A Little Bit of Help With AI Jargon

From Healthcare IT News:

Cognitive computing
If AI is the umbrella term, machine learning and cognitive computing are two bits of phraseology that often cause confusion.

As Steven Astorino VP of development, private cloud platform and z analytics at IBM explained in a blog post, “Think of machine learning as a set of libraries and an execution engine for running a set of algorithms as part of a model to predict one or more outcomes. Each outcome has an associated score indicating the confidence level at which it will occur.”

Cognitive computing, meanwhile, refers to “the ability of computers to simulate human behavior of understanding, reasoning and thought processing,” he explained. “The ultimate goal is to simulate intelligence though a set of software and hardware services to produce better business outcomes.”

Machine learning: Supervised, unsupervised and more
With machine learning, the technology “derives knowledge from the data,” he explained, “to uncover new insights.”

Or, as another IBMer put it, machine learning refers to computers’ ability to get smarter “without being pre-programmed after a manual.” That could be through any number of algorithmic models that can “learn from data and create foresights based on this data,” as Copenhagen-based IBM exec Peter Sommer explained.

But wait, there’s more. Within machine learning, there are several specific subtypes. Supervised, unsupervised, semi-supervised and reinforcement. Again, it’s OK if you’re saying, “Huh?”

With supervised machine learning, the insights derive from both existing data and a specific outcome that might be associated with that data, scientist John Guttag, head of the Data Driven Inference Group at MIT’s Computer Science and Artificial Intelligence Laboratory, told Healthcare IT News in 2017.

For example, “We’re given all the people who have Zika infections and then we know which of the women have children with birth defects and which don’t – and maybe from that we could build a model saying that if the woman is pregnant and has Zika, what’s the probability that her baby has a birth defect,” he explained. “We have a label about the outcome of interest.”

In other words: “You have data about a problem, and information about certain outcomes; you’re essentially trying to predict or classify or diagnose the outcome from the data you have access to,” as Zeeshan Syed phrased it. “That’s why it’s called supervised: You’re learning with the knowledge of what the outcome is.”

Unsupervised learning leaves a bit more to the imagination. “We just get data, and from that data we try to infer some hidden structure in the data,” said Guttag. “Typically the nice thing about unsupervised learning is you find things you weren’t even looking for.”

Or, as Syed explained, “you basically just have a bunch of data, and the goal is to find interesting structure in that data. It’s not necessarily related to any particular outcome, but it’s just what are the interesting characteristics of it, what are the anomalous records in a set of data you have…”

(Read more (there’s a lot more) )

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