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.

Object Detection and Segmentation

In recent years, object detection and segmentation have accelerated significantly. Today, smart algorithms can find and classify countless individual objects within a video or an image. Although it was incredibly difficult for machines to do, it’s now part of our daily existence.

Both object detection and segmentation are powered by Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). In this scenario, convolutional neural networks can locate and identify the class each item belongs to within an image.

It has also evolved to be much more than an intelligent algorithm that can recognize objects in photographs stored in a database. It can now find and classify objects in real-time to enable things like self-driving autonomous vehicles and more.

How to Train AI Models

When most people think about artificial intelligence (AI), they think of two possible futures. A positive future where self-driving cars help us navigate our roads and robot servants help us maintain our homes. Or a more negative one, where machines take away our jobs and employment. AI systems won’t replace humans in the workforce, but rather they’ll exist alongside humans as invaluable sidekicks.

While self-driving cars are advancing towards commonality, other grand AI aspirations await realization. Integral to achieving these goals is understanding how to train AI models effectively. For those looking to delve deeper into machine learning datasets, which serve as the backbone for training AI models, our machine learning dataset services provide invaluable resources.

Fortunately, it looks like the negative future isn’t one that we have to worry about. AI systems won’t replace humans in the workforce, but rather they’ll exist alongside humans as invaluable sidekicks. While self-driving cars are advancing towards commonality, other grand AI aspirations await realization. Integral to achieving these goals is understanding how to train AI models effectively.

How Does Face Detection Work?

Face detection technology has come a long way over the past few years. From unlocking your iPhone by scanning your face to automatically tagging photographs, most of us have encountered and benefited from it (in one way or another).

However, there’s a lot more we can do with technology than just recognizing faces. For example, it is now a tool used in marketing to help improve sales and customer experiences. Or you can use this technology in offices to mark employee attendance or provide access to secure areas automatically. To understand the nuances of how online face recognition AI works and its applications, click here for an insightful read.

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

The development of face recognition technology and the role of adequate training data

Face recognition is a technology that is used to identify people by their faces and is a type of biometric software. It is often used in security settings, but also has other uses such as in social media and photo tagging.

In order for AI to be able to recognize a person by their face, it needs to be presented with enough training data, or data that shows the AI how to recognize people by their faces. The training data needs to be accurate, and it must be large enough to provide a large variety of examples.

Speech Recognition Technology and Its Applications in Sales

Voice commerce has arrived. Today, retailers can sell products by designing experiences that leverage speech recognition technology and its capabilities.

According to Meticulous Research, wthe speech recognition market is forecasted to be worth approximately $26.79 billion by 2025 (growing at a CAGR of 17.2%). The number of voice assistants used around the world is also expected to reach a whopping 8.4 billion by 2024.

As such, speech recognition technology, like the dotcom boom, is well-placed to create a new avenue for sales teams around the planet. But before we get ahead of ourselves, let’s first define it.

Development (history) and applications of speech recognition systems

Walking into many houses around the world, you’re likely to find one or perhaps more ubiquitous little speakers, scattered around. For the residents of these homes, these devices have become a key part of their lives, sharing details about meetings, travel plans, grocery lists and even weather reports. We’ve come to depend on them to help simplify our lives and entertain us.

Crowdsourced voice recordings and their relevance for the development of speech recognition systems

Crowdsourced voice recordings have evolved to play a critical role in the development of speech-controlled apps. As speech recognition rapidly grows from a novelty to a daily necessity, you can expect the demand for both voice recordings and voice-activated systems to rise concurrently.

According to Grand View Research, the demand for voice-activated systems and devices is expected to be worth approximately $32 billion by 2025. But what exactly are voice recordings used for? Why are crowdsourced voice recordings important?

Uses of Speech Recognition Systems for Disabled Persons

Today, thanks to technological advances, many disadvantaged and disabled individuals are able to use technology to make their lives easier and more livable. One promising area is speech recognition. This technology is at the heart of personal voice assistants like Siri, Amazon Echo, and Google, that we’ve become accustomed to using either on our smartphones or through a smart speaker in our home.

The Role of Voice Biometrics in Enterprise Security Systems

Artificial Intelligence (AI) has been around for decades. However, we didn’t reap its true benefits until recently. With chatbots, voice bots, and more, AI is now a force to be reckoned with.

When we think of AI-based voice systems, we think of voice-based assistants Alexa and Siri. These voice assistants engage in Internet searches, switch lights on and off, play music at our homes and just make life a little easier. However, speech recognition isn’t the same as voice biometrics.