If you launched a product that took 50 years to gain traction, it would likely be considered a failure. But it has taken some-50-odd years for machine learning to grow from concept to one of the hottest topics in tech, and it would be hard to find anyone who considers it a flop.
Machine learning was first introduced in the 1950s. The idea was to create systems that allow machines to learn how to complete tasks on their own. While this sounds like the foundation for an android race that will overtake and subjugate humans, it’s actually powering many of the platforms that protect us (and some that are just convenient and fun).
You can find machine learning in:
- Spam filters – to keep opportunities like “Earn $40/hr. No experience & no ID required” from making their way into your inbox.
- Fraud detection – to flag that order for 15,000 “Golden State Warriors 2016 NBA World Champions” shirts that was placed on your Visa last June by an enterprising entrepreneur.
- Voice recognition – to answer all your pressing questions. “Hey Siri, who is Taylor Swift dating today?”
- Security cameras – to recognize suspicious activity (like your downstairs neighbor who steals your AmazonFresh deliveries) and alert authorities.
Machine learning, marketing and you
Machine learning has transformed the world of marketing as well. Today there are systems that crunch through millions of gigs of data to provide you with predictive analytics. These nuggets of information give you insight into the actions your customers are likely to take, giving you the opportunity to optimize communication and experiences to generate better results. Data from these experiences can then be crunched to generate even more accurate predictions!
As this cycle repeats, these continually refined predictions could – in theory – get us to a place where we can perfectly target and convert every qualified prospect.
One of the wonderful things about this field is how rapidly things are moving thanks to an open community that freely shares research, ideas and breakthroughs. In fact, some of the most robust and advanced machine-learning software libraries, such as TensorFlow and Theano, are open-source platforms that allow anyone to apply this science to their own applications.
Key(words) to the future
Take Bynder AI as an example. It utilizes machine learning to recognize the image and provide a list of recommended keywords to use as tags. This gives teams a smarter, faster way to add the critically important metadata that ensures assets are always organized and easy to find.
Bynder AI is just the beginning. Imagine a DAM that analyzes user habits to better predict their actions the same way a company like Amazon does with its customers. That means surfacing things like the most popular content, similar assets or the assets users need to complete their jobs. Tomorrow’s DAM could automatically serve up the “perfect” assets to users while removing the guesswork from and reducing time spent searching for content manually.
Machine learning has revolutionized so much of the technology we interact with on a daily basis. And as this field continues to advance, it’s opening up more and more possibilities for software platforms of all types. Bynder is actively involved in utilizing machine learning to make our products quicker, easier and even more effective – you might say we’re in the middle of a DAM revolution.
