2019 AI Index
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.
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.
Manufacturing
Decades ago, industrial manufacturers were the earliest adopters of industrial robots, which tended to be simple, automated systems performing repeatable tasks. Today’s robots are more intelligent, equipped with computer vision, cloud connectivity, sensor suites, and bespoke hardware. A new category of intelligent robots (collaborative bots, or cobots) is emerging that can more safely work alongside humans in unstructured environments.
Manufacturing also has non-obvious AI applications. Manufacturers use ML systems for predictive maintenance and quality assurance. Digitized factory operators can use AI to predict possible disruptions and optimize operational efficiency, which includes controversial tactics like surveilling and algorithmically managing floor workers.
Big picture: As global demand for goods climbs, so will the robotization of factories. More manufacturing automation could improve quality while decreasing costs. It could also displace up to 20 million jobs globally by 2030, which will disproportionately impact poorer and more rural countries.
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%, according to MacroPolo.
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.
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:
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.
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.
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.”
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.
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.
Tech Brew keeps business leaders up-to-date on the latest innovations, automation advances, policy shifts, and more, so they can make informed decisions about tech.