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Introduction
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.
TREND 1 Artificial Intelligence and Machine Learning
The One-Sentence Definition
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.
What Is Artificial Intelligence and Machine Learning?
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
