.jpg)
.png){kind=icon}
Artificial intelligence. It's a term everyone knows, but few understand. It's a term Hollywood dramas have coined to represent robots like in Westworld and The Matrix. It's also one that—like blockchain—is slapped onto a company's value-add so they can ride on the coattails of the hype.
Today's technology news is saturated with articles about AI. So much so that when we read about AI in the press, most people associate the technology with anthropomorphic systems that will take over the world. The reality of AI is that this technology is slowly permeating everything we do, whether it's how we communicate on a social network, how we prioritize our work, or how the data trail we create is being analyzed to determine what we might do next.
But AI isn't and shouldn't be construed as onerous. Companies are working on innovative applications of AI that could improve our lives. So how do we take a thoughtful approach to a loud, tired trend and rise above the noise?
Here are three ways.
1. Take a step back
When every week we read about new natural disasters plaguing parts of the world, it's challenging to inspire readers (and journalists) with a story about how AI is helping companies make more money. While predictive analytics in its own right can be an exciting storyline, it's important to take into account the context with which we tell a story.
Many people have privacy concerns about AI technology, especially individualized privacy. In today's landscape, readers are hypersensitive to the disturbing aspects of AI from the spreading of fake news to the amplification of racist and sexist biases. So a story about how AI is helping companies make more money by replacing human jobs with computers can come off as tone-deaf or trite. Whenever you're looking to tell a story, regardless of whether it's AI-related or not, it's important to take a step back and gain a broader perspective. How does this story positively affect the world at large?
Palo Alto-based satellite imagery analysis company, Orbital Insight, has AI baked into its core offering. The company uses neural networks and machine learning to analyze the thousands of satellite images beaming down from space every day to gain a better understanding of the world, and most importantly, a better understanding of how the world changes. Orbital Insight's technical chops are the result of a CEO formerly at Google Books and NASA and a whole team of engineers, machine learning experts, and data analysts working towards this common goal: using AI to take the pulse of economic and societal activity in the world.
While Orbital Insight has many interesting use cases from monitoring global oil supply to tracking sales at large retail chains, most recently, the company measured flooding during Hurricane Harvey by analyzing a new type of satellite technology called synthetic aperture radar. SAR imagery allows Orbital Insight to see through clouds and explain what's happening on the ground, which is particularly useful when about 70% of the earth is usually covered in clouds. In the case of Hurricane Harvey, Orbital Insight was able to measure the extent of flooding during the hurricane so that insurance companies can react accordingly.
Similarly, Orbital Insight works with nonprofits such as the World Bank and World Resources Institute, on poverty and deforestation mapping. These projects have historically been large undertakings for organizations and have typically resulted in semi-accurate data because of the lack of resources and infrastructure in the regions being monitored. With the help of Orbital Insight and AI, these organizations can not only more accurately track but also predict where deforestation and poverty are going to show up next, which is immensely useful for a better allocation of resources.
Stories like these—where the human element is at the forefront, and AI takes a backseat—are one of the most successful ways to tell a tech story. So often, the tech industry gets caught up in technology for the sake of technology that we lose sight of why we are innovating in the first place.
2. Dig into the technology
Going back to how everyone knows about AI but few fully grasp what AI is, there's a missed opportunity for many AI companies who don't dig into the technology. Artificial intelligence, machine learning, predictive analytics, computer vision, bots, neural networks, algorithms; there are many names for what we consider AI and they're all used interchangeably without little or no regard for the real technical differences between each other. AI is the umbrella term that broadly encompasses the various ways machines attempt to replicate functions of the human brain like learning, problem-solving, and predicting.
With all of the confusion about what AI consists of, it is our responsibility to communicate the technical capabilities (and limitations) of the technology, and the more specific we get, the better. For example, Mapillary is a computer vision (AI that mimics our vision capabilities by analyzing imagery or video) startup that crowdsources images to build maps of the world. A couple of years ago, while most people were talking about the algorithms as the "special sauce" for AI, Mapillary's founder, Jan Erik Solem, a computer vision expert, dug into the importance of the data. He argued that data is more powerful than algorithms and for the need for collaboration within the industry to properly train AI algorithms.
“Every system you train is going to be biased based on the inputs,” said Solem to TechCrunch. “The output is directly correlated to the input. You can train a system in the U.S., but it’s not going to be the same in the rest of the world. If you don’t have tuk-tuks in your training sets on the streets in Michigan, you’re not going to detect a tuk-tuk on a street in India.”
3. Counter an existing storyline
Another creative way to talk about AI is to take a different opinion to the consensus. For example, there are two leading schools of thought about the future of AI, which can be boiled down to Mark Zuckerberg versus Elon Musk. In the Musk point of view, there's a call for regulation because AI represents a “fundamental risk to the existence of civilization.” While usually brushed off as an alarmist "doomsday" opinion, this stance more broadly encompasses the idea that companies are spending too much time on the development of AI to reduce costs and cut corners and not enough time thinking about the greater societal consequences of doing so.
In the Zuckerberg camp, you have a more optimistic point of view, where AI is going to affect every industry from healthcare to automotive positively. The reality is of course, somewhere in the middle. As media specialists, it's our job to pay attention to the storylines being discussed and find a way to add to the discussion.
A favorite storyline is how AI is taking away jobs. While this is true of all technology—think about how farming machines changed agriculture—this was a very common storyline when AI first went mainstream. However, like all good stories, AI is more nuanced than that. After the first-wave AI freak-out settled down, reporters and tech companies alike began asking smarter questions about the Fourth Industrial Revolution and the future of work. How is AI augmenting human productivity? How can AI technology create opportunity? How can we take a new approach to education in light of job automation? What role could (and should) the government play during these rapidly changing times?
Early stage VC fund, Bloomberg Beta did just that when it partnered with The New America Foundation to bring together 100 leaders in technology, business, policy-making, and culture to imagine what the future of work might look like in 10 to 20 years. Roy Bahat, the founder of Bloomberg Beta, said that the commission wanted to embrace the complexity of the topic by building out various scenarios of the future of work ranging from traditional employment to the gig economy and income instability. The result was a collection of situations and solutions that were both complementary and contradictory. “I think we live in a time of paradox,” Bahat told Fast Company, admitting that the topic isn't black and white.
While it's natural to ask the easy questions first—"Is AI replacing humans?" "Is AI good or bad?"—there are many ancillary and more nuanced questions that we need to be asking as well. Ultimately, if you seek to find answers to the more thoughtful questions, you'll be able to play a vital role in moving the discussion forward, rather than just offering another wall for the echo chamber.
