Deep Learning, Machine Learning, and Artificial Intelligence—some people are confused about the difference between them. All three words are synonymous with disruptive innovation in computer science. But what is the difference? In this article, we will explore the differences between machine learning and deep learning and their applications in business.

Deep learning is the subset of machine learning

Artificial intelligence is a study that enhances computers to mimic a human’s mind. The exploration of AI began in World War II with Alan Turing and his Enigma machine.

Source: the-scientist.com

Machine learning is a study that trains computers to learn with statistical data. It is a subset of Artificial Intelligence. Tony and Stephanie created this visualization of how machine learning works.

As mentioned in ACADGILD’s YouTube video, there are supervised and unsupervised learnings in Machine Learning. In supervised learning, machines predict outcomes with the help of a data scientist. Meanwhile, unsupervised learning is where machines predict outcomes by themselves using patterns from the data they analyze.

Finally, deep learning is the study of imitating a human brain in computers, using multi-layered analysis to conclude and solve a problem. It is a subset of Machine Learning.

Learn more- Everything You Need to Know about Artificial Intelligence

The trends and what the big companies are doing

Companies like Google, Amazon, Apple, and Microsoft have invested in Machine Learning AI technologies. Deep Mind, which was acquired by Google, mentioned how they wanted to give back to the community with Artificial Intelligence through real-world impacts.

Elon Musk, CEO of SpaceX, created Neuralink in 2016. One of its products is a chip that can be planted into human brains. One use of such a product is to help people with brain damage regain control. For example, a video by Real Engineering depicts how one can grab a can of soda without moving her hands.

We can also look at the ‘smaller’ impacts of Machine Learning. Personal Assistants like Siri, Google Now, and Alexa use Machine Learning to serve humans better. Facebook and LinkedIn use “People You Might Now” with algorithms based on learning: they learn whose profiles you look at, whose friends you connect with, your interests and activities, and more. Regarding security, there is also Face Recognition and spam/malware detection. All of these use Machine Learning to build better products and services for customers.

Learn more- Everything You Need to Know About IoT and User Experience

Deep learning and neural networks

Deep learning requires a larger data set and a longer work time than machine learning. In its methodology, deep learning constructs layers with increments in information abstractions. It uses neural networks, a network that tries to mimic how the brain works. Information is transferred from one layer to the other through connecting channels.

Serokell mentioned that Deep learning has been used in multiple fields, such as Natural Language Processing, portfolio management, drug discovery, self-driving cars, and robotics. Geeks for Geeks added more: automatic text generation, automated machine translation, and earthquake prediction.

Learn more- Why You Need Neural Networks to Advance Product Customer Experience

Customer-facing and employee-facing apps

In implementing machine learning and deep learning, we can look at two different types of apps: customer-facing and employee-facing. The key here is that we train computers to learn, which will assist us in making decisions.

Customer-facing apps

Customer-facing apps benefit users with easy-to-access information/reports for purchasing decisions.

Bonlook is one of the eyewear companies that use virtual try-on features. Customers can record a video of their face and later use it as a try-on for different eyeglasses. The purpose is to help customers decide which glasses to buy without visiting stores. In this customer-facing app, the computers first recognize the customer’s faces. With smart technology, the app fits the glasses with the face as it moves around.

Apple Health reports health statistics based on the user’s movement captured by the iPhone’s sensors. They even went further with the Apple Watch, where the data can help predict the next seizure the user most likely experience. Users or their loved ones can contact hospitals and prepare for the event. The company can better serve the healthcare industry by amalgamating Apple’s data science data.

Learn more- The Future of AI and User Experience Design

Employee-facing apps

Employee-facing apps benefit companies by enhancing their capabilities and increasing productivity.

With the help of machine learning, Google and Facebook can now target ads to different customer segments. The algorithms essentially learned about customers and their behaviors: sites they visit, online friends, interests, etc. When a business owner wants to advertise his products and services, Google and Facebook promote the news to targeted segments.

Persist IQ is one of the many email automation tools sales teams can use. One of its features is intelligence, which allows Persist IQ to identify existing prospects. It can avoid misformatted emails and identify and categorize response emails.

Your business stage and ML/DL application

As you launch your business, you begin with the “Launch” stage, when you establish your brand. The next stage is “Mature,” where many customers purchase your products and services. Finally, you are challenged to the next stage, “Growth,” where you experiment in a new field to cater to the customers’ needs. Learn more about which team should own the product experience here.

Early stage

You can start by creating the learning algorithm. You can set up the computers to categorize activities and recognize speech and text patterns. For example, the machine can categorize a repeat customer versus a one-time buyer or show related results when a customer uses a search bar. It is important at this stage to build your IT infrastructure. If you don’t have the right data set for your algorithm, you can borrow some free data that is publicly available. In this case, you will train the computers with free data before putting in your customer data later as you collect them.

Mature stage

In the Mature stage, you might want to use more advanced algorithms. The goal is to enhance customer experience with new features, such as chatbots and personalized recommendations. You can also offer an image recognition feature. Chase and Bank of America are examples of many banks that allow check deposits through mobile apps by capturing an image of the check. You can try text recognition as well. For example, Google launched a mobile app called Voice Search to help ease search experiences. You can read an overview by Wordstream here.

Growth stage

Lastly, the Growth stage is the turning point for your business. Here, you are challenged whether you want to grow or remain where you are. Companies like Blackberry and Nokia decided to stay on the course and failed to capture the new trends of touch-screen phones. But if you are susceptible to changes, then you should think ahead of the game. In the growth stage, you want to incorporate machine and deep learning into your experimentation. You would look at a customer segment you want to focus on and offer new products and services to them. A quick example would be Generation Z’s ability to navigate the online world. Here, you want to incorporate fun and useful activities into your apps. An idea would be to launch a social media-integrated app for shopping. For example, they can take a selfie or record a video of themselves hanging in a store and quickly get price tags for the products around them.

Learning during an economic downturn

Downturns are inevitable. We live in a world where change is the only constant variable in life. In that case, we need to be always prepared for downturns.

A good case study about facing changes is a publishing company with which the Designial team worked. The challenge is the ever-growing demand for portable eBooks at lower prices. Designial’s solution is to embrace instead of rejecting the digital revolution. The publishing company specializing in reference books on medicine, business, and engineering then offers an app for customers to ‘try’ the books. This helps customers decide whether to invest in their learning and buy books.

One practical thing you can do in times of downturn is to invest in IT infrastructure. Technology will advance, and starting early will help you save more in the future. You can rent computers and research on several Cloud Computing platforms, such as Google Cloud, Amazon AWS, Microsoft Azure, and IBM Cloud Pak & Data.

For Machine Learning and Deep Learning, you can start building your algorithms and later use a data source to train the computers.

User experience with ML and DL

As we know, analytics are very useful for predicting an event’s likelihood to occur based on given data. However, it does not necessarily assist us in making decisions.

For example, we know that Tesla is the leader in self-driving cars. Regarding autonomy, their cars are at level 2 on a scale of 0 to 5. With a forward-facing camera, the car can see two cars in front of it. Should the one in the very front hit a brake, it can predict whether the second one (the one right in front of the user) would turn left, right, or hit a brake. Knowing that humans have a gap in reaction time, Tesla cars use that time to activate an algorithm for safety, and deep learning helps us make decisions. At the same time, the analytics of traffic accidents only inform us to be aware.

There is a study about the relation between visiting primary care doctors often and the less likely the person to be hospitalized. Taking a step further, deep learning can study a user’s behavior: how many visits we take to primary care doctors. With every visit, we get to know our bodies better, knowing if we have a disease and need to recover from it. Deep learning can change human behavior and decision-making.

Both cases show how deep learning is helpful for customer experience. Previously, we only had analytics and user testing. User testing means asking 8-10 users for their experience, also called qualitative testing. However, we can scale the effect to many users with deep learning.

Learn more- Why Do You Need To Use Mixed Reality?

Conclusion

Machine learning and deep learning are both parts of Artificial Intelligence. While machine learning is a study to train computers with statistical data, deep learning uses neural networks or multi-layered filters to process information. To implement both, you can start small in your organization by building the learning algorithms and later leverage it as you mine more data.

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Editor’s note: This post was originally published in July 2020 and has been updated for comprehensiveness.