The Use of Artificial Intelligence in Google Search Engine Algorithms

The search engine giant Google has always been focused on understanding and responding to its users’ needs. Over the past few years, we have witnessed countless changes in Google’s algorithm, and it’s always been something important to industries.

However, while most of Google’s algorithms changes weren’t earth-shattering, this current evolution just might be it. The introduction of its latest Artificial Intelligence (AI) algorithm called MUM is something we haven’t seen before.

Stemming, Stop Words and SEO

House or houses, the house or just house – how do these small differences affect SEO? Stemming and stop words have been a controversial topic for search engine optimization for years. Is it worth paying attention to these nuances? Or are inflections, prepositions and articles irrelevant for a successful ranking on Google?

7 Unusual Use Cases for AI

Whenever we discuss the key benefits of artificial intelligence (AI), we think of its application in connected cars, FinTech, and healthcare. While we first encountered smart algorithms in the form of Amazon product recommendations and personal assistants like Siri, this technology has evolved to become so much more.

Some use cases in healthcare and software development were groundbreaking (to say the least). However, every now and then, we come across some surprising applications for new technologies.

Let’s take a look at seven unusual real-world use cases for AI.

How the Big Players Are Deploying AI

While the last couple of years has undoubtedly been difficult for all types of businesses, it didn’t slow down development within the artificial intelligence (AI) and machine learning (ML) space

According to IDC, as much as 65% of organizations have accelerated the use of digital technologies this year. In this case, technologies like AI will transform existing business processes to boost employee productivity, drive customer engagement, and enhance business resiliency.

What is Active Learning and How Does it Work?

Active Learning is a machine learning (ML) technique where the algorithm can select data it wants to learn from. In active learning, the algorithm chooses which subset of examples to label next from the pool of previously unclassified data. Passive learning is different in that a user gives all known parameters as training labels without considering how useful this will be for future prediction.