What is AI? The Human's Handbook to Computers that Think

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 subdiscipline, 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. Computers that see, hear, sense, and speak

Before we move on, a word on artificial general intelligence, which does not exist. AGI is a theoretical AI system that could perform every human intellectual task at parity with us or (more likely) at a superhuman level. By Emerging Tech Brew’s estimation, we’re still decades away from that tipping point—known as the singularity—assuming it happens at all.

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.

III. 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%.

China has invested billions to reach technological parity with the U.S.. In 2017, China released a national strategy called the New Generation AI Development Plan. In the “Made in China 2025” industrial strategy blueprint, the country said it aims to be the global AI leader by 2030.

• “AI will be the first GPT in the modern era in which China stands shoulder to shoulder with the West in both advancing and applying the technology. During the eras of industrialization, electrification, and computerization, China lagged so far behind that its people could contribute little, if anything, to the field,” AI expert Kai-Fu Lee predicts in his book.

China is competitive with the U.S. in AI commercialization in 2020. But it’s not driving as many fundamental research breakthroughs—and likely needs until 2025 before it could reach a tipping point. The countries have distinct competitive advantages:

• U.S.: Highest quality algorithms, best talent pool, superior R&D. Also, the key AI hardware sectors—like semiconductors—are dominated by U.S. companies.

• China: Fewer privacy constraints, largest Internet market by users, huge state subsidies, and a more coherent overarching strategy.

The EU’s domestic tech sector is not as robust as its American and Chinese counterparts. But the continent is a top producer of the best AI researchers, a key market for tech companies, and a powerful regulatory bloc creating its own rules for data and AI governance.

At a macroeconomic level, AI can boost productivity and wealth. It also increases job destruction and inequality. The U.S.’ Rust Belt corridor shows the physical pains of the country losing 5 million manufacturing jobs since 2000, due to the twin forces of automation and globalization. Midwest states also have the U.S.’ highest rates of robot density.

What’s new with today’s intelligent automation? The scope of physical and cognitive work that can be automated. Some jobs could be engineered into obsolescence, although most will be reskilled and not outright deskilled. In 2017, McKinsey predicted 15% of the global workforce’s “current activities” will be automated by 2030.

• Policymakers and Big Tech firms are weighing a range of responses, including universal basic income and mass reskilling programs.

• Worth noting: The most valuable companies in the world are highly labor efficient, meaning they don’t need many employees.

Wharton School at University of Pennsylvania professor Lindsey Cameron told Emerging Tech Brew that when cars arrived on the scene, “people wondered what was going to happen to blacksmiths, and what happened was displacement but then eventually the creation of new jobs. And I think in the long run, that’s what I see with AI.”

“There will be a lot of new jobs created, but in the short term there will also be a lot of pain because upskilling can’t happen in time.”

At a social level, we’re all key players. We’re end users of AI, but we’re also subject to the whims of imperfect algorithms. AI is used to identify potential terrorists, make hiring decisions, set bail, predict criminal recidivism, and recommend medical treatments. When algorithms misfire in high-stakes situations, the consequences are disportionately shouldered by communities of color and women.

AI ethics experts have ideas for mitigating risk: Companies should build diverse technical teams and open up their algorithms for audits and independent oversight. Technologists should use data reflective of an entire population when they train and deploy algorithms.

• The EU is moving to more closely scrutinize and regulate AI in high-risk applications like healthcare, policing, and transportation.

• In the U.S., the Pentagon adopted five ethical principles to steer its AI usage—it needs to be responsible, equitable, traceable, reliable, and governable.

• The EU’s GDPR established a legal framework for data processing and obtaining user consent. Some U.S. politicians believe AI vendors should obtain consent from users before processing sensitive personal or biometric data.

Nkonde, who worked on the Algorithmic Accountability Act, told us regulation is necessary to reign in harmful AI, both around what can be released into the marketplace and around algorithmic transparency.

“Transparency in terms of consumer products is very important,” she said. “When we’re dealing with algorithms I think it’s unfair to make people into computer scientists in order to understand what they’re buying. When we’re thinking through AI systems it needs to be something that’s really explainable—three points or less—that speaks to the social impact.”

IV. 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.