Human-level machine intelligence would turn out to be far harder to achieve than the ­early ­pioneers imagined. During the ­following ­decades, AI hype would be followed by AI winter in a cyclical pattern. Several prominent AI researchers, among them Marvin Minsky, had predicted human-like ­intelligence by the 1980s. When those predictions were proven wrong, some were deflated, but the Japanese government was eager to have Japan take the lead. In 1981, Japan initiated the Fifth Generation Computing Project, pouring 850 ­billion dollars into AI research, with the ­stated goal of creating machines that could carry on conversations, translate languages, interpret ­pictures and reason like human beings.

Progress was made during this time, ­primarily with limited systems tuned to play chess or give ­expert advice in narrow fields of inquiry. But anything even vaguely resembling the dynamic and wide-ranging intelligence of humans remained out of grasp. After the Fifth Generation Project came to an end, without fulfilling its promise, the field again found itself at a low point late in the 1990s.

Luckily, an offshoot of AI research was about to gain new traction. In parallel to the mainstream research going on at prestigious universities, well-funded government programs and hyped-up companies, some scientists had taken an interest in artificial neural networks. The network architecture was thought to resemble the human brain, offering new ways of representing machine thoughts compared to the strictly algorithmic manipulation of symbols of conventional AI systems. A neural network could be trained on appropriate data sets, much like a child learns, until its maze of internal connections becomes suitable for its designated tasks.

A fatal flaw

Artificial neural networks had a fatal flaw, however. As soon as you started to scale a network to do something ­interesting, its complexity increased exponentially, and the system ground to a halt. The computer hardware of the time, with architecture very different from human brains and far less processing power, simply could not keep up. So, this line of research remained theoretical, dormant for decades until, deep in the 2010s, the time had come for the AI field to enter a new era of machine learning.

Three developments of the new millennium came together to finally make neural networks practical:

Computer hardware kept getting faster, smaller and more energy efficient, as predicted by Moore’s Law.

Computer scientists developed more ­sophisticated architectures and algorithms for artificial neural networks.

An immense trove of digital text, images and sounds accumulated online, an all-you-can-eat buffet of information for neural networks to be trained on.

Looking back at what was then envisioned, artificial intelligence is finally living up to its name.

With the recent work of DeepMind, OpenAI, Google and Microsoft, we arrive at today’s state of the art. Artificial intelligence may have missed the deadline of Japan’s Fifth Generation Project, but looking back at what was then envisioned – or indeed, what the Dartmouth Workshop sought to achieve – artificial intelligence is finally living up to its name. ChatGPT and its rivals can easily hold conversations with humans; Google Translate and its ilk can translate text and speech in the blink of an eye; and many neural networks not only ­interpret images but also create beautiful pictures from natural­-language prompts.

Several fundamental questions do remain, however. Can these machines truly reason? Can they think? Can they feel? Will they ever?

The French seventeenth-century philosopher René Descartes famously formulated a dualist theory where mind and body are metaphysically separate. He was inspired by the automatons on display in Paris at the time and concluded that mind and body must be different substances. The latter can be replicated by automatons, while the former is singular to man and intimately tied to what makes us us. We think, therefore we are.

Unexpected leaps

With AI science advancing – at times inching forward incrementally, sometimes striding with unexpected leaps – software engineers are getting closer to imitating the human mind as well. Chat GPT has repeatedly ­defeated the Turing test, designed by the British computer pioneer Alan Turing to settle the question: “Can machines think?”

Refined algorithms, humon­gous data sets and ­clever reinforcement ­learning techniques are pounding at the walls of ­dualism. Perhaps, as the Dartmouth Workshop proposed, the ­human mind is a mere machine after all. And if this is the case, why would we not be able to replace it with more efficient machines?

The history of ­artificial intelligence is a tale of scientific progress, of engineering failures and of triumphs. But it is also the story of our struggle to understand our own minds. Are we truly unique? Are our brains, like our ­bodies, simply machines governed by ­electrical ­impulses? When we dismiss the “thinking” of large ­language models as simply a series of predictions of what comes next, are we absolutely certain that this does not also apply to ­human minds?

It seems inevitable that we will soon be able to create genuine thinking machines – if we haven’t already.

At this point (as at every point in the history of AI) it seems inevitable that we will soon be able to create genuine thinking machines – if we haven’t already. There is still some disagreement about whether we can create feeling machines, however. Conscious machines. Machines that can do and experience everything that a human can and more.

Some aspects of this may be harder than we can foresee. On the other hand, it may be within our power sooner than we think, emerging incidentally as our models become increasingly complex, combining techniques from neural networks with symbolical AI.

Mary Shelley would be delighted to see ­modern scientists still hard at work ­trying to realise the ancient dream of godlike ­creation. The full original title of her ­famous horror novel is “Frankenstein; or, The Modern Prometheus.” The subtitle alludes to the crafty Titan from Greek ­mythology who stole fire from the Olympian gods and gave it to man. Fire, in this old tale, symbolises knowledge, invention and ­technology – powers Zeus had deter­mined must be reser­ved for gods and withheld from humankind.

In some versions of the myth, Prometheus gives us more than fire; moulding the human race from clay, he also gives us life. Millennia later, the fire he gave us is still burning bright, and we are now the ones doing the moulding. Not from clay, but from code.

And she believes that ChatGPT is a perfect example of how bad things can go when you share “fun” new techno­logy openly, without having had ­discussions about its implications.

“I believe we have a serious problem when pupils are now thinking, ‘Why should I write a text if there is technology that can do it better?’ How will we now make young people understand that an idea starts with a thought in your head and that you need to grow and communicate that idea to get other people to understand it? And if you can’t do that, then you won’t be able to function in society.”

That might sound just as dystopian as the future scenarios. But her point is that we can and we must take the lead here in the Nordics and in Europe when it comes to discussing the role we want technology to play.

“If we can’t manage to figure out how to use what we develop here, then we will end up using solutions developed by tech giants that we are unable to influence.”

Impact across the society

And these discussions, she says, should involve experts from across the board – politicians, social scientists, economists, legal experts, ethics, apart from technologists – since the impact will be felt across all parts of society.

This is also why she thinks it’s so important that as many of us as possible understand what this is about and how the technology works. The things she explains in her book.

“My dream is that anyone can read it. That a woman past 80 would read it and think, ‘I can understand this if I want to.’ I have this passion to ­empower people on this subject. To make them see that they can look after their own interests.”

New class issues

What also has become clear to her, in discussions after writing the book, is that AI can spur a new kind of class issue. That the world might be divided between those who are able to use the technology for their own benefit and those who aren’t.

“Someone said that AI will not take the journalists’ jobs. But journalists who know how to work with AI will take the jobs from the journalists who don’t.”

Inga got into the science world as a physicist. She wanted to understand the universe. Then, when she took her bachelor’s at NTNU, she noticed there was a field of study on artificial intelligence, and curiosity led her that way.

“My driving force is to find out what is possible. The main reason that I’m still in this field is that I see the consequences, and they are extremely complex.”

Even though she thinks about this complexity day and night, she also finds time to use that ­curiosity and energy on other things – mainly outdoor activities. Her social media is filled with pictures of her mountain biking, climbing and hiking. And those things are important.

“No matter what happens with technology and politics, there is one important thing that we can’t forget: to have a nice life. Human happiness must be our common goal – if not, nothing else matters. That’s very important to me to remember, every day.”

The toe is broken

I was once playing with my son. I chased him around the apartment, arms straight ahead, like a zombie. As I made my way around the kitchen ­table, my little toe hooked onto a chair leg. There was no doubt that the toe was broken. It pointed straight out to the side. Still, I spent most of the day in the emergency room.

The reason for this was a bottleneck in the system.

When people come in with ­minor fractures or just a suspicion that they’ve broken something, for that matter, an X-ray is taken. A doctor (the bottleneck) must study each individual image to see if there is a fracture or not. If there is no fracture, the doctor sends the patient home. If there is a fracture, the patient is placed somewhere on a priority list.

However, minor fractures are not life-threatening. If there is a lot to do in the emergency room, the X-rays will be given low priority until there is more time to look at them. When the doctor finally has time, he or she will study the picture and determine that there is no fracture – in about 70% of cases. The patient, who by then may have waited seven hours, is then told to go home, take two ibuprofen and three glasses of red wine (which my dentist once recommended), and turn on Netflix.

It’s things like this that artificial intelligence can solve much faster and better. And it’s already doing it, actually.

Level up

The other day I was visiting a ­hospital in Bærum, just outside Oslo. An ­enthu­siastic, young, bearded radiologist pointed to an X-ray image on a screen in front of us. The picture showed a foot, and it looked quite similar to the picture taken once upon a time of my foot (except that the little toe didn’t point straight out to the side).

But one thing was different. The ­image had been assessed by an arti­ficial intelligence.
Above the ankle bone, a yellow square had been drawn, lined with the text “FRACT.” That means there’s a fracture there. The software goes through all the X-rays as they come in. Seven out of ten patients are told immediately that they can go home. The rest automatically end up in a priority queue.

Doctors do not have to spend valuable time finding out that everything is okay, and patients do not have to wait. This is an extreme efficiency improvement in a health service that will experience greater and greater strain in the decades to come.

Should this have been banned? Some think so.

Sense and sensibility

A few months earlier, two Norwegian politicians warned that artificial ­intelligence leads to everything from polarisation to ­eating disorders, and perhaps even the extinction of humanity. The government should immediately “impose a public ­sector moratorium on adopting new ­commercial tools based on artificial intelligence,” they argued.

This is an absurd approach to ­artificial intelligence. The pressure on the healthcare system ­only increases as people age. To have any hope of maintaining good health services for the population, we must make use of the tools at our ­disposal. The AI tool at Bærum Hospital ­happens to be delivered already ­fully trained from abroad. All patient data is ­deleted, so as to avoid all privacy issues. Of course, there shouldn’t be a ban on such things. But the two politicians still had a good point:

“The development of AI has for a long time taken place ­without ­adequate regulation from the authorities.”

Now it’s happening

There has been a Wild West approach from the tech companies. Naturally. Development is rapid, and work on laws and regulations is slow. But the EU has been working diligently on the issue.

The EU’s first draft regulation of ­artificial intelligence, the so-called AI Act, was presented two years ago. It is likely to be formally approved ­within 2023. The EU is adopting a risk-based approach. For ­services that pose a low risk, it’s full speed ahead. Unacceptable risk means it’s prohibi­ted. And for everything else in between, there are two more levels: acceptable risk and high risk.

The purpose of the AI Act is to ensure that artificial intelligence ­systems are safe and respect fundamental rights and values. This means, for example, that facial recognition in public spaces is banned. It’s not ­allowed to single out citizens for the authorities to keep an eye on in case they do something illegal. Stuff like that.

AI should be open and honest, not closed and manipulative. The resistance the AI Act has faced from tech companies suggests that regulation is needed. For example, Sam Altman, the man behind OpenAI and ChatGPT, has threatened to pull out of Europe if the regulations become too extensive.

Perhaps now it’s time to revisit the crystal ball.

A willingness to solve problems

In September 2023, Norway’s Prime Minister, Jonas Gahr Støre, held a press conference where he proudly announced that his government would allocate one billion Norwegian kroner to artificial intelligence research, to be used over the course of five years. On the same day, the government leaked that it would spend five billion on a new tunnel to shave a few minutes off the drive between two villages in the mountains of western Norway somewhere. But OK, a billion is money too.

A large and important part of the ­research will focus on how artificial intelligence can be used for innovation in industry and in the public ­sector. Like in hospitals, when people come in with sore fingers and toes. Or in building applications, so people don’t have to wait several months for the overworked caseworker to get far enough down the pile. Or to provide public services with a faster, larger and better basis for decision-making. Or to improve data security, in fact, and not worsen it.

And in so many other ways that I can’t possibly imagine.

That’s what politics is all about. To follow social developments and ­govern society in a way that makes it as good as possible for as many ­people as ­possible. Norway is just an obvious example because that’s where I live. The same goes for every other country and continent, and globally, for that matter.

As in other areas of society, international resolutions and treaties and sanctions must be adopted to ensure that artificial intelligence is used in a way that solves humanity’s problems, rather than create new ones.

That work is underway.

OK, here’s what the ­crystal ball says

If I’m going to allow myself to try to look 27 years into the future, to 2050, I’d guess that people are more concerned about themselves and their nearest and dearest, and not so much about what people were thinking back in 2023. But those who bother to read old newspapers might chuckle a bit at the banal discussions we had about artificial intelligence ’way back when.’ And the fact that many were either for or against. Maybe it’ll be the demand for a ban and the call to halt development that everyone will laugh at (try asking Putin to stop the development of ­artificial intelligence, by the way).

I’m guessing that my future grandchildren will experience an education system much more attuned to each student’s learning disabilities, learning styles and skills. That their health will be taken care of much better than by the GP they see every two years. That potential health problems will be discovered before they become major and serious. I’m guessing the car will be a safer driver than the human. That public transport will be much better adapted to people’s needs. That ­precise weather forecasts will control the heating in houses. That everyone will be better protected from abnormal activity, whether it’s in their bank accounts or in their apartments. Maybe I won’t have to think about shopping for food or cleaning the house anymore.

I’m guessing it will seem strange that society spent so much time and resources on having people perform repetitive and simple tasks. And that major and important decisions were made on a razor-thin knowledge base.

I am absolutely certain that ­artificial intelligence will be subject to inter­national regulations. And that artificial intelligence will lead to global, ­regional, local and personal changes that are difficult to imagine today.

Because by then humanity will know better.

If, of course, it still exists.

Several academic studies have ­already explored the impact of ­using ChatGPT across various professions, including law, customer support, ­programming, and creative writing. The consensus is clear – AI significantly enhances the productivity of lower-performing individuals, enabling them to accomplish more with better quality. High performers see less improvement, and in some cases, even a drop in productivity. Interestingly, early indicators suggest this productivity boost is consistent across many, if not all, white-collar disciplines.

This can be attributed to two primary factors. First, chatbots have become remarkably adept at simplifying complex tasks. Second, Gen-AI ­enhances creativity. While there’s ­ongoing debate in the scientific community about whether large language models can truly be creative, from a ­productivity standpoint, this is a moot point. After ­experiencing ChatGPT’s ­“creativity,” it’s clear that it’s quite adept at it.

Something bigger

But is the so-called AI revolution merely about increasing productivity by using ChatGPT and its counterparts in office work? Or is there something bigger at play here?

Comparing the CPU, the ­central processing unit of a ­computer, with the human brain, we find that they complement each ­other ­remarkably well. The CPU excels at rapidly executing instructions provided in code with structured data – tasks that humans find challenging.

Conversely, we humans excel at learning, a capability entirely absent in a CPU. We possess agency, intuition, creativity, and are multi-modal, meaning we process input and output through most of our senses.

The LLM sits somewhere between these extremes. It’s as fast as a CPU, but also capable of learning in the sense that it can be trained and fine-tuned. It possesses contextual understanding, a characteristic more akin to our brains than a CPU.

Low costs

The key takeaway is that we now have access to human-like intelligence at nearly zero cost. It’s more than just about chatbots. Large language ­models enable us to infuse human-like analysis, creativity, decision-making and more into workflows and processes at virtually no cost.

With this perspective, the advancements we’ve made in the past 50 years will likely pale in comparison to what we’ll achieve in the next 50 years, or even the next 15 years, for better or worse.

How can all of this play out more concretely, in one of Schibsted’s core business areas – news media?

The answer to this is that its practical implications will be vast and far-reaching. The expected transformations will challenge the very core of our traditional business models. To grasp the full breadth of AI’s potential impact, let’s first consider the fundamental business structure of the industry.

Most online businesses can be ­simplified into three core activities:

• Creation of goods
• Customer acquisition
• Distribution of goods
• From a financial perspective, these activities respectively translate into:
• Cost of goods sold
• Sales and marketing expenses
• Other operating expenses

Historically, the advent of the internet drastically reduced distribution costs in the news media, triggering substantial shifts in how content reached consumers and removing most barriers to entry into the market. Now, as we usher in the era of AI, we stand on the precipice of another profound change: a potential collapse in content creation costs. The ramifications of such a shift could be as transformative, if not more so, than the internet’s earlier influence on the business models and the broader ­industry landscape. In the short term, I predict several phenomena that are set to drive our transformation:

Democratisation of programming

Anyone can develop software using tools like ChatGPT and Replit. All it ­requires is a bit of curiosity and cou­rage. This democratisation signifies not just more efficient programming, but an increase in the number of programmers, which will further accele­rate digitalisation and innovation. As Sam Altman, CEO of OpenAI, puts it:

“I think the world is going to find out that if you can have ten times as much code at the same price, you can just use even more.”

Automation of content creation

Content with predictable production processes and performance, often format-driven, such as news summaries, listicles and interviews, will likely be generated either entirely by AI or more commonly in collaboration with journalists.

Unbundling of research and narrative

Traditionally, journalism involved ­researching facts and weaving them into a cohesive narrative. With AI, we can separate these processes. For instance, we can publish research material alongside articles, enabling readers to explore the underlying ­research through an AI-driven chat interface. Newsrooms may even have teams dedicated solely to establishing and verifying facts and other information building blocks, which are then used to automatically create content using AI.

Writing of previously unwritten stories

Many individuals possess important stories that remain untold due to a lack of competence in content production. With AI, these barriers between ­lower and higher performers are ­reduced, ­allowing many more voices to be heard.

Personalised consumption

Every individual has unique consumption preferences. With AI’s ability to transform text into various formats, we can cater to these individual needs more effectively, especially when mastering the arts of unbundling ­research and narrative, as well as the automation of content creation.

With the collapse in costs and barriers in distribution and content creation, customer acquisition becomes the primary competition area for both new and incumbent brands.

To succeed in this new paradigm, I’ve identified at least four distinct, but not mutually exclusive, strategies that news media brands can deploy.

1. Creating an addictive product

Develop a service so engaging that it captures users’ attention far more than traditional news outlets. The prime ­example is TikTok, which holds users’ attention for an average of 90 minutes daily. Achieving this is extremely, challenging, likely impossible, but the payoff is tremendous if accomplished.

2. Fostering a movement

Tap into deeper emotions such as fear and hope to capture audiences’ energy and passion, generating extraordinarily high engagement and loyalty. Fox News, for better or worse, has done this. There is no doubt that in these times of high uncertainty, audiences are yearning for hopeful narratives.

3. Nurturing a trusted brand

This is the go-to strategy for established brands. Establishing and maintaining credibility in an era of information overload should be rewarding. However, in a future hostile media landscape, no matter how strong the brand is, brands will require greater degrees of discipline, transparency, and accountability than before.

4. Building a community

In a world of increasingly personalised experiences, individuals will seek shared interactions and rewarding experiences. This insight isn’t new for news media, but most previous ­attempts to build communities encountered scaling issues as the community grew, leading to its downfall. This para­dox may be resolved if we can leverage AI to address the challenges that arise as the community expands.

Technology of the present

AI is not a technology of the future anymore; it’s very much a technology of the present. Every media organisation must actively engage with AI tools and platforms. Training your teams on platforms like ChatGPT or similar AI tools can lead to innovative storytelling techniques, streamlined content production, and a deeper understanding of audience behaviour.

On a personal level, embracing this new paradigm means integrating AI ­into your daily routine. You need to incorporate it into your life to such an extent that you automatically turn to it whenever you face challenges that require collaboration, or that can be solved faster and more effectively than you or your colleagues can do on your own. Only when it becomes an integral part of your life will you be able to fully understand it and its potential.

Rethink the pipeline

The barriers to software development are being lowered every day. Embrace this demo­cra­tisation by encouraging your teams to experiment. Host inter­nal hackathons or workshops. Foster a culture of prototyping; this not only breeds innovation but also promotes a fail-fast mentality in which learnings are quickly integrated.

With AI’s capabilities, media orga­ni­sations have the opportunity to ­rethink their content production pipeline. Centralising certain production ele­ments can help maintain ­consistency while leveraging AI can ensure content is tailored to audience preferences. Moreover, AI can assist in identifying content trends and predicting audience interests.

The transformative power of AI in the journalism industry is ­undeniable. We stand at a crossroads, facing a horizon with enormous uncertainty, limitless opportunities and inevitable challenges. The technological power that AI presents has profound implications on how we produce, distr­ibute and consume news. As AI shapes a new paradigm for humanity, it becomes imperative for the journalism industry to not just adapt but lead the way. By wholeheartedly embracing AI, media brands can redefine their narrative in this new era. This journey won’t be without pitfalls, but the ­rewards – both for the industry and society at large – are immense. The future of journalism, powered by AI, awaits.