Machine Learning and AI are undoubtedly two of the biggest buzzwords of the past few years. Besides constantly redefining our experience with products across all ecosystems, they became the ‘Holy Grail’ for companies, marketers, analysts, and programmers around the world. But what are they really about?
People often wrongly use the terms interchangeably, which may lead to confusion and a poor understanding of their definitions, which are fundamentally different.
According to the Merriam-Webster dictionary, Artificial Intelligence is a branch of computer science dealing with the simulation of intelligent behavior in computers, or the capability of a machine to imitate intelligent human behavior.
On the other hand, Machine Learning is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” (progressively improve performance on a specific task) with data, without being explicitly programmed.
If we look closely at the two definitions, we can understand why the terms are not interchangeable: machine learning is a subset of AI, it can exist without artificial intelligence, but AI cannot exist on its own (unless the developer can write millions of rows of incredibly complex code, covering every scenario possible). Simply put, Machine Learning represents a way to ‘reach’ AI.
When ML gets to a point where the program can interact with people in a convincing way AND make decisions on its own, it can be elevated to ‘Artificial Intelligence’.
Let’s take a real-life example. If you run an image recognition software that’s able to detect a common denominator in every data-set according to some defined criteria, that’s machine learning. However, if you give that program the ability to interact with a person, maybe answer some questions, and make a decision on its own whether or not to tag the image, then we’re already talking about Artificial Intelligence.
Every time a machine learning algorithm makes a decision, it’s a case of artificial intelligence.
Nowadays, the business world is heavily reliant on data, which has become one of the most important assets of an organization and the most valuable currency in the world.
People have aspired since ancient times to reach Artificial Intelligence, but the tremendous progress it’s made in recent years is mainly due to the exponential increase of computing power and data available.
Even if we don’t realize it, we’re constantly using machine learning in our daily lives, which is fueling the development of AI. From browsing social media to seeing an ad or checking the price of a product, we’re surrounded by algorithms designed to know us better and to give us a more targeted experience.
Of course, every company aims to leverage machine learning to compel us into completing their goals – buy something from their site, land on their pages, read their content, etc. But in their quest to boost their revenues, it’s often the customers who actually benefit.
Predictive analytics is one of the hottest areas of machine learning technology. By harvesting rapid data analysis, ML algorithms are able to drastically reduce the time spent to discover trends by examining prior user behavior.
Companies such as Netflix or Facebook use predictive analytics to recommend new content that users might like, based on previous consumption patterns. Retailers such as eBay or Amazon use powerful machine learning recommendation engines to boost their sales and show people products they might be interested in, based on what they’ve already viewed or purchased.
Today, more than 40% of brand marketers argue that social media is critical to their daily business. But the relevancy of social media is mainly due to machine learning algorithms, which show users what they’re interested in. And this is what companies are betting on.
Machine learning paved the road for advertisers to target users specifically with ads based on their interests, rather than firing blindly. This drastically increased the conversion rate, since marketers and advertisers can now choose which group of people will be seeing their ads, based on relevant data about their behavior and ML-based technology.
Companies have also been using machine learning to detect security issues and to fix any users’ problems. This can save companies hundreds of hours of work and prevent possible catastrophic losses. Identifying any security threats or software problems and alerting them in time can have an unmeasurable impact, enabling the company to act before it’s too late.
Another example of machine learning use cases is for an online retailer which has tens of thousands of products for sale, and pricing can be a real burden. Machine learning algorithms can help retailers dynamically correlate price and sales trends, along with other variables, such as available inventory.
Two of the most active industries when it comes to using AI and machine learning are Finance and Healthcare. The finance world is perfectly suited for machine learning, considering the enormous volume of data, historical records, and patterns that need to be analyzed. Machine learning is used in so many different ways in the world of finance, from approving loans, fraud detection, managing assets and automated trading to customer service with AI chatbots.
The first thing you need to do in order to see how your business can benefit from machine learning is to do a data audit. Is your data clean? Is it properly tracked? Can you start labeling it as a ‘supervised learning’? Do you generate enough new data to help the algorithm learn? Can it learn from historical data, or has a critical variable (such as price or product offerings) drastically changed?
Then you need to figure out what sort of business problems you want a machine learning algorithm to address. For example, you can set up email spam filters, or add a product recommendation engine on your website. For online advertising, you can add real-time bidding, or if you’re worried about frauds, you can add a credit card purchase fraud detection possibility.
Data analytics is also an incredible use for machine learning since it can drastically expedite the process of generating insights and it connects the company with the best leads. You can then focus on the users that are most likely to generate the highest ROI.