With machine learning you have to create a culture that can ask data science questions.
In one of my previous articles What Is Machine Learning? I explain the basics of machine learning (ML) and point out the fact that big data plays a key role in ML. However, I stopped short of explaining the connection between ML and big data in detail. In this article, I take a deeper dive into the important role that big data plays in machine learning.
Machine learning requires the following four key components:
Machines learn in the following three ways:
As you can see, data is important for machine learning, but that is no surprise; data also drives human learning and understanding. Imagine trying to learn anything while floating in a deprivation tank; without sensory, intellectual, or emotional stimulation, learning would cease. Likewise, machines require input to develop their ability to identify patterns in data.
The availability of big data (massive and growing volumes of diverse data) has driven the development of machine learning by providing computers with the volume and types of data they need to learn and perform specific tasks. Just think of all the data that is now collected and stored — from credit and debit card transactions, user behaviors on websites, online gaming, published medical studies, satellite images, online maps, census reports, voter records, financial reports, and electronic devices (machines equipped with sensors that report the status of their operation).
This treasure trove of data has given neural networks a huge advantage over the physical-symbol-systems approach to machine learning. Having a neural network chew on gigabytes of data and report on it is much easier and quicker than having an expert identify and input patterns and reasoning schemas to enable the computer to deliver accurate responses (as is done with the physical symbol systems approach to machine learning).
In some ways, the evolution of machine learning is similar to how online search engines developed over time. Early on, users would consult website directories such as Yahoo! to find what they were looking for — directories that were created and maintained by humans. Website owners would submit their sites to Yahoo! and suggest the categories in which to place them. Yahoo! personnel would then review the user recommendations and add them to the directory or deny the request. The process was time-consuming and labor-intensive, but it worked well when the web had relatively few websites. When the thousands of websites proliferated into millions and then crossed the one billion threshold, the system broke down fairly quickly. Human beings couldn’t work quickly enough to keep the Yahoo! directories current.
In the mid-1990s Yahoo! partnered with a smaller company called Google that had developed a search engine to locate and categorize web pages. Google’s first search engine examined backlinks (pages that linked to a given page) to determine each page's relevance and relative importance. Since then, Google has developed additional algorithms to determine a page’s rank; for example, the more users who enter the same search phrase and click the same link, the higher the ranking that page receives. With the addition of machine learning algorithms, the accuracy of such systems increases proportionate to the volume of data they have to draw on.
So, what can we expect for the future of machine learning? The growth of big data isn't expected to slow down any time soon. In fact, it is expected to accelerate. As the volume and diversity of data expand, you can expect to see the applications for machine learning grow substantially, as well.
With machine learning you have to create a culture that can ask data science questions.
The symbolic systems approach is one of the original approaches to artificial intelligence. Symbolic AI is rule-based approach that has become less popular than machine learning.
In one of my previous posts "The General Problem Solver," I discuss the debate over whether a physical symbol system is necessary and sufficient for intelligence. The developers of one of the early AI programs were convinced it did, but philosopher John Searle presented his Chinese room argument as a rebuttal to this theory. Searle concluded that […]