The Future of Marketing is AI

With the rapid adoption of intelligent technology, marketers now have access to a wealth of data-driven insights that were previously unimaginable. These platforms help them to better understand their target audiences while also relieving the workload on team members so they can focus more time on converting customers into raving fans! It’s hard to imagine a company function that could benefit more from artificial intelligence than marketing (AI Marketing).

Its core activities include understanding customer needs, matching those up with products and services available on the marketplace, and persuading people to buy what you’re selling.

Top 9 Ways to Overcome or Prevent AI Bias

Smart algorithms are only as good as their training data sets. As such, it’s not surprising that algorithmic bias (or Bias in Artificial Intelligence = AI Bias) increasingly pops up when Artificial Intelligence (AI) and Machine Learning (ML) models go into production.

AI bias is dangerous because it could easily lead to poor decisions with disastrous consequences. I’m sure you have come across examples of AI bias in the news, like AI’s inability to recognize minorities and so on. So, it’s not hard to imagine businesses finding themselves in a legal nightmare.

How do you overcome or prevent AI bias?

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.

SEO keywords: dead or alive?

Content, search intent, user experience, differentiated user signals, and artificial intelligence (AI) — Google’s ranking criteria are becoming increasingly complex. So where does that leave keywords? Have the keywords in the text outlived their usefulness as ranking factors? The relevance of keywords for search engine optimization has changed. Online copywriters are now facing new challenges.

Artificial intelligence – key technologies for the financial industry

From robots on the factory floor to decision-making in investment banks, technology has always driven the financial service sectors. John McCarthy first coined the term artificial intelligence in 1956, but for many this concept from the world of science fiction is only becoming a reality today.

The potential of this technology has driven billions of dollars into research and development around the world; however, there are no clear examples or benchmarks that show us exactly where we may end up regarding making machines think like humans.

Artificial intelligence (AI) is a crucial tool in the financial sector. AI covers everything from chatbot assistants to new systems and tools designed to quickly detect fraud. In addition, AI tools can be used to improve task automation in the financial industry, helping to increase efficiency. While AI may provide a lot of obvious advantages, it’s important to recognize that even now, a significant amount of a bank’s manual procedures are still being done manually.

Top 5 Common Training Data Errors and How to Avoid Them

In traditional software development, the code is the most critical part. In contrast, what’s crucial in artificial intelligence (AI) and machine learning (ML) development is the data. This is because AI training data models include multi-stage activities that smart algorithms must learn in order to successfully perform tasks .

In this scenario, a small mistake you make during training today can cause your data model to malfunction. This can also have disastrous consequences—for example, poor decisions in the healthcare sector, finance, and of course, self-driving cars.

So, what training data errors should we look out for, and what steps can you take to avoid them? Let’s look at the top five data errors and how we can prevent them.

Emotion Recognition – How computers see through our emotions

Emotion recognition or emotion detection is a method of detecting sentiments based on images, videos, audio, and text leveraging artificial intelligence (AI). In this scenario, technology uses data from different sources like photographs, audio recordings, videos, real-time conversations, and documentation for sentiment analysis.

Emotion recognition has become increasingly popular in recent years. In fact, the global emotion detection market is forecasted to grow to $37.1 billion by 2026.

Part of the “affective computing” family of technologies, the primary objective is to help computers or machines interpret human emotions and affective states. This is done by examining non-verbal forms of communication like facial expressions, sentence constructions, the use of language, and more.