By 2028, the global machine learning market will grow to $152.24 billion. As the digital economy continues its massive growth, events like the COVID-19 pandemic further accelerated consumers' demand for digital services. As a result, nearly all businesses across all sectors will seek to harness the power of artificial intelligence (AI) and machine learning. Before businesses can venture deep into the sci-fi movie-inspired possibilities of machine learning, it is important to be aware of its underlying principles and what makes it work. At the heart of every machine learning initiative, data labeling forms a key foundation of this disruptive technology.

Everyone seems to be talking about Artificial Intelligence (AI) and Machine Learning (ML). Our clients often ask: "What exactly do these terms mean?" and "How can I take advantage of these capabilities to improve my business? Artificial Intelligence is the overarching term used to describe when machines execute tasks that typically require human intervention. Although the terms are often used interchangeably, Machine Learning is actually a type of artificial intelligence that enables computers to get into a mode of active self-learning beyond what is initially programmed. When exposed to new data, these computer programs learn, grow, change, and develop by themselves to become more effective and efficient at achieving desired outcomes.

An MIT-sponsored study in 2019 pointed out that 7 out of 10 companies that invested in Artificial Intelligence (AI) initiatives say that it had minimal or no impact on their business. Let that sink in for a moment. AI and machine learning have taken center stage in the digital economy and as a result, nearly every business wants to ride the wave. However, simply diving in headfirst to invest in AI capabilities doesn't always lead to successful outcomes. One of the most common ways companies hide their failure with AI implementation is to simply blame the technology. The truth is that technology is not the reason why many AI initiatives fail to realize their intended objectives and goals.

Be it for on-road GPS navigation or voice-assisted speakers, speech-activated devices are gaining more prominence in today's digital world. Globally, the market for speech and voice recognition is estimated to be valued at $1.38 billion (in 2021) and is projected to grow to $3.89 billion by 2026. So, how do machines recognize spoken language or sounds? A subset of data labeling or annotation, audio annotation can be performed on different types of voices and is an integral part of natural language processing (NLP). With more companies deploying NLP, the global NLP market was over $12 billion in 2020, and is projected to grow at an annual rate of 25% to $43 billion by 2025.