In the fast-paced technological world, people don’t care about behavior as much as they do their own interests. For example, I’m sure you’ve had the chance to use famous hook-up tools like Tinder, Hitch and Aisle, where people aren’t worried about the behavior of the person they’re talking to, but are more interested in knowing whether they’re ready to hook up or not. That’s the direction in which our world is heading. Data, not behavior, now helps us in getting acquainted with each other’s interests, and plays a critical role in deciding how we move forward with our future.
What Components Make Machine Learning Intelligent?
In artificial intelligence, algorithms are designed to evolve and change based on data that’s either inputted into a system or collected in real-time. This algorithm processes the data, draws conclusions and makes inferences to provide users content that’s tailored specifically for them.
Recommendation systems, personalized content and accurate searches are some of the common systems where Machine Learning is making a huge difference. The most common examples of machine learning in effect are “similar products” lists on eCommerce sites like Amazon, “friends suggestions” on Facebook, and “video suggestions” on YouTube.
Let’s look at an example from eCommerce. Information collected during a single purchase is enormous, ranging from last login date, browsing history, purchase history, cart details, saved items, product filtering history, color, brand and size preference, preferred mode of payment, and much more. The data collected can be as explicit as ratings given, favorites, language, comments, upvotes and downvotes, or as implicit as usage trends such as the pages most frequently visited, frequently used devices, time spent browsing categories, and products purchased.
Systems can process this information and connect various data with each other to drive inferences. For example, matching last login details with the discounts run by the eCommerce store reveals a purchase trend that allows a site to send similar discounts next time that user logs in. Effectively, the system will learn what makes a customer happy and
continue to improve their experience by adapting their interface to fit the preferences inferred from the connections revealed in the data.
Machine learning can
improve customer experience in a big way, but the overall user experience won’t improve
until you use your data to create a personalized interface for your users via UX design. Experts have to cleverly use data to tweak different elements of UI, overall layouts, and information architecture to make their products more user-friendly. Brilliant minds are using the best of machine learning and UX techniques to make their product a success. Let’s take close look at how UX-driven machine learning is saving our time and adding great value to our lives.
Redefining Information Architecture with Google and Netflix:
How the heck did I end up wasting my entire Saturday watching movies on Netflix again? Gosh!
Netflix knows exactly which movies will glue me to my couch and popcorn every weekend.
Netflix‘s instant streaming is a great example of machine learning for content-driven sites, using an algorithm to recommend the most relevant content to its users. They’re leveraging machine learning to predict what users want based on their viewing history.
The rumors of
Google’s active involvement in machine learning started when they took over Deepmind, opened a machine learning center in Zurich, and revealed their AI powered virtual assistant and the self-driving intelligent cars. Whoops!
Google has given us plenty of reasons to confirm its aggressiveness in the Machine Learning space.
But
Google’s Personalized Search is the most widely known implementation of machine learning, tracking what kind of results you’ve clicked in the past to optimize future search results. If you regularly browse
Quora, your search listings will automatically place
Quora-related links at the top of your results.
Simplifying Interactions with Auto-corrections
Machine learning can also simplify the use of a product, tracking frequently-performed tasks and using that data to automate procedures. One of Google’s most popular examples of machine learning is its
Android keyboard auto-correction function. Words that are frequently used are suggested more often. Even if you type in dual language, the system suggests the right words almost every time. Apple Maps also automates auto-corrections based on user habits,
drawing on travel history to instinctively suggest destinations and estimate traffic on frequently used routes.
Understanding People and Taking Smart Guesses for People
Machine learning is all about reducing cognitive load and giving smart guesses to what people are looking for based on a wealth of data. Gmail segregates emails into promotional, social and primary by scanning through the subject lines. Similarly, we no longer have to create a speed-dial list, as Android automatically adds your frequently dialed numbers to speed-dial. Even Siri gives you suggestions based on your phone activity, listing your frequently used apps and numbers.
Transforming Interactions to Match People’s Potential
Machine learning has also transformed the way we interact with products. Voice commands are a good example of this. Whether it’s Siri, Cortana, or Google Now – a blend of Natural Language Processing (NLP) and Artificial Intelligence is paving the way for
easy and more natural communication in human machine interactions. The system is not just trying to understand the language and accent spoken by a user, but also attempting to understand it within the context in which the user is speaking to streamline the completion of a task.
Similarly, the world is designed to suit right-handed people, historically leaving left-handed people struggling to operate machines. But now, with the aid of machine learning, these devices are automated to learn user-preferences to accommodate those formerly unfortunate left-handed individuals.
Machine learning make it possible but UX make it USABLE
Effective machine learning demands a blend of algorithmic assessment and UX intelligence, making a product optimally usable. However, most industry players are giving more importance to algorithms and ignoring the value of UX, especially the banks and Big-Data companies.
But it’s
UX design that makes things usable in both the digital and physical world. Pick anything from your toothbrush to your car and you’ll find that it’s the design and usability that compels you to buy the product. Ask yourself why you chose an iPod over a normal MP3 player, or a Mac over a PC and you’ll realize just how important UX design is in impacting your daily decisions.
What’s Next for Machine Learning?
Our world is being remodeled by machine learning. We no longer have to teach machines, we just have to build data-systems that allow them to learn on their own. But machine learning is still in its nascent stage. Increasing emphasis on UX will increase the practical value of machine learning and
add required intelligence to make IoT and Big Data space more usable and likeable.
Fast and easy big-data analytic systems will help us make sense of huge amounts of data and ultimately allow companies to find patterns and trends to improve decision-making.
