Tech Trends in Practice

Bernard Marr

📚 GENRE: Business & Finance

📃 PAGES: 304

✅ COMPLETED: January 14, 2024

🧐 RATING: ⭐⭐⭐

Buy Book

Short Summary

Bernard Marr believes we are in the midst of a fourth Industrial Revolution, this one led by the incredible technology resources now available to us. In Tech Trends In Practice, Marr explores AI, machine learning, blockchains, smart robots, 3D printing, and more, providing a brief look at each piece of technology and how it’s changing the world. 

Key Takeaways

1️⃣ Data Drives Technology — Data is behind the rapid advancements in AI and other technologies. So many devices we use are collecting data, whether it’s our smartwatch, phone, streaming services, internet searches, or even our refrigerators and thermostats. AI and machine learning are ultimately just functions of the data that is fed to them. Data can mean numbers, text on the Internet, faces, photos, videos, song and show selections, etc. These streams of data are what allow us to train and develop some of the sophisticated technologies, like AI, that are changing our world. AI is able to scan the data, spot patterns, and deliver actionable insights — all in a blink of an eye. 

2️⃣ AI Rules — AI is involved directly in most, if not all, of the 25 technology trends discussed in this book. AI can be used to do so many different things: write content, create graphics, control robots, answer customer questions, take phone calls, design buildings. Some traditional jobs will become extinct because of AI, while new jobs will be created by it. AI’s impact continues to grow, but it should be viewed as a compliment to our daily lives, helping us be more efficient and productive, rather than a replacement. 

3️⃣ Automation & Customization — These are two of the book’s big themes. Customers increasingly want products and services that are customized, and the massive amounts of data being collected around the world can help us deliver that kind of personalized service. Additionally, most of today’s technology, led by AI, helps people and companies become more efficient in their work. That’s how these tools should be used: with efficiency and automation in mind. Your goal should be to leverage these technologies to create a more seamless and efficient life, opening you up to do more things that you enjoy. 

Favorite Quote

“But what is it that makes data, well, ‘big’? After all, data isn't exactly a new thing. What's new is the unprecedented digitization of our lives, where almost everything we do leaves a digital footprint. This is largely thanks to the rise of computers, smart phones, the internet, the IoT, sensors, and so on. Think of everyday activities like shopping online, reading the news in an app, paying for the morning coffee by card, messaging friends and family, taking and sharing photos, watching the latest show on Netflix, asking Siri a question, swiping right on a potential love match... we’re all generating data all the time.”

Book Notes 📑

Introduction

  • Bernard Marr — Bernard is the author of this book. He is an internationally best-selling author, popular keynote speaker, futurist, and strategic business and technology advisor to governments and companies. Bernard is a regular contributor to the World Economic Forum, writes a weekly column for Forbes, and is a major social media influencer. He is basically a tech guru. 
  • A Fourth Industrial Revolution — The main point of this book is to outline some of the technologies that are redefining the world here in the 2020s. Whether it’s AI, blockchains, smart robots, 3D printing, or any of the other trends covered in this book, today’s tech is changing how we do things, just like the first three industrial revolutions did previously. This book is designed to take readers through 25 of the most important tech trends to know about.
    • Quote (P. 1): “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.”

Ch. 1: Artificial Intelligence and Machine Learning

  • AI & Machine Learning — Artificial intelligence (AI) and machine learning are essentially 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. The key word here is ‘data’, because AI is nothing without it. AI and machine learning essentially read, interpret, and act on massive data sets, and they can do it in a tiny amount of time. But they need data. AI is the process of applying an algorithm to data sets in order to accomplish tasks, make product recommendations, etc. 
    • Quote (P. 4): “In very simple terms, Al 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 Al, and it involves creating machines that can learn (‘Machines,’ by the way, may include computers, smart phones, software, industrial equipment, robots, vehicles, etc.).”
  • AI & Daily Life — AI is all around us at this point. Alexa, Siri, Amazon product recommendations, Netflix and Spotify’s personalized recommendations, Google searches, YouTube recommendations, dating apps, fitness trackers, facial recognition, Chipotle’s chatbot… all are driven by AI. In fact, AI and machine learning are talked about first in this book because they are the foundation for a lot of the other technologies that are evolving right now.
  • AI & Machine Learning: How It Works — 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. Al — 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, just like humans. And just like humans, the more data a machine has to learn from, the smarter it becomes.
  • AI Needs Data — Just to drive this point home, AI needs data in order to do anything. And, here in the 21st century, we’re now creating more data than ever before. This largely explains the big advancements in AI the last few years. So many different things are collecting and sharing data (e.g. smartwatches, phones, iPads, streaming services, the Internet as a whole, etc.). Data means a lot of things — numbers, written text, photos, faces, etc. The combination of more data and advances in computing power are driving the growth of AI and its capabilities. For example, Spotify is using AI to look at every song, album, and playlist you’ve ever listened to (data) when it recommends certain songs to you. 
    • Quote (P. 7): “Content platforms like Netflix and Spotify are built on Al — they use Al 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.”
  • Transforming Jobs — AI and automation are already having a big impact on jobs, and that will only continue as we go forward. Many current human jobs will no longer exist in 10 or 20 years. But AI will also enhance the work of humans, and new jobs will arise to replace displaced jobs. It may have the same kind of impact on jobs as the Internet did when it first became a thing; nonessential jobs were no longer needed, but the Internet helped create a lot more jobs. There’s also a chance that AI, by doing predictable tasks that can be automated anyway, will put a premium on human creativity and critical thinking, because AI can’t do those things (again, it is operating from a data set). 
  • AI: Games & Healthcare— AI is already beating humans at their own games. Several articles have highlighted how AI has been victorious in head-to-head matchups with the world’s best Jeopardy! players, chess players, and more. In 2019, an AI machine solved a Rubik’s Cube in 1.2 seconds. A study published in The Lancet in 2019 found that Al is as good as human experts when it comes to diagnosing disease from medical images. MIT researchers have even taught AI how to reverse engineer pizza. In the future, you may be able to show AI a photo of a meal and have it tell you how to make it. 
  • Key Challenges — There are a lot of potential challenges with AI. Some of the most prominent right now include the following: 
    • Regulation — How do you regulate the use of this thing? More and more companies are using it, and right now there aren’t a lot of rules and guidelines in place for using AI.
    • Privacy — 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. Some companies have already fallen short here, in the process exposing data that some customers wanted to remain private. 
    • Lack of Explainability — We don’t really know how exactly AI systems arrive at their findings most of the time. For a doctor who uses AI to help a patient make a decision that ultimately backfires, who is responsible if the doctor can’t even explain how AI produced the answer that he recommended? 
    • Data Issues — 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. This means companies need to make sure their data is as unbiased, inclusive, and representative as possible. This issue popped up in the early days of facial recognition — the technology was initially better at identifying the faces of white males than women and colored people because the initial dataset that trained the technology featured faces that were 75% male and 80% white. This was fixed when programmers added a more diverse range of faces to the dataset.  
  • Putting AI to Use — Companies are generally using AI in three primary ways, as outlined below. No matter what, however, the key to using AI effectively is pinpointing what the business is trying to achieve, then identifying ways to use AI to help deliver on those overarching strategic goals. 
    • Developing Smarter Products
    • Delivering Smarter Services 
    • Making Business Processes More Intelligent

Ch. 2: The Internet of Things

  • The Internet of Things (IoT) — The Internet of Things (IoT) refers to the increasing number of everyday devices and objects that are connected to the internet and are capable of gathering and transmitting data. These are sometimes referred to as “smart devices,” and the list of smart devices in the world is now very long: TVs, watches, fitness trackers, thermostats, refrigerators, machinery, etc. Worth noting, the IoT does not refer to actual computers; rather it refers to all of these new smart devices that we don’t think of as actual computers. 
  • The IoT & Data — The rapid proliferation of smart devices (one report predicts that 75 billion devices will be connected to the Internet by 2025) is one of the reasons there is now more data than ever to feed AI and machine learning systems; literally almost everything is equipped with a sensor and can gather and transmit data. The data is collected by machines, not people. What happens is that the smart device or gadget or machine is gathering information and communicating it to the Internet — an example of this would be your fitness tracker automatically sending activity data to an app on your phone. 
  • IoT & Automation — Smart devices are designed to simplify and automate a user’s life, making everything more efficient. These devices use their sensors to gather data, and some of them are even smart enough to act on that data by anticipating your needs and responding to your behavior. A few examples of this in action:
    • Google Nest — Google-owned Nest’s learning thermostat tracks how you use your home so that it can regulate your home’s temperature accordingly
    •   Orro — The Orro intelligent light switch can tell when you’re in the room and switch the lights on and off without you having to do anything. It’ll also adjust the lighting based on the time of day.
    •   August Smart Lock Pro — This device allows you to lock and unlock your home from anywhere, without a key. It automatically locks the house when you leave and unlocks it when you come home and can integrate with voice assistants like Alexa and Siri.
  • IoT & Healthcare — One of the areas the IoT has made a big impact in is healthcare via wearable devices, like the Apple Watch. There are now a wide variety of devices patients can wear that will track almost anything a doctor wants to see. The data being collected by these devices is being used by doctors to make decisions on medication, surgery, and more. 
    • Quote (P. 19): “These IoT devices can be used to help monitor patients, inform caregivers in the event of an emergency, and provide healthcare professionals with data that could inform diagnosis and ensure patients follow doctors’ orders. For example, IoT devices can track vitals and heart performance, monitor glucose and other body systems, and track activity and sleep levels. Think about the impact of this for a second — instead of doctors relying on what the patient tells them, the IoMT gives healthcare providers an incredible insight into what’s really going on with the patient’s health and lifestyle.”
  • IoT & Business — The number of IoT devices connected to the Internet is becoming wild. So many things can be created to hook up to the Internet; even things like machines and farming equipment. Businesses around the world are creating smart devices for almost anything in order to gain valuable insights via the data that the device collects. These insights can then be used by the business to create better products and service, streamline their operations, and reduce costs. A few examples:
    • Rolls Royce — Rolls-Royce installs sensors in the jet engines it manufactures, so it can better understand how airlines use those engines
    • John Deere — John Deere has developed intelligent farming solutions where sensors continuously monitor soil health and other factors, and give farmers advice on what crops to plant where, and so on
    • Google Nest — Through its smart thermostat, Google Nest collects a ton of valuable data that they sell to utility companies. The data collected by the smart thermostat is very, very valuable for utility companies. 
  • Smart Dust — Wireless devices the size of a grain of salt that are equipped with tiny sensors and cameras are already a reality. Microelectromechanical systems (MEMs, or motes, as they’re sometimes called) are very real and have the potential to multiply the loT millions or billions of times over. In the future, MEMs may be used in settings like agriculture, manufacturing, and security, as well as in robotics and drone technology. Think about that: tiny specs that can connect to the internet and collect data! 
  • Data Is Valuable! — One of the big takeaways from the first two chapters is just how valuable data is. AI acts on data, and the massive number of smart devices that make up the IoT collects it. This data then allows businesses to create improved products and drive efficiencies at every level of the organization. 

Ch. 3: From Websites to Augmented Humans

  • Wearables & Augmented Humans — This trend harnesses AI, the Internet of Things, Big Data, and robotics to create wearable devices and technology that help to improve the physical — and potentially mental — performance of humans, and help us lead healthier, better lives. Maybe the most prevalent example of this is smart watches and fitness trackers. But wearables extend out to “smart” clothing like shoes and robotic prosthetics. As technology continues to get smaller and smarter, you’re going to see even better wearables going forward. 
  • Human Robots? — Some of the wearable technology is wild, and some of the emerging devices can be placed on or inside the body to track our signals and give us a boost. There’s talk that some disabilities we know of today won’t be a thing in the future as we continue to merge into augmented humans powered by robotic devices. 
    • Quote (P. 26): “Sound far-fetched? Not at all when you consider that we already have advanced robotic limbs that can replace human limbs and, thanks to Al, be controlled by the wearer’s thoughts. And we won’t just be looking at physical augmentations, either. Al for the human brain is already in development. Companies like Facebook are racing to develop brain-computer interfaces that could, in theory, allow you to type your Facebook status update using your mind instead of your fingers.”
    • Quote (P. 26): “So in the future, we may find ourselves permanently attached to our smart phones, but in a more literal way — because the technology could be implanted into our bodies and capable of constantly scanning our thoughts, emotions, and biometric data to understand what we want to do next. Al chips implanted in our brains could help us make smarter, faster decisions. And physical augmentation could make us stronger, faster, and who knows what else.”
  • Smart Clothes — Under Armor, Ralph Lauren, and Tommy Hilfiger are examples of clothing companies that have already developed wearable clothing. These “smart clothes” can help you track sleep data, measure heart rate during exercise, control the volume and get directions on your phone, and much more. And of course, there are “smart glasses” out there as well. 
  • Tech & Augmented Humans — From prosthetics that help restore amputees’ motor functions to industrial equipment that helps employees work smarter and safer, wearable technology goes way beyond everyday smart watches or clever yoga pants. Some of these devices even go INSIDE the body. A few examples:
    • Exoskeletons — Ford, Volkswagen, and other companies around the world have started developing exoskeletons, which are full-body robotic suits that allow workers in factories to lift super heavy weight without a problem. 
    • Bionic Lenses — Ocumetrics has created a bionic lens that can improve a person’s vision three-fold. Samsung has a patent for a lens that can take photos and record videos. 
    • Augmented Humans — Several companies, including Facebook and Neuralink, are attempting to make technology that essentially merges the human brain with AI, allowing people to type and do things just by thinking.
    • Quote (P. 32): “As the examples in this chapter show, we’re clearly well on our way to augmented humans. The prospect of humans merging with machines no longer seems like the imaginative plot of a sci-fi movie — but is a genuine goal for some technology companies.”

Ch. 4: Big Data and Augmented Analysis

  • Big Data & Augmented Analytics — “Big Data” refers to the explosion in the amount of data being generated in this increasingly digital age, while “augmented analytics” refers to the ability to automatically work with and generate insights from that data. As mentioned in previous chapters, data is critical to all of these new technologies (e.g. AI, machine learning, etc.). In many cases, these technologies are acting on the data that’s being gathered around the world.
    • Quote (P. 37): “Without data, the massive leaps we’ve seen in these trends — and many other technology trends — wouldn’t be possible.”
  • Data: Why It’s Valuable — Data is so valuable because it allows you to make better decisions about everything, whether it’s building better products and services, creating more efficient internal processes, making better recommendations to your customers, anticipating what your customers want, optimizing pricing, forecasting demand, reducing employee turnover, boosting productivity, and so much more. Netflix recommending movies and shows to you based on your watch history is an example of data being used to anticipate what customers want. Also, by being able to use data to see what you’re doing well and not doing well, you can improve your ROI, therefore making the most of your time, money, and resources.
    • Quote (P. 37): “At the heart of big data is the idea that the more data you have, the easier it is to gain new insights, and even predict what will happen in the future. By analyzing masses of data, it’s possible to spot patterns and relationships that were previously unknown. And when you can understand the relationships between data points, you can better predict future outcomes, and make smarter decisions on what to do next.”
  • What’s Driving Big Data? — According to reports, 90% of the data available today was generated in the last 2-3 years, and the amount of data we’re producing is doubling every two years. This explosion is why it’s called “big data.” The reason this explosion is happening is due to all of the Internet of Things (IoT)/smart devices that are generating data. Almost everything generates data now.
    • Quote (P. 38): “But what is it that makes data, well, ‘big’? After all, data isn’t exactly a new thing. What’s new is the unprecedented digitization of our lives, where almost everything we do leaves a digital footprint. This is largely thanks to the rise of computers, smart phones, the internet, the IoT, sensors, and so on. Think of everyday activities like shopping online, reading the news in an app, paying for the morning coffee by card, messaging friends and family, taking and sharing photos, watching the latest show on Netflix, asking Siri a question, swiping right on a potential love match… we’re all generating data all the time.”
  • Different Kind of Data — Our increasing digital footprint has also given rise to another interesting aspect of big data: the fact that there are many new types of data that can be analyzed. We’re no longer just working with numbers in spreadsheets, or entries in a database; today, “data” includes photo data, video data, conversation data (i.e. asking Alexa to play a certain song), activity data (such as browsing online), and text data (like social media updates). 
  • Augmented Analytics — Because the types of data listed in the previous bullet are complex and not straightforward at all, it is very difficult to analyze that kind of data. This is where augmented analytics comes into play. Augmented analytics essentially uses AI and machine learning to analyze data and draw insights from it. This allows businesses to understand the data, spot trends in the data, and make decisions based on those insights.
    • Quote (P. 39): “So, in theory, with an augmented analytics tool, a non-tech expert could simply ask the system a question — like ‘Which of our employees are most likely to leave in the next 12 months?’ — and the system would automatically generate a response.”
  • Protecting Data — Because AI, machine learning, and so many other forms of technology operate based on the data they are being fed, it is critical that the data is protected from attack. If the data is manipulated in anyway, you will get bad results that may be biased in some way. 

Ch. 5: Intelligent Spaces and Smart Places

  • Intelligent Spaces & Smart Places — Intelligent spaces and smart places are physical spaces (such as homes, office buildings, or even cities) that have been bulked up with technology to create an intelligent, cool, and connected environment. Entire buildings are being transformed into connected spaces that respond intelligently to the people who live and work inside. Even cities are becoming smart, thanks to initiatives like smart street lighting and intelligent traffic networks.
  • Intelligent Spaces: Using AI — An intelligent space is one that is very automated and responds to the activity of the people inside. These are now more and more common as AI and other technologies have become more prominent. It’s the combination and acceleration of AI and other advances that allows us to create spaces in which humans and technology interact in a more intelligent, connected automated way. 
  • Intelligent Spaces: Benefits — The benefits of smart places include increased energy efficiency, increased productivity, greater quality of life, increased safety, and simplified processes. Generally, the idea is to make everyday life easier and better for the people who use these spaces (be they residents, commuters, employees, clients, etc.), while maximizing efficiency and the use of resources.
  • Examples of Intelligent Spaces — There are many examples of intelligent spaces around the world. Again, an intelligent space is basically one that is using AI, sensors, and other technologies to save energy and automate everything, making things easier and more efficient for humans inside them. A few examples include:
    • Smart Homes
    • Smart Offices/Buildings
    • Smart Lighting
    • Smart Climate Control Systems 
    • Smart Desks 
    • Smart Chairs
    • Smart Locks
    • Smart Cities

Ch. 6: Blockchains and Distributed Ledgers

  • Blockchains — A blockchain or distributed ledger is a kind of highly secure database — it’s essentially a new and improved way of storing data. In today’s digital age, storing, authenticating, and protecting data presents serious challenges for many organizations. Blockchain technology is a practical solution to this problem, providing a secure way to authenticate information, identities, transactions, and more. This makes blockchain an increasingly attractive tool for industries like banking and insurance, among others. In fact, blockchain can be used to provide a super-secure real-time record of pretty much anything: financial transactions, contracts, supply chain information, and even physical assets.
  • Blockchains: How They Work — Records in a blockchain are called “blocks” and each block is linked to the previous block, forming the “chain.” Every block has a time and date stamp, noting when the record was created or updated. The chain itself can be public (like Bitcoin) or private (like a banking blockchain). And when changes are made to a block, the whole blockchain is kept in sync and each user’s copy of the blockchain is updated in real time. Whether a blockchain is public or private, users can only edit parts of the chain by possessing the cryptography key needed to alter the file. 
    • Quote (P. 62): “To illustrate how this works in practice, I often use medical records as an example. Imagine a digital medical record as a blockchain. Each entry (e.g. a diagnosis and treatment plan) is a separate block, with a time and date stamp that marks when the record was created, and only people with the cryptography key can access the information in that block. So, in this case, the patient might have the key that allows them to give a consultant and their general practitioner access to the information. Information can be shared with another party — say, between the consultant and the general practitioner— but only with permission, using the secure key.”
  • Blockchains, Decentralization, and Bitcoin — What makes blockchain technology so secure and safe is that data is distributed (duplicated) across many computers and is decentralized, meaning there’s no one central point of attack for hackers to target. It also means that data can be verified by peers rather than by one central administrator. No discussion of blockchains is complete without mentioning Bitcoin, a digital currency that functions using blockchain technology to facilitate transactions. Bitcoin was the first example of blockchain in use. 
    • Quote (P. 63): “Here’s the key difference: blockchains are generally public, which means anyone can participate in the chain and anyone can validate information — it’s a truly decentralized, democratic system with no one body or person being ‘in charge.’ Bitcoin is the perfect example of this kind of public blockchain. With Bitcoin, transactions aren’t verified by a trusted organization, like Visa or Mastercard, but by the Bitcoin community in a peer-to-peer system.”
  • Blockchains & The Future — While it’s true that early adopters of blockchains have been using the technology for financial transactions, we’re likely to see blockchain being used far more widely in the next few years. Medical records, transfer of ownership records, property transactions, HR records — any process of recording, overseeing, and verifying information could be enhanced by blockchain technology. This is a trend to watch in the coming years.

Ch. 7: Cloud and Edge Computing

  • Cloud Computing — Cloud computing means storing and processing data on other people’s computers (e.g. data centers) via a network (e.g. the internet), which gives companies the ability to store massive amounts of data and process it in nearly real time. To put it simply, the cloud is “other people’s computers.” You’re storing your data (texts, photos, apps, notes, etc.) on somebody else’s (e.g. Apple) computers and data centers, and this data can then be accessed on any device (e.g. your iPhone, iPad, Mac) at any time, as long as you have an internet connection. 
  • Edge Computing — Edge computing refers to the processing of data ON devices such as smart phones, which are getting more powerful and therefore no longer need to outsource as much processing to the cloud. Edge computing is about leveraging the processing power of devices close to the source of data rather than sending the data to the cloud. 
  • Edge Computing & Automated Cars — A good example of edge computing can be found in the self-driving vehicles that are becoming popular. These cars need to have edge computing capabilities so they can process data on the spot and make life-or-death decisions on the road quickly. If these cars had to send data to the cloud before its computers could make a decision, there would be delays that could cause major accidents. Edge computing allows the computers inside the cars to process data from its sensors on the spot. 
  • Cloud Computing: How It’s Used — Cloud computing is being used by individuals and companies around the world. By storing data in the cloud, you’re able to limit how much data is stored on your actual devices. Amazon, Apple, Google, and Microsoft are the leaders of cloud computing. With cloud services from these providers available, it’s no longer necessary or desirable to host all of your IT infrastructure within your organization’s walls. At Cambridge, we use Microsoft 365 and the SharePoint/OneDrive cloud services it offers. I also personally use Apple’s cloud services and Google Drive for my own stuff. 
    • Quote (P. 73): “Cloud providers host all of the tools for you, allowing you to access them wherever and whenever you need to. Not only does this mean you take advantage of their expertise at maintaining and updating tools, you also benefit from their world-class security and support facilities. You also get access to the huge amount of computing power and storage resources that cloud service providers have at their disposal — the amount of computing resource at your disposal can be scaled up or down, as demand for your own service changes.”

Ch. 8: Digitally Extended Realities

  • Extended Reality (XR) — Extended reality (XR for short) includes virtual reality (VR), augmented reality (AR), and mixed reality (MR). These forms of extended reality involve the use of technology to create more immersive digital experiences. Some information about the different realities: 
    • Virtual Reality (VR) — VR is all about putting a user in an immersive simulated digital environment. VR is typically used with a special headset that basically transports the user into any digital environment they can think of, whether that’s walking on the moon or wandering down the streets of 18th century Venice. The experience feels real physically. 
    • Augmented Reality (AR) — AR is rooted in the real world, not a simulated digital environment like VR. With AR, information and images can be overlaid onto what the user is seeing in the real world. A great example is the Pokémon GO game/app, where users could “see” and chase Pokémon characters on the street via their smart phone. AR can be delivered via smart phones, smart glasses, tablets, web interfaces, smart screens, or smart mirrors. 
    • Mixed Reality (MR) — MR is a bit of a blend of VR and AR. It’s essentially an extension of AR that brings the virtual and real worlds together, creating a more connected experience in which the virtual and real elements can interact with each other. With AR, users can’t interact with the information or objects being overlaid onto the real environment; with MR, they can. The user can play around with virtual elements like they would in the real world, and the 3D digital content responds and reacts accordingly. Think Ironman here. Ironman’s headset allowed him to interact with digital components that popped up in his field of view. Also, think Apple’s new Vision Pro headset.
  • Extended Reality: A Great Marketing Tool — Although gaming and entertainment were the first industries that embraced extended reality, this technology is now being used in fun and creative ways by companies all over the world to create cool experiences for customers and employees. The technology has become a great tool for marketing, brand building, and enhancing the overall customer experience. A few examples of how the technology is being used in creative ways:
    • Mercedes Benz — Mercedes created a sleek virtual experience of driving its SL model down California’s beautiful Pacific Coast Highway. Customers were able to put on a headset and experience what that feels like. 
    • IKEA — IKEA has created an AR app that lets customers bring IKEA furniture to life in their own home. Called IKEA Place, the app allows you to scan your room and place IKEA objects in the digital image of that room, creating a whole new look. The Home Depot is also doing this; while considering new flooring options, I was able to use the company’s augmented reality settings to see what each option looked like inside my home. 
    • GAP — GAP’s Dressing Room app lets shoppers input their body dimensions and try on clothes virtually in a simulated GAP changing room
    • Gatwick Airport — The passenger app for London’s Gatwick Airport has won awards for its creative use of AR technology. Passengers can use the app to navigate through the busy airport.
    • Public Speaking — For those who dread public speaking, Oculus’s VirtualSpeech VR tool provides immersive training that helps people deliver better sales pitches, network more effectively, become a more confident speaker, and more
    • Law Enforcement — Law enforcement officers in New Jersey are using a system that allows them to train for various scenarios, ranging from routine traffic stops to being shot at
    • Recruiting Employees — Even recruitment can be enhanced through XR. Food service company Compass Group created a VR experience for campus events that lets students take a virtual tour of its office and participate in a video interview.
  • Extended Reality: Challenges — Maybe XR’s most significant challenge is the impact it may have on people’s mental health. By allowing users to immerse themselves in a virtual reality outside of the real world, the technology may create even more disconnect than what we’ve already seen with social media. Will people prefer to spend more time in their virtual realities than in their real world realities? That’s something that has to be monitored as the technology becomes more advanced in the coming years.
  • Interesting Fact — In 2019, the World Health Organization (WHO) classified gaming addiction as a mental health disorder. 

Ch. 9: Digital Twins

  • Digital Twins — A digital twin is a digital copy of an actual physical product, process, or ecosystem that can be used to run virtual simulations while using data to update and adjust the digital copy to reflect any changes in the real world. To put it more simply, a digital twin is a digital copy of a physical thing. Digital twins allow you to test certain things by running simulations and tests on the digital model to see what happens before actually trying those things on the real physical version. 
    • Quote (P. 96): “The idea (with a digital twin) is that it lets us see what might happen if we make adjustments that might be too expensive, dangerous, or uncertain to try out in a real-world scenario. By altering the variables under which the digital twin is operating, the changes can be observed in the digital world without putting money or safety at risk.”

Ch. 10: Natural Language Processing

  • Natural Language Processing (NLP) — NLP refers to the technology that allows computers to understand human language. NLP is a subset of AI and is used to help computers read, edit, and write text. When you type a question into an app like OpenAI and it returns a well-written response, that’s NLP at work. The technology also powers computer “speech,” as seen in voice interfaces (e.g. Amazon’s Alexa) and chatbots (e.g. Chipotle’s Pepper chatbot). 
  • NLP: How It’s Used — NLP is very versatile and can do a lot of different things. It is great at processing and extracting meaning from human language. Again, advances in AI and machine learning are what is driving NLP. A few of the ways this technology is making an impact:
    • Research & Summaries — NLP can review huge amounts of written text on the Internet and summarize it very concisely and intelligently in less than a second. This is what happens when you ask OpenAI and other AI apps a question: It scours the Internet, summarizes the information, and delivers it to you in a very well-written answer. In this way, you can use AI and NLP to do research for you. For example, ask it to “write a few paragraphs summarizing the top five cybersecurity best practices,” and it will do the research for you and provide a well-written answer. 
    • Creating Content — Linked to the point above, NLP can write articles, books, reports, newsletters, emails, and basically any other type of written content you can think of. The technology is already having a big impact on journalism and marketing; NLP (driven by AI) can create well-written content in seconds. Humans can then take pieces of the content and mix it with their own, draw inspiration from what AI spits out, or just copy/paste the content entirely. Bloomberg, Forbes, and other news outlets are leveraging AI tools to help create content and produce strong headlines. 
    • Digital Assistants — NLP is what allows Alexa or Siri or Google Assistant to understand human requests. And natural language generation, or NLG, is what allows Alexa, Siri, and friends to respond with human-like speech. NLG (also a subset of AI) takes data and transforms it into language that sounds natural, as if a human was writing or speaking. 
    • Spam Filters — NLP powers spam filters and search engines. By being able to summarize information quickly, NLP tools within email systems are able to filter your email into different buckets automatically. This is how Gmail is able to send your inbound emails to the ‘primary’, ‘social’, or ‘promotional’ buckets accurately. 
    • Search Engines & Autocorrect — Search engines use NLP to understand your search request and return relevant results. It also powers the predictive search function that anticipates the rest of your search query as you’re typing. The same technology is what drives autocorrect and autocomplete functions in email, word processing, and phone applications (like the Notes app). As I type this right now, my iPhone is constantly trying to predict the next word I’m about to type. That’s NLP at work. Grammerly is completely built on NLP. 
    • Awesome Transcribing — There are AI tools that use NLP to help humans transcribe interviews. These tools listen in on a conversation and are able to not only provide a written record of everything that was said but also produce a high-level overview of the conversation with bulleted highlights of what was discussed. Journalists are using this for their interviews with subject matter experts and other sources. Financial advisors are also using this for their conversations with clients. 

Ch. 11: Voice Interfaces and Chatbots

  • Voice Interfaces & Chatbots — Voice interfaces and chatbots are computer programs that allow humans to converse and interact with computers through either spoken commands or written text. Both voice interfaces and chatbots work in a similar way, using AI, machine learning, Big Data, and natural language processing (NLP) to both understand and respond to human speech. The massive advances in AI and machine learning are what have allowed these technologies to become part of our everyday use. Although they use the same technologies, there is a difference between voice interfaces and chatbots:
    • Voice Interfaces — These are things like digital assistants (e.g. Siri, Alexa, etc.). These take in spoken language commands and are able to verbally respond in an intelligent manner. This category also includes AI bots who take phone calls from customers, like the Mayo Clinic’s AI bot that handles scheduling. The voice on the other end of the line sounds very similar to a human and can help customers with simple tasks. 
    • Chatbots — Chatbots interact with people through written interface. Chipotle’s ‘Pepper’ chatbot is a very simple example. The chatbots use AI and NLP to understand the question or request, find a suitable answer, and respond quickly. 
  • Voice Interfaces & Chatbots: How They’re Used — Many companies have adopted voice interface and chatbot technology to handle routine customer service tasks, like scheduling appointments, answering simple questions, cancelling memberships, and much more. The speed of the technology often enhances the customer experience and allows humans to do other, more important work. A few examples of how these are being used:
    • Traveling — Travel company Hipmunk has a digital virtual assistant (called Hello Hipmunk) that helps users book flights, hotels, and car rentals to plan the perfect trip without talking to a human travel agent.
    • Human Resources — Chatbot Polly is designed to improve workplace happiness by conducting surveys and gathering employee feedback, allowing organizations to keep track of how employees are feeling about the workplace and nip morale problems in the bud before they escalate.
    • Sales Calls — The Voca voice interface system allows companies to reach out to customers and potential customers on a large scale with a personal, human voice that is generated by a computer. Voca can fulfill a number of tasks, including making repetitive cold calls and passing promising leads onto human sales reps. This frees up the sales force to work on the most valuable leads only. 
    • U.S. Army — The United States Army is using a chatbot named SGT STAR to quickly answer questions about joining the service and help enlist the soldiers of the future.
    • Healthcare— The HealthTap chatbot responds to medical questions, concerns, and symptoms. And if the chatbot is stumped by a user’s query, it’ll be referred to a human health professional for answers.
    • Google Duplex — Using voice interface technology, Google Duplex can call your hairdresser, dentist, local restaurant, etc. to make appointments and enquiries for you. The voice interface responds to the person on the other end of the line in a way that’s very authentic.
  • Voice Interfaces & Chatbots: Why They’re Used — Voice interface and chatbot technology is used because it can open up more time for humans to do more important work. The technology can help companies create a more efficient and personalized customer experience by assisting with routine tasks like answering customer questions and making appointments. The key is to find the sweet spot; the human touch is still very important, and there are certain parts of the customer experience that should only be handled by humans. But, if used well, this technology can really help save a lot of time and enhance the overall customer experience. 

Ch. 12: Computer Vision and Facial Recognition

  • Computer Vision & Facial Recognition — Computer vision is described as computers, phones, and other machines being able to “see” and interpret the world around them. Facial recognition (which uses computer vision to identify people) is a prime example.
  • Computer Vision, AI, and Data — Computer vision is, like most of the trends outlined in this book, ultimately a form of AI. Because it’s a form of AI, it relies heavily on data. What makes computer vision unique is that the data it relies on is mainly visual (photos and videos). The sheer volume of photo and video data we’re generating every day is a major driver in the advancements in computer vision. We are constantly snapping and uploading pictures, which adds to the data that computer vision technology can learn from. 
  • Computer Vision & Machine Learning — To analyze photos and video with accuracy, computer vision technology relies on machine learning. In other words, it uses pattern recognition to figure things out. For example, in 2012, Google used a neural network to identify cat videos on YouTube. To learn to recognize cats, the system needed lots of images, some containing cats and some without cats. Because of deep learning — meaning the computer system learns to train itself — programmers didn’t have to tell the system what signifies a cat (i.e. whiskers, tail). Instead, the system learned for itself by analyzing millions of cat images.
  • Facial Recognition — Facial recognition is a subset of computer vision. It is being used all over the world for various reasons. Probably the most popular example is the iPhone’s ability to unlock based on a quick scan of a user’s face. In China, they are using facial recognition to replace tickets for transportation like buses and the Beijing Subway. Also in Beijing, police officers are using augmented reality glasses to cross-reference faces against the national database to spot criminals on the fly. 
  • Computer Vision: How It’s Used — There are many ways computer vision is making its way into our daily lives. A few interesting examples include: 
    • Autonomous Vehicles — Computer vision is partly what enables autonomous vehicles (like those being produced by Tesla, BMW, and Volvo) to safely drive on roads, navigate around objects, change lanes, and “see” road signs and traffic signals. The cars are using multiple cameras and sensors to interpret what’s going on around them and respond accordingly.
    • Hotel Check In — Two Marriott hotels in China are using facial recognition to speed up the check-in process. Guests can check in using kiosks equipped with facial recognition technology. After guests scan their ID, the system takes a photo and confirms their identity; then the kiosk dispenses their room key.
    • Amazon Go — Amazon is eliminating the checkout process altogether in its small, but growing, chain of Amazon Go grocery and convenience stores. Customers scan themselves in (using the Amazon app on their smart phone) at a turnstile when they enter the shop, pick up what they want from the shelves, then leave — no queuing at checkouts, no handing over cash. Cameras track you as you shop, monitor what you take, and the cost is charged to your Amazon account automatically.

Ch. 13: Robots and Cobots

  • Robots & Cobots — Robots are intelligent machines that can perform tasks autonomously. Collaborative bots, or cobots, are also robots, but they are defined as robots that assist humans in their work. For example, Amazon uses cobots to bring items to humans for packing, which helps make everything more efficient. As with all of the trends in this book, AI is what has been driving the advances in robotics. 
    • Quote (P. 140): “Today’s robots are not only physically more robust and flexible than early industrial robots, they’re also much smarter. We have delivery robots, robots that can perform surgery, robots for space exploration, demolition robots, underwater robots, search-and-rescue robots, and more. We have robots that can walk, run, roll, jump, and even backflip.”
    • Quote (P. 148): “Robotics provides exciting opportunities to reduce costs, increase capacity, boost efficiencies, and reduce errors. In the future, I believe humans will no longer be employed to carry out those jobs that robots can do safer, faster, more accurately, and less expensively.”
  • Robots & Dirty Work — In the future, robots will likely take all of the jobs that are dull, dangerous, and dirty. Any job that is very repetitive and tedious will likely be overtaken by robots. This is already starting to happen. Factory jobs are a good example. By taking these repetitive and tedious jobs, humans are freed up to spend more time on creative and high impact tasks that require more skill. Robots should be approached as tools to help make human lives more efficient. A few placed robots are already having a big impact:
    • At Home — There are robots that can sweep your floors and do other fairly easy cleaning duties. There are also robots that can roll around the house and take photos and videos, which is great for making ensuring children and pets are safe. 
    • At Work — Robotics are becoming more and more common in factories and on assembly lines. They can help make everything more efficient. 

Ch. 14: Autonomous Vehicles

  • Autonomous Vehicles — An autonomous vehicle is a car, truck, ship, or other vehicle that can sense what’s going on around it and operate without human involvement. Autonomous cars are the way of the future; every major car manufacturer is investing heavily in self-driving technology. Someday, humans will be able to load up in the back of a self-driving car, sit back, and relax while the car takes them where they need to go. 
  • Autonomous Vehicles: How They Work — Self-driving cars are fitted with cameras that can read road signs, recognize road markings and provide an accurate view of the car’s surroundings. Together, these technologies (along with others, like GPS) help the vehicle scan and map its surroundings, navigate a route, carry out maneuvers, and avoid obstacles. 
  • Autonomous Vehicles: The Future — Tesla is one of the car companies leading the charge in autonomous vehicles, as is Google with its Waymo cars. Down the line, you could see self-driving cars become a viable alternative to flying when it comes to traveling short distances. The ability to hail a car, climb in the back, and chill out while the car drives you to a different city or state might be preferred over going to the airport to fly. 

Ch. 15: 5G and Faster, Smarter Networks

  • 5G Network — 5G is the fifth generation of cellular network technology, which together with other network innovations give us much faster and more stable wireless networking, as well as the ability to connect more and more devices and enable richer, more varied streams of data. 
  • More Bandwidth, More Power — As bandwidth and coverage have increased, more has become possible, from email to web browsing, location-based services, and streaming video and games. Today it’s all about sending a constant stream of real-time data back and forth between ourselves, the apps, and devices we use, and the cloud services which power them. 5G is what powers all of that. 

Ch. 16: Genomics and Gene Editing

  • Genomics & Gene Editing — Genomics is a field of biology that focuses on the understanding and manipulation of DNA and genomes of living organisms. Gene editing is a group of technologies that enables genetic engineering in order to change the DNA and genetic structure of living organisms. The human genome was first accurately sequenced in 2003. Since that time, advancements in computers and technology have allowed us to do some amazing things, and editing genes is one of them. 
  • A Biology Refresher — To add context to this trend, it’s helpful to recap a few important biology concepts. All living cells contain DNA that determines the traits (or code) that a cell will pass on when it divides. As we have a better understanding of how sequences of DNA (genes) are passed on during that division process, we can have a better idea of the impact it will have on a living organism’s ability to cope with injury, allergies, food intolerances, hereditary diseases, or any number of internal or external factors. The study of this field of science is known as genomics.
  • Manipulating DNA — Biotechnology is advancing to the point where it’s possible to alter the DNA encoded within a cell. This will influence the characteristics or traits (phenotypes) that its descendants will have after it reproduces by cell division. This alteration can be done by removing — by physically cutting — sections of DNA from a strand. The strand will then naturally heal, and pass on an “altered” version of the DNA when it divides, meaning the new cell will develop with altered characteristics. In plants, this could affect the number of leaves or their coloring, while in humans it could affect their height, eye color, or their likelihood of developing diabetes. 
  • Why It Matters — We are pursuing gene editing because it is useful if “bad” genes are detected that could endanger the health of an organism/human. Any characteristic can be changed via gene editing. Diseases could be prevented, crops can be developed that are resistant to pests and diseases, and medicines could be tailored to individuals according to their own genetic makeup.
  • Key Challenges — There are many challenges and moral dilemmas associated with the idea of gene editing, but one of the most important involves the health of future generations. If we accidentally screw up a person’s genes, those genes will be passed on to their kids, and their kids’ kids. When it comes to something like manipulating genes, we have to be very careful and tread lightly.  

Ch. 17: Machine Co-Creativity and Augmented Design

  • Machine Co-Creativity & Augmented Reality — This trend is all about AI and machines helping humans do their work faster and better. These technologies can assist humans tremendously, as discussed earlier in the section about AI creating excellent written content. AI can now also create digital graphics, video, building designs, and a lot more. The possibilities are almost endless. 
    • Quote (P. 187): “In other words, for now at least, machine creativity is being largely used to augment and enhance the human creative process. This is what we mean by co-creativity or augmented design — deploying AI alongside human creativity, rather than replacing human creativity altogether. Think of it as a little extra creative ‘muscle,’ if you will.”
  • Data Is Big Again — Once again, data plays a big role in creating graphics. The technology has been fed millions and millions of photos, to the point it can now understand how to create a really solid digital graphic of a cat (for example). All a designer has to do is insert their request and the desired specs; AI will do the rest.
  • It’s About Collaboration — The key is to understand that AI and other related technologies should be used to HELP create cool things. These technologies should never REPLACE a human’s ability to create. It’s about finding the right balance. Humans are great at establishing the creative vision for a project, and AI is helpful for building and providing options that can help meet that vision.  
    • Quote (P. 193): “Where humans excel is usually in coming up with a creative vision, connecting with their target audience, and making complex decisions on which design (or song, or artwork, or whatever) is most likely to resonate with that audience. Al can support this process by coming up with multiple options that fit the determined style or parameters — quicker, easier, and more effectively than a human could.”
    • Quote (P. 193): “When considering potential applications, remember that its about using AI to complement and enhance the work of humans – not to replace human creativity altogether. It’s about finding ways for humans and Al to work together to come up with something more incredible than either could create alone.”

Ch. 18: Digital Platforms

  • Digital Platforms — Digital platforms are networks that facilitate interactions and exchanges between people. These exchanges include communicating and sharing information, selling products, or offering services. Facebook, Uber, Amazon, and Airbnb are all well-known examples of digital platform businesses. What do they all have in common? They aid valuable interactions between people, meaning participants may use the platform to sell goods or services to each other, collaborate on projects, give advice, share information, or cultivate friendships.
  • Changing the World — Digital platforms have completely changed the world. The traditional business model is all about physical assets, inventory, and raw material. Much of the company’s value came from those areas. Now companies must be very aware of their digital presence. Take Airbnb and Marriott. Marriott’s business model is highly predicated on physical assets, buying and selling hotels, and managing employees. Airbnb is a digital platform; the platform IS the business. Airbnb acts as a facilitator for the crowd, making interactions possible, easy, and safe. Examples of digital platforms include:
    • Airbnb
    • Instagram
    • Twitter
    • Google
    • Amazon
    • Expedia

Ch. 19: Drones and Unmanned Aerial Vehicles

  • Drones — Drones are being developed for almost any use you can think of, including farming, military, travel, safety, construction firefighting, police work, marketing, package delivery and so much more. Amazon and a few other companies are planning to one day use drones to deliver packages that aren’t too heavy. 

Ch. 20: Cybersecurity and Cyber Resilience

  • Cybersecurity and Cyber Resilience — Cybersecurity is a company’s ability to avoid the increasing threat from cybercrime, such as cyberattacks or data theft. Cyber resilience is a company’s ability to mitigate damage and carry on once systems or data have been compromised.
    • Quote (P. 220): “So, what is the difference between cybersecurity and cyber resilience? The simple way to think about the difference is that cybersecurity is about stopping threats before they cause damage. Cyber resilience, on the other hand, is about mitigating the potential damage that can be done when your security measures fail.”
  • Growing Cyber Threats — As AI and other means of hacking, phishing, and developing ransomware continue to improve, cyber attacks will only get more and more sophisticated and tricky. Cyber attacks can cause serious issues, and data can be lost or stolen. It’s more important than ever to have a strong cybersecurity plan, especially with how many different devices are connecting to the Internet via the Internet of Things (IoT) today. 
  • Ransomware — Ransomware is one of the many cyber threats being perfected by hackers. Essentially, it’s where attackers encrypt personal or business files, then demand a ransom to unlock them. The attackers usually ask to be paid in anonymous cryptocurrency.
  • Protecting Against Cyber Threats — The first step of cybersecurity is being prepared. Companies should ensure all devices are running on the most up-to-date firmware, that fire-walls, VPNs, and anti-virus/malware software is running, and all of your software and tools are fixed with the latest patches is an important first step.

Ch. 21: Quantum Computing

  • Quantum Computing — Quantum computing harnesses the peculiar phenomenon observed to take place when operating at a subatomic level — such as quantum entanglement, quantum tunneling, and the ability of quantum particles to apparently simultaneously exist in more than one state. 
  • Quantum Computing: More Power — Quantum computing sounds cool, but what does it mean for the average person? It means computing power and processing is about to get much, much stronger soon, which will unlock a ton of new possibilities in the future. Quantum computing is still very far away, though. We may not see this become impactful for another decade or two. 

Ch. 22: Robotic Process Automation

  • Robotic Process Automation (RPA) — RPA is technology that can automate business processes. RPA is about minimizing the time spent on routine, manual activities that are currently performed by humans but could be given to software robots. The aim of RPA is to improve productivity, reduce human error rates, and free people up to do the creative, higher value work that can’t yet be performed by robots. 
  • RPA & AI — RPA is basically using AI to open up more time to do higher ROI tasks. Just as physical robots have been developed to help humans carry out physical, manual work, software robots can be used to carry out repetitive and tedious digital tasks. Again, it’s about using AI and other technologies to help you become more efficient in your day-to-day life. These technologies are ultimately a compliment to humans, not a replacement. Examples include much of what has already been discussed in this book: chatbots, spellcheckers, auto-completing forms, content creation, etc. 

 

Ch. 23: Mass Personalization and Micro-Moments

  • Mass Personalization — Mass personalization is all about offering products and services that are customizable. People love to customize their stuff when they buy. Nike’s custom shoe builder is an example. But it goes one step further. Mass personalization is also about being able to create content, emails, offerings, recommendations, etc. that are highly personalized to each person. 
  • Mass Personalization & Data — Data once again plays a huge role in mass personalization. Because we now have access to an unprecedented amount of data on customers, companies can personalize content and offerings effectively. The incredible volume of data available on people allows marketers to segment customers based on highly granular demographics like interests, occupation, and sex, locations, and more. Examples of this trend in action include: 
    • Personalized emails from companies
    • Show and movie recommendations on Netflix
    • Article recommendations from Apple News
    • Suggested products when you’re shopping on Amazon
    • Personalized web searches when using Google 
  • Micro-Moments — Micro-moments are opportunities to put solutions in front of customers at the exact time they need them. This is similar to mass personalization. Data once again plays a big role. Because companies can basically track a person’s activity via search history and other data, they are able to anticipate a customer’s needs and deliver suggested recommendations in real time. When you’re scrolling through Facebook and an ad for something you just looked into pops up, that’s an example of a micro-moment. It’s kind of creepy, but the idea is to give people what they want before they even know they want it. 
    • Quote (P. 252): “The perfect micro-moment occurs when an offer of a product or service pops up in front of us exactly at the time when we are trying to solve a problem that it might help us with.”
    • Quote (P. 253): “The internet giants like Google, Facebook, Netflix, Amazon, and Spotify have pioneered the trends of personalization and identifying micro-moments by learning to serve up personal recommendations at the time were likely to want them. When you search for a product on Amazon or scroll through the movies available on Netflix, you’re being offered a sample of what is available based on what the service provider thinks you will want.”

Ch. 24: 3D and 4D Printing

  • 3D & 4D Printing — 3D printing (also known as additive manufacturing) means creating a 3D object from a digital file by building it layer by layer. Meanwhile, 4D printing is similar but more complex and very far away from being widely adopted.
  • 3D Printing vs. Traditional Manufacturing — 3D printing has the potential to completely change the way we manufacture goods. Unlike traditional manufacturing, where objects are essentially cut out of a piece of raw material, 3D printing builds the object up, layer by layer. This is why it’s called “additive manufacturing”; you’re adding layers via the 3D printer until the object is complete.  
    • Quote (P. 262): “In other words, you start from nothing and build the object up bit by bit, as opposed to starting with a block of material and cutting or shaping it down into something. . . But let’s take a step back from that. Before printing anything, you need a 3D model of the object you’re trying to create — a digital blueprint, if you will. That blueprint or model is then ‘sliced,’ essentially dividing the model into hundreds (or potentially thousands) of layers. This information is fed to the 3D printer and, hey presto, it prints the object slice on top of slice on top of slice.”
  • 3D Printing: Potential Impact — If 3D printing becomes widely used in manufacturing plants around the world, it could have a huge impact on everything.  For example, using 3D printing the factories of the future could quickly print spare parts for machinery on-site, without having to wait for those parts to be shipped halfway around the world. Entire assembly lines could be replaced with 3D printers, allowing objects to be created on the spot rather than having to order them and wait. 
    • Quote (P. 268): “At the time of this writing, 3D printing is far from commonplace, but as the examples in this chapter have shown, the technology has the potential to challenge traditional production methods.”
  • 3D Printing: How It’s Being Used — You can use almost any material in the 3D printer: plastic, metal, powder, concrete, liquid, and even chocolate. Some labs are even experimenting with creating body parts. The possibilities are almost endless with 3D printing. A few other interesting ways 3D printing is being deployed: 
    • Manufacturing — 3D printing is really catching on in the car manufacturing business. Three out of four automotive companies in Germany and the US — including companies like BMW and Ford — are using 3D printing to mass produce car components and spare parts.
    • Human Tissue — At the Wake Forest Institute for Regenerative Medicine, researchers have been able to print bones, muscles, and ears — known as bioprinting — and implant them successfully into animals. What’s key is that the printed tissue survived after being implanted and became functional tissue.
    • Food — Choc Edge sells 3D printers that allow chocolatiers to design and produce amazingly inventive chocolates in pretty much any shape or design. As with any 3D printing process, the shape is sliced into layers, which are then built up by the printers in one ultra-thin layer of melted chocolate at a time. The chocolate cools and sets as it’s printed. Hershey has also been experimenting with 3D printed chocolate.
    • Buildings — Dubai has set an ambitious goal of 3D printing 25% of buildings by 2030 and is working with 3D printing construction firm Cazza to achieve that aim. Using 3D printing robots, the company is planning to create new large-scale developments of low-rise buildings in Dubai.

Ch. 25: Nanotechnology and Materials Science

  • Nanotechnology & Materials Science — Nanotechnology essentially means controlling matter on a tiny scale, at the atomic and molecular level, while materials science is the study of materials — characteristics, properties, uses, and so on — to understand how various factors influence a material’s structure. These trends go hand in hand: understanding how atoms work and at the molecular level (nanotechnology) allows us to create materials that are stronger, more flexible, etc. (materials science).
  • Nanotechnology — A quick review of nanotechnology is in order. Everything around us (e.g. chair, sofa, shoes, socks, book, etc.) is made of atoms and molecules (which are atoms linked together). Nanotechnology is about looking at the world on such a tiny scale that we can not only see the atoms that make up everything around us (including ourselves), but we can manipulate and move those atoms around to create new things. In this way, nanotechnology is a bit like construction but on a tiny scale. 
  • Nanotechnology: Manipulating Atoms — A diamond and the graphite in a pencil are good examples of the core ideas around nanotechnology. Both items are made of carbon: when the carbon atoms bond in one way, you get a diamond; when they bond another way, you get graphite. Nanotechnology is about manipulating atoms and molecules in a way that allows us to create better materials, thereby improving our lives. Today, tiny, computer chips, transistors, and smartphone displays are all being built using nanotechnology and materials science.
    • Quote (P. 274): “In another example, silk may feel incredibly soft and delicate to the touch, but zoom in to a nano level and you’d see it’s made up of molecules aligned in cross-links, which is what makes it so strong. We can then use knowledge like this to manipulate other materials at a nano level to create super-strong, state-of-the-art materials like Kevlar. Or products that are lighter. Or fabrics that are stain resistant. This is where the technology bit of nanotechnology comes in — using our knowledge of materials at a nano level to create new solutions.”
    • Quote (P. 275): “Many of the practical applications of nanotechnology are seen in manufacturing, where the technology is being used to create innovative products that are stronger, lighter, and more durable — products that perform better, in other words.”