AI is changing every field we have, but not all those fields are embracing it with the same enthusiasm. The proliferation of artificial intelligence has a lot to do with what society deems relevant or fascinating at the moment. The AI Index recently highlighted a report (CAPIQ, Crunchbase, QUID, 2019) outlining which sectors feature the most AI investments – with a few surprising results.
The Deeper Significance of Self Driving Cars
We’re still after the mythical self-driving car, even with some notable and dangerous failures. Self-driving vehicles have the potential to work with smart cities to ease traffic even as more people head to urban living. They could also cut down on labor costs for deliveries while aiding the same-day status we crave (and expect, now thanks to Amazon).
The problem is more than just simple algorithms. Computers are still largely inefficient at transferring learning across multiple circumstances. The pursuit of self-driving cars is also, in some ways, the pursuit of computers that learn like humans do.
The algorithms are getting closer, but for now, it remains disconcertingly easy to fool a computer’s processors with some well-placed stickers or even data with glaring oversights. As we continue the race for the perfect self-driving car, we’re also looking to perfect the learning curve for machines in general.
That type of learning would be valuable across industries and disciples, and organizations know it. Autonomous driving takes the most significant chunk of the investment pie because there’s so much at stake here. Robot automation, a related field, has also shown the most significant growth, according to Crunchbase.
Drugs and cancer study occupies the next biggest piece of AI investment. While it’s nowhere close to autonomous driving in terms of numbers, this is a significant field for a few different reasons.
Investments that reduce the overall cost of experimentation have the chance for enormous windfalls when the algorithms turn out to be right. Drug companies could counteract years of criticism for “price gouging” popular drugs, and the constant quest for new, improved medicines, which takes a significant toll on operations.
AI, combined with experts in the field, offers the best chance to build new methods of drug discovery, including the ultimate prize, breakthroughs in cancer research. And because cancer is such a mystery to us, it could take the efforts of massive data processing – something humans can’t do – to unlock patterns we’ve missed.
Companies like Fresenius Medical Care, Flatiron Health, and Alexion Pharmaceuticals, for example – all of which have representatives delivering talks and workshops at ODSC East this year – understand the way AI will play a role in the future of healthcare, biotech, and pharmaceuticals. With the emergence of massive amounts of unstructured data, AI could prove to be the only way forward.
The Controversy of Facial Recognition
Facial recognition is constantly in the news, but investments continue to pour in backing new software. These investments come from a surprising variety of places – anyone from governments to law enforcement to in-house advertising, despite the controversy.
Facial recognition isn’t all dystopian novels, but where our investments come from could direct usage for generations after. It’s not the Minority Report quite yet, but we could be looking to balance our privacy concerns with general safety and the social contract as these investments continue.
Facial recognition is taking the third biggest piece of the pie, but the consequences of this development remain to be seen. As governments scramble to take a stance on privacy concerns and enact regulations to control software with potential military applications, it could be a growing field.
One of the most surprising ways AI investments appear is in digital content. For a long time, we thought writing was a human endeavor, but machines have continually surprised us with their clarity. Quite a few major organizations are using AI to write the type of content human labor isn’t fast enough to create, providing up to date news and analysis.
On the opposite end of the spectrum, the supply chain continues to occupy the lowest rungs of AI investments despite the loud promise of efficiency and reduction in downtime. And despite marketing’s love affair with chatbots, this “next-generation marketing” method showed the lowest growth in AI investing.
So what does this tell us? In some cases, it hints at a very human way of making decisions – if it isn’t broken, don’t fix it. Digital media has struggled to remain profitable in the era of online sales, and one way to remain solvent is by generating the content people crave without increasing the budget for human writers. Problem equals solution.
For the supply chain, the field still operates on industrial revolution principles that don’t “not” work. Still, managers must take a long hard look at real numbers for product waste, downtime, and unplanned obstacles to unseat the old practice of handling it. Until that happens, those investments aren’t likely to spike.
Chatbots are plateauing thanks to advancements in NLP that allow people to interact with those bots long enough to free up human customer service for higher-order tasks. For the moment, there’s nothing to innovate. It works, and that could be a reason for stagnating investments.
Looking Forward with AI
Investment trends tend to follow a reactive attitude, i.e., a problem becoming too much to handle the old way, but not all of our investments go that route. Investing strategies can sometimes come as a surprise.
What we are seeing is a continued commitment to developing AI to automate, create efficiency, and cut down on waste, things humans aren’t always so great at. Even in creative fields like digital content, AI can ease burdens for rote content and provide pathways for information providers to stay profitable in a changing digital world. The takeaway from this study? AI is here to stay.
Check out the ODSC East 2020 Virtual Conference for even more ways AI is playing out in a variety of industries. With training on NLP, deep learning, and data visualization (among many, many more), you’ll get a good look at how data science is transforming real-world industries beyond theory.