This edition first published 2020
© 2020 by Bernard Marr
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To my wife Claire, and my children Sophia, James, and Oliver;
and everyone who will use these amazing technologies
to make the world a better place.
We have never lived in a time of faster and more transformative technological innovation. Incredible technologies like artificial intelligence, blockchains, smart robots, self-driving cars, 3D printing, and advanced genomics, together with the other tech trends covered in this book, have ushered in a new industrial revolution. Similarly to how steam, electricity, and computers have respectively been the driving forces of the first three industrial revolutions, this fourth industrial revolution is driven by the 25 technologies featured in this book. And as with the previous industrial revolutions, this fourth industrial revolution will change businesses, reshape business models, and transform entire industries. These technologies will change how we run our businesses, what jobs we will do, and many other aspects of how we function as a society.
For most leaders it can be very challenging to keep up with the speed at which many of these new technologies are emerging. As a futurist and strategic advisor to many of the most innovative companies and governments in the world, it is my job to help leadership teams understand and prepare for the impact of these technologies. With this book, I want to provide an easy-to-understand, state-of-the-art overview of the key technologies underpinning this fourth industrial revolution and outline how they are practically used by businesses today, as well as provide some tips on how to best prepare yourself and your organization for the transformation they bring.
I have chosen these 25 technology trends because I believe that they are the key ones every business leader needs to be aware of today. There are some technologies in this book that are more foundational – like big data, 5G, and artificial intelligence – and then there are others that overlap with or use technologies like big data, 5G, and artificial intelligence – like self-driving cars, chatbots, or computer vision. My aim is to discuss the key technologies and applications that are having the biggest impact on businesses today and the medium-term future.
Before you dive into the various future tech trends, I just want to say that the fourth industrial revolution offers us huge opportunities to make our world a better place and use these technologies to address some of the world’s biggest challenges – from climate change, to inequality, and from hunger to healthcare. We shouldn’t waste them.
As with any new technologies, there is also huge scope to exploit them for evil and we have to put in place safeguards to ensure that doesn’t happen. What is sure is that all these technologies will change businesses, reshape business models, and transform entire industries.
With many of the technologies featured in this book the rate of innovation and development is simply mind-boggling. Every week there are new breakthroughs and new applications even I didn’t think possible just a few years ago. It is my job to keep a close eye on all this and I share my insights in my Forbes articles, YouTube videos, and across my social media channels. I would like to invite you to connect with me on LinkedIn, YouTube, Instagram, Twitter, and Facebook. I also have a weekly newsletter in which I share all the latest developments. If you would like to keep up to date then you can sign up to the newsletter on my website www.bernardmarr.com, where you can also find many more articles, videos, and reports on future tech trends.
Artificial intelligence (AI) and machine learning refers to the ability of machines to learn and act intelligently – meaning they can make decisions, carry out tasks, and even predict future outcomes based on what they learn from data.
Speaking in 2016, Stephen Hawking said, “Success in creating AI would be the biggest event in human history.” Now, it’s no secret that technology trends often create a lot of hype. But in the case of AI, the hype is warranted. Like Hawking, I believe AI will transform our world and how we live in it.
AI and machine learning already plays a bigger role in everyday life than you might imagine. Alexa, Siri, Amazon’s product recommendations, Netflix’s and Spotify’s personalized recommendations, every Google search you make, security checks for fraudulent credit card purchases, dating apps, fitness trackers … all are driven by AI.
AI and machine learning is the foundation on which many other technology trends in this book are built. For instance, without AI, we wouldn’t have achieved the amazing advances in the Internet of Things (IoT, Trend 2), virtual reality (Trend 8), chatbots (Trend 11), facial recognition (Trend 12), robotics and automation (Trend 13), or self-driving cars (Trend 14) – to name just a few.
But what exactly is AI and machine learning, and how does it work? In very simple terms, AI involves applying an algorithm (a rule or calculation) to data in order to solve problems, identify patterns, decide what to do next, and maybe even predict future outcomes. Crucial to this process is an ability to learn from data and get better at interpreting data over time. And this is where the machine learning part comes in. Machine learning is a subdiscipline of AI, and it involves creating machines that can learn. (“Machines,” by the way, may include computers, smart phones, software, industrial equipment, robots, vehicles, etc.)
The human brain learns from data, not a preprogrammed set of rules. We humans are continually interpreting and learning from the world around us. We generally get better at this process over time, learning from our successes and failures. And we make decisions or take action based on what we’ve learned. AI – or, more specifically, machine learning – replicates this process, but in machines. So, rather than just giving a machine a set of rules to follow, machines can now “learn” from data. Deep learning is another AI-related term that you might have heard. If machine learning is a subset of AI, deep learning is a subset of machine learning – it’s essentially a more cutting-edge form of machine learning, involving more complex layers of data processing. (For the purposes of this chapter, both machine learning and deep learning will be wrapped up in the umbrella term AI.)
Like humans, the more data a machine has to learn from, the smarter it becomes. That explains why AI has made such dramatic advances in the last few years – advances that we might not have thought possible 10 or even five years ago. Modern AI needs data to function. And we’re now creating more data than ever before (see Big Data, Trend 4). This continual expansion in data, along with advances in computing power, is fueling a rapid acceleration of AI capabilities.
AI isn’t just infiltrating our everyday lives; it’s going to transform our industries and businesses. According to one survey, 73% of senior executives see AI, machine learning, and automation as important areas to maintain or increase investment in.1 (Governments, too, are prioritizing AI investment. In 2019, the White House launched a National AI Initiative directing government agencies to commit to advancing AI.2)
As well as transforming entire businesses and industries, AI is also going to transform many human jobs. IBM predicts that more than 120 million workers globally will need to be retrained in the next three years due to AI.3 AI-enabled automation (see Trend 22) will have a particularly significant impact and may lead to the displacement of many jobs. But rather than subscribe to a vision of a dystopian future where all human jobs are given over to robots, I believe AI will make our working lives better. Yes, jobs will be impacted by automation and AI, and many current human jobs will no longer exist in 10 or 20 years’ time. But AI will enhance the work of humans, and new jobs will arise to replace displaced jobs. (Just think how computing and the internet led to the demise of some jobs but created many more new roles.) What’s more, as machines become more intelligent and capable of carrying out more human tasks, I believe that our uniquely human capabilities – things like creativity, empathy, and critical thinking – will become all the more precious and valuable in the workplaces of the future.
AI gives machines the ability to carry out a wide range of humanlike processes, such as seeing (think facial recognition), writing (think chatbots), and speaking (think Alexa). And as machines’ ability to act intelligently gets better and better, AI will infiltrate even more of our lives.
Because AI underpins so many other technology trends, throughout this book you’ll find lots of specific examples of how AI is used across different businesses and industries. Here, I want to briefly whet your appetite and set out just a few of the amazing things AI can already do.
Machines battling man is the theme of many a sci-fi movie. In real life, AI research and development has seen intelligent machines beat their human opponents in some significant (but thankfully less harmful) ways.
In August 2019 the UK government announced it would dedicate £250 million to funding AI in the National Health Service.9 Here are just a few examples of how AI is beginning to transform healthcare.
Content platforms like Netflix and Spotify are built on AI – they use AI to understand what viewers most want to watch or listen to, make personalized recommendations, and (in Netflix’s case) create new content based on what it knows users enjoy. Here are a few other examples of AI infiltrating our hobbies and downtime.
In 2019, Microsoft announced it was plowing $1 billion into AI research lab OpenAI – which was founded by, among others, Elon Musk.16 What’s behind such a big investment? OpenAI is dedicated to creating something called artificial general intelligence (AGI), widely considered to be the “holy grail” of AI.
While AI can do some incredible things when it comes to “general intelligence,” AI lags way behind the human brain. In other words, AI is great at learning to do specific things, but AI systems can’t just apply that knowledge to other tasks in the way humans can. This is the goal of AGI – to create an AI system that’s as generally intelligent and flexible as the human brain. It’s not been done yet – in fact, we don’t know if AGI is even possible – but Microsoft’s investment shows it’s certainly a serious goal.
I opened this chapter with a Stephen Hawking quote, “Success in creating AI would be the biggest event in human history.” Hawking immediately followed that up with, “Unfortunately, it might also be the last, unless we learn how to avoid the risks.”
AI isn’t without its challenges and risks. For one thing, there are potentially huge risks for society and human life as we know it (particularly when you consider some countries are racing to develop AI-enabled autonomous weapons). But let’s focus on the key challenges that everyday businesses will have to overcome if they’re to deploy AI successfully.
There will no doubt be regulatory hurdles to negotiate as regulators begin (quite rightly and belatedly) to take a greater interest in the application of AI. Until now, some of the early adopters of AI have played a bit fast and loose with the technology (Facebook, for example, is facing legal action over its use of facial recognition technology for auto-tagging photos, without gaining user consent).17 That sort of behavior can’t continue, and business leaders will have to take an ethical, responsible approach to AI.
Part of using AI ethically means making sure you respect individuals’ privacy, gain consent to use their data for AI applications, and make it clear how you are using their data. Again, this is where some big players have fallen short in the past. Amazon, for example, faced consumer outrage over the news that contractors were listening to people’s Alexa requests. Individuals could not be identified by their audio, and Amazon stressed the practice was necessary to help develop Alexa’s capabilities, but the fact remains that most users had no idea that anyone would ever hear their private audio. Amazon has since introduced a “no human review” option to its Alexa settings, which allows users to opt out of their audio being manually reviewed.18
Remember I said that AI can now solve a Rubik’s Cube in just 1.2 seconds? Interestingly, the researchers who built the puzzle-solving AI can’t quite tell how the system did it. This is known as the “blackbox problem” – which means, to put it bluntly, we can’t always tell how very complex AI systems arrive at their decisions.
This raises some serious questions around accountability and trust. For example, if a doctor alters a patient’s treatment plan based on an AI prediction – when he or she has no idea how the system arrived at that prediction – then who is responsible if the AI turns out to be wrong? What’s more, under GDPR (the General Data Protection Regulation legislation brought in by the European Union), individuals have the right to obtain an explanation of how automated systems make decisions that affect them.19 But, with many AIs, we simply can’t explain how the system makes decisions.
New approaches and tools are currently being developed that help to better understand how AIs make decisions but many of these are still in their infancy.
Put simply, AI is only as good as the data it’s trained with. If that data is biased or unreliable, then the results will be biased or unreliable. For example, facial recognition technology was found to be generally better at identifying white males than women and people of color, because a leading data set used to train facial recognition systems was estimated to be more than 75% male and 80% white – something that programmers were able to correct by adding a more diverse range of faces to the training dataset.20 This means companies will need to ensure their data is as unbiased, inclusive, and representative as possible if they’re to get the best results from AI.
Finally, one area in which many companies will struggle is finding the right AI talent. There’s a shortage of people who can develop these complex AI systems – and what talent there is tends to be scooped up by the Googles and IBMs of this world. AI-as-a-service (AIaaS) could be part of the solution. AIaaS offerings from companies like IBM and Amazon allow companies to make use of AI tools, without having to invest in expensive infrastructure or new hires, which makes AI much more accessible to businesses of all shapes and sizes.
AI is going to revolutionize almost every facet of modern life, including business. Therefore, despite the challenges involved, businesses cannot afford to overlook the potential of AI. So how might you use AI in your business? Broadly speaking, companies are using AI to improve their business in three ways:
Every business should consider whether they can use AI to improve their business in one or, ideally, all of these ways. But you’ll need a robust AI strategy in order to get the most out of AI – and a good AI strategy should always be linked to your overarching business strategy. To put it another way, you need to look at what the business is trying to achieve and then see how AI can help you deliver those strategic goals.