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.

10 Companies Using Machine Learning in Cool Ways

If science-fiction movies have taught us anything, it’s that the future is a bleak and terrifying dystopia ruled by murderous sentient robots.

Yelp’s machine learning algorithms help the company’s human staff to compile, categorize, and label images more efficiently – no small feat when you’re dealing with tens of millions of photos.

Twitter has been at the center of numerous controversies of late (not least of which were the much-derided decisions to round out everyone’s avatars and changes to the way people are tagged in @ replies), but one of the more contentious changes we’ve seen on Twitter was the move toward an algorithmic feed.

Tell me why in the comments, you lovely weirdos.) These days, it’s probably easier to list areas of scientific R&D that Google – or, rather, parent company Alphabet – isn’t working on, rather than trying to summarize Google’s technological ambition.

selection of images created by Google’s neural network The most visible developments in Google’s neural network research has been the DeepMind network, the “machine that dreams.” It’s the same network that produced those psychedelic images everybody was talking about a while back.

According to Google, the company is researching “virtually all aspects of machine learning,” which will lead to exciting developments in what Google calls “classical algorithms” as well as other applications including natural language processing, speech translation, and search ranking and prediction systems.  For years, retailers have struggled to overcome the mighty disconnect between shopping in stores and shopping online.

In addition to streamlining the ecommerce experience in order to improve conversion rates, Edgecase plans to leverage its tech to provide a better experience for shoppers who may only have a vague idea of what they’re looking for, by analyzing certain behaviors and actions that signify commercial intent – an attempt to make casual browsing online more rewarding and closer to the traditional retail experience.

natural language processing system One of the most interesting (and disconcerting) developments at Baidu’s R&D lab is what the company calls Deep Voice, a deep neural network that can generate entirely synthetic human voices that are very difficult to distinguish from genuine human speech.

Far from an idle experiment, Deep Voice 2 – the latest iteration of the Deep Voice technology – promises to have a lasting impact on natural language processing, the underlying technology behind voice search and voice pattern recognition systems.

Anyone who is familiar with HubSpot probably already knows that the company has long been an early adopter of emerging technologies, and the company proved this again earlier this month when it announced the acquisition of machine learning firm Kemvi.

AI and machine learning HubSpot plans to use Kemvi’s technology in a range of applications – most notably, integrating Kemvi’s DeepGraph machine learning and natural language processing tech in its internal content management system.

This, according to HubSpot’s Chief Strategy Officer Bradford Coffey, will allow HubSpot to better identify “trigger events” – changes to a company’s structure, management, or anything else that affects day-to-day operations – to allow HubSpot to more effectively pitch prospective clients and serve existing customers.

Salesforce Einstein allows businesses that use Salesforce’s CRM software to analyze every aspect of a customer’s relationship – from initial contact to ongoing engagement touch points – to build much more detailed profiles of customers and identify crucial moments in the sales process.

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