If you engage in a conversation about technology trends with a founder, business executive, or software engineer, you'll probably hear them talking about artificial intelligence (AI) and machine learning (ML). And they'll probably tell you how these transformational technologies have the power to transform any business.
Machine learning is gaining such momentum that the total funding allocated to ML worldwide during the first quarter of 2019 was $28.5 billion. Moreover, it's been found that 49% of companies are exploring or planning to use ML.
According to research:
- One-third of IT leaders intend to use ML for business analytics.
- 25% of IT leaders plan to use ML for security purposes.
- 16% of IT leaders want to use ML in sales and marketing.
With these mind-boggling statistics in mind, businesses are forced to dive deeper into the concept of ML and learn how this technology can help them remain relevant.
Table of contents
What is Machine Learning and Why Now?
Let’s start with the basics: What's machine learning?
In essence, machine learning is a subset of artificial intelligence that trains a machine how to learn. It's the proposition that systems can learn from data, identify patterns, and come to a decision with only minimal human involvement.
- Have you heard about the self-driving Google car? That's machine learning!
- Or the "recommended for you" section on Netflix? Machine learning at work!
More and more companies across all verticals have been adopting the technology with the speed of light. All in efforts to drive innovation, make smart processes, and get ahead of the competition.
NL holds a chest of benefits for those who understand the applications of machine learning within their businesses. Working with large amounts of data, companies can now have computational processing that is cheaper and more powerful, as well as affordable data storage.
Meaning, by using machine learning, companies will be able to build models that can analyze bigger, more complex data, and deliver faster, more accurate results. Hopefully, they'll have a better chance of identifying profitable opportunities and avoiding risks.
Image, Facial and Speech Recognition
Thanks to the emergence of deep learning, image, facial, and speech accuracy took a giant leap forward.
Many companies' core business today is based on machine learning and image/speech recognition. Google, for example, uses ML in image recognition for Google Photos and speech recognition for Google Home and Google Assistant.
Another company that heavily relies on ML is Apple. Today, millions of people talk to Siri, Apple's own virtual assistant. The company has recently extended the application of its virtual assistant through its newer product HomePod, a smart home device. Additionally, Apple has been investing in ML startups, such as Vocal IQ, a platform for voice interfaces, and Emotient, a leader in emotion detection.
Apple has also been using deep learning for face detection since 2016 with the release of its iOS 10. And with the release of their Vision framework, iOS developers can use the technology in their apps.
Personalization & Search
In the last few years, forward-thinking companies have completely changed the online experience. Thanks to businesses such as Netflix and Spotify, customers can now enjoy the ultimate customer experience, one-on-one personalization.
Personalization is an essential ingredient of Netflix. It allows each subscriber to have a different view of the content. By using a range of machine learning and recommendation algorithms, the homepage adapts to the subscriber's interests and can help expand their interests over time. They are continuously conducting online A/B testings and offline experiments to improve subscribers' unique experiences further.
Spotify is another company that relies on personalization. Up until recently, Spotify's curated playlists didn't include any personalization. There was one official playlist, and it appeared on everyone's screens. However, now Spotify is making those playlist part curated and part personalized. Meaning, human editors will pick and choose which songs are perfect for which list, but not every song will appear to every listener. Instead, Spotify will automatically adjust the playlist to each listener's preferences.
Customer Service
Customer service is critical for the success of any business. Why? Because it's an essential ingredient for retaining customers and increasing profits. In fact, one study has found that an increase in customer retention of 5% can lead to an increase in profit of at least 25%.
We can see many companies using support-focused customer analytics tools enabled with machine learning to provide a higher level of convenience for their customers. One such company is Zendesk.
The company's customer service focuses on self-service. Why? Because according to research, 81% of customers prefer to help themselves rather than speaking to an agent. Thanks to the advances in ML, their customers can now get support from chatbots, virtual assistants, and other AI-enhanced tools. These AI-enhanced tools can simulate conversations with customer service agents and help customers find more accurate and helpful answers.
Zendesk has also been using ML to aid with content creation. It analyzes the data that comes in from support tickets and uses that to create actionable tips in the form of help articles. Additionally, it's been using machine learning to add a predictive element to some support analytics. By using predictive customer service analytics, the company can use the previous data from its users to determine what the quantitative results may be in the future.
Security and Fraud Detection
In the last decade, there's has been an explosion in the number of people entrusting companies with their personal data. However, there also has been an explosion in the number of data breaches and hacks.
In 2016, 3 billion Yahoo accounts were hacked in one of the largest breaches of all time. In that same year, Uber reported that hackers stole the information of over 57 million riders and drivers.
With its power to analyze millions of files, companies have been heavily investing in machine learning to help them uncover threats and automatically get rid of them before they can wreak havoc.
Cisco is one of those companies. They've managed to significantly improve their security and fraud detection by using the machine learning and analytics engine called Cognitive Intelligence. The engine it's used to detect threats on a network by continually monitoring the behavior of the network for anomalies. In addition to detecting threats, it can also offer endpoint malware protection by analyzing the attributes and behavior of older malware.
Another company that's been making smart investments in machine learning is American Express. As it has 100+ million cards in operation and processes $1 trillion transactions, they rely on data analytics and machine learning algorithms to detect fraud in real-time. By doing so, they're saving millions of dollars in possible losses.
Adeva has recently introduced the AI Talent Hub, a platform that connects companies with a global network of AI experts. This partnership empowers organizations to quickly adapt to the latest developments in AI and build a workforce that is both technologically proficient and strategically aligned with their AI goals.
Key Takeaways
Machine learning is slowly revolutionizing the world. From personalized experiences to virtual assistants, it's making our lives easier and more entertaining. And for many tech leaders, artificial intelligence and machine learning form the core of all their strategic decisions.
It helps them provide exceptional customer support, reduce risks, and remain relevant.
One thing is for sure: with improvements in computing, algorithms, and statistical modeling, machine learning will only evolve to become more efficient. And those companies that embrace it will be the ones that remain relevant in a hyper-changing world.
