How to Accelerate ML Development with Pre-Trained Data Models

December 2, 2021

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.

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Top 5 Common Training Data Errors and How to Avoid Them

November 19, 2021

Avoid training data errors

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.

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Emotion Recognition – How computers see through our emotions

November 12, 2021

Emotion Recognition

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.

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Artificial Intelligence – Sentiment Analysis Using NLP

November 3, 2021

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).

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Top 7 Facial Recognition Technologies

October 21, 2021

Facial Recognition Technologies

In recent years, biometric tools like facial recognition technologies have witnessed some groundbreaking innovations. From unlocking your mobile phone to automatically tagging photographs to diagnosing patients with genetic conditions, the possibilities are now endless.

The research firm Markets and Market expects the global facial recognition market to be worth $8.5 billion by 2025. However, the success of all these face recognition tools depends largely on adequate training data. In this scenario, the more extensive and more inclusive the data is, the better.

But before we get ahead of ourselves, let’s first define it.

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