Updated: Jul 5
Compiled from Emerging Tech Brew | Ryan Duffy | August 2020
Computers got a lot smarter between 1968 and 2017. Artificial intelligence is not new, but it’s increasingly influential. We’ll return to definitions later, but for now, think of AI as the capacity of a machine to simulate human intelligence.
AI is already ubiquitous in your day-to-day life, ranking blue links on Google searches, blocking spam from your work inbox, providing your boss with marketing and sales leads, suggesting Amazon products and Netflix shows, sorting Facebook and TikTok feeds, and navigating you from📍A to📍B. That’s just the tip of the iceberg.
Now that we have your attention, we’ll turn down the galaxy-brain knob a bit. This guide provides the overview of what you need to know about AI today. No more, no less. Despite how far it’s come, AI is far from general intelligence or its anthropomorphized pop culture depictions.
I. How to Conduct Your Own AI Sniff Tests
Not everyone agrees on what’s considered AI. The goalposts are constantly shifting. We have five concepts that will help you be discerning in the real world.
Catch ‘em all: The field of “AI” is a catch-all computer science category, composed of tools and techniques that vary in sophistication. The field has grown and changed over the decades. The quest to engineer ever-smarter machines encompasses philosophy, biology, logic, neuroscience, and evolution. AI is a sticky term that ends up applied to bits of all of these disciplines, rightly or wrongly.
The AI effect: Also known as the “odd paradox,” this essentially means that a software technique loses its AI label once it becomes mainstream. According to this line of thinking, AI is only any task that machines can’t do yet. If a machine can do it, it’s not AI anymore.
And to clarify a few misconceptions:
AI isn’t inherently unbiased: In the U.S., the AI community skews white and male. This affects how AI systems are built and designed, as well as what training data they’re fed. Data can often be fundamentally biased itself. When bias creeps into algorithms, it can reinforce and even accelerate existing inequalities—especially in regard to race and gender. Ethical AI is a rapidly growing sub-discipline, which we’ll explore later.
For now, we’ll leave you with a story: In 2016, research scientist Timnit Gebru attended NeurIPS, a prestigious machine learning and computational neuroscience conference. She counted five Black attendees in the crowd of ~5,500 researchers. She says Black attendees’ representation at NeurIPS has increased but that it’s still relatively low.
AI ≠ full automation: Autonomy is a machine’s ability to do a task on its own. But it’s not a binary—it’s a spectrum. A system becomes more autonomous as it tackles more complex tasks in less structured environments.
Automatic systems can handle simple tasks, typically framed in terms of Yes/No.
Automated systems can handle more complex tasks, but in relatively structured environments.
Autonomous systems can perform tasks in unstructured, complex environments without constant input or guidance from a user.
A case study from cars: Automatic systems (transmission, airbags) do their thing after a certain trigger. Automated systems (Tesla’s Autopilot or GM’s Super Cruise) handle specific driving functions and must have human oversight. A fully autonomous vehicle can sense, decide, and act without human intervention. Just enter the destination.
Snake oil: One programmer’s AI may be another’s linear regression. Some startups, marketers, and sales departments are keen to exploit the fluidity of AI as a concept, dressing products up as “AI-enabled” even when it’s not true.
Companies have exaggerated the degree of automation even when their software still has mostly or only humans in the loop. And a 2019 survey found that 40% of European “AI startups” didn’t actually use the technology.
AI policy analyst and researcher Mutale Nkonde told us, “The truth is that much of what we’re buying is snake oil. We’re prepared to buy it because it taps into this fantastical piece of our brain, but we need to be very very suspicious of something that we cannot audit. And until those audit processes are in place, we shouldn’t assume that it does what it says it can do.”
II. Machines Go to School: A Brief History
AI hype and rosy exuberance are nothing new. In periods of retrenchment—famously known as “AI winters”—government funding and private investment in basic research dried up. Algorithmic innovation and performance plateaued. Media and the general public lost interest.
And while winter comes for AI, so does spring: AI innovation skyrocketed in the 2010s. This timeline captures just a fraction of recent AI developments.
III. Computers that see, hear, sense, and speak
AI is a grab bag of many techniques and terms. Here we’ll provide clarity about what matters for the business world. We’ll start with the key definitions first.
Today’s AI systems are “narrow” or “weak,” meaning they can handle specific problems. That doesn’t mean AI systems can’t cognitively compete with us and/or achieve superhuman performance levels in particular tasks. AI has bested humans in checkers, Jeopardy!, chess, Go, and complex role-playing video games.
IV. Putting AI to work
AI is frequently described as a general platform technology. GPTs, such as electricity and the internet, reshape entire societies, economies, and industries.
While we do believe AI has applications across virtually every industry, we don’t want to keep you here forever. We’ve handpicked 14 industries that AI could reshape. Our methodology = largest total addressable market. Simple as that.
V. The key players
At a geopolitical level, competition has been a primary driver of government AI investment and strategy. The world’s top two economies are also its AI superpowers.
It’s difficult to quantify AI sophistication, but talent is a good proxy. The U.S. has 59% of the world’s top-tier AI researchers, while China has 11%.
VI. One hundred years of AI
When world leaders invoke AI, they often describe its impact at a civilizational level. Executives from Silicon Valley to Shenzhen are equally animated when discussing the technology. That’s not a coincidence—the world’s leading technology firms are all AI powerhouses.
Investors are quick to fund new entrepreneurs in the space. In the second quarter of 2020, U.S. AI startups received $4.2 billion in funding, per CB Insights. Chinese companies received nearly $1.4 billion.
All this activity is a giant leap from the 1950s, when “artificial intelligence” was aspirationally coined on the leafy campus of Dartmouth. Today’s deep learning and neural nets required many decades of if-then statements, iterations, and new techniques. And yes, today’s AI systems are narrow, flawed, and at times harmful. But they’re layered across more devices, services, and businesses than ever before.
The people (or robots) writing the history books in 2050 probably won’t link a superpower’s rise and fall to its AI strategy. But they’ll definitely dissect AI’s technological disruption of jobs, economies, and societies. That narrative will have some good and some bad, but it’s truly impossible to predict.