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Deep Learning for Non-Technical People: 5 Facts You Need to Know

It is no easy feat staying up to date in today’s swiftly changing business environment, with the exponential rate at which technological change takes place. With more and more innovations, computer processing speed doubles almost every 18 months.

Many consumer market insights managers and marketers are not up to date with what technology in the form of artificial intelligence and deep learning is capable of. Here's a few fundamental facts to help you get up to speed.

Artificial Intelligence — Important Definitions You Need to Know

One baffling trait that goes hand in hand with new ideas is how people talk about a new topic. Artificial intelligence is still at its infant stage, consumers—and even professionals—use these buzzwords interchangeably. That can lead to a lot of confusion.

You may have heard about deep learning but also artificial intelligence and machine learning. Now, they are related but not entirely the same, let’s clarify that for you:

Artificial Intelligence: This is an umbrella term for computer science that relates to simulating intelligent behavior in modern computers. Both machine learning and deep learning form parts of artificial intelligence.

Machine learning: Machine learning is a subset of artificial intelligence. It is the ability some AI computers have of modifying their functioning when they receive more data. They learn how to act by analysing the data, such as simulating tasks performed by human workers, even if they were not initially programmed to do this.

Deep learning: In a practical meaning, deep learning is a subset of machine learning. It functions in a similar way, but its capabilities are different.

Deep Learning Explained

Figure 1 An illustration of deep learning from input layer to hidden layers within the deep neural networks to the output layer from left to right. 

‘Deep’ refers to the various layers you’ll find in a neural network. This closely relates to how our brains work. Only, instead of neurons that connect you find different layers and connections. There are specific directions to propagate data.

Deep learning can learn data features in the hierarchy. Through algorithms, this can solve problems that have an application in areas such as image or audio recognition.

Figure 2 Deep learning technology used in image recognition feature.

Instead of people trying to match factors and construct an image, deep learning can do it automatically- and that is only one application. Deep learning can also enhance your work life.

5 Important Facts: How Does Deep Learning Affect Businesses Today?

  1. All Industries Need Deep Learning

The first mistake many make is to think this complex technology does not apply to their niche. However, in the same way other AI functions have become more and more prevalent in companies over the years, the same is bound to happen to deep learning.

Artificial intelligence still seems futuristic to many business owners who do not realize it, from driving automation, enabling robots to manage your phone lines, to polling without hiring additional employees.

So where will you find deep learning in your company?

For instance, almost every business has an app these days. When consumers realise they can access menus without clicking, but simply by speaking, they will prefer chatbots that support this feature. For that, deep learning technology is required.

2. Market Research Can Be Simpler Than You Think

One thing central to any business is knowing your audience. Market research is a tedious process with traditional methods. With the help of deep learning, it is not only becomes more effective, but faster and effortless for you.

Already, sales teams try out this technology for market intelligence research. The ability of deep learning to extract intricate information from big data’s neural nets show them which individuals should be contacted to obtain the best results. This includes all important sentiment analysis to gauge individuals’ attitudes towards your brand.

Instead of manually monitoring your audience, digital ethnography gives you a true representation of people’s actions and perspectives. Deep learning can do this by analysing data of people’s online activities.

3. Starting Up Before Benefiting From Deep Learning

When you engage in this technology it won’t be a simple task of downloading a program and enjoying the results. To make it work, you need an infrastructure consisting of in-depth data which you need to collect in order for deep learning technology to extract the information you want. This is a network of GPUs (Graphic Processing Units). This requires capital investment.

A surprisingly large percentage of revenue in the tech industry over the past few years comes from people investing in deep learning. This shows that companies are adjusting to the change and embracing the possibilities.

4. Challenges You Will Face During the Implementation Process

While basic deep learning is accessible to almost anyone, to get the most out of a system you’ll need experts on your payroll. Occasionally you may not know why deep learning makes a certain choice. This can be problematic when the outcomes affect lives, such as in the pharmaceutical industry. Using simpler systems at first may be an option.

5. The Benefits You’re Missing Out on

The outcomes of deep learning in the form of reports can be applied in many scenarios. Of course, some tasks can be done by humans, but with deep learning, it will be faster which saves you and your employees time. It is more accurate with less chance of human error.

Despite people’s fears that ‘robots’ will take away humans’ jobs, this actually breaks open a new field for those seeking employment. You’ll need a deep learning expert on your team so one of your employees can garner a new skill.

The Progression of Deep Learning and Its Relevance to You

As deep learning becomes more refined, we will see even more advanced applications of artificial intelligence. Expect to see even more innovative applications of deep learning in the near future, and expect machines to provide even better personalised assistance to both businesses and customers.