AI News, A Platform Strategy Won’t Work Unless You’re Good at Machine Learning

A Platform Strategy Won’t Work Unless You’re Good at Machine Learning

Just consider that a few tweets from the president caused Amazon’s market capitalization to fall by about $40 billion, or that Russian influencers were able to reach 126 million people through Facebook.

At OpenMatters, we spend a lot of time studying network orchestration—business models where companies facilitate relationships and interactions, rather than serving up all the products, services, and pieces of content themselves.

Today it’s machine learning that solves this problem, by organizing what the network offers up, bringing users carefully tailored results, and flagging bad behavior.

On one end are “wild west” companies that merely aggregate everything served up by the network, or using simple rules like up-voting to elevate content.

On the other end are companies that use machine learning to review and organize the data, services, or products that flow in, and serve them up to their customers in a customized way.

The pile of resumes was too large, and the simple algorithms attempting to serve up relevant content were insufficient for the size and varied needs of the user base.

This data, combined with machine learning, helps the company learn more about its customers, better target advertising and content, and better match people and opportunities.

Netflix uses machine learning to make personalized recommendations, which reduce churn by keeping customers happy, and even reduce cost by allowing Netflix to better use the content it has already purchased.

In addition to using machine learning to parse and understand data generated by a network, platform companies are now seeing the importance of AI for detecting and preventing misuse.

Twitter has had to take steps to curb abuse, Yelp and LinkedIn are working on filtering out fake content, and Facebook is likely at the beginning of a long journey to prevent misuse following the Russian influencing scandal.

If your organization wants to enter adopt a platform strategy and begin taking advantage of the networks effects it offers, you had better recognize that curation is an essential part of the journey and make sure you have the machine learning competency needed to make it happen.

Machine Learning vs. Deep Learning

If you’re new to the field of data science, it may seem like there’s a lot of jargon to keep track of.

William High said duringthe opening panel at DataScience: Elevate, “AI implies a level of system control and orchestration of multiple models and rules.'We can think of machine learning as an important subset of AI, encompassing the techniques and strategies that work to answer the questions that AI is trying to answer.

Some of these use cases were discussed during DataScience: Elevate's opening panel, and they range from building recommendation engines for curated email content at Quora, to employing natural language processing in chat logs at Riot Games, to building predictive models focused on customer churn at Verizon Wireless.

But you don’t necessarily have to say that a cat is something with cute ears or whiskers,” explained Verizon Wireless Data Scientist Aurora LePort during the opening panel of DataScience: Elevate.Before you start thinking about deep learning, however, it’s important to first fully understand the concept of a neural network.

The difference between a neural network and a deep learning network is contingent on the number of layers: A basic neural network may have two to three layers, while a deep learning network may have dozens or hundreds.

API-driven services bring intelligence to any application

Developed by AWS and Microsoft, Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

More seasoned data scientists and researchers will value the ability to build prototypes quickly and utilize dynamic neural network graphs for entirely new model architectures, all without sacrificing training speed.

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