Let’s start with the basics so we can move on to more tricky specifics. Take your everyday computer, its chip uses what is called ‘bits’—not to confuse with the saying ‘to do bits with someone’ commonly used in Love Island. The bits we’re talking about here can either be in the ‘off’ position, which is represented by a zero, or in the ‘on’ position, which is represented by a one.

Every website we visit, app we use, and even photograph we take is ultimately made up of a lot of these bits in some combination of ones and zeros. To put it simply, which is what we’re aiming to do, everything a computer does is based on ones and zeros—all code is binary.

And although so far it’s worked pretty well for us, this binary code represents a problem now. Our world, our universe, aren’t that simple. Things are uncertain (even though some people might argue that everything is predictable, this is another complicated topic) and uncertainty means that our computers—even our supercomputers—are struggling to deal with.

Why is that a problem, you might wonder. Well, because, in the last hundred years, as physicists discovered more and more about our world, they found out that if you wish to go down to a really small scale, we’re talking really small, things stop working correctly. That’s where the problem is.

In order to solve this problem, physicists have developed a whole new field of science to try and explain these malfunctions: quantum mechanics. Quantum mechanics is the foundation of physics which underlies chemistry, which is the foundation of biology—meaning quantum mechanics is a pretty big deal.

That’s exactly why scientists need help from a new technology that can handle making calculations while taking our world’s uncertainty into the equation. And that’s what quantum computers are here to—the dirty work! Just as when you typed in 12 + 8 into your calculator in math class because you couldn’t be bothered, scientists are using quantum computing to do some crazy calculations.

Hopefully things are a bit clearer by now; either that or we’ve lost you already! On to the next one: quantum computers and the way they function. So, you’ve read about bits for ordinary computers. Quantum computers use something called qubits. As we explained before, the problem with bits was that they can be either on or off. 

Qubits can be on and off too, but can also be in ‘superposition’, which means they’re both on and off at the same time, or somewhere in between. This in-between option that a qubit offers means that it allows for uncertainty, and that’s exactly what scientists are looking for.

The best way to explain the difference between an ordinary computer and a quantum computer is by using the metaphor of the maze. If you asked an ordinary computer to figure out which path will take it out of the maze, it would have to try every path individually, one after the other, in order to finally find the right one. A quantum computer, on the other hand, would be able to simultaneously go down every path of the maze, making the whole process way quicker.

But qubits can do even more than taking uncertainty into consideration; they can do what is called ‘entanglement’. Physicists still don’t fully understand how or why entanglement works, but it means that you can move information around, even if it contains uncertainty.

For example, a quantum computer can take two separate spinning coins and perform complex calculations by linking two particles together, even if they’re physically separate. This means that, by stringing together multiple qubits, the quantum computer can solve problems that would take our best computers millions of years to crack.

But quantum computers are not only here to do quick maths. They’ve allowed us to achieve things that even supercomputers would have not been able to do.

At the moment, supercomputers, which are the fastest and most powerful type of ordinary computer you can find, can only analyse the most simple molecules. On the other hand, quantum computers actually use the same quantum properties as the molecules they’re trying to simulate. This means they have no problem handling even the most complicated reactions.

From helping in the search for a cure for Alzheimer’s and improving solar panels to rapidly accelerating the development of artificial intelligence (AI), quantum computers are, without a doubt, going to be a vital element in furthering our advancement.

Quantum computing comes with another key application: cryptography. At the moment, many encryption systems rely on the difficulty of breaking down large numbers into prime numbers, which is called ‘factoring’. Factoring for ordinary computers takes time, is expensive and often impractical.

Quantum computers can perform factoring easily, but this means that it could put our data (even more) at risk. In response to this problem, quantum encryption keys would protect data, making it unbreakable and impossible to hack.

Don’t get us wrong, the next iPhone probably won’t have a quantum chip. As amazing as quantum computers sound, they’re also incredibly sensitive to interference. They have to be kept isolated from any other form of electrical interference and chilled down to a temperature colder than outer space. Long story short, you probably will never have access to a quantum computer—sorry about that.

We still have quite a while to wait before quantum computers can do all the things they promise. At the moment, the best quantum computers have around 50 qubits, which is enough to make them incredibly powerful. However, they also have incredibly high error rates resulting from interference problems.

Quantum supremacy, which is the point at which a quantum computer can outperform a classical computer, has not yet been achieved. In 2019, Google published a paper suggesting it had achieved quantum supremacy. Soon after, IBM disputed the claim and said that Google had not yet tapped into the full power of modern supercomputers.

Quantum computing is here to change the world, but, for now, the devices themselves still require a lot more work. Until then, using a ‘simple’ computer remains the easiest and most economical solution for tackling most problems.

In recent years, the digital media landscape has seen an array of trends—the rise of clickbait and fluff articles emerging from ad-generated revenue models, and positive feedback loops created by social media middlemen in the grand scheme of timeline catering. Changes in business models led to the thriving success of some and the bankruptcy of others. Throughout this volatility, the world of journalism has been disrupted by technology just like any other industry, and with that, the era of digitisation might have saved the dying world of the newspaper, but the introduction of AI journalism might very well be able to provide the news industry with a solution to its newest problem: keeping up with the speed of information.  

In many main news publications, AI has been a crucial aspect of their growth strategy for the last few years. The immediate creation of financial reports, sports articles, and pieces focused on national disaster are being handed over to our machine counterparts. Soon, robot journalism will seep into just about anything that falls under some sort of numbers-based reporting.

The rise of machine-generated journalism is inevitable, and one that we shouldn’t want to push away. Currently, about one-third of all content on Bloomberg News uses some sort of automated technology, while Forbes is testing out an AI technology that will help create rough drafts and templates for reporters. The WSJ and Dow Jones are both experimenting with technology that can transcribe interviews, and Wired constantly plays around with AI written science fiction stories and scripts. Some extremities have even seen an attempt at replacing human news anchors with machine ones, like the recently introduced AI news anchor in China.

With the world around us bursting with upgrades, it seems that gradually everything around us is becoming excitingly infused with the technology of the future. Within this constant tech conversation, the world of news and its gizmos is no exception. What the use of AI journalism in some of the world’s staple media publishers shows is just how much more intrinsically connected robots are to journalism than we currently assume. So why is our initial feeling towards robot written articles ones of uneasiness?

At the end of the day, if done properly, AI is able to crunch numbers better and faster than we’ll ever be able to. As robot generated journalism takes over the responsibility of producing these reports and articles, journalists will have more time to tackle investigative, in-depth pieces that demand humility and a moral compass (so you would hope). Arguably, now more than ever there is a need for journalists to be able to provide think-pieces that hold governments and powerful players accountable.

The integration of AI journalism will quite possibly lead to a strengthened trust in news and journalism, as the intelligence landscape of news outlets becomes more competitive. The journalistic standard has and always will stay the same, and the integration of AI will only help us better achieve that level of standard.

With the current pattern of those consuming the news being reading small, mainstream, information-heavy pieces, the focus has been on utilising human capital to create those repetitive and simple pieces. With the projected use of automation though, computer-authored journalism will give way for journalists to pursue less mechanical stories, and focus instead on ones that are of higher quality and of a more investigative nature.  

The upcoming technological reshaping in the newsroom is going to be incredibly disruptive. Robots will be able to automate certain aspects of reporters and their jobs, but more importantly, augment their abilities to do real investigative, opinion-based journalism. With the automation of redundant and labour-intensive reports, the availability of human capital to focus on less repetitive work will result in humans being able to do what we do best: having an opinion, providing perspectives, being curious, and extracting some sense other than numbers and figures from what’s happening around us.