Two Ways Machines Can Evolve the News Media Industry

Blade Runner predicted that in 2019 we would have cyborgs so realistic they would challenge everything we know and think about ourselves.

Granted, it’s now 2022 and there’s not a single cyborg in sight (unless they’ve gone straight to realistic models), but pretty much every talk of the future now includes an iteration more advanced artificial intelligence (AI). This future is not a fiction or a film, it is a rushing reality.

The move away from third-party cookies promises another wave of technological evolution looming on the horizon. And it’s a safe bet that AI, machine learning, deep learning, and micropayments will define the characteristics of what this next technological evolution will look like.

For the news media industry in particular, new technologies offer a valuable opportunity to get a head start and better establish the industry for the future. If we play our cards right, these innovations can be the key to solving persistent problems that have plagued the industry for years, such as rising costs and expenses, as well as declining revenues.

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In terms of higher costs, the shift to online news (with audiences now used to reading everything for free) has left a financial hole and created a need to generate much larger volumes of content.

These factors mean newsrooms are under immense pressure to create more journalism than ever before for more readers than they ever had, all while juggling the fact that money is tighter than ever. it hasn’t been in the past.

But this is where digitization can be a boon, giving publishers the chance to monetize their offerings and free up more time for journalists.

Take a natural disaster, which unfortunately we see a little too often these days. A custom AI piece can already create a brief article with the basic facts of the event – time, location, severity, etc. For me, this is an extremely powerful application of technology that can reduce the higher costs faced by news media companies. It is absolutely essential that news outlets publish this information quickly and accurately – but is it a waste of a journalist’s time if they have to write these basic articles? The same goes for other one-to-one stories like stock market movements, where millions of man-hours are currently consumed to produce important, yet basic, information.

Getting the facts out is important, but by embracing this technology, the media can free up their most valuable resource, their reporters, to go out into the field and talk to the people in the field. Humans talking to other humans and creating a more nuanced understanding of the situation is the most powerful form of journalism and will create more differentiation in a crowded news market. This is something that no technology is capable of, even advanced technology like GPT3 which is able to write like people.

This technology is also starting to impact the monetization side of the publishing equation, giving publishers a chance to increase their revenue.

Despite the vast array of technology solutions available to publishers, it can be easy to lump them all into one basket and think they’re interchangeable. Any company looking to use machine learning needs to think deeply about the technology that powers their business and understand the subtle differences between different platforms. By using a diverse mix, they can actually create much better results for their ad partners.

Take, for example, deep learning technology, which you might think is interchangeable with machine learning. However, deep learning is a more complex technology and is inspired by the human brain.

Machine learning, on the other hand, refers to computers learning from data and performing tasks without being explicitly programmed. Humans are able to change parameters around machine learning, while deep learning has an engine that can learn on its own. Either of these technologies can come in handy when it comes to featured articles, ensuring readers have a highly personalized and relevant online experience.

These technologies can affect how publishers set prices and list inventory. The systems “think” differently and therefore offer a wider range of solutions. This allows publishers to diversify their revenue streams and better monetize what they offer.

But if you’re not quite on board with AI or machine/deep learning just yet, don’t worry. While it’s a safe bet that we’ll see many more of these innovations, there are plenty of other opportunities for publishers to use new technologies to better monetize the news model. This is where micropayments come in.

Think back to a time when on your morning commute you bought your usual newspaper at the newsstand and saw a newspaper you weren’t used to getting with a flamboyant “exclusive” in black block letters screaming at you to buy it too. You could buy this journal for a few dollars to satisfy your curiosity.

But if the only option to buy this newspaper was to pay for all of its editions for a month, you probably wouldn’t have cared, would you? That’s what the current subscription model looks like, but I think audiences might be better served by micropayments, allowing readers to buy a story online for pennies with no obligation to subscribe for any length of time. longer.

It’s not exactly about creating a bespoke piece of AI to cover stock market news, but it’s just one way new technologies are helping publishers rethink current monetization methods while increasing income.

Most of us were caught off guard during the great leaps in technology of the past, but with the chipping away of the cookie, we have a chance to be prepared. For the news media, it’s a chance to come out ahead of the pack and set a standard for everyone else to follow.

Progress happens in the blink of an eye – so keep your eyes peeled.

Outbrain’s doctor in Japan, Masahiro (Max) Ueno