You may have already read our piece on the basic fundamentals of AI. This piece will dive a little deeper. If you are planning an AI development project, or simply want a more in-depth understanding of AI, this is the article for you. Developers and IT project managers will also find some things of interest. Here, we’ll go into more detail on AI technology, and dig a bit deeper into the inner workings of artificial intelligence.
AI Definition: An Advanced View
To the average layperson, the term AI is often used to describe any computer function that seems more intelligent than what the user is used to As a result, people use the term artificial intelligence without truly understanding its meaning. This can skew the public debate around AI as many don’t have the foundational knowledge required to understand it.
It’s important first of all to understand that AI wasn’t created as a program or series of programs. Instead, it was formed as an academic discipline that was dedicated to the creation of intelligent machines by combining science and engineering with a focus on computer programs. This discipline goes as far back as the 1950s.
Why the sudden surge in AI technology in real-world applications today? That’s thanks in large part to the large amounts of data. For AI technology, data is like food. This combined with faster processing capabilities, high powered computing, and intelligent algorithms make it much easier than ever before to leverage AI technology.
Differences Between AI Machine Learning and Deep Learning
Lay people often use these phrases as if they all mean the same thing. That’s not quite accurate. It may help to think of AI as a broader concept. ML is a part of that, and then drilling down a bit further is deep learning which is a subset of machine learning.
Machine Learning is the science behind making computers that can act without receiving specific programmatic instructions. Deep learning is a specific methodology for doing this. It involves the use of neural networks to help machines complete a variety of tasks with a high level of accuracy. These tasks include translation, object recognition, and speech recognition. Thanks to these deep neural networks, deep learning allows computers to learn directly from data sets. For example, thanks to DL, computer systems can extract physical features from data sets that feature thousands of pictures of faces. During this process, the computer is essentially training itself to extract information, categorize it, and learn. In the end, it learns directly from data without having to follow instructions given to it directly from computer code.
Three Types of Machine Learning
There are three types of machine learning. Below, we’ll go into some details on each of these.
Supervised Learning. With supervised learning, the system is provided with an output label for each category applicable to the data set. For example, the categories of red, blue, red, and yellow in which to sort differently colored balls. This is called supervised learning because example data is used to ‘teach’ the system by tagging the output correctly. When something goes into your spam filter by mistake, you move it back to your inbox. This is an example of supervised learning.
Unsupervised Learning. Just like you might assume, there are know known outputs, and no feedback influencing how any data might be sorted or classified. This is used in cases where you may not have any example data, and simply don’t know what the output classifications may be. This type of learning is often used when machines must identify unknown patterns.
Reinforcement Learning. Here, you reward or ‘punish’ the machine with feedback when it makes the right or wrong choice. One example of reinforcement learning is Google’s DeepMind project. In this project, supervised learning is combined with reinforcement learning. Supervision allows the machine to learn what is good (winning) and what is a punishment (losing)
Understanding Deep Learning
Deep learning employs neural networks to extract useful information from raw data. When compared to machine learning, there are more complex algorithms involved. Deep learning also requires more space and processing power.
Who Works in AI Development
Of course, all of this academic information is fascinating, but if you’re tasked with building an AI development team, you probably want to know what that team will look like. Let’s take a look at the different specialists who work to take AI technology, and create useful applications.
CAIO or AI Manager - Depending on the amount of AI development you plan to do, and the role of AI in your business strategy, you may want to designate a Chief AI Officer or another management level position.
AI Developer - An AI developer will have a variety of skills that enables them to develop apps that are driven by artificial intelligence. They will likely have mastery of at least some of the following:
- Understanding of probability and statistics
- Distributed computing competency
- Understanding of signal processing methodologies
- Solid mathematical background and understanding of complex algorithms
- Expertise in one or more of the following: Python, Java, C++, R, Scikit, Spark
- Unix proficiency
Data Engineer - This is the person who has the ability to manipulate large amounts of large data so that the information within it can be analyzed.
Data Analyst - The person in this row looks at the data and bridges the gap between data and business needs. In other words, they determine how data can and should be used to solve business problems.
Final Thoughts
Hopefully, with a deeper understanding of AI, you can begin to see ways in which you can apply this technology to help your business meet its goals. AI technology has a variety of real-world applications, you simply need to make a plan for your business, and assemble the right development team.