The future of AI and synthetic life … China’s new world order and the trust economy … How to explore the tech trends, their implications, and apply them to business

March 16, 2020

When is a strategic trend, just a passing fad?

Distinguishing between strategic shifts and current fads is not easy. The easiest way to cut through hype is to remember that trends are driven by fundamental shifts in demographics, the economy, technology, politics and social movements. They are new manifestations representing our fundamental human needs. Trends form steadily over many years, and they do not necessarily follow a linear path. Fads are much more transient.

Strategic trends share a set of conspicuous, universal features, which Amy Webb in her book The Signals Are Talking: Why Today’s Fringe Is Tomorrow’s Mainstream calls the Four Laws of Tech Trends:

1. Trends are driven by basic human needs.
2. Trends are timely, but they persist.
3. Trends are the convergence of weak signals over time.
4. Trends evolve as they emerge.

Typically all four features are present in an authentic strategic trend.

Disruptions drive trends

Disruption usually stems from influential sources of macro change. It is a way of understanding where disruption is coming from and where it’s headed next. The sources of macro change represent external uncertainties—factors that broadly affect business, governing and society. They can skew positive, neutral and negative.

  • Wealth Distribution: The distribution of income across a population’s house- holds, the concentration of assets in various communities, the ability for individuals to move up from their existing financial circumstances and the gap between the top and bottom brackets within an economy.
  • Education: Access and quality of primary, secondary, and post-secondary education, workforce training, trade apprentice- ships, certification programs, the ways in which people are learning and the tools they’re using and what people are interested in studying.
  • Infrastructure: Physical, organizational, and digital structures needed for society to operate (bridges, power grids, roads, wifi towers, closed-circuit security cameras), the ways in which the infrastructure of a city, state or country might impact another’s.
  • Government: Local, state, national, and international governing bodies, their planning cycles, their elections and the regulatory decisions they make.
  • Geopolitics: The relationships between the leaders, militaries and governments of different countries, the risk faced by investors, companies and elected leaders in response to regulatory, economic or military actions
  • Economy: Shifts in standard macroeconomic and microeconomic factors.
  • Public Health: Changes in the health and behavior of a community’s population in response to lifestyles, popular culture, disease, government regulation, warfare or conflict and religious beliefs.
  • Demographics: Observing how birth and death rates, income, population density, human migration, disease and other dynamics are shifting communities.
  • Environment: Changes to the natural world or to specific geographic areas, including extreme weather events, climate fluctuations, sea level rise, drought, high or low temperatures and more. (We include agricultural production in this category.)
  • Media and Telecommunications: All of the ways in which we send and receive information and learn about the world. This includes social networks, news organizations, digital platforms, video streaming services, gaming and e-sports systems, 5G and the boundless other ways in which we connect with each other.
  • Technology: Not an isolated source of macro change, but rather, as the connective tissue linking business, government and society. For that reason, we always look for emerging tech developments, as well as tech signals within the other sources of change.

Mapping the future

The Future Today Institute uses a Time Cone to represent years and certainty on the axes, and then a matrix to translate trends into implications for organisations and how they can embrace them depending on their tangibility:

With the benefit of both hindsight and strategic foresight, frameworks such as these identify risk and highlight the best emerging opportunities. The challenge for business leaders is to make connections between emerging trends, business and society, especially when there is such a huge mass of technologies and trends that will shape the world of tomorrow.

Tech trends that matter 

You can download the Future Today Institutes fabulous new 386-page report here: Tech Trends 2020.

Some of the big trends emerging in the report include

  • AI systems that can be trained in hours rather than weeks
  • Widespread availability of algorithmically-traded funds
  • Off-planet human civilization
  • Bioengineered animals, plant-based proteins and indoor robot-powered farms
  • Autonomous cars, trucks, ships and fighter jets
  • Exascale computing
  • Quantum computing
  • Functional 5G networks

Here is a summary of some of the more interesting insights:

#1:  The Synthetic Decade.

From digital twins to engineered DNA to plant-based pork sausages, a deep push to develop synthetic versions of life is already underway. We will look back on the 2020s as an era that brought us synthetic media, such as AI-generated characters whose storylines we follow on social media and humanlike virtu-
al assistants who make our appointments and screen our calls. Soon, we will produce “designer” molecules in a range of host cells on demand and at scale, which will lead to transformational improvements in vaccine production, tissue production and medical treatments. Scientists will start to build entire human chromosomes, and they will design programmable proteins. Foods made from synthetic techniques rather than artificial ingredients will make their way to the mainstream, and you’ll have a wide array of choices: humanely engineered foie gras, flora-derived ice cream and molecular whiskey made in a lab. Every indus- try will be impacted as our synthetic decade brings new business opportunities and strategic risks. Companies will need to ask challenging ethical questions and examine the security risks for synthetic material in order to earn public acceptance, government approvals and commercial appeal.

#2:  Augmented hearing and sight.

While you shouldn’t expect to see everyone wearing smart glasses by this time next year, you will certainly start to notice some important developments throughout 2020, beginning with audio augmented reality (or AAR). Think of it as augmented reality for audio. Smart earbuds and glasses will digitally overlay audio (like directions, notifications, and verbal descriptions of what — or who — you’re looking at) without others hearing and while you continue to hear what’s going on around you. Not only will AAR help runners stay safe, it offers a sophis- ticated alternative to traditional hearing aids. Smart glasses won’t look like the minimalistic Google Glass headband, but rather a stylish pair of frames you’d find at your local optometrist’s office. Google, Amazon, Apple, Microsoft and Face- book all have connected systems on their product roadmaps. The connected glasses and AAR ecosystem offer tremendous new business opportunities—and could signal disruption to longtime market leaders in frames, prescription lens- es, hearing aids and headphones.

#3:  AI-as-a-Service and Data-as-a-Service

The future of digital transformation is rooted in two key areas: AI-as-a-Service and Data-as-a-Service. Microsoft, IBM, Google, Amazon, Facebook and Apple are all developing new services and tools ranging from robotic process automation to offering GPUs (graphics processing unit) in the cloud. Amazon’s upcoming project, AWS For Everyone—a low-code/no-code platform built to enable anyone to create business applications using their company data—will be a huge differentiator when it launches.

#4:  China’s new world order

The growth of China’s economy might be slowing, but it would be a mistake to assume that the People’s Republic of China has lost its influence. In the past two decades, China overtook the U.S. as the world’s dominant exporter on every continent with the exception of North America. Its imports matter, too: This year China should surpass the U.S. and become the world’s largest movie mar- ket, with a projected $10 billion in revenue. China has a rapidly-expanding middle class, an educated and trained workforce and a government that executes on long-term plans. China will continue to assert prolific dominance in 2020 across multiple areas: diplomacy throughout Southeast Asia, Africa, Latin and South America and Europe; the development of critical digital infrastructure; artificial intelligence; data collection and scoring; bioengineering and space.

#5:  Home and office automation

An Alexa in every pot and a self-driving car in every garage? Nearly 100 years ago Herbert Hoover promised Americans they would prosper under his presidency: a chicken in every pot, and a car in every garage. Today, AI-powered digital assistants, home security systems and voice-controlled microwaves are being manufactured—and priced—for the masses. Robots used to be the stuff of science fiction, but this year major appliance manufacturers, component makers, and of course, the big tech companies will make compelling arguments for why our homes and offices should be outfitted with sensors, cameras and microphones. Next-generation network infrastructure should speed adoption. The global market could reach $214 billion by 2025. Which company’s operating system controls all those devices, and what happens to the data being collected, will spark public debate.

#6:  Everyone is being scored

In order for our automated systems to work, they need both our data and a framework for making decisions. We’re shedding data just by virtue of being alive. From our social media posts, to our unique biology (posture, bone and capillary structures, vocal pitch and cadence), to our credit card debt, to our travel habits, thousands of data points are analysed to score us. Automated systems use our scores to make decisions for or about us, whether it’s what price to show us on e-commerce sites or if we might pose a security risk at a football game. We anticipate that in the coming year, regulators will take a deeper inter- est in scoring.

#7:  The rise of fear

In the 2010s Facebook, Instagram, Snapchat, Reddit, Foursquare and Twitter caused a “fear of missing out.” Those very same networks (save for the now-defunct mobile social app Foursquare) are being used for intentional—and some- times unwitting—scaremongering. On Facebook, Baltimore Mayor Bernard “Jack” Young helped propagate a wild—and totally false—story on Facebook about a white van abducting girls for human trafficking and for selling body parts. Numerous times, President Donald Trump has used Twitter to stoke fear, telling the public about armed “large [sic] Caravans” that were “invading” America. On Twitter, he has publicly threatened the leaders of other countries: “North Korean Leader Kim Jong Un just stated that the “Nuclear Button is on his desk at all times.” Will someone from his depleted and food starved regime please inform him that I too have a Nuclear Button, but it is a much bigger & more powerful one than his, and my Button works!” on January 2, 2018. Social media posts like these are often repeated at rallies and protests, which only serve to amplify our fear. The Anti-Defamation League discovered a 226% increase in acts of vandalism and hate crimes in the counties hosting Trump rallies. We’re continually told that we need protection: from unsafe countries, people and even our neighbors. Fear is good for business. Amazon bought smart doorbell company Ring for $1 billion, and it now has lucrative partnerships with more than 400 U.S. police departments to share recognition tech and surveil- lance video from users’ homes.

#8:  Nothing is forgotten

After a decade of posting photos, videos and written messages on social media, it’s now clear that our recent histories will persist far into the future. It isn’t possible to truly delete or erase our pasts. A centerpiece of the European Union’s landmark internet laws, the “right to be forgotten,” emerged as a stan- dard intended to force search engines to delete links to personal information if it wasn’t in the public interest. But in 2019, the European Court of Justice ruled in Google’s favor, making it much harder for people to request that negative, pri- vate or misleading information about them is removed from internet searches. A Google search team member put it more bluntly: “We’re not a truth engine.”

#9:  The new trust economy

We will soon see a host of new tools built to engender and ensure—but also ma- nipulate—our trust. In the wake of deepfake videos and other manipulated con- tent, a new ecosystem devoted to trust is in the process of being formed. There’s a lot at stake: After hearing an AI fake his CEO’s voice on the phone, a gullible employee transferred $243,000 to a scammer. In the coming year, sentinel surveillance systems will algorithmically detect manipulated content—for a fee. Meanwhile, governments and interest groups around the world will try to shape the future development of A.I. and blockchain technology, proposing legislation and “bill of rights”manifestos.

The Future of AI

Artificial intelligence represents the third era of computing, one that could usher in a new period of productivity and prosperity for all.

It has potential to act as a force multiplier for good, helping to address humanity’s most complex challenges: how to mitigate climate change, how to increase the global food supply, how to develop safer infrastructure, how to manage cyberse- curity threats and how to diagnose and eradicate diseases.

However, AI also carries risks: gender, race and ethnic bias continues to negatively influence the criminal justice system; countries differ in their regulatory approaches; it enables the creation and spread of fake news and misinformation; it threatens privacy and security; and it will inevitably displace swaths of the workforce. There is no central agreement on how AI should develop during the next several decades.

In its most basic form, artificial intelligence is a system that makes autonomous decisions. AI is a branch of computer science in which computers are programmed to do things that normally require human intelligence. This includes learning, reasoning, problem solving, understanding language and perceiving a situation or environment. AI is an extremely large, broad field, which uses its own computer languages and relies on computer networks modelled on our human brains.

The global AI market should grow 20% annually between 2020 and 2024, while global economic growth generated by AI could reach $16 trillion by the end of this decade.

Weak and Strong AI

There are two kinds of AI: weak (or “narrow”) and strong (or “general”). Narrow AI systems make decisions within very narrow parameters at the same level as a human or better, and we use them all day long without even realizing it. The anti-lock brakes in your car, the spam filter and autocom- plete functions in your email and the fraud detection that authenticates you as you make a credit card purchase— these are all examples of artificial narrow intelligence. Artificial general intelligence (AGI) de- scribes systems capable of decision-mak- ing outside of narrow specialties. Dolores in Westworld, the Samantha operating system in Her, and the H.A.L. supercomputer from 2001: A Space Odyssey are anthropomor- phized representations of AGI—but the ac- tual technology doesn’t necessarily require humanlike appearances or voices.

There is no single standard that marks the distinction between weak and strong AI.

This is problematic for researchers covering AI developments and for managers who must make decisions about AI.

In fact, we have already started to see real-world examples of functioning AGI. In 2017 researchers at DeepMind, a lab owned by the same parent company as Google, announced that A.I. had taught itself how to play chess, shogi (a Japanese version of chess) and Go (an abstract strategy board game)—all without any human intervention. The system, named AlphaZero, quickly became the strongest player in history for each game. The team has been publishing important discoveries at an impressively fast pace. Last year, the DeepMind team taught AI agents to play complex games, such as the capture the flag “game mode” inside the video game Quake III. They, like humans, had learned skills specific to the game as well as when and how to collabo- rate with other teammates. The A.I. agents had matched human player ability using reinforcement learning, in which machines learn not unlike we do—by trial and error.

While we haven’t seen an anthropomorphic AI walk out of DeepMind’s lab, we should consider these projects as part of a long transition between the narrow AI of today and the strong AI of tomorrow.

Neural Networks and Deep Neural Networks

A neural network is the part of a system in which information is sent and received, and a program is the set of meticulous instruc- tions that tell a system precisely what to do so that it will accomplish a specific task. How you want the computer to get from start to finish—essentially, a set of rules—is the “algorithm.”

A deep neural network is one that has many hidden layers. There’s no set number of layers required to make a network “deep.” Deep neural networks tend to work better and are more powerful than traditional neural networks (which can be recurrent or feedforward).

Machine Learning and Deep Learning

AI pioneer Arthur Samuel popularized the idea of machine learning in 1959, explain- ing how computers could learn without being explicitly programmed. This would mean developing an algorithm that could someday extract patterns from data sets and use those patterns to predict and make real-time decisions automatically. It took many years for reality to catch up with Samuel’s idea, but today machine learning is a primary driver of growth in AI.

Deep learning is a relatively new branch of machine learning. Programmers use spe- cial deep learning algorithms alongside a corpus of data—typically many terabytes of text, images, videos, speech and the like. Often, these systems are trained to learn on their own, and they can sort through a variety of unstructured data, whether it’s making sense of typed text in documents or audio clips or video. In practical terms, this means that more and more human processes will be automated, including the writing of software, which computers will soon start to do themselves.

Nine big tech companies—six American, and three Chinese—overwhelmingly drive the future of artificial intelligence. In the USA, it’s the G-MAFIA: Google, Microsoft, Amazon, Facebook, IBM and Apple. In China it’s the BAT: Baidu, Alibaba and Tencent. Those nine companies drive the majority of research, funding, government involvement and consumer-grade applications of A.I. University researchers and labs rely on these companies for data, tools and fund- ing. The Big Nine AI companies also wield huge influence over AI mergers and acquisitions, funding AI startups and supporting the next generation of developers.




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