Deep learning, machine learning, artificial intelligence — all buzzwords that represent the future of analytics. In this post, we will explain what machine learning and deep learning are at a high level with some real-world examples. In future posts, we will explore vertical use cases. The goal of this is not to turn you into a data scientist but to give you a better understanding of what you can do with machine learning. Machine learning is becoming more accessible to developers, and data scientists work with domain experts, architects, developers, and data engineers, so it is important for everyone to have a good understanding of the possibilities. Every piece of information that your business generates has the potential to add value. This post and future posts are meant to provoke a review of your own data to identify new opportunities.
What Is Artificial Intelligence?
Throughout the history of AI, the definition has been continuously redefined. AI is an umbrella term (the idea started in the 50s); machine learning is a subset of AI and deep learning is a subset of ML.
In 1985, when I was a student interning at the NSA, AI was also a very hot topic. At the NSA, I even took an MIT video (VCR) class on AI about expert systems. Expert systems capture an expert’s knowledge in a rules engine. Rules engines have a wide use in industries such as finance and healthcare, and more recently for event processing, but when data is changing, rules can become difficult to update and maintain. Machine learning has the advantage that it learns from the data, and it can provide data-driven probabilistic predictions.
How Has Analytics Changed in the Last 10 Years?
According to Thomas Davenport in the HBR, analytical technology has changed dramatically over the last decade, with more powerful and less expensive distributed computing across commodity servers, streaming analytics, and improved machine learning technologies, enabling companies to store and analyze both far more data and many different types of it.
Technologies like Apache Spark speed up parallel processing of distributed data even more with iterative algorithms by caching data in-memory across iterations and using lighter weight threads.
Graphical Processing Units (GPUs) have sped up multi-core servers for parallel processing. A GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously, whereas a CPU consists of a few cores optimized for sequential serial processing. In terms of potential performance, the evolution from the Cray-1 to today’s clusters with lots of GPU’s is roughly a million times what was once the fastest computer on the planet at a tiny fraction of the cost.
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