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.

Speech recognition in our everyday life

Over the course of the past decade, the world has gone through a radical transformation. Technologies that were once something popularized by science fiction have now become a facet of our daily life. One technology that has now become so commonplace, most don’t even give it a second thought is speech recognition.

Nowadays, AI powered voice assistants like Alexa, Siri and Google are scattered throughout our homes and smartphones – but it wasn’t always this way. For quite a long period of time, voice control was very hit and miss.

Leveraging Big Data: Important Data AI Researcher Should Track

Big Data appeared before everyone even realized its existence, and so far, it is the core element of modern technologies. This article will explain the intersection where big data meets AI as well as the key elements of their inseparable connection and the prospects for the further development of AI by using big data.

AI Data Set creation, labeling and verification, and its importance for Machine Learning & Artificial Intelligence (AI)

Data scientists continue to work tirelessly to try and replicate human intelligence through the algorithms they create.
Neural networks are systems with autonomous or intelligent behavior. They are able to perform tasks and solve problems independently (so-called artificial intelligence / AI). Before that, the neural algorithms have to be trained using sample data. AI systems learn from these data and can generalize them and apply what has been learned to new tasks.
The more accurate and extensive the amount of AI training data is, the better the first results of AI systems are.

Does Artificial Intelligence Optimize Human Beings?

The concept of the perfect human is as old as humanity itself. But self-learning algorithms are bringing the realization of this idea a little closer now, just as they entail the fear of the omnipotence of machines. Does artificial intelligence make us smarter, better looking, healthier and happier? Four questions and four answers.