Human in the Loop: The Human in the Machine

Man in the machine – a buzzword familiar from science fiction novels of the early 20th century. What this term is about in the 21st century is clear: it is about Artificial Intelligence and Machine Learning. The development and training of AI requires the intervention of natural intelligence at many points: human in the loop. In this loop, the human acts in a similar way to a teacher.

5 Ways to Integrate AI in Your eCommerce Shop

The still-ongoing pandemic has pushed many people towards online shopping permanently. And this gave a staggering rise to the ecommerce industry. During the pandemic, there has been a 50% growth in the ecommerce market.

And, as more and more people opt for ecommerce businesses over conventional retail, it is becoming important to integrate artificial intelligence (AI) for efficiency. Taking such a step can prove beneficial for many reasons; driving customer engagement, detecting fraud, retaining customers, and enhancing the shopping experience.

Big players are already deploying AI, and here are five ways you can start leveraging it to grow your ecommerce business.

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. To understand more about how to avoid training data errors that might lead to these biases, consider reading up on the importance of high-quality AI datasets at Clickworker’s guide on avoiding training data errors.

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?

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.

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.

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.

Artificial Intelligence – Sentiment Analysis Using NLP

Artificial Intelligence is becoming more and more prominent in our everyday life. From Google Assistant to Apple’s Siri, we can interact with computers, smartphones, and other devices as if they were human beings.

However, while a computer can answer and respond to simple questions, recent innovations also let them learn and understand human emotions.

One of the latest uses of Artificial intelligence is sentiment analysis using natural language processing (NLP).

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?

Artificial intelligence for efficient support in translation work

Artificial Intelligence (AI) is becoming an ever more important part of our lives. Whether it is in our homes with smart speakers and automation or in the business world, its impact in our lives cannot be dismissed.

However, while the benefits of AI are obvious, in the past, using the technology with language translation was difficult, if not impossible. Language translation is an area that has always required human intervention. There’s simply too much nuance in language for a machine to understand without a lot of training, most often done painstakingly by hand.

In recent years, that situation has started to change. With new advances in Machine Learning (ML) along with the development of neural networks, this once-difficult task is now much more possible.