As artificial intelligence makes its way into editorial products and processes, media organizations face new challenges. They need to find out how to use this new computational toolbox and how it can contribute to creating quality content.

Do you find human-like robots creepy? You wouldn’t be the first to feel that way. ’The Frankenstein Complex’ was introduced in novels by Isaac Asimov already in the late 1940s as a representation of human beings’ intricate relationship to humanoid robots. While coined in science fiction, this term has found its footings in very real scenarios today, based on key areas of concern related to robots replacing our jobs.

The concerns are not unfounded for. There are indeed a wealth of robots (or programmable machines) employed across the globe, rendering many human workers in sectors, such as manufacturing, transportation and healthcare, obsolete. These industries are undergoing rapid transformation through the use of robotics and technologies such as artificial intelligence.

Creators and consumers of news express unease about the potential downsides of AI

These concerns extend into the media industry as well, where both creators and consumers of news express unease about the potential downsides of AI. To deal with these concerns, it is about time that we offer an alternative narrative to the Frankenstein Complex!

We might as well start with the basics. Robots are highly unlikely to enter newsrooms any time soon. What is already there, though, is a great new computational toolbox that can help human reporters and editors create and share high quality news content.

AI technologies are currently used in newsrooms in a myriad of ways, from data mining to automated content production and algorithmic distribution. While the possibilities know no bounds, the applications tend to be geared towards information processing tasks like calculating, prioritizing, classifying, associating or filtering information – sometimes simultaneously.

With recent advances in technological domains such as natural language processing and generation (NLP/NLG), the potential to leverage AI in editorial products and processes is increasing rapidly. In Schibsted, we are currently exploring the use of AI in news work in various ways, such as helping editors decide when to put content behind paywall, supporting journalists in tagging their articles and optimizing something as old school as printed papers in order to maximize sales and minimize waste.

AI learn from the past

The opportunities offered by AI are vast, but the technologies won’t help with every newsroom task. To responsibly leverage the potential of AI, reflecting on the unique traits of humans and machines becomes key.

AI systems are incredible tools for identifying patterns in data. However, this feature also renders AI technologies susceptible to reinforcing biases. And through technology such as face recognition systems and language trans­lations, we have uncovered a key limitation to AI: the learn from the past.

Journalists, on the other hand, shape the future. They introduce new ideas through stories and reporting, often subtly influencing the ways our societies and democracies progress.

In order to recognize the unique skills (and limitations) brought by both sides of a human-machine relation­ship, we need to equip ourselves with reasonable expectations. We need to stop portraying AI as flawless human-­like robots excelling at any task given to them. Instead, we should offer a narrative in which human beings are assisted by computational systems.

Let’s use a kitchen metaphor. If you are expecting an AI system to bring you a perfect omelet, you are bound to be disappointed. But if you are expecting the AI system to help prepare your ingredients – crack the eggs, grind some cheese, chop an onion – you are more likely to end up with a great lunch. You might have had to pluck some eggshells out of the mix or do a second round of onion chopping, but the overall process was smoother with the help of AI.

Training is needed

The idea of humans and machines working together is a topic gaining traction in academia, not least in the field of journalism where the term hybridization is increasingly used. One way of enabling constructive hybridization is to routinely practice decomposition. This means breaking down big news projects into smaller, more tangible tasks, so that news professionals can more easily identify what can be done by the machine and what requires human expertise.

To get to this point, news professionals should be offered appropriate training and information about the potential and the limitations of AI technologies. Introductory courses such as Elements of AI are a great starting point for anyone looking to familiarize themselves with the terminology. However, news organizations (Schibsted included) need to go beyond that and step up their game in terms of culturally and practically upskillling the workforce, aiming to bridge gaps between historically siloed departments.

We need to bring our full organizations onboard to understand how to responsibly leverage these new technologies. Schibsted is currently part of multiple research efforts at Nordic universities, such as the University of Bergen, NTNU in Trondheim and KTH in Stockholm, where we explore both technological and social aspects of these new technologies. Just as we do in academia, we need to take an interdisciplinary approach when equipping our organization with the skills needed to thrive with AI.

It is time for news organizations to take the lead in the industry’s AI developments

We put ideals such as democracy and fair competition at risk if we allow the global information flow to be controlled – implicitly and explicitly – by a few conglomerate companies. It is time for news organizations to take the lead in the industry’s AI developments. This does not mean that we need to match big tech’s R&D funding (as if that was an option…), but we need increased reflection and engagement regarding how we want AI to impact our industry, organizations, readers, and ultimately, society.

A pressing task for the news media industry is to ensure that AI in newsrooms is optimized for values that support our publishing missions. To do so, we have to stop talking about robots and focus on how newsrooms – and just to be clear, the human beings in them – can benefit from the capabilities of these new technologies. One such attempt can be found in the global industry collaboration JournalismAI run by Polis at the London School of Economics, which Schibsted is part of. There, newsrooms from across the world are joining forces to experiment and test the potential of applying AI to achieve newsroom goals. The collaboration serves as a great illustration of what would make a nice bumper sticker: Power to the Publishers!

With more and more people to feed in a less reliable world, the farming industry is the key to change. For years, the food production system has been one of the largest producers of emissions and a main reason behind reduced biodiversity. But new technologies to produce more food in a smart way are already here. Now it’s about scaling and putting things in order for sustainable food production.

In 1973, when I was born, there were 3.9 billion people in the world. Today there are 7.8 billion. Twice as many. Consider that for a minute. In 47 years humanity has doubled. And it keeps on growing.

The UN estimates that there will be 9.7 billion people on Earth in 2050, and that we will reach peak population of eleven billion in the year 2100. As if that wasn’t enough, the population growth will not be evenly spread. For example, it is expected that the population of Africa, south of Sahara, will double before 2050.

Furthermore, things are going the wrong way in many areas. Rising temperatures mean less land for growing wheat, one of the most important sources of calories in the world. More extreme weather destroys cultivation, while flooding and intense farming leads to soil erosion. The use of pesticides is ruining bio-diversity both on the surface and underneath the soil.

The timing couldn’t be worse. It will be difficult to keep pace with food production with 200,000 more mouths to feed every day, year round. The irony is that those who are producing the food are, to a large extent, the same people who are destroying the possibility to produce food. Globally, 23 percent of the man-made climate emissions come from farming, forestry and other land use, according to the UN. But the destruction caused by farming is not limited to emissions.

In September 2020, when the WWF published the report Living Planet, many people spilled their morning coffee in surprise (coffee, by the way, might be in short supply in the future). In the stock studied by WWF, there are on average 68 percent fewer animals than 50 years ago.

The main reason, the report says, is a change in area farming. The natural habitats are gone. Species are made extinct. Biodiversity is reduced. Pollinators are gone. The seas are warming and containing less oxygen, and they are being destroyed by over-fishing, pollution and contamination.

The food system is a large part of the problem, which means that it is also a large part of the solution

With today’s food system, the world is moving towards catastrophe and everything that entails, from suffering and political uproar, to conflicts, wars and migration. But the report is giving a bit of hope as well. It is possible to turn the trend around and increase the biodiversity on the ground, in the soil and in the water.

”We know that it will take a global, collective effort; increased conservation efforts are key, along with changes in how we produce and consume our food and energy”, the report says.

In other words; the food system is a large part of the problem, which means that it is also a large part of the solution. How do you produce more food in a smarter way? How can you reduce the energy consumption, throw away less food and reduce the greenhouse gas emissions? How can we use less water and fewer pesticides that destroy the microbiology of the soil? How do you use fewer fertilizers to block drainage and problems with the groundwater? And what about acreage? Is it possible to produce much more food in a much smaller place?

All around the globe, researchers and innovative centers are asking these questions. They find answers too. There are so many things going on within agricultural technology right now that there is reason for optimism. Here are three of the most important ones:

1. Biotechnology

Possibly the most revolutionary development in agriculture is occurring right now in genetic research. Earlier, genetic research has been somewhat primitive, mostly about moving DNA between various species to give plants or animals the characteristics one wanted. But changing the gene pool can be risky and that is why the opposition to so called GMO has been strong.

But developments have been rolling on and today I can hardly think of a field where the distance between researchers and the general public is wider. The agricultural sector is no stranger to genetic modification. The food plants of today and production animals are a result of generations of cross-breeding in order to have plants with the desired characteristics, which in many cases are completely different from their ancestors in nature.

The idea that modern gene modifications is ”fiddling with nature” is therefore rather confusing, because people have been ”fiddling with nature” ever since they went from being only hunters to being only gatherers who selected and grew plants with specific characteristics, about 12,000 or so years ago.

The really big breakthrough came with Crispr, the technology that makes it possible to enter DNA sequences and make changes in absolutely every living organism, whether bacteria, virus, insects, fish, human beings or other mammals. Actually, this technique has been developed by nature itself. The researchers have merely copied it. Already, seed has been developed that is resistant to fungus infection, potatoes containing less acrylic (a substance that can induce cancer), mushrooms that don’t go brown, corn that can survive a drought and pigs that are resistant to common virus infections, to mention a few.

Agrisea, an unbelievably exciting British start-up, is a splendid example of how gene technology can be used to get food to a growing population. They have developed a rice plant (and heaps of other plants, of course) that can grow in salt water and make use of the nutrients in the sea. Thus the food can be growing in a floating compound in the sea without soil, without fertilizers and without having to add fresh water which, as we know, is a scarce commodity in many places. The first test installation will be run towards the end of 2020.

Crispr opens up endless possibilities for better food production and makes it more sustainable. With plants that are more robust, one can produce more food on a smaller area, with less loss and waste. Better animal health makes for better animal welfare and less wasted feed and energy. The question is how one can secure all these improvements without losing control.

The next step for gene modification is to have regulations that make it possible put it to use. There are no international regulations. So far, EU has said that Crispr should be treated with the same level of severity as GMO with imported DNA, that is with serious restrictions. In other places in the world, like the USA, a difference is made between GMO and gene modification. Plants that could have been cultivated with traditional methods, but are improved with Crispr or some other genetic tool, are not hampered by any special restrictions. American authorities treat them as they treat all food from conventional farming.

With good regulations, gene technology has the potential to make a strong contribution to a more efficient and sustainable food production.

2. Precision agriculture

People like to think of agriculture as something a bit old-fashioned, close to nature and a constant. In reality, farming has been well on its toes when it comes to technology development dating back to the industrial revolution. Today it is common to have both milking robots and self-driving agricultural machinery. But big changes are on their way. The biggest problem with the farming technology of today is that it is too coarse. A huge field is usually treated as if there were no variation in the entire field. Even if some part can have more than enough humidity and another part too little, the field is watered equally much everywhere. Pesticides are being sprayed all over the place, sometimes even from an airplane. Fertilizers are evenly spread too. Giant, heavy trucks are driving on the fields, compressing the soil so hard that it is difficult to grow anything there.

Next generation farming is much more precise and less harmful. Unmanned planes and drones can scan the cultivating areas, collecting and analyzing data to find out which places need watering, fertilizing or spraying. Down on the ground, all-electric light robots roll along between the plant rows studying plants on leaf level, sowing or spraying only on the exact spots where that is needed – and then rolling back to charge itself.

This technology makes it possible to have large plantations with a better quality on the same acreage, and at the same time reduce the use of pesticides and fertilizers by almost 95 percent.

This will improve the soil quality, which in turn will mean large benefits. When the soil offers good conditions for microbes and living organisms, sufficient content of organic material and a good soil structure, it will be able to prevent erosion, produce better crops, create better conditions to store water and to drain off excessive water and, not least, ensure better conditions to store carbon.

3. Internet of things

When everything is connected to the net, that is because there have been strong, simultaneous developments in many technological fields. Mobile tech, location tech, sensors and data storage are only a few of these key technologies. If you combine them you can make rather funky things.
Connected sensors can obviously be used in the field. But it can also be used to establish a more sustainable meat production.

A long, long list of companies are now developing solutions that will ensure better animal health, animal welfare and yield in meat production. What many of these projects have in common is that they put a sensor on the animal, gauging the animal’s body temperature, movements and level of activity. The data is collected and treated in real time. When, for example, a cow has a slightly increased body temperature, is moving less and lowers her head, that could mean that she is about to fall ill. Such early warning signs can be next to impossible to detect in a herd, and the earlier the animal receives treatment, the easier it is to limit the passing of the infection in the stock and apply a treatment that stops a serious illness. Some systems even have a lamp on the sensor in the cow’s ear. It lights up when illness is suspected, to make it easier for the farmer to find the right cow among all the others.

To sum up, one might say that the constantly growing human population is facing an enormous challenge. Biodiversity must increase. The protection of natural habitats must step up. The soil health must improve. Plants and animals must become more robust. The production must be more reliable in a less reliable world.

I don’t think it will happen through innovation. The solutions are here already. It is rather the scaling that will cause a problem. To make authorities invest, regulate and put things in order for sustainable, efficient food production and protection of nature. If they do this, they are contributing to saving the world, no less.
You reap what you sow.

Artificial intelligence is behind countless services that we use every day. But how close is it to really understanding human emotions? Affective computing has already come a long way – and as in many areas, big tech is in the lead.

A somber, suited man stands in a cemetery. Softly, he strokes a gravestone before throwing his arms up toward the sky, howling in sorrow. The inscription on the stone reads:

Clippy. 1997 – 2004.

The scene is from a Microsoft commercial for their Office software. In reality, however, few people mourned the demise of the paper clip-formed Office assistant, tasked with aiding Microsoft users in their screen work.

Unfailingly pseudo-helpful, Clippy may be the most ubiquitously reviled piece of software ever created. Not because a digital assistant is inherently a bad idea, but because its tone-deaf servility pushed Microsoft users closer and closer to insanity.

Designed to respond intuitively

Ever since computers became everyday tools, tech companies have been investing heavily in improving the ways humans and machines interact. We have gone from the days when using a computer required impressive technical skills and hours hunched over dense user manuals, to the plug and play era where software is designed to respond intuitively to our needs and wishes.

Even so, digital computers and human emotions have never gotten along very well. Too many computer engineers have made the cool rationality of computers the standard to which humans need to adjust. But as algorithms become more and more intertwined with every aspect of our lives, things are changing. For better and for worse.

In 1995, the American computer engineer Rosalind Picard wrote a pioneering paper, ”Affective computing”, about a nascent research field investigating the possibilities of computers learning human emotions, responding to them and perhaps even approximating human emotions to more efficiently make decisions.

Any algorithm that takes human behavior as input is indirectly responding to human emotions. Take Facebook for example, and the way its algorithms feed on human agitation, vanity and desire for companionship. Their algorithms systematically register the actions these emotions trigger (likes, shares and comments, commonly referred to as engagement), and then attempt to amplify and monetize them.

Making tech less frustrating

The field of affective computing, however, is ideally less about manipulation and more about making tech less frustrating and more helpful, perhaps even instilling in it some semblance of empathy. Counter-intuitively, one key to making affective computing work well may be to avoid anthropomorphizing the interface. Humanizing Clippy did not make people relate better to their Microsoft software, quite the opposite. And while chat bots are popular among companies hoping to slash customer service costs, for customers they are less like magically helpful spirits and more of a needlessly convoluted way of accessing information from an FAQ.

Affective computing endeavors to understand us better and deeper, by analyzing our calendars, messaging apps, web use, step count and geolocation. All this information can be harvested from our phones, along with sleep and speech patterns. Add wearable sensors and cameras with facial recognition, and computers are getting close to reading our emotions without the intermediary of our behavior.

In the near future this could result in consumer technology such as lightbulbs that adjust to your mood, sound systems that find the perfect tune whether your feeling blue or elated, and phones that adjust their notification settings as thoughtfully as a first-rate butler – just to name a few possible applications. It could also be used for surveillance of employees or citizens, for purposes malicious or benign.

Rosalind Picard is currently a professor at Massachusetts Institute of Technology, running the Affective Computing Research Group. She is also the co-founder of two groundbreaking startups in this space: Affectiva in 2009 and Empatica in 2014. Through her work she has become keenly aware of the potential to use affective computing for for both humanitarian and profit-driven purposes.

Affectiva’s first applications were developed to help people on the autism spectrum better understand facial expressions. Later the company developed technology to track the emotional state of drivers. And after Picard had moved on to form Empatica, a company hoping to address the medical needs of epilepsy patients, Affectiva has been attracting clients like Coca-Cola – who use the technology to measure the effectiveness of their advertising – and political campaigns who want to gauge the emotional response to political debates.

Simulate human emotions

Microsoft’s doomed Clippy was neither the first nor the last anthropomorphized bundle of algorithms. Robots have often been envisioned as synthetic persons, androids that understand, exhibit and perhaps even experience human-like emotions. There are currently countless projects around the world in which robots are developed for everything from education and elderly care to sex work. These machines rarely rely on cutting-edge affective computing technology, but they nevertheless simulate a range of human emotions to please their users.

If science fiction teaches us anything about synthetic emotion it is a bleak lesson. Ever since the 19th century, when a fictional android appeared in Auguste Villiers de l’Isle-Adam’s novel ”The Future Eve”, they have tended to bring misery and destruction. In the ongoing HBO series ”Westworld”, enslaved robots rise up against their makers, massacring their human oppressors. In the acclaimed British author Ian McEwan’s 2019 novel ”Machines Like Me”, the first sentient androids created by man gradually acquire human emotions, and then commit suicide.

Of course, we should celebrate the ambition to create software that adjusts to our needs and desires – helps us live and learn a little bit better. But it is worth keeping in mind the failure of Clippy, and perhaps even the warnings from concerned science fiction writers. More than that: at a time when big tech companies are hoarding personal data and using that data to manipulate us, affective computing will inevitably be a double-edged sword. After all, why should we trust Facebook’s or Google’s algorithms to ever understand empathy so long as the companies themselves show little capacity for it?

Technology is giving us tools to alter reality in more and more areas. You might soon not only eat artificial meat but also interact with your personal double. And – not least consume more and more information created by AI. Welcome to the Synthetic Decade.

The idea that we’re entering a new era, is established by futurist Amy Webb and her team at the Future Today Institute. She states: ”Not only will we eat beyond burgers, but we will consume synthetic content, or train the next generation of AI with synthetic data sets”.

Recent developments within AI, prove them right. AI will impact the way we consume, get informed and envision health and life span. It’s not in a distant future, and you might already have encountered what is now defined as ”synthetic content”. If you’ve ordered a beyond burger you had synthetic meat, if you used a face swap filter on your phone you produced synthetic media.

Editing DNA

As we will progress into the synthetic decade, synthetic experiences and relationships will shape greater parts of our life. A really good example is the development of synthetic biology and the ability to engineer living systems and structures, by programming DNA with Crispr, to design and re-design organisms to do what we want them to do. Editing DNA is possible since 2010, but it is a very laborious task. Synthetic biology promises to automate the editing process. As Amy Webb puts its ”In this, decade synthetic biology is going to allow us to read, edit and write life. We will program living biological structures as we build tiny computers.” This is not science fiction, and we can envision many positive use cases for improving our own health and life span, and also helping our living structures adapt to new conditions such as global warming or pandemics.
Looking into one of these fields – synthetic media – many of the trends behind the synthetic decade are uncovered. It has started to unfold, and it tells us a lot about the potential outcomes and the many questions it triggers, blurring the line between what we consider ”real” or ”virtual” even more.

2017 was a landmark for synthetic media, with Vice reporting the emergence of pornographic videos altered with the use of algorithms to insert the faces of famous actresses. The term ”deep fake” was coined soon after, bringing a lot of attention to the phenomenon and its harmful potential for misinformation. It then triggered a fundamental discussion, that will likely be at the core of synthetic media, about ethics and the potential harm around the ”forgery” of content through AI. A very famous example is a deep fake video of Obama, created by Buzzfeed and enacted by Jordan Peel, warning us that ”We’re entering an era in which our enemies can make anyone say anything at any point in time.” – and indeed we are!

The potential impact of synthetic media lies in the automation of editing

Synthetic media is the term used for content created using artificial intelligence. With an initial set of data, algorithms learn to reproduce, and create, pictures videos, sound, gestures, text and more. The result is realistic-looking and sounding artificial digital content.

Looking closer at the tech behind synthetic media, the past few years have shown significant advancements in deep learning and generative adversarial networks (GANs) have accelerated their growth. Synthetic media is mostly based on GAN technologies, even if there are many different techniques being developed. This has resulted in the quality of synthetic media improving rapidly, and soon it might just be indistinguishable from traditional media.

The potential impact of synthetic media lies in the automation of editing which makes it possible to create content at scale. The cost to create synthetic media has considerably lowered due to the wide availability of the techniques. Open source software already enables any­one with some technical knowledge and a powerful-enough graphics card to create a deep fake. This has led to a drastic improvement of synthetic media quality (check out thispersondoesnotexist.com), without countless tedious hours of work.

A meaningful trend

If we also think about new behaviors such as how we consume media on social channels, how we expect even more personalization and accessibility or the fact that we have normalized virtual spaces for socializing (see the rise of Fortnite, or Animal Crossing as social media during the quarantine period), we have a very favorable ground for synthetic content to be a meaningful trend and impact the way we create and consume content online.

This again raises the familiar question if synthetic media is bad. It is a delicate yet fundamental question, and the answer is the same as with most tech: it’s not harmful in itself, it depends what we are using it for. Synthetic media has a lot of potential because it is not just deep fakes, there is a growing interest in how it could be used to support new business and creative areas. The industry around synthetic media is blooming and many companies and investors are looking into the trend, believing strongly in its future.

For now, entertainment applications are the entry point for larger audiences. We all have the possibility to create synthetic media in our pocket today. For example, Snapchat released their gender-swap filter in 2019. Russian app, Faceapp made us look older and in China ZAO released a deep fake app that can engrave the user’s face into some clips from famous films or series. It’s not hard to imagine the next iteration of a social media app being one where users can transform their voices, create their own synthetic character, or pretend to be their favorite celebrities.

Synthetic media could become a leverage for the media industry

But it’s about more than just entertainment – synthetic media could become a leverage for the media industry starting with automated news reporting and delivery.

In today’s newsroom, some types of reporting are extremely tedious and straightforward – human opinion and effort are not adding value. Weather reporting is a very good example. In the UK, the BBC blue lab has been exploring how synthetic media could help weather reporting. Given the growth of digital assistants and the industry’s drive for greater personalization, they are betting that in the future, we might expect that a video response to a query will be digitally generated. To try this out, the editorial department collaborated with the AI firm Synthesia and created an experiment where the presenter reads the names of 12 cities, numbers from -30 to 30 and several phrases to explain the temperature, to the camera. You can then pick your city and get a personalized, but synthetically created weather report.

Within Schibsted several of our media houses have simpler, but also automated services, reporting on weather, sports and real estate.
Another application that is very promising is automated, real time translation and dubbing. In that field, Synthesia is one of the most prominent companies looking into real time automated translation, with use cases ranging from education to customer service.

With improvement in synthetic voices, we can also imagine a rapid adaptation of voice technology in traditional media production pipelines. Particularly in video games and audio books which are markets that today face significant challenges scaling human voice over. Overall synthetic media could be a powerful technology for businesses that are reliant on content and would like to adapt their offering to different audiences. Today what would require many hours of work could be done through synthetic content creation.

Texts and dialogue are prominent use cases of Synthetic media. Hence, we are seeing the development of more realistic and accurate conversational and companionship technologies. From a simple bot, which generates a tailored conversation to a virtual double, the potential for service or leisure conversation opens up.

Having a conversation with an AI

Right now, most of our interactions with AI are transactional in nature: ”Alexa, what’s the weather like today”, or ”Siri, set a timer for ten minutes”. But what about developing a profound conversation with an AI? A stunning example is from a conversational bot called Replika which is programmed to ask meaningful questions about your life and to offer you emotional support without judgment. Since its launch more than two million people have downloaded the Replika app.

Digital assistants could be used for companionship purposes, but also education or training. It could for example help us recreate a learning environment, especially when working remotely. What if you could interact with simulated persons to learn from them or practice management techniques? And – would you invite a synth to a dinner party?

For all of this to happen and to convince us to interact with our virtual counterpart, the improvement of virtual human character and emotional response is crucial. The more these companions will look, talk and listen like humans the more we will be inclined to interact with them. For example, Samsung’s virtual human ”Neon” which they describe as their ”first artificial human” is here. These Neons can go out-of-script and develop their own ”personality”. It can generate new expressions, gestures, and reactions out of this unique ”personality”.

Producing quality synthetic content is still very costly and tech intensive, but companies that specializing in synthetic content are emerging, allowing businesses and individuals to buy and rent synthetic media.

Synthetic media is rather new and it’s moving fast. So fast that regulation has not followed yet. Whether it is about deep fakes, synthetic voices used for customer service, or entertainment pieces we will need to lay some ground rules about the ownership of such content and establish the responsibilities that come along. So far, many questions are still left unanswered such as, who will ”own” the content produced? How will copyright laws apply on a reproduction of a celebrity? Who would be held responsible if a digital assistant hurts someone in real life?

Still in early stages

Synthetic content has already made its way into our lives. But not all part of its ecosystem is moving at the same pace. The synthetic media sub trend, has already emerged to mainstream audiences, the technology powering it has left the research lab to find very concrete business applications. From strong ethical fears, to concrete valuable use cases, this development tells us a lot about the potential trajectory, outcomes and questions of the synthetic decade. Other areas such as biology are still in early stages, but their applications alter our lives even more profoundly. Overall, the technology underlying synthetic media, synthetic biology and other fields of synthetic content – namely AI, computer vision, deep learning etc., are the same. This means, the early questions that have risen with synthetic media are indicating the fundamental discussions we will face during the synthetic decade. In every field will arise interrogations and debate around the rights to edit, create and use what is created, determining ownership of the content, what is considered ethical or not. This also means we still have some agency to decide what comes next, and the synthetic decade to come will not necessarily be dystopian.

Get an overview of some of the most interesting ongoing tech trend with Schibsted’s top ten trends-list!

1. Splinternet: our new, fractured online life

The web is dead, so said the cover of Wired Magazine in August 2010. Ten years later it is still around, but there is no denying that its original form – the free, global, hyperlinked internet – is a thing of the past. Governments around the world are increasingly asserting control of the digital realm. China’s ”Great Firewall” and other censorship efforts are prime examples. Other countries are in turn responding to China’s global ambitions by banning Chinese apps. India banned 59 Chinese apps in July, and the US threatens to ban some of the most popular: Wechat and Tiktok. Meanwhile, as the EU is trying to make American tech companies play by the rules of GDPR legislation, Facebook recently responded that they may leave the EU if they cannot store Europeans’ data on American servers. Threat or promise? Many European tech startups would no doubt be thrilled to see Facebook go, hoping for a chance to create new social media platforms for European users.

2. Rise of the super apps

In Asia, ”super apps” collect many services within proprietary eco-systems. Wechat, Alipay, Grab and Gojek all compete in this space; Wechat is the front runner with over one billion monthly users and one million mini-apps on its platform. Watch out for companies like Amazon and Facebook trying to bring this ­winner-take-all trend to the West.

3. Shopping goes online

The pandemic has forced many brick-and-mortar stores to close, but according to data from IBM, this has fast-forwarded e-commerce growth by about five years. As new user groups learn to shop everything from groceries to fashion online, the pressure is on retailers to up their game, offering friction-free payments and same day delivery.

4. Games become social

Online gaming has been a refuge during the pandemic. For gamers around the world, hits like Fortnite and Animal Crossing offer more than just game mechanics. The games themselves, as well as Twitch streams, Youtube play-by-plays and Discord gaming chats, are spaces of connection and camaraderie – important forms of social media.

5. 5G changes everything

The battle for 5G supremacy may have stolen the spotlight for now, with several countries banning Huawei from their telecom infrastructure. But the more interesting news is that 5G speed (and bandwidth) changes the game for augmented reality and the internet of things. Finally, these hyped technologies have a chance to live up to our expectations.

6. Dining in  the cloud

We may not meet up with friends at the restaurant as often these days, but we still need to eat. Thus, delivery services such as Foodora and Uber Eats are keeping busy. There is also new opportunity for nimble food startups, foregoing dining spaces (and expensive rent) and instead setting up efficient kitchens and selling food online to stay-at-home diners.

7. Deeper authenticity

Our lives are increasingly cloud-based and intertwined with algorithms. Despite this – or because of it – research shows that millennials crave authenticity: real people, real connections. The race is on to solve digital identity, ensuring users can own their online identity and data. And it may be a race where blockchain tech finally wins out.

8. No hiding from Big brother

500 million surveillance cameras track the Chinese people, along with a country-wide network of human informers. The social credit program aims to create a record of the entire population’s trustworthiness. Just wait for security-minded western politicians, and managers eager to check in on their work-from-home staff, to take a page out of the Chinese playbook.

9. Deep fakes

In September, The Guardian published an op-ed on why the human race must not fear artificial intelligence. The twist? It was written by an AI. Synthetic media is not coming, it is already here. Time to get used to AI-generated text, audio and video, created by learning algorithms, powered by engines from companies including OpenAI and Deepmind.

10. Smile for the camera

We waited decades for video chat to take off, in schools, in the workplace and just for fun. The pandemic was the tipping point that finally made video tech like Zoom, Skype and Google Meet indispensible everyday tools. Watch this space for a burst of innovation as screen sharing and fun filters evolve into sophisticated AR applications.