I recently did an Innovation Evangelist podcast with Tom Raftery, covering the top innovation trends that I find interesting in business and society today.
Here’s a summary of some of the points we covered in the video above:
1. We have a golden opportunity to make the world a better place
Technology is accelerating, and it’s amazing opportunity for win-win-win opportunities. We can improve customer outcomes, increase productivity and profits AND make the world a better place (come listen to one of my presentation in order to hear some real-life examples!). Our challenge is to ensure that the benefits are shared so that everybody is better off, not just a small global elite.
2. Weaponized algorithmic addiction
Some of the most talented, brightest people in the world are helping make the world a worse place because of bad business models. Maximizing engagement is great for ad clicks, but it’s an awful metric for society.
Casinos use analytics to find out exactly what it takes to get you back to the gaming table after losses — so that you can lose more. Social media and gaming companies are doing the same thing, at massive scale, to make their platforms as sticky — i.e. addictive — as possible.
More engaging does not mean “better”. For example, hate is a virus and we’re spreading it more efficiently than ever before thanks to modern algorithmic targeting.
Fake news is also very engaging — by definition, it’s more interesting than the mundane nuances of reality. And as Jonathan Swift remarked over 200 years ago, even if the lies are eventually debunked, the damage is already done:
“Falsehood flies, and the Truth comes limping after it; so that when Men come to be undeceiv’d, it is too late; the Jest is over, and the Tale has had its Effect…”
Throughout history, unethical people haven’t hesitated to use our human failings against us — but never before have been able to do so at such a massive scale.
3. Asymmetric Social Warfare
In traditional fighting, smaller armies using guerrilla tactics win much more often than would be expected. Now we’re seeing asymmetric social warfare, where small groups with an agenda can destabilize society, such as the antivax movement.
Given the low cost and efficiency of modern disinformation campaigns, any group wanting to destabilize another country would be crazy not to use targeted astroturf social movements to increase societal divisions, whether it’s about immigration in the US, the Yellow Vests protests in France, Brexit in the UK, or Catalonian independence in Spain. All the research shows that we’re becoming more polarized than ever before, with technologists providing the weapons to increase the gaps.
How do we fix this? I don’t know — but I’m convinced that a big part of it is business models based on ads and clicks. It’s easier and more tempting than ever to get rich by faking authenticity, putting morals firmly to one side and lying with abandon.
4. Predictive maintenance for people
This is THE biggest outcome of modern technology: collectively, we’re going to live longer, happier lives. Wearable devices and analytics techniques that can detect subtle signs among dozens of different data sources mean personalized treatments that can head off the worst of heart attacks, mental health problems, and more. Science and technology has always helped improve healthcare, but with always-on monitoring we can find out what really works and what doesn’t, for each individual, on a massive scale.
5. AI ethics
First, it’s important to point out that it would clearly be incredibly unethical not to use technologies like machine learning and artificial intelligence, because of the massive efficiency benefits they can bring. Researchers such as McKinsey believe that up to 70% of some business processes can be automated using the latest technology, making better use of the world’s limited resources.
But it would also be unethical to ignore the potential downsides. The benefits and problems are not shared equally. For example, while machine learning will probably increase employment overall, it’s clear that it will eliminate some jobs that exist today, causing personal misery and hardship.
And algorithms are psychopaths — that is, they can behave “intelligently” and make decisions, but they have no awareness of the human consequences of those decisions. They are perfectly happy to replicate existing human biases and mistakes, making the wrong decisions faster and more efficiently than ever before.
Algorithms do what you say, not what you mean — and can be tricked. We’re used to humans making simple but consequential mistakes because of misunderstandings — just wait until you see what algorithms can do!
AI needs to remain firmly under human control, with clear responsibility for any automated decisions that are being made. Organizations need to work with ethicists and other experts like never before, and adopt the same principles as medical doctors since Hippocrates: “first, do no harm”.
6. Personal data sovereignty
Data is more important than ever. But who “owns” or controls the data that I generate as an individual? How do I stop it from being abused?
Recent legislation such as the European GDPR framework tries to help — but it’s clear that the implementations are far from perfect, and that we ultimately need a much larger approach such as “self-sovereign identity“. But can such approaches work in the real world?
In many ways, it’s about getting back to the levels of privacy we take for granted in the non-digital world. For example, I can stroll into a store and pay cash for a newspaper without the bank, the newspaper, or the vendor knowing who I am. Will I ever be able to subscribe to a newspaper online with that transaction remaining anonymous to both my bank and the content provider? Why can’t an ID service vouch that I am, say, an adult, or a citizen, or that I don’t have a criminal record, without having to also leak all of my other personal information?
7. Surveillance society
Throughout history, every new method of control and surveillance has been abused by authorities, until the abuse was discovered and eventually legislated against — from habeas corpus onward: mail, telegraph, telephones, cell phones, internet, GPS on cars, and more. This is unfortunately bound to continue — and potentially get much worse — with AI-powered technologies such as face recognition.
The ability to bring together many disparate information sources to get a single view is a huge boon to businesses — but can be a societal nightmare in the wrong hands.
Businesses around the world are already being held to ransom using flaws that were kept secret and deliberately hoarded by government agencies to use against their enemies. But those agencies were themselves hacked, and the tools have spread around the world.
Attitudes such as “I don’t care because I have nothing to hide” or “Great, it means that criminals will be caught more easily” are dangerously naive. For example, I guarantee — based on the weight of historical precedent — that there are currently senior political and business leaders around the world that are being blackmailed and coerced using evidence of crimes or embarrassing personal information that hackers or state actors have dug up on them. We just won’t find out who, and what the consequences are, for decades to come.
And today’s secrets won’t necessarily stay that way. Almost every aspect of your entire life is stored in various databases around the globe, just waiting for somebody with bad intentions to misuse it. It’s rumored that governments are sucking up large quantities of encrypted data, confident that new quantum-computer approaches will be able to crack existing factorial-based encryption methods in the next decade.
In the meantime, researchers are trying to find new approaches to protect sensitive data, such as lattice-based cryptography — but so far none of these have been proven to be safe from the discovery of new mathematical shortcuts in the future.
8. Team AI
Algorithms are powerful. Teams of algorithms will be much, much more powerful.
A lot of the recent developments in AI involve generating lots of different algorithms and then let them fight against each other to find the most effective approach. For example, the generative adversarial networks (GANs) used to create “deep fake” images use one algorithm that creates fake faces, battling against another algorithm that try to detect fake faces. The result, after many generations, is startlingly realistic images.
Today machine learning is typically used in business to automate complex, repetitive decisions in a single process, such as automated invoice matching. But most human activities involve teamwork, and teams of algorithms working together may turn out to be better at teamwork than we are.
For example, a team of (simulated) jet fighters using genetic fuzzy algorithms thoroughly outclassed human pilots, using tactics such as deliberately firing missing shots in order to induce the human pilots to take evasive maneuvers that ultimately puts them in untenable positions — literally, playing 3D chess with rockets.
Teams of AI players are thrashing professional human teams at games such as DOTA2 and Starcraft — by being perfect “team players”, sacrificing themselves for the greater good of the team.
And teams of AIs working with each other can lead to problems — for example, different automatic price algorithms in an industry can end up behaving like and illegal cartel — in ways that might be even harder to spot than when humans do it.
9. A new golden age for knowledge workers
We’re used to the notion that machines can help scale up physical labor. A single person with a modern tractor can work fields that previously required hundreds of people.
Over the last few decades, computers, spreadsheets, and other applications have also helped knowledge workers to be more productive. But the new generation of on-demand cloud infrastructures means that individuals now have the same computing power at their fingertips as massive organizations or governments. It means a single person can operate a multi-million dollar company, and a company with around a dozen employees can be worth billions.
It means that people with smart ideas can take them further, and benefit more from them personally. It’s the start of a new golden age for knowledge workers.
10. Disparity and diversity
The profits from technology are flowing to the segments of society that are already the best educated, richest, and most powerful, increasing the disparity between the haves and the have-nots. Big inequalities bad for us all. We’re wasting some of the best minds on the planet because they don’t have access to basic resources. We need to find new ways to spread the riches that technology brings.
Is technology good or bad? It depends! — it depends on us making the right choices as a society.
What do you think? What did I miss?!