Machine learning has been defined by Stanford University as “the science of getting computers to act without being explicitly programmed.” It’s machine learning that is now behind some of the greatest advancements in technology, driving new industries like autonomous vehicles.
From machine learning, a whole new world of concepts has developed, including supervised learning and unsupervised learning, as well as algorithm development to build robots, Internet of Things devices, chatbots, analytics tools, and more. Here are five application of machine learning at work right now:
Virtual Personal Assistants
Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice. All you need to do is activate them and ask “What is my schedule for today?”, “What are the flights from Germany to London”, or similar questions. For answering, your personal assistant looks out for the information, recalls your related queries, or send a command to other resources (like phone apps) to collect info. You can even instruct assistants for certain tasks like “Set an alarm for 6 AM next morning”, “Remind me to visit Visa Office day after tomorrow”.
Machine learning is an important part of these personal assistants as they collect and refine the information on the basis of your previous involvement with them. Later, this set of data is utilized to render results that are tailored to your preferences.
Virtual Assistants are integrated to a variety of platforms. For example:
- Smart Speakers: Amazon Echo and Google Home
- Smartphones: Samsung Bixby on Samsung S8
- Mobile Apps: Google Allo
Real-Time Mobile Personalization
Digital personalization is becoming a more sought-after process to engage prospects and customers, as well as enhance the overall experience so they regularly return to buy your products or services. This has become particularly important in the mobile environment with the advent of tablets, smartphones, and wearables.
Now, mobile marketers and app developers are looking for a way to leverage all the information they can find about each customer’s context so they can develop a highly personalized mobile experience that pleases the consumer and delivers a greater return. Enter machine-learning applications.
Flybits is one company that uses machine learning to enable companies to deliver real-time personalization. This context-as-a-service product allows you to have instant cloud access to internal and external data to develop personalized mobile channels.
Yet as Facebook’s recent experience has shown, companies that fail to protect consumers’ personal data can expect a backlash. According to Hossein Rahnama, founder and CEO of Flybits, “Flybits promotes data transparency and a proactive approach to privacy. Our enterprise customers want to protect the privacy of their customers, and Flybits makes this easy. First, our customers maintain full control over their data — we do not own it. In addition, we follow Privacy by Design to embed security into our software and use tokenization to anonymize all customer data. Our customers have total control over the opt-in choices that they offer.”
With consumers’ growing preference for shopping online, criminals have gained an enormous opportunity to commit more fraud. Businesses have employed many types of online security measures but are finding that more are needed. The rise in online transactions also means that many of the measures available to check them make each transaction take longer and slow down the purchase experience — and still often don’t work to stop fraud. The result is increased chargebacks that cost money and impact a brand’s reputation.
Luckily, machine learning is available to improve the fraud detection process. For example, PayPal is using machine-learning tools to look for fraudulent transactions (including money laundering) and to help separate these from legitimate transactions. Machine learning assists by examining specific features in a data set and then building models that provide the basis for reviewing every transaction for certain signs it could be fraudulent. This helps stop the fraud in process before the transaction is even completed.
Email Spam and Malware Filtering
- There are a number of spam filtering approaches that email clients use. To ascertain that these spam filters are continuously updated, they are powered by machine learning. When rule-based spam filtering is done, it fails to track the latest tricks adopted by spammers. Multi Layer Perceptron, C 4.5 Decision Tree Induction are some of the spam filtering techniques that are powered by ML.
- Over 325, 000 malwares are detected everyday and each piece of code is 90–98% similar to its previous versions. The system security programs that are powered by machine learning understand the coding pattern. Therefore, they detects new malware with 2–10% variation easily and offer protection against them.
Natural Language Processing
There are so many functions where it would be great to have a stand-in to take care of tedious tasks. These include tech support, help desks, customer service, and many others. Thanks to machine learning’s capability for natural language processing (NLP), computers can take over. That’s because NLP provides an automated translation method between computer and human languages. Machine learning focuses on word choices, context, meaning, slang, jargon, and other subtle nuances within human language. As a result, it becomes “more human” in its responses.
Using this capability, chatbots can step in and serve as communicators in place of humans for various roles. In addition, NLP applies to situations where there is complex information to dissect, including contracts and research reports.
As these examples show, machine learning is ready to step in and make many business areas more efficient, effective, and profitable. The time to implement the technology of tomorrow is today.