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?

What is Competitive Analysis and How can I Conduct it Online?

Learning from the competition also means learning how to win. This has been true for a long time. Moreover, the Internet offers many opportunities to conduct a competitor research analysis quickly, comprehensively, and reliably.

But the question is, how does it work? The following provides answers to questions about online competitive analysis.

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.

The new Google search: MUM

MUM — these three letters stand for Multitask Unified Model. MUM is the new Google algorithm for capturing search queries. What is it all about? What is changing in Google search? And what impact will MUM have on search engine optimization and online marketing?

How to Accelerate ML Development with Pre-Trained Data Models

Recent advancements in artificial intelligence (AI) like autonomous systems, computer vision, natural language processing (NLP), and predictive analytics are all powered by machine learning (ML). In those scenarios, ML helps to move data in the value chain from the informational level to the knowledge level.

Most smart systems you’ve interacted with today were probably developed leveraging supervised learning. Supervised learning is all about building ML models from scratch. However, this approach isn’t always the best. Many AI and ML projects fail because of a lack of resources and, of course, a lack of useful AI training datasets.

Supervised learning demands time, money, and significant human effort to make it work. That’s why it’s vital for enterprises to find viable alternatives to supervised learning. While for many years there has been no way around this problem, ML engineers have recently identified new ways to optimize ML models.

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, emotion recognition technology can use data from different sources like photographs, audio recordings, videos, real-time conversations, and documentation for sentiment analysis.

In recent years, emotion recognition has become increasingly popular. 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 by examining non-verbal forms of communication like facial expressions, sentence constructions, the use of language, and more.

Diary studies – valuable insights for marketing

According to an old saying, you can only find real truths in diaries. Modern market research makes use of this wisdom. A diary that relates to the use of a device, app or software can provide valuable insights for marketing. How do diary studies work and what makes them so successful?

Optimizing Your Business Site for Smoother Customer Experience

In 2021, there are anywhere between 12 to 24 million online shops across the internet, with more being created every day. With this amount of competition, online businesses must discover effective strategies to attract more customers and maintain their loyalty.

One crucial factor that affects your brand’s success is customer experience. A Walker study found that at the end of 2020, customer experience overtook price and product as the key brand differentiator.

This article will dive deeper into what customer experience is and its importance to your business. We will also take a look at nine ways to improve customer experience on your business website.

5 marketing tactics every eCommerce business should be using

If you’re working in eCommerce in 2021, you should be focusing on using the most effective digital marketing tactics in order to sell your products or services. In this article, we’re going to outline different tactics you can use to improve your sales. Let’s get started.