices started to sublime, turning directly from solid to gas. This activity is how a comet gets its tail: the nucleus becomes shrouded in a diaphanous “coma” of dust and debris from its sublimated surface, which is then blown away from the Sun by the solar wind.

But comet 12P/Pons–Brooks didn’t activate gently and smoothly. Instead, it produces several large outbursts of activity, each time, emitting vast amounts of gas and dust in a very short period of time before settling down again.

In the first of those significant outbursts, on July 20, 2023, the comet brightened by a factor of a hundred times, shedding an estimated ten million metric tons of dust and ice.

The solar wind pushed the resulting dust, gas and debris away from the Sun, giving the comet an unusual appearance. To some, the comet looked like the Millennium Falcon from Star Wars. To others, it looked vaguely demonic – sporting the cometary equivalent of horns.

The moniker of “devil comet” took hold in media articles and appears to have stuck – even though the comet’s horned appearance is now a thing of the past.

Where (and when) should I look?

Over the last few days, the first confirmed sightings of 12P/Pons–Brooks have come in from around Australia. It is currently visible low in the western sky after sunset, albeit almost lost in the glow of twilight.

In the next few weeks, the comet will slowly climb higher in the evening sky. The two videos below show the location of the comet’s head at 6:30pm from mid-April through to mid-June, as seen from Toowoomba and Melbourne.

Remember, the comet is a diffuse object, rather than a single point of light. The head is where the comet is brightest (centred on its nucleus). The comet’s tails point away from the Sun – so will rise upwards from the western horizon in the evening sky.

While the comet is visible with the naked eye, you really need to know where to look. The best bet is to search with binoculars. Make sure to wait until the Sun is well below the horizon. Once you find the telltale blur of the comet, you will know where to look, and can switch to see if you can spot it with the naked eye.

For me, the most exciting time with Pons–Brooks will come during the first two weeks of May. At that point, the comet will be passing underneath the constellation Orion, which will serve as a signpost.

That period will be prime astrophotography season, so I expect to see many spectacular images of the comet’s tails cutting through the celestial hunter, shining next to the spectacular nebulae dotted throughout Orion’s body.

But wait… there’s more!

While comet 12P/Pons–Brooks currently basks in the limelight, a potentially great comet is currently moving sunward, promising a spectacular show later this year.

That comet, C/2023 A3 (Tsuchinshan-ATLAS), was discovered in January 2023, and astronomers soon realised it has the potential to become truly dazzling.

Comet behaviour is hard to predict, so take the following with a pinch of salt, but things still look really promising.

Current predictions suggest Tsuchinshan-ATLAS will be at least as bright as the brightest stars in late September and early October this year. During that time, it will pass almost directly between Earth and the Sun. It might even briefly become visible in broad daylight at that time.

In the days following that chance alignment, the comet will gradually become visible in the evening sky and could be an incredible sight, up to a hundred times brighter than Pons–Brooks at its best.

So, with any luck, the current apparition of 12P/Pons–Brooks is merely the warm-up act, with an even greater spectacle to come later this year. Fingers crossed!

Jonti Horner, Professor (Astrophysics), University of Southern Queensland

This article is republished from The Conversation under a Creative Commons license. Read the original article.

If you look from above, you can see thousands of stone structures dotting the landscape of the Arabian peninsula. On the ground, you can find a bounty of stone tools and ancient fireplaces scattered along the edges of ancient lakes, as well as rock art depicting hunting and herding scenes in the surrounding mountains.

Despite the visibility of these sites, only in the past decade or so have archaeologists taken a dedicated interest in them. Some of the structures have now been dated at up to 10,000 years old.

However, the arid climate, baking days and freezing nights, and intense wind erosion are not kind to some of the other relics archaeologists prize. To date, there has been little found in the way of fossils or the kind of deeply buried, layered deposits that can open a window onto the history of a place.

Until recently, no archaeologists had surveyed any of the hundreds of caves and lava tubes recorded across northern Arabia. In 2019, our team began to look in these subterranean locations – and in a new study published today in PLoS ONE, we report on the first documented occupation of a lava tube in the Arabian Peninsula.

The Umm Jirsan lava tube

The Umm Jirsan lava tube lies some 125 kilometres north of the city of Madinah, in the Harrat Khaybar lava field. The tube formed long ago by cooling lava. It winds an impressive 1.5 kilometres, and reaches 12 metres in height and 45 metres in width in some sections.

The first thing you notice when venturing into the tube’s dark and meandering tunnels is the sheer number of animal remains. The floor is strewn with piles of bones containing thousands – if not hundreds of thousands – of exceptionally preserved fossils.

These bone-piles are the work of striped hyenas, which drag bones underground to eat, stash away for times of food scarcity, or process and feed to cubs. This process, repeated over millennia, has produced some of the most incredible accumulations of fossils seen anywhere in the world.

But it’s not all just bones. When we surveyed the entrances of Umm Jirsan – essentially areas where the roof has collapsed, providing access to the lava tube – we uncovered hundreds of stone artefacts made from obsidian, chert and basalt.

Although exciting, these artefacts were all surface finds, making them extremely difficult to date. We needed to look deeper.

Digging in

We excavated in the mouth of the eastern passage, near a series of semi-circular stone structures of an unknown age or function. The excavation uncovered more stone artefacts – all made from fine-grained green obsidian – as well as animal bones and charcoal.

Most of the stone artefacts came from a discrete sediment layer roughly 75 centimetres beneath the surface. Radiocarbon dating of the charcoal, and dating of the sediments using a method known as optically stimulated luminescence dating, revealed this main occupation phase likely occurred between 7,000 and 10,000 years ago.

We also found some interesting objects in the surrounding landscape. These included more stone artefacts and circular structures, as well as a so-called “I-type” structure. These constructions are believed to date to around 7,000 years ago, based on their association with large rectangular structures known as mustatils, which we believe were used for ritual animal sacrifices.

We also found the first rock art discovered in the area. This includes depictions of herding scenes of cattle, sheep and goat, and even hunting scenes involving dogs. This art has similarities with other rock art in Arabia from the Neolithic and the later Bronze Age. It includes overlapping engravings, suggesting people visited the area repeatedly over thousands of years.

We also found human remains at Umm Jirsan, which we dated to the Neolithic and Bronze Age periods. By analysing the carbon and nitrogen in these remains, we found these people’s diets were consistently high in protein – though they ate more fruit and cereals over time.

Interestingly, this change in diet appears to coincide with the arrival of oasis agriculture in the region. This saw the emergence of sophisticated farming and water management techniques that enabled people to settle in the deserts more permanently and cultivate plants such as dates and figs.

We made another interesting finding after coming home from the dig. Studying maps of archaeological structures in the wider area, we noticed Umm Jirsan sits along a “funerary avenue” connecting two major oases.

These funerary avenues, which consist of chains of tombs stretching hundreds of kilometres, are believed to have been routes used by Bronze Age pastoralists as they transported their herds between water sources.

We think Umm Jirsan may have been a stopping-off point for pastoralists, a place that offered shelter and water in an otherwise dry and harsh environment.

Archaeologists have made remarkable finds in Arabia in recent years, in settings such as ancient lakebeds. Our finds at Umm Jirsan add another important element to the story of Arabian societies over time, and how they interacted with this dramatic landscape.

Mathew Stewart, Research Fellow, Australian Research Centre for Human Evolution, Griffith University; Huw Groucutt, Lecturer in Mediterranean Prehistory, University of Malta, and Michael Petraglia, Director, Australian Research Centre for Human Evolution, Griffith University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

See the word “musician” on a resume and you might not immediately think “stellar employee” or “exceptional leader”.

Perhaps the word evokes the image of a rock star, in trouble for chucking a television out of a hotel room window. Or else someone struggling along in life, who should have picked a “real job”.

But is there more to the profession than meets the stereotype?

It is well known many musicians work simultaneously in arts and non-arts roles, often to create some income security. Less understood is just how well the extensive skillset developed in music transfers to a non-arts, professional workplace.

My nationwide survey sought to find out. I began by conducting 15 in-depth interviews with musicians simultaneously working dual-careers - one in music, and one somewhere else.

The preliminary findings, which are yet to be peer-reviewed, showed dual-career musicians have a plethora of distinctive workplace skills that had been enabled by their musical education and experience. This was verified by their non-musician co-workers.

The discovery sparked an ongoing second stage of research, investigating the experience of a larger group. These include:

• dual-careerists
• musicians who have exited the profession
• musically-educated individuals who never pursued a music career.

With 165 respondents so far, the emerging results are significant – music education and experience lays the foundation for high levels of future aptitude in a range of workplaces.

Practice makes perfect

One of the most powerful traits instilled by a music education is a deep sense of professionalism. 85% of survey participants identified the trait as the skill that most influenced expectations of themselves and others, and the quality of their work.

A common industry saying about rehearsal reflects this attitude of consistency and punctuality – “early is on time, on time is late, and late is left behind.”

Other notable skills included autonomy and self-direction, resilience and perseverance, and creativity.

Participants attributed the development of these strengths to the disciplined and focused attention required to learn music, and the intrinsic motivation needed to practise and perfect an instrument over a long period of time.

This is increasingly relevant in an age where screens and social media steal our concentration and cost employers’ productivity.

Another key trait identified was creativity, which may reflect findings that the musically trained have more neuronal matter, and therefore increased brain activity.

Interviews with non-musician colleagues showed that out-of-the-box thinking was particularly prevalent amongt jazz musicians, singer-songwriters and composers.

It takes a whole orchestra to play a symphony

Participants unanimously said ensemble work – playing in bands, chamber music, orchestras and so on – has contributed positively to their workplace team dynamics.

As one respondent put it:

It taught me how to be part of something bigger than myself.

Experiences in music translated to a greater appreciation of diversity and inclusivity, exceptional leadership and deep listening skills, and the ability to manage “difficult” conversations respectfully within a team.

Respondents said an ongoing passion for music improved their mental health and workplace resilience. Unsurprisingly, their non-musician co-workers reported better workplace morale and an infectious positive energy from their musical colleagues.

Growth mindset

All respondents reported a healthy relationship with failure – experiences in music had taught them to remain curious, embrace learning and “fail forward” while owning errors.

This attitude directly enhances one’s ability to upskill in a non-music careers, something many respondents have had to do. 82% of the exited and dual-career musicians had re-accredited, and 71% admitted to “learning on the job”.

Their live performance experiences cemented their ability to work under pressure in a variety of situations – public presentations, deadlines, project management. To them it was a no-brainer: “the show must go on.”

Co-workers’ perceptions further verified the musicians’ transferable skills, suggesting they possessed high professional values, a strong work ethic, high intelligence, willingness to learn, and the ability to take the initiative.

Survey respondents work in a much greater variety of fields than you might expect, including:

• health
• science and academia
• building and engineering
• business and finance
• law
• technology
• government
• transportation
• administration
• and religion.

Many also hold leadership positions. Economically, these findings suggest having a musician on the books may be linked to increased productivity, innovation, and profitability.

Lessons for educators and policymakers

The skills of our country’s musically educated are relevant to far more than just the arts. Employers and recruiters need to respect and appreciate the professional contributions of the musically trained.

But the ongoing decline in school music programs and nominal music teacher training, as well as a shift in tertiary training focus to STEM at the expense of the arts, suggest we’re heading the wrong direction.

Including a well-informed understanding of music skills in the proposed National Skills Passport would be a big step forward. So would ensuring all school children have access to proper music education, and that school leavers are not discouraged from pursuing further music training.

Who knows, when the next pandemic comes, it could be a musician who designs the next life-saving vaccine.

Diana Tolmie, Senior Lecturer of Professional Practice, Griffith University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

So weighed down have Americans become by student debt, and so potent a political issue has it become in the US, that President Biden plans to waive interest or write off money owing by 30 million of them.

He is doing it bit by bit, in the face of resistance from the US Supreme Court. He has already axed or wound back 4.3 million debts, and on Friday cancelled 277,000 more.

The benefits, as he keeps telling anyone who will listen in the lead-up to the November election, are likely to be increased consumer spending, better mental health and credit scores for borrowers, and increased home ownership.

In Australia, Prime Minister Anthony Albanese is under pressure to do something – anything – for Australians under the same sort of pressure.

Every June, the amount owed jumps

Every June the amount that someone who has borrowed under the Higher Education Loan Program (HELP) jumps. Because the jump is linked to inflation, and because inflation has been low for decades, in most Junes the jump has been small, until last June.

On June 1 2023, Australians who had made no payments over the previous year faced a jump of 7.1%. Someone who had owed $25,000, suddenly owed $26,770, and so on.

A quarter of a million Australians have signed a petition asking for change.

The good news is there’s likely to be some change, and we are likely to hear about it soon, in the lead-up to the May budget.

The bad news for borrowers is it won’t be debt relief of the kind Biden is offering.

It’s worse in the US

While in both Australia and the US it’s the government that lends to pay student fees rather than private lenders (who don’t like the risks) in the US the loans are really onerous, requiring fixed monthly repayments over a set period of time, regardless of the borrower’s circumstances.

In Australia and the United Kingdom and New Zealand and some other countries that have copied Australia’s system, the loans are income contingent, meaning they only need to be repaid when the borrower’s income rises to a certain level.

At the moment Australia’s repayment threshold is A$51,550 per year, meaning anyone who earns less than that doesn’t need to repay a cent, perhaps forever if their income never climbs that high.

Where payments are required, they are taken out in the same way as income tax is, each fortnight for pay-as-you-earn employees.

Buried within Biden’s announcement is a decision to move towards an Australian-style plan he has called SAVE, which stands for Saving on a Valuable Education.

If it becomes law, single Americans won’t have to repay until they earn US$32,800. For an American supporting a family of four, the threshold will be US$67,500. It will be an Australian-style system.

Easy wins for Albo

While Australia’s system is much better than the one in the US and has been copied around the world, it is far from perfect.

A simple change, identified by the report of the Australian Universities Accord delivered to Education Minister Jason Clare, in February is to increase the amount owing each year by either the rate of increase in prices or the rate of increase in wages, whichever is lower.

Usually, prices increase by less than wages, which is why the system was set up in 1988 to index amounts owed to prices.

But last year, unusually, prices increased faster than wages. In those years it would be simple to lift the amount owed only in line with wages, as the report recommends.

The amount owed needs to increase in line with something, because otherwise its value would shrink rapidly as prices rose. The government doesn’t charge interest (which would hurt) so instead it lifts the amount owed in line with prices to ensure that compared to other things it remains little changed.

Make repayments more like tax

Although we repay student loans through the income tax system, we don’t do it like income tax.

Here’s how it works for tax: on our first $18,200 of income we pay nothing, then we pay 19 cents in the dollar for each extra dollar we earn up to the next threshold, and so on. The key words here are “for each extra dollar”. We continue to pay nothing on the first $18,200 we earn.

Higher education loans work differently. For them, we repay nothing until we earn $51,550, and then at that point, even if we earn just one dollar more, we pay one per cent of all our annual income, the entire $51,550 (which amounts to $515).

It’s a repayment cliff that sends us backwards. It means earning an extra dollar costs more than $500 in that year That’s an effective marginal tax rate of 500%.

The cliff matters. Each year, there’s an impressive cluster of taxpayers who happen to be earning just under the threshold. More likely to be women than men, they might be deciding not to work in order to keep their incomes below the threshold.

Make it easier to get home loans

Britain and other nations that copied Australia’s system don’t impose large repayments in one hit, and the economist who designed Australia’s system now says that part of the system was “an error, a mistake”.

That economist, Bruce Chapman, has suggested a redesign that would require collections only on extra rather than total incomes, a proposal the report to the government endorses.

And there’s something else Albanese can do. Right now Australia’s banking regulator requires banks to count student loans as debt for the purpose of determining who can get a housing loan, knocking some former students out.

Chapman says it would make more sense to treat the compulsory payments as tax, which is how they function. All they do is reduce after-tax income, and for low earners, they don’t even do that. It’d get more people into housing.

Now it’s over to Albo.

Peter Martin, Visiting Fellow, Crawford School of Public Policy, Australian National University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

For millions of years, giant animals or megafauna roamed the lands that are now Australia and New Guinea. Many were like much larger versions of modern animals.

There was a four-metre goanna called Megalania (Varanus priscus), for example, which likely ambushed its prey. This beast disappeared by around 40,000 years ago along with almost all the other megafauna aside from remnants such as the red kangaroo and the saltwater crocodile.

Some of the now-vanished kangaroo species were quite massive. The short-faced kangaroo Procoptodon goliah grew as tall as three metres and may have weighed more than 250 kilograms.

There was another genus of extinct kangaroos, Protemnodon, which were more like the grey and red roos we know today, but little has been known about their lives. In a new study, my colleagues and I describe three new species of these vanished marsupials – and shed some light on where they lived and how they got around.

A 150-year puzzle

The first species of Protemnodon were described in 1874 by the British naturalist Richard Owen. As was standard at the time, Owen focused chiefly on fossil teeth. Seeing minor differences between teeth from different fragmentary specimens, he described six species of Protemnodon.

However, Owen’s species have not stood the test of time. Our study agrees with only one of his species — Protemnodon anak. The first specimen of P. anak to be described, called the holotype, still resides in the Natural History Museum in London.

Fossils of individual Protemnodon bones are not uncommon, but more complete skeletons are rare. This has hampered palaeontologists’ efforts to study the creatures.

The question of how many species there were, and how to tell them apart, has not been fully answered. This has made it hard to say how the species differed in their size, geographic range, movement and adaptations to their natural environments.

I set out to solve this problem in my PhD project. With fellow PhD student Jacob van Zoelen, I visited the collections of 14 museums in four countries to gather data.

We have now seen just about every piece of Protemnodon that exists above ground, photographing, scanning, measuring, comparing and describing more than 800 specimens collected from all over Australia and New Guinea.

Finding the key

Among all this study, the key to the Protemnodon problem turned out to be buried in the dry bed of Lake Callabonna in northeastern South Australia. Three expeditions to Lake Callabonna from 2013 to 2019 found a megafaunal boneyard: complete skeletons of giant kangaroos, giant wombats, and Genyornis newtoni, a 250kg flightless bird, were scattered among the remains of hundreds of Diprotodon optatum, a rhino-sized marsupial herbivore. This lake likely preserves animals that died while searching for water during prolonged drought.

I accompanied the 2018 trip, which returned with all manner of amazing articulated fossils. These fossils allowed me, then a PhD student in my first year, to begin the process of picking apart the identities of the kangaroos.

Two of Richard Owen’s species, Protemnodon brehus and Protemnodon roechus, were only known from their teeth, which were extremely similar. We unearthed and compared several kangaroos with teeth that could have belonged to either of Owen’s species, but the skeletons didn’t match.

By the international rules of species naming, this meant we had to describe two new species. These are Protemnodon viator from central Australia and Protemnodon mamkurra from southern Australia.

Big kangaroos with big differences

Our study reviews all species of Protemnodon, finding surprising differences. We concluded that there are seven species in the genus, adapted to live in very different environments and even hopping in different ways. This level of variation is unusual within a single genus of kangaroo.

Protemnodon viator was a large, long-limbed kangaroo that could hop fairly quickly and efficiently. Its name, viator, is Latin for “traveller” or “wayfarer”. Its elongated hind limbs were muscular and narrow, perfect for supporting the kangaroo as it hopped long distances.

Protemnodon viator was well-adapted to its arid central Australian habitat, living in similar areas to the red kangaroos of today. Protemnodon viator was much bigger, however, weighing up to 170 kg, about twice as much as the largest male red kangaroos.

Our study suggests two or three species of Protemnodon may have been mostly quadrupedal, moving something like a quokka or potoroo – bounding on four legs at times, and hopping on two legs at others.

The newly described Protemnodon mamkurra is likely one of these. A large but thick-boned and robust kangaroo, it was probably fairly slow-moving and inefficient. It may have hopped only rarely, perhaps just when startled.

The best fossils of this species come from southeastern South Australia, on the land of the Boandik people. The species name, mamkurra, was chosen by Boandik elders and language experts in the Burrandies Corporation. It means “great kangaroo” in Bunganditj language.

The third of our newly described species is Protemnodon dawsonae, a woodland-dwelling kangaroo from eastern Australia and the probable ancestor of P. viator and P. mamkurra. It is named for kangaroo palaeontologist Lyndall Dawson.

By about 40,000 years ago, despite their many differences, all Protemnodon were extinct on mainland Australia. We don’t yet know why they died out when similar animals such as wallaroos and grey kangaroos survived – but our study may open the door for further Protemnodon research that will find out more about how they lived and why they died.

Isaac A. R. Kerr, Research Assistant at Flinders University Palaeontology Laboratory, Flinders University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

After a decade of silence, one of the most powerful magnets in the universe suddenly burst back to life in late 2018. The reawakening of this “magnetar”, a city-sized star named XTE J1810-197 born from a supernova explosion, was an incredibly violent affair.

The snapping and untwisting of the tangled magnetic field released enormous amounts of energy as gamma rays, X-rays and radio waves.

By catching magnetar outbursts like this in action, astronomers are beginning to understand what drives their erratic behaviour. We are also finding potential links to enigmatic flashes of radio light seen from distant galaxies known as fast radio bursts.

In two new pieces of research published in Nature Astronomy, we used three of the world’s largest radio telescopes to capture a host of never before seen changes in the radio waves emitted by one of these rare objects in unprecedented detail.

Magnetic monsters

Magnetars are young neutron stars, with magnetic fields billions of times stronger than our most powerful Earth-based magnets. The slow decay of their magnetic fields creates an enormous amount of stress in their hard outer crust until it eventually fractures. This twists the magnetic field and releases large amounts of energetic X-rays and gamma rays as it unwinds.

These exotic stars were initially detected back in 1979 when an intense gamma-ray burst emitted by one was picked up by spacecraft across the Solar System. Since then, we’ve found another 30 magnetars, the vast majority of which have only been detected as sources of X-rays and gamma rays. However, a rare few have since been found to also emit flashes of radio waves.

The first of these “radio-loud” magnetars goes by the name XTE J1810-197. Astronomers initially discovered it as a bright source of X-rays after an outburst in 2003, then found it emitted bright pulses of radio waves as it rotated every 5.54 seconds.

Unfortunately, the intensity of the radio pulses dropped rapidly, and within two years it had completely faded from view. XTE J1810-197 remained in this radio silent state for over a decade.

A wobbly start

On December 11 2018, astronomers using the University of Manchester’s 76-metre Lovell telescope at the Jodrell Bank Observatory noticed that XTE J1810-197 was once again emitting bright radio pulses. This was quickly confirmed by both the Max-Planck-Institut’s 100-metre Effelsberg radio telescope in Germany and Murriyang, CSIRO’s 64-metre Parkes radio telescope in Australia.

Following confirmation, all three telescopes began an intense campaign to track how the magnetar’s radio emission then evolved over time.

The reactivated radio pulses from XTE J1810-197 were found to be highly linearly polarised, appearing to wiggle either up and down, left to right, or some combination of the two. Careful measurements of the polarisation direction allowed us to determine how the magnetar’s magnetic field and spin direction are oriented with respect to the Earth.

Our diligent tracking of the polarisation direction revealed something remarkable: the direction of the star’s spin was slowly wobbling. By comparing the measured wobble against simulations, we were able to determine the magnetar’s surface had become slightly lumpy due to the outburst.

The amount of lumpiness was tiny, only about a millimetre off from being a perfect sphere, and gradually disappeared within three months of XTE J1810-917 waking up.

Twisted light

Normally, magnetars only emit very small amounts of circularly polarised radio waves, which travel in a spiral pattern. Unusually, we detected an enormous amount of circular polarisation in XTE J1810-197 during the 2018 outburst.

Our observations with Murriyang revealed the normally linearly polarised radio waves were being converted into circularly polarised waves.

This “linear-to-circular conversion” had long been predicted to occur when radio waves travel through the super-heated soup of particles that resides in neutron star magnetic fields.

However, the theoretical predictions for how the effect should change with observing frequency did not match our observations, though we weren’t too surprised. The environment around a magnetar in outburst is a complicated place, and there are many effects that can be at play that relatively simple theories aren’t designed to account for.

Piecing it all together

The discovery of the slight wobble and the circular polarisation in the radio emission of XTE J1810-197 represents an exciting leap forwards in how we can study the outbursts of radio-loud magnetars. It also paints a more complete picture of the 2018 outburst.

We now know that cracking of the magnetar surface causes it to become distorted and wobble for a brief period of time, while the magnetic field becomes filled with super-hot particles whizzing about at almost light speed.

Combined with other observations, the amount of wobble could be used to test our theories of how matter should behave at densities much higher than we could ever hope to replicate in labs on Earth. The inconsistency of the linear-to-circular conversion with theory, on the other hand, motivates us to devise more complex ideas of how radio waves escape from their magnetic fields.

What’s next?

While XTE J1810-197 remains active to this day, it has since settled into a more relaxed state with no further signs of wobbling or linear-to-circular conversion. There are however hints that both phenomena may have been seen in past observations of other radio-loud magnetars, and might be a common feature of their outbursts.

Like cats, it’s impossible to predict what a magnetar will do next. But with current and future upgrades to telescopes in Australia, Germany and North America, we are now more ready than ever to pounce the next time one decides to awaken.

Marcus Lower, Postdoctoral Research Fellow, CSIRO; Gregory Desvignes, Postdoctoral Researcher, Fundamental Physics in Radio Astronomy, Max Planck Institute for Radio Astronomy, and Patrick Weltevrede, Lecturer In Pulsar Astrophysics, University of Manchester

This article is republished from The Conversation under a Creative Commons license. Read the original article.