The business world often uses the terms machine learning, deep learning, and artificial intelligence as interchangeable buzzwords. The problem? Each is uniquely different from its siblings. With so much terminology describing different pieces of the same AI puzzle, it’s easy to misunderstand various components.
AI has been around for decades in business and government, but it’s still a relatively new addition to many sectors. The lines between data science and machine learning begin to blur for those unfamiliar with the sector, but it’s increasingly important for professionals to understand this technology that’s changing our world.
For instance, Facebook uses AI to scan photos (as does Google) to match people and information with advertisers. Netflix uses this technology to recommend programming and drive its content decisions. You’d be hard-pressed to name a major brand that is not at least researching how to leverage and implement AI into its business model.
Before shopping around for solutions, it’s helpful to have some knowledge of the mechanics behind this seemingly magical technology.
How are Machine Learning and Deep Learning Related?
My team has worked with machine learning for the past two years. In fact, we were among the first developers to build an AI chatbot — ours is called ShoutOut — for Google Home. Our bot allows users to utilize verbal cues to dictate birthday cards to family and friends in about 60 seconds.
This software relies on powerful machine learning algorithms. We coded our chatbot to recognize names, phone numbers, and natural language messaging — it understands slang and contextual language, among other things. The more real-world data we fed the bot, the more feedback we could collect. Over time, the software learns and improves upon the results it gives.
Natural language processing (NLP) is a powerful segment of machine learning, enabling software to detect the nuances of human speech, both verbally and in text. According to a recent study, 40 percent of large businesses use NLP for tasks like data analytics and customer service.
Here’s where deep learning comes into play.
Deep learning, while still a subset of machine learning, is a new and more complex way of analyzing massive amounts of data, which allows us solve problems that were impossible to solve before.
Deep learning algorithms parse data to make informed decisions, serving as the basis of automation. Ever wonder why Netflix seems to predict the shows you'll enjoy so accurately? Those recommendation engines have become so refined that more than 80 percent of the shows users watch on the service are because of a recommendation.
Google’s AlphaGo Zero is another great example of deep learning. AlphaGo Zero recently beat the world champion of the ancient Chinese game of Go. After taking down the best in the world, Zero continued to improve by playing games against itself and learning from those bouts. Not only is it considered the best in the world, but its neural network is also constantly getting smarter.
Imagine that technology transforming not only entertainment and competition, but also being leveraged by every industry. The potential is limitless — provided you’re ready to embrace this brave new world.
Applications and Advancements of AI
Still unsure whether you need to climb aboard the AI train? You don’t need to look far to find numerous examples of companies, both large and small, dipping their toes in the water. Global spending on AI system is expected to reach $46 billion by 2020, according to one study.
HubSpot, the very website you’re browsing now, has been investing in machine learning and artificial intelligence. HubSpot recently acquired Kemvi, a machine learning startup that’s developing a platform to help companies build deeper relationships with potential customers through the power of AI. In fact, Gartner estimates that 30 percent of companies will use AI in their sales department by 2020.
While Google's TensorFlow and Facebook's PyTorch are the main developer tools being used in the AI arena, both pieces of software are still fairly complex.
Remember that your AI efforts do not have to be overly complicated. The best place to start in terms of automation is manual processes that demand a significant administrative effort. Financial processes, data entry, fraud detection, and customer loyalty are only a few areas in which AI can make a tremendous difference.
Better Living Through Automation
By plugging several AI systems into your business, you can cut costs, raise productivity, and become a more efficient enterprise. Once machine learning takes on the bulk of your team’s busywork, people are liberated from mundane tasks and able to focus energy on innovation to drive the business forward.
In particular, your marketing team will appreciate the opportunity to spend more time on ideation rather than tedium. There is no shortage of automation in marketing, and there are plenty of tools already available to solve almost any problem.
Email marketing algorithms, for example, can determine content most likely to elicit specific responses. Platforms like Phrasee and Persado allow companies to use NLP to create automated subject lines, body copy, and calls to action.
These AI-generated headlines outperform humans 95 percent of the time, and the engagement rate of the content outperforms humans 100 percent of the time. That’s impressive improvement for just one marketing platform. Imagine the impact if you were able to roll out multiple tools at once!
AI and machine learning are going to unlock a whole new suite of tools to help marketers — and businesses in general — grow and excel. The true beauty of the plug-and-play AI solutions is the way that they complement each other. Due to their shared reliance on data, these platforms become smarter, more accurate, and efficient over time while adding more value to the business cycle. The AI revolution is underway, but the truly exciting advancements are yet to come. There’s never been a better time to explore the wealth of possibilities.