Tech Skills

Neural Network and AI Skills: What Your Business Needs to Know

Kirill Eremenko and Sebastian Moncada

Udemy for Business instructors on AI

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Tech Skills

May 24, 2018

How long do you think it will take for machines to surpass human intelligence? What technologies would be key for this amazing feat actually to happen? These are some of the major questions that are dominating current debates in the technological world, specifically, in fields such as artificial intelligence (AI).

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If you think about it (positively), it’s exciting to know that there will come a time when you will be able to relax in your living room as you wait for a machine to prepare your dinner and other sci-fi scenarios that we can’t even contemplate yet. However, this “robot dinner” scenario is possible and it might happen sooner than expected, especially considering the rapid growth of artificial intelligence technology.

In the workplace, AI is already helping with mundane data entry and providing data insights about the business, freeing up humans to be more strategic, collaborative, and creative. See Automation Skills Your Workforce Needs Today. Through analyzing and optimizing large data sets, AI is changing the game in research & development and product design at companies from pharmaceuticals to consumer goods–bringing products to market faster.

A revolution is already happening and it’s time for business leaders to understand the implications for your business and workforce skills. As neural networks continue to change the world as we know it, what technologies should you pay attention to and what skills will your workforce need to ride this wave of change?

First, what are “artificial” neural networks?

According to Dr. Robert Hecht-Nielsen, Neural Networks can be defined as:

“…a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.”

The network is meant to emulate the human brain structure in terms of its modeling, structure, and functionality. This means neural networks mimic the way the human brain processes, stores, and retrieves information—learning along the way and becoming “smarter” over time. Alpha Go is a well-known example of how AI learned the rules of the game Go and then beat the world’s human Go champion.

However, it’s important to point out that neural networks mimic the structure of the human brain at a smaller scale, especially if you consider that the human brain has billions of neurons.

The neural network processing devices consist of only thousands of processor units, including actual hardware or even algorithms, still a far cry from a billion or so connections tied to our brain.

Why are neural networks gaining ground?

Most people think that artificial neural networks are a new invention, but it might surprise you to learn that the technology was discovered more than 70 years ago! The main reason for the delay in the dissemination of this technology was slow processing speed, which limited its capabilities.

Previously, neural networks were mostly applied in the design of learning algorithms such as computer vision algorithms that allow Tesla cars to drive around without anybody monitoring it.

However, modern graphics cards have evolved as well, and they have significantly boosted the speed of neural networks, and in turn, increased AI capabilities.

In addition, in the past, computer scientists had to train their algorithms to be able to perfect whatever task they wanted it to complete. Let’s say you wanted to train an algorithm to recognize and classify images of animals. It would take thousands and thousands of iterations before the algorithm would be able to master this task.

With time, as algorithms got better and data scientists improved their skills, modern neural networks can now be easily trained without having to start the whole process from the beginning each time.

Moreover, the availability of a wide range of pre-trained network libraries now exists to help avoid this slow and time-consuming initial “training” stage. These code libraries enable AI developers to build upon existing creations. Currently, it is easy to go online and find pre-trained off-the-shelf networks and start one step ahead when coming up with your applications.

These pre-trained networks have “already learned” a rich set of features which can be applied to similar tasks. For example, a network trained to recognize millions of animal images can be repurposed and used for a similar image classification task.

With all these new innovations coming together in 2018, neural networks and AI are poised to become pervasive across all industries and in every facet of businesses.

Real-world applications

Today, the neural network technology has successfully been used in many industries. Some of the major applications include data analysis, optimization, pattern recognition, decision-making, and forecasting.

IT companies are using the creation of natural reaction algorithms to better understand user behavior and improve their services. In addition, neural networks are widely applied in speech synthesis and image recognition. (Think facial recognition technologies for airport security and border control.)

Other notable applications of neural network technology include algorithms for self-driving vehicles, industrial robots, navigation systems, as well as algorithms for protecting information systems from malicious online attacks.

How neural networks can change our lives

In the coming years, the latest trends in machine learning algorithms will likely change our lives in pretty significant ways (and often fantastically when you dig deeper into it). The following are some of the artificial neural network trends that you can expect will transform our lives in the next few years.

  • Image processing for art filters and photo effects

  • Elimination of language barriers through simultaneous interpreter bots for personal use and conferences

  • Intelligent self-learning systems for devices management and production processes

  • Using a photo to find someone on the internet

  • Video analytics services for monitoring and security systems

  • The Internet of Things concept through development of voice interaction interfaces

  • Development of artificial neural network personal assistants, technical support, and bots-consultants

  • Object recognition and classification of images.

Key skills required for neural network projects

Implementing neural network projects requires key AI skills that can be acquired through training, courses, and actual field experience. It’s important to point out that AI skills might vary from one company to another depending on the company’s business and its needs, but there are some core competencies that every AI expert or engineer must have in order to handle neural network projects.

The key qualifications are derived from skills sets and education. The initial requirements necessary for an artificial neural network expert are:

  • Problem-solving and math skills as the fundamentals

  • A deep understanding of algorithms and the logical sense behind it

  • The ability to search for patterns from large amounts of data—and be able to draw conclusions from it.

10 neural network and AI skills

In addition, an artificial neural network expert must be proficient in the following 10 key AI skills (not all, but the more the better!) Each skill is linked to our courses or those of our peers on Udemy and Udemy for Business to help your employees master these important Neural Network skills.

1. Machine Learning

2. Python

3. R

4. Data Science

5. Hadoop

6. Big Data

7. Java

8. Data Mining

9. Statistical Analysis

10. Linux

Machines will make life and work easier for humans

In the quest to replicate something as complex and wonderful as the human brain, artificial neural networks are a great step forward, and we can easily expect that its best applications are yet to come. We often wonder if human qualities and abilities that are inherently human—traits such as self-awareness, information processing capabilities, and ability to respond to stimulation—would stay completely human. Neural networks have proven that they have the ability to function like human beings, albeit on a lower scale.

By making machines more human, neural networks have been able to increase the usefulness of computer systems and more benefits are sure to come. However, it is important to point out that the technology is far from replacing the human brain in terms of functionality and reliability. More work still needs to be done to improve the technology. Machines won’t replace humans. Humans will still be uniquely human and possess traits that machines won’t be able to do well. However, machines will make life and work easier for humans.

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