We are entering a new era of medicine, where AI will revolutionise healthcare and treatment. It might also lead to more empathetic doctors.

Pancho was 20 years old when he was paralysed and lost his ability to speak. He was a Mexican-born amateur football player and a field worker in the California vineyards, until one summer Sunday after a match, he was in a car crash. After surgery, he suffered a stroke and his life changed.

When he woke up from his coma, he tried to move. He couldn’t. He tried to talk and discovered he couldn’t form a word. Then he started to cry, but he couldn’t make a sound.

At that point he wished he hadn’t come back from the coma at all, he recently told a New York Times reporter. Pancho felt like his life was over.

Today Pancho can speak again

Fifteen years later, his life would change again, when a group of American scientists implanted a chip with 128 electrodes into his brain, plugged a cable into his skull and trained deep learning software to read his mind. Today, Pancho can speak again. When he thinks of words, the software decodes the signals from his brain and his words are spoken in a gravelly, synthetic voice.

Interpreting his brain signals and translating them into words is only possible thanks to an artificial neural network interacting with Pancho’s organic one. The AI software was trained on his thoughts during 50 sessions in which Pancho was trying to speak words, until it recognised the patterns of brain signals that corresponded to certain words. (Thanks to his new ability to speak, the patient has expressed that Pancho is the nickname he prefers, to protect his privacy.)

Pancho’s AI-assisted rehabilitation may seem like the stuff of science fiction. It is all the more remarkable for being real, and part of an AI revolution in medicine and health care.

The AI research field was conceived at Dartmouth College in 1956, a year when James Dean ruled the silver screen and Elvis Presley had his first number one hit, Heartbreak Hotel. Since then, AI has had its ups and downs, exaggerated expectations followed by troughs of disillusionment. The term “AI winter” was coined specifically to denote periods of disenchantment, when investment in AI dried out and the pace of progress slowed.

Notably, AI systems have defeated humans at games like go, Dota and Jeopardy.

Since Pancho’s accident in 2003, however, AI has made great strides thanks to the advent of artificial neural networks. The theory behind it had been established decades earlier, the idea that software networks mimicking the arrangement of neurons in the human brain would provide an important step in the evolution of thinking computers. But it wasn’t until the 2000s that it bore fruit, when processor speeds, storage capacity and data availability finally reached a tipping point in which it became feasible to train large neural networks on vast amounts of data, quickly leading to improvements in image recognition, automatic translation, and other domains.

Notably, AI systems have defeated humans at games like go, Dota and Jeopardy. Social media corporations have used machine learning to find the best methods to keep us refreshing our feeds in endless loops of distraction, and self-driving cars are inching ever closer to arriving on our streets. But only in the last few years has medical AI started taking off in a big way. And if you think hooking a cable to Pancho’s skull and reading his mind is something of a miracle, well, that’s just the tip of the iceberg.

Alphabet’s AI research company Deepmind is perhaps best known to the public for creating Alphago, the go-playing AI that runs circles around human go masters. But their work on Alphago has turned out to be useful in another domain as well. In 2020, Deepmind entered their tweaked AI, Alphafold, in a contest to solve the problem of protein folding. To nobody’s surprise, Alphafold crushed the competition.

Mapped out proteins of the Corona virus

Biologists have hailed Alphafold’s success as the first time AI has solved a significant scientific problem. An improved understanding of protein folding is already significantly reducing development timelines for drugs and vaccines. For instance, during the Covid-19 pandemic, Alphafold’s predictions were used to map out the proteins of the virus.

Currently, AlphaFold is being used to help develop new drugs for tropical diseases. It has already helped scientists find a safe drug for treating sleeping sickness, replacing a previous drug that is highly toxic.

Other areas where AI is currently being used include:

Medical imaging

Interpretation of visual data is key in fields such as dermatology and radiology, and fertile ground for machine learning algorithms. Tech companies, hospitals and universities around the world have developed machine learning algorithms that surpass human experts at detection of various skin conditions, cancers and other abnormalities. While human specialists can be highly skilled at examining rashes and reading x-ray plates, they require years of training and they are a scarce resource. AI speeds up the process, makes these skills more accessible and can, in some cases, detect anomalies that human doctors cannot.

Health records analysis

Electronic health records create new opportunities for machine learning algorithms to find previously unknown patterns in symptoms, diagnoses, medications and treatment effects. Researchers in Uruguay have developed a system that analyses the text a doctor enters in a health record and pulls up similar cases that may contain valuable insights. Spanish researchers have created a system that analyses a patient’s health record, and predicts risks for diseases based on their historical data along with data from their family’s health records. As health record data grows at a rapid pace, human physicians cannot keep up, but they can learn from the analysis performed by AI systems.

Diagnosis

Symbolic AI was first tailored to diagnose disease in the 1970s, using rules-based decision trees to create “expert systems” to assist professionals in their decision making. Recent methods instead rely on artificial neural networks and have proven themselves useful in diagnosing a vast range of diseases, relying on techniques such as image recognition, symptom evaluation and natural language processing.

Disease control

On December 31, 2019, the Canadian medtech company Blue Dot alerted its clients about a new flu-like virus spreading in Wuhan, China. They were a week ahead of the World Health Organisation and the Centers for Disease Control, thanks to their use of machine learning algorithms that were processing 100,000 news items and disease reports in 65 languages every day. AI-supported systems such as the Blue Dot platform can monitor the spread of new diseases, but also predict future trajectory based on data such as travel patterns, hospital admittance, historical data from previous epidemics and mathematical models.

Drug interactions

For patients taking a large number of medications, drug interactions can be potentially harmful as well as hard to track by individual physicians. Spanish and Chinese researchers have shown that machine learning algorithms can scan databases, medical literature and electronic health records and find patterns indicating adverse reactions to drug combinations.

Drug discovery

AI has proven its ability to speed up drug discovery dramatically. It is useful in several ways, such as predicting the 3D structure of target proteins, predicting drug-protein interactions and toxicity risks, designing biospecific drug molecules as well as assessing drug activity. While Deepmind has turned its attention to neglected tropical diseases, British medtec start-up Exscientia and Japanese pharmaceutical firm Sumitomo Dainippon Pharma used AI to create a treatment for obsessive-compulsive disorder. Development took only twelve months (compared to the expected timeline of five years) and the drug is now in human trials. Many other research teams around the world are currently using AI to find new drugs, and Andrew Hopkins, CEO of Exscientia, recently predicted that all new drugs will be created with the help of AI by 2030.

Neural prosthetics

With machine learning, Swiss researchers have trained algorithms to interpret the muscle signals of an amputee, to control a prosthetic hand. The task is complex because muscle signals are noisy and traditional approaches have yielded clumsy results. Machine learning changes the game, and the AI-powered prosthetic is more precise and quicker to react than previous prostheses. Advances in this field are bearing fruit in many research labs and Pancho’s new ability to talk is another example of this approach, called a speech neuroprosthesis.

As Pancho’s example and all the ongoing research demonstrates, we are entering a new era of medicine where machines can read the signals of our bodies, unfold amino acid molecules, replace lost abilities and tailor drugs to the protein structure of specific viruses. There will be disputes over privacy and security, as AI systems crave massive amounts of data to further increase their capabilities. But the technology is so powerful and the stakes are literally life and death. As evidence of AI’s potential continues to grow, odds are that privacy advocates will be fighting a lost cause. After all, no government would prioritise privacy over a cure for cancer or prevention of the next pandemic.

Humans doctors can relate

Will friendly and competent doctors be replaced then, by robodocs with superior diagnostic skills and knowledge of drug interactions? Well, not so fast. If there is one domain where humans are irreplaceable, it’s emotional work. No algorithm will feel genuinely sympathetic about your knee pain. If you want cold, hard facts, go see Dr Alphacure. If you want someone who can relate to your problems, go see someone made of the same stuff as you: flesh, blood, bone.

Sometimes, you see your doctor just to get your prescription renewed. But often, when you go because of a new ailment or injury, you are not only hoping for an intelligent analysis of your symptoms. You want to be taken care of, by someone who understands – from the inside – what it is to have your body starting to fail. You want someone who knows what it is like to wake up at 4 a.m., chest pounding, fearing that your expanding mole might be a mark of death. In time, AI could be taught to simulate these emotional skills. But fake empathy, in the end, is just manipulation.

While human expertise can be replaced by AI, empathy cannot. But as AI unburdens medical practitioners from many of the mechanical tasks of the profession, physicians may have greater opportunity to develop their human-to-human skills. In this way, medtech AI will not only revolutionise drug research, give voice to the speechless and improve diagnostic accuracy. It may just lead to more empathetic doctors, too.

Leaders need to create an environment for exploration and experimentation – and set up teams for failure, says David Gill, CPO at Schibsted in Finland. This is how companies working with tech will survive.

Our users’ expectations and needs grow every day. What was a relevant solution yesterday can suddenly be considered outdated and ripe for disruption today. There is just one problem: across consumer technology companies, most of the improvements we put in front of our users are completely useless. Various internal benchmarks and academic studies show that even among the world’s strongest technology companies, many of the changes made to their products do not deliver value to users. As measured through user behaviour and metrics, most new functionality isn’t used and some even lead to lower user engagement. You have probably been in this situation yourself: your favourite app suddenly changes, introduces meaningless new features, or even messes up the navigation.

So, how do you avoid these well-intentioned but wasteful missed opportunities? How do we lower the risk of spending our time and energy building features that no user will ever use? And on the other hand, how can we speed up our ability to quickly identify the most pressing problem to be solved for our users, and determine the solution that best solves this problem?

We need to set up our teams for failure. They need to fail a lot, ideally many times a week. Why? Let’s remember the data: even the most user-centric, smart and well-equipped product teams will ship a lot of improvements that do not solve any relevant problem well enough. Only one out of four ideas will positively move the needle, at least in its first iteration. That means three out of four ideas should not have been built in the first place!

Since we do not have a magic crystal ball to know which three ideas will not work, we simply must test them out for ourselves. This means that the best teams in product development are the ones that work through and discard the bad ideas the quickest. The teams that creatively and efficiently spend the least time determining that a certain idea will fail to meet user needs, are the teams that ship the most meaningful improvements. As you might have guessed, failures and critical learnings are almost as important as finally shipping that one improvement that really moves the needle.

The right environment is crucial

For our leaders, this means providing the right environment for teams tasked to tackle this hard and time-consuming journey of exploration and experimentation. By studying some of the most successful technology companies in the world and reflecting on my own experiences, there are some key enablers that need to be in place for your teams to have the user-centric, strategically relevant impact of our dreams:

1. Focus on outcome

Set goals based on the outcomes you want to achieve, not the functionality you want to build. As tempting it might be for a leader to measure progress in tangible feature releases (we built something! I can see it on the app!), it’s incredibly important that the teams are instead empowered to achieve positive user and business outcomes as measured by metrics. This recognises the fact that the first idea/feature will probably not work, while also giving the team the freedom to decide on solutions while also boosting the responsibility of delivering results.

2. Open atmosphere

Psychological safety: many leaders know that a key trait of any successful team is the degree of psychological safety they feel when interacting with each other. This basically means having permission to speak your mind with your colleagues without any fear of repercussions or judg­ment. This is hard to establish, possibly even harder to achieve remotely, but it’s a critical foundation if you want your team to reap the benefits of being diverse and cross-functional.

3. Product discovery

Get really good at product discovery. Product discovery is the art and science of finding out which problems are most worth solving and whether the proposed solution will work. It’s a series of techniques ranging from user interviews, surveys, prototyping and design validation, technical feasibility, competitor benchmark and other types of experimentation. This requires both time, skills, empathy and patience. I have yet to see a company doing enough product discovery.

4. A clear strategy

Develop a clear vision and strategy. So, it’s super hard to figure out what to build. And it needs quite a bit of time-consuming product discovery. How do we avoid the pitfall of straying too far from the purpose and mission of the company? How do we avoid the teams coming up with all kinds of ideas that make the totality of your product look like Frankenstein’s monster? The answer is through serious strategic immersion between leaders and the team. It’s not enough to share a fancy slide in a company all-hands a few times a year. The strategy needs to be discussed, questioned and familiarised with everyone in the team until the strategic direction of the company is understood. And most importantly, it needs to be clear how each individual team will contribute to this strategy.

5. Time and focus

Give enough time and focus. We know that successful product development is high in complexity and low in predictability. The process is messy and requires both creativity and resilience. It also requires investment in learnings and development, and that the teams reflect often and deeply on how they can improve their ways of working together. This means that overworked or stretched teams will have a much harder time achieving this tough task. Ensuring that they have enough support and slack in their agendas is fundamental for their success.

Product development is a tricky business. It requires talented and motivated people, strong leadership, a curious company culture, sharp strategy, patience and a bit of luck. However, it can also breed team engagement, happy users and positive business outcomes. And you know what? Much of the above isn’t exclusive to product development. Almost all the work we do these days has the same high degree of complexity, which makes these five tips applicable to your entire organisation!

When the futures we imagine confront our logic and business models – it might help to look at gaming
to break free from the gravity of the present.

The full moon hangs over the horizon, and a light rain begins to fall from heavy clouds. Your boots squelch in the mud as the pitter-patter of raindrops lulls you into a false sense of safety. A monstrous howl breaks the calm night, followed by several more, echoing from the forest around you. Whatever creatures lurk in the darkness have you and your companions surrounded. What do you do?

Over the last decade successful TV shows like Game of Thrones and Stranger Things, as well as live-streaming platforms like Twitch, have brought tabletop role playing games (TTRPGs) out of the basement and into the mainstream, as exemplified by the commercially successful Dungeons and Dragons which has developed a cult-like following.

Propelled by collaborative storytelling, TTRPGs share three characteristics. First, players take on the role of imagined characters, each with their own histories, motivations, strengths and weaknesses. Second, characters inhabit a fictional setting with its own internal logic, covering everything from medieval fantasy to space opera. Finally, these games use elements of chance and improvisation to resolve conflicts and create consequences of the characters’ actions.

Players often use their characters to explore parts of themselves or experience things to which they aren’t normally exposed. This can be an incredibly energising experience; imagine what it would feel like to actually save the world with your friends? Other times, this experience can be quite personal. In the game “14 Days”, players take on the role of someone living with severe migraines, and experience what the unpredictability of chronic pain means for them over the course of the two-week game. Simply using narrative and imagination can take us one step closer to empathy and experience.

What sets these games apart from their digital cousins is the near-complete freedom they offer. Not bound by the limitations of a script or computer program, the question “what do you do?” gives players the chance to take unconventional paths that they would otherwise miss. It builds a sense of agency and allows stories to unfold in emergent ways. Unconventional strategies can reward unique narrative turns.

In many games, this freedom is tempered by a Game Master (GM), who acts as referee of sorts, describing the context the characters are in, acting as any people they may meet in the world, and adjudicating the results when the outcome of the characters’ actions is uncertain. With multiple players making individual and collective decisions, the GM helps maintain the tone and direction of the narrative, providing believable consequences for the characters’ actions. Collaborative storytelling, meaningful cause and effect, and emergent plot twists are what makes these games unique.

While it has a very different goal, Strategic Foresight is a discipline that follows a very similar logic to TTRPGs. Used by leading companies and public institutions worldwide, including here at Schibsted by our Tech Experiments team, Strategic Foresight helps us imagine a range of possible futures and develop robust, forward-looking strategies in times of uncertainty. Like TTRPGs, it uses fictional, collaborative scenarios to explore different actions and imagine possible outcomes.

The process follows four core steps. First, we scan the present for signals of change, including market trends, news stories, and user behaviour. Second, we use these signals to identify trends affecting our organisation. Next, we extrapolate and create future scenarios, imagining our place in them. Finally, we identify and prioritise actions we can take today to move us towards our preferred future.

This can be a powerful approach to anticipating future trends, risks, and opportunities. It can also be an unsettling one. When the futures we imagine confront our logic and business models, how can we break ourselves free from the gravity of the present and imagine different paths going forward? This is where Strategic Foresight can draw lessons from TTRPGs.

Make it experimental

Part of what sets role-playing games apart from books, films or computer games is the feeling that you and your friends were actually there, living the story, even if it is fantastic and unbelievable. When working with Strategic Foresight, paradigm-shifting futures can be hard to believe; if we don’t have parallel experiences to draw on, how can we imagine what that future might feel like? This is where we can use design and storytelling to simulate those experiences. Building prototypes of future products and services that we can touch and experience can bridge the gap and make challenging futures more believable.

Make it divergent

Engaging stories are full of choice, conflict, failure and success. Role playing games embrace this and allow us to find opportunities we might have disregarded otherwise. In Strategic Foresight processes, it can be tempting to focus on the most preferable or probable future we identify. If we give ourselves the chance, these processes also provide low-investment, low-risk opportunities to explore novel paths and alternative strategies. This kind of divergent thinking helps challenge our approach to business-as-usual, adapt to unpredictable events and identify novel areas for exploration and investment.

Make it emergent

Collaborative storytelling means that no one at the table knows what will happen next. It builds excitement and a sense of ownership, and the collective choices of the players can lead to truly unpredictable twists. In parallel, one of the main challenges for Strategic Foresight is preparing organisations for unpredictable, so-called ‘Black Swan’ events, like global pandemics, as well as unforeseen areas of opportunity, like NFTs. To foster emergent thinking, Strategic Foresight processes should involve a diverse range of participants from across the organisation in a safe space for open discussion. Truly novel ideas can arise only when a mix of experiences, expertise and perspectives have a seat at the table and are given the chance to imagine together.

Last fall I became a cyborg. Now, when I tap twice on my left ear, I answer a phone call or hang up. If I tap twice on my right ear, I activate Siri. My new hearing aids make me feel superpowered. Improving our senses and bodies will be the future for all of us, says Sven Størmer Thaulow.

As a kid I handled the fact that I was born with a reduced hearing by compensating with lip-reading and being seated in the first row in the classroom. Later, in the army and at university – exposed to the myriads of Norwegian accents and most likely some effects of playing in bands – it became more tiresome to compensate, so I started using hearing aids.

Until last fall it has been a varied experience. They serve as an important support, but they lack in refinement – and always make me acutely aware of my handicap. But then something happened. For years we´ve talked about cyborgs and wearables, while techies implanted RFID chips under their skin to demo the future and numerous smart glasses with AR have been tested. But now I’m convinced that it’s within the audio space that things will take off. Superpowered hearing aids will become consumer electronics – filling the missing link in the audio interface ecosystem.

But the road to make me a cyborg has been long.

It all started with “in ear”-aids, devices that practically plugged my ears so that no natural sound could enter. In the mid-90s, I got the first programmable devices that could amplify sound on six different frequency bands. Then, in the early 2000s, came the tiny aids that hung behind my ear and that had a speaker connected by a fairly invisible chord. At that time, the buds stuck into my ear were full of holes, and amplification was much better, resulting in a more natural sound picture. These are still the default mode today. But all this time I still felt handicapped, and I didn’t like that they were visible – so I kept my hair long. Being a vocalist in a band was a good combination.

Around 2014, the first mobile connected hearing aid entered the scene. It was connected to the mobile through Bluetooth, not only in an app, but into the operating system IOS. The hearing aids worked like headphones and the voice audio was excellent – but they didn’t have a microphone. I had to hold up the phone to my mouth when speaking. You could also control different programs and the volume, either through an app or directly in the control centre on Iphone. I could also stream music from my Iphone – but the sound was optimised for voice, so it was treble-crap. But this started to become cool. So, I cut my hair.

Still, the potential was so much greater. When could I drop my airpods? Why weren’t the hearing aids truly connected two-ways so I could talk in them, talk to Siri, and get answers? I kept asking every year for innovations and I told my audiologist Heidi to call me when any great leaps were made on functionality.

Then came the breakthrough. Heidi called me in November 2020, asking me to come over. She handed me a hearing device called Phonak from the Swiss company Sonova Group. And now, after 15 years with hearing aids – I have officially become a cyborg. I don’t feel like I have a handicap anymore. I feel superpowered. Privileged. And I am sure this is the future for all humans.

I can use the aids as I do airpods when I talk on the phone. It’s almost a problem as people don’t have any visual cue that I am on a call. The audio quality is also close to the quality from airpods. I have now used these hearing aids for about twelve months, and I haven’t used my airpods at all. And I am listening to a lot of music.

But what’s probably the coolest thing about my new hearing aids is that they are gesture activated. If I tap twice on my left ear, I answer a phone call or hang up. If I tap twice on my right ear, I activate Siri and can ask whatever I want – hoping she will understand. I use it mostly for controlling Spotify, beefing up the volume, setting a timer, jotting down a to-do, sending a simple text message, etc. A few times I’ve asked questions like “who is xx”, but what you can do with this new audio interface is limited to the intelligence of Siri and not to the content or the value chain, which is super smooth.

For 17 of the 24 hours in a day, I am connected to the internet – inside my brain practically. It saves me loads of friction during the day; I probably pick up the phone 40–50 percent less. I don’t need to charge or look for my airpods either.

In my view, the true breakthrough of connected humans will come from medtech. And it starts here, with audio. And while I’m walking around with some fellow hearing-impaired cyborgs, waiting for my audiologist, Heidi, to reveal yet another breakthrough – here are some predictions about the soon-to-come future of the audio interface:

In five years from now you will be able to buy “Invisible Airpods” from Apple or equivalent. They will be the priciest Airpods you can get hold of, but way cheaper than my Phonaks (which are hovering around 1,400 USD in Norway).

“Invisible Airpods” will be the primary audio interface toward the internet. It’s always on you and it’s personal – so why bother with Google Home?
Siri will become a lot smarter and tailored towards “non-screen” communication. This means you won’t need to pick up your phone to browse through what Siri has found on the Internet when asking her a question. Just imagine you are about to have a meeting with someone and you’d like to get some information about them. Siri will be able to provide that, directly into your ear.

App providers will build in Siri support on loads of functions so that it’s possible to use functionality inside the apps without picking up the phone. Today there are very few apps that have implemented functions towards Siri, which is why “she” has limited reach on our phones.

I can’t wait for the next innovation in this space!

Cryptocurrencies have made decisive moves towards mainstream adoption in recent years. A May 2021 report by New York Digital Investment Group showed that 17 percent of American adults owned at least a fraction of Bitcoin. Among millennials, nearly half own some form of cryptocurrency.

Moving at a much slower pace behind buzzy cryptocurrencies such as Bitcoin and Dogecoin is the notion of decentralised finance (defi). Billed as finance for the internet age, it boils down to the notion that anyone in the world can lend, save, invest and borrow blockchain assets. Unlike today’s financial systems, defi is run on peer-to-peer networks where financial transactions take place through smart contracts – programs on the blockchain that only run when conditions between the buyer and the seller have been met. The users define the rules of engagement, not the institutions.

Anyone have access

The advantages of defi are numerous. Without institutions having the final word on financial transactions, anyone can access banking services. The same permissionless feature applies to the developers who build on these decentralised platforms. There’s also the added benefit of transparency, as software built on these decentralised networks is open-source, and transactions on the blockchain are recorded for all to see.

Furthermore, defi promises the benefit of 24/7 accessibility, unlike traditional financial institutions. Instead of subjecting their financial history to the whims of a bank manager, a defi user could use their Ethereum tokens (the crypto that powers many defi protocols) as collateral for the loan.

Global adoption of defi would have a disruptive effect on our current financial institutions

And rather than opening a savings account with a paltry interest rate, a user could stake certain coins, earning interest rewards far beyond those determined by central banks. By adding your tokens to the blockchain’s liquidity pool, you provide the capital to power other defi services. Many of today’s blockchains operate with these so-called Proof-of-Staking models, an environmentally friendlier alternative to Bitcoin’s Proof-of-Work framework. The user (and their crypto) help serve as the infrastructure.

Disruptive effect

Global adoption of defi would have a disruptive effect on our current financial institutions, reengineering everything we understand about costly financial services such as transfers and global remittances. Banks are particularly exposed to risk – even digital-savvy banks still largely operate by a traditional set of rules. Not surprisingly, some banks have begun commissioning analysis studies and stating that it’s “time to cooperate” with defi.

Defi still has a steep barrier to entry though. It requires a high level of internet savvy to understand. Another downside is the inherent volatility of many cryptocurrencies. The Ethereum tokens you stake on a pool, for example, could drop 20 percent in value over the course of a week (although the opposite is also true).

As I write this, Defi Pulse reports that there are 83 billion USD of assets locked up in defi protocols. Q1 2021 saw 1.5 trillion USD worth of transactions settled with Ethereum, an amount that the venture capital firm Andreesen Horowitz noted represents 50 percent of Visa’s payment volume. Given the sheer volume of capital involved, it’s a question of when, not if, defi will start to play a central role in how we handle our money.