Cool Ways in which Companies are Using Machine Learning/AI

Artificial intelligence and machine learning are among the most significant technological developments in recent history. Few fields promise to “disrupt” (to use a fancy term) life as we know it quite like machine learning, but many of the applications of machine learning technology go unseen.

1. Yelp — Image Curation at Scale:

While Yelp might not seem to be a tech company at first glance, Yelp is leveraging machine learning to improve users’ experience.

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.

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.

In 2015, Pinterest acquired Kosei, a machine learning company that specialized in the commercial applications of machine learning tech (specifically, content discovery and recommendation algorithms).

Today, machine learning touches virtually every aspect of Pinterest’s business operations, from spam moderation and content discovery to advertising monetization and reducing churn of email newsletter subscribers. Pretty cool.

3. Twitter — Curated Timelines

Twitter has been at the centre 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.

Whether you prefer to have Twitter show you “the best tweets first” (whatever that means) or as a reasonably chronological timeline, these changes are being driven by Twitter’s machine learning technology. Twitter’s AI evaluates each tweet in real-time and “scores” them according to various metrics.

Ultimately, Twitter’s algorithms then display tweets that are likely to drive the most engagement. This is determined on an individual basis; Twitter’s machine learning tech makes those decisions based on your individual preferences, resulting in the algorithmically curated feeds, which kinda suck if we’re being completely honest. (Does anybody actually prefer the algorithmic feed? Tell me why in the comments, you lovely weirdos.)

4. 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 demonstrates this better than its renowned AI, Watson.

An example of how IBM’s Watson can be used
to test and validate self-learning behavioural models

Watson may be a Jeopardy! champion, but it boasts a considerably more impressive track record than besting human contestants in televised game shows. Watson has been deployed in several hospitals and medical centres in recent years, where it demonstrated its aptitude for making highly accurate recommendations in the treatment of certain types of cancers.

Watson also shows significant potential in the retail sector, where it could be used as an assistant to help shoppers, as well as the hospitality industry. As such, IBM is now offering its Watson machine learning technology on a license basis — one of the first examples of an AI application being packaged in such a manner.

5. Salesforce — Intelligent CRMs

Salesforce is a titan of the tech world, with a 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.

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 touchpoints — to build much more detailed profiles of customers and identify crucial moments in the sales process. This means much more comprehensive lead scoring, more effective customer service (and happier customers), and more opportunities.

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.

So, what’s next in machine learning trends?

Machines That Learn More Effectively

Before long, we’ll see artificial intelligence 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 modelling 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 fewer data to “learn,” resulting in systems that can learn much faster with significantly smaller data sets.