A recent Bloomberg Businessweek article titled “The Future of AI Depends on a Huge Workforce of Human Teachers” discussed what to expect in the future of artificial intelligence.
It’s clear from this article that the tech industry and professional investors are betting big on AI. But what the Bloomberg article shows is they are specifically putting their money (more than $50 million in 2017 alone) towards data labeling startups who collectively employ over 1 million people worldwide to train AI software. The bet they’re placing is on a technique known as supervised machine learning.
Supervised machine learning is a subsection of artificial intelligence and one of two popular ways which computers learn:
- In supervised machine learning, a computer model is fed raw inputs that are already labeled. This method is the equivalent of technology learning like a student working with a teacher. The teacher (or human) provides examples to the student (a computer) of what is right and wrong, correcting the student’s work along the way. In this method, the student learns the general rules of a subject and applies these lessons to predict the right answer to a new question in the future.
- In unsupervised machine learning, a computer is fed raw inputs without any labels to analyze. The result is that the computer must find patterns in that data on its own without any human assistance. This method is the equivalent of trying to learn a foreign language by reading millions of pages of untranslated text without the assistance of an instructor, verb conjugations, or vocabulary dictionary.
If you look at the best applications of AI, they need humans to provide feedback on what is right and wrong. For example, Tesla Autopilot, a driver assist feature offered by Tesla Motors, uses supervised machine learning to train its self-driving technology. In this case, Tesla Autopilot is taught how to drive by the human owners who are operating their cars every day. As Tesla owners drive their cars, sensors in their vehicles collect hundreds of millions data points on driving, from GPS coordinates to actual videos captured from the car’s front and rear cameras. The vehicles then wirelessly send this data back to Tesla’s Autopilot data model, creating a massive library of knowledge around how to drive. The human feedback loop is essential here because if there is an error, the damage could be catastrophic.
By creating this library with the help of human drivers, Autopilot can use visual techniques to break down the videos and understand why drivers reacted the way they did. So, when a ball or child crosses the self-driving car’s path, Autopilot recognizes the pattern and reacts accordingly—stop!
Utilizing AI in Media Intelligence
Many automated media monitoring solutions say they use artificial intelligence and machine learning. But if there is no dedicated human analyst anywhere in the process to check or label the input data (i.e. your organization’s media), it means one of two things:
- The results you’re getting from your company’s media intelligence solution are inaccurate because it uses unsupervised machine learning. The solution using AI is teaching itself and you probably shouldn’t be counting on it to make informed decisions without labeled data.
- You, the customer, are expected to be like the coders discussed in the Bloomberg article. This means taking the time out of your own schedule to train the algorithms on what is right and wrong or hiring someone to do so.
Some people say a supervised machine learning solution is expensive, but supervised machine learning boosts media intelligence accuracy and helps communications pros make better decisions. To get accurate data, you need to either hire an analyst to train the algorithm so that it learns the right way, or spend time doing it yourself. Otherwise, you may end up paying in a different way with inaccurate results.
How AI Can Affect Media Intelligence in the Future
AI and machine learning are going to have an enormous impact on public relations and media intelligence. AI will give you more than just analytics, it will give you answers to what is happening in your media coverage and why – all on demand. Not only that, but it will forecast what topics could be a problem in the future, where those problems will occur, and how long the problems may last. These predictive analytics will make you proactive rather than just reactive.
As AI gets “smarter”, it will make you better and more prepared. But AI will only become as smart as the human teachers who train it along the way. It’s a virtuous upward cycle of humans and technology making each other better. Humans can train the machines, and pick up the last pieces of the most complex analyses that AI can’t like idiomatic phrasing and sarcasm. If done right, the benefits are truly worth the investment.
PublicRelay continues to invest in AI. In fact, supervised machine learning is embedded into our solution both in terms of making the analysis better and faster as well as in the outputs to the client.
Ted Ziemer is the VP of Strategy & Business Development at PublicRelay