10 Companies Using Machine Learning in Cool Ways

1. Yelp — Image Curation at Scale

Few things compare to trying out a new restaurant then going online to complain about it afterwards. This is among the many reasons why Yelp is so popular (and useful).

2. Pinterest — Improved Content Discovery

Whether you’re a hardcore pinner or have never used the site before, Pinterest occupies a curious place in the social media ecosystem. Since Pinterest’s primary function is to curate existing content, it makes sense that investing in technologies that can make this process more effective would be a priority — and that’s definitely the case at Pinterest.

3. Facebook — Chatbot Army

Although Facebook’s Messenger service is still a little…contentious (people have verystrong feelings about messaging apps, it seems), it’s one of the most exciting aspects of the world’s largest social media platform. That’s because Messenger has become something of an experimental testing laboratory for chatbots.

4. Twitter — Curated Timelines

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.

5. Google — Neural Networks and ‘Machines That Dream’

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.

6. Edgecase — Improving Ecommerce Conversion Rates

For years, retailers have struggled to overcome the mighty disconnect between shopping in stores and shopping online. For all the talk of how online retail will be the death-knell of traditional shopping, many ecommerce sites still suck.

7. Baidu — The Future of Voice Search

Google isn’t the only search giant that’s branching out into machine learning. Chinese search engine Baidu is also investing heavily in the applications of AI.

8. HubSpot — Smarter Sales

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.

9. IBM — Better Healthcare

The inclusion of IBM might seem a little strange, given that IBM is one of the largest and oldest of the legacy technology companies, but IBM has managed to transition from older business models to newer revenue streams remarkably well. None of IBM’s products demonstrate this better than its renowned AI, Watson.

10. Salesforce — Intelligent CRMs

Salesforce is a titan of the tech world, with strong market share in the customer relationship management (CRM) space and the resources to match. Lead prediction and scoring are among the greatest challenges for even the savviest digital marketer, which is why Salesforce is betting big on its proprietary Einstein machine learning technology.

The Future of Machine Learning

One of the main problems with rapid technological advancement is that, for whatever reason, we end up taking these leaps for granted. Some of the applications of machine learning listed above would have been almost unthinkable as recently as a decade ago, and yet the pace at which scientists and researchers are advancing is nothing short of amazing.

Machines That Learn More Effectively

Before long, we’ll see artificial intelligences that can learn much more effectively. This will lead to developments in how algorithms are treated, such as AI deployments that can recognize, alter, and improve upon their own internal architecture with minimal human supervision.

Automation of Cyberattack Countermeasures

The rise of cybercrime and ransomware has forced companies of all sizes to reevaluate how they respond to systemic online attacks. We’ll soon see AI take a much greater role in monitoring, preventing, and responding to cyberattacks like database breaches, DDoS attacks, and other threats.

Convincing Generative Models

Generative models, such as the ones used by Baidu in our example above, are already incredibly convincing. Soon, we won’t be able to tell the difference at all. Improvements to generative modeling will result in increasingly sophisticated images, voices, and even entire identities generated entirely by algorithms.

Better Machine Learning Training

Even the most sophisticated AI can only learn as effectively as the training it receives; oftentimes, machine learning systems require enormous volumes of data to be trained. In the future, machine learning systems will require less and less data to “learn,” resulting in systems that can learn much faster with significantly smaller data sets.

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    D. Shewan

    Written by

    D. Shewan

    Marketing and Entrepreneurship

    Tips & News on Social Media Marketing, Online Advertising, Search Engine Optimization, Content Marketing, Growth Hacking, Branding, Start-Ups and more.