Stanford University, Wolfram Alpha, and Pinterest are among the companies aiming to take advantage of advances in machine learning technology – all enabled by Amazon Web Services (AWS) infrastructure.
Dr. Matt Wood, general manager for artificial intelligence at AWS, told attendees at the company’s New York summit yesterday about how machine learning usually undergoes a renaissance every several years. This time, through the technological capabilities of cloud computing, it seems to have stuck.
“The reason [machine learning] has started to stick in this iteration is that the cloud has enabled machine learning and customers to overcome the single largest point of friction, which is almost always around scale,” he said. “Much like we did in the early days of AWS… we want to put this magical technology into the hands of every developer.”
By providing that scale, there are some fascinating projects being undertaken as a result.
Researchers from the Byers Eye Institute at Stanford University – whose professors include Andrew Ng, who has recently launched an online deep learning course – has developed a deep learning model to help prevent diabetic blindness.
Identifying the warnings signs of diabetic retinopathy involves looking at scans of behind the eye and checking for areas of swelling in the retina’s blood vessels in mild cases, and the proliferation of new blood vessels inside the surface of the retina in more severe cases. According to a report from Science Daily, about 45% of diabetic patients are likely to suffer from diabetic retinopathy, yet fewer than half of patients are aware of their condition.
According to the study, published in the journal of the American Academy of Ophthalmology, the algorithm developed could identify all disease stages with an accuracy rate of 94%.
Wolfram Alpha may be best known as the company which integrates with Siri to help answer a multitude of users’ questions. The company provides a computational knowledge engine which offers an almost limitless list of solutions far greater than a regular search engine.
The company utilises AWS, with Wood explaining: “When we’re talking about the challenges of handling inference at scale, with complicated deep learning models, this is the sort of scale you can achieve today through AWS.”
Pinterest is using machine learning to get a better grip on visual search; finding visually similar pins and then being able to recommend them to other users.
“Machine learning can not only determine the subject of an image, it can also identify visual patterns and match them to other photos,” Steven Melendez wrote in Fast Company towards the end of last year. “Pinterest is using this technology to process 150 million image searches per month, helping users find content that looks like pictures they’ve already pinned.”
Going back to Stanford, a post from Helgi Hilmarsson earlier this month describes the process in a little more detail. ‘Pinnability’, the term to describe the overall process, relies on machine learning models such as logistic regression, support vector machines, gradient boosted decision trees and convolutional neural networks.
“With these major improvements in building the discover engine in the home feed, Pinterest has now started to expand the use of their Pinnability model to improve its other products,” Hilmarsson wrote. “We are definitely looking at state of the art models and with these constant improvements one has to wonder where the limits and applications are.”
You can take a look at a slide featuring the more than 60 companies using AWS for machine learning below:
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