Tackling some really tough problems with machine learning by Derrick Harris.
From the post:
Machine learning startup Ayasdi is partnering with two prominent institutions — Lawrence Livermore National Laboratory and the Texas Medical Center — to help advance some of their complicated data challenges. At LLNL, the company will collaborate on research in energy, climate change, medical technology, and national security, while its work with the Texas Medical Center will focus on translational medicine, electronic medical records and finding new uses for existing drugs.
Ayasdi formally launched in January after years researching its core technology, called topological data analysis. Essentially, the company’s software, called Iris, uses hundreds of machine learning algorithms to analyze up to tens of billions of data points and identify the relationships among them. The topological part comes from the way the results of this analysis are visually mapped into a network that places similar or tightly connected points near one another so users can easily spot collections of variables that appear to affect each other.
Tough problems:
At LLNL, the company will collaborate on research in energy, climate change, medical technology, and national security, while its work with the Texas Medical Center will focus on translational medicine, electronic medical records and finding new uses for existing drugs.
I would say so but that wasn’t the “tough” problem I was expecting.
The “tough” problem I had in mind was taking data with no particular topology and mapping it to a topology.
I ask because “similar or tightly connected points” depend upon a notion of “similarity” that is not inherent in most data points.
For example, how “similar” are you from a leaker by working in the same office? How does that “similarity” compare to the “similarity” of other relationships?
Original text (which I have corrected above):
I ask because “similar or tightly connected points” depend upon a notion of “distance” that is not inherent in most data points.
For example, how “near” or “far” are you from a leaker by working in the same office? How does that “near” or “far” compare to the nearness or farness of other relationships?
I corrected the original post to remove the implication of a metric distance.
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