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Human-Assisted Machine Learning

Advancements in machine learning have come a long way in recent years, but they haven’t done it alone. Humans are largely responsible for how intelligent AI and machine learning systems have become and will continue to pave the way for more improvements in the future.

An increasingly important factor of technology for businesses large and small, an Accenture survey confirms that machine learning will only become more valuable. In it, they project that 85% of business executives plan to invest in AI over the next three years with most of the investments focused on revenue-critical business processes. One question that many companies working with or using AI technology today are wondering is: Will the day ever come that these machines won’t need the help of humans to make them as powerful as they can be? The short answer – certainly not any time soon. A Datanami article stated it best:

“Whether you view it as machine-augmented human cognition, or human-assisted machine cognition, it comes back to one simple fact: artificial intelligence needs people.”

Humans are completely necessary in order to make data better. With human-assisted machine learning, AI engines can factor in truly human elements, such as emotion and nuanced visual cues; using a machine learning algorithm by itself simply doesn’t work. Human input is absolutely critical to how these engines perform successfully. 

Here are five of the most innovative and future-forward use-cases for organizations using human-assisted machine learning today.

1. Voice & Virtual Assistance

A familiar system to many individuals, families, and organizations today, voice assistance technology has quickly become commonplace. With Siri, Alexa, and others in our homes, offices, and mobile devices, the ability to access information and virtual assistance using just your voice is more powerful and widely used than ever before.

While it may feel as though these systems have simply improved, it’s not without the work of a vast team of real, live specialists; in the case of Eva, an AI virtual assistant created by Voicera, this was especially true. By utilizing human speech-to-text services from TranscribeMe to help train their voice recognition engines, the Eva virtual assistant is more effective than ever, powered by the ability to truly understand human speech patterns, anticipate actions, and more.

2. Intelligent Photo Tagging

Image tagging is a mission-critical step for the categorization, filtering, and analysis of photos. Providing context to an image by tagging it with a word or phrase that best describes it within the metadata leads to a roster of benefits, such as improved search and discovery online. But, why are humans necessary for such a task? The short and sweet answer is that machines and algorithms still have a long way to go before getting everything right.

Annotating and verifying the data found within specific photos, as well as objects within the photos. This helps the AI technology to improve its own system of identification moving forward so it makes less mistakes, like being able to accurately ID if you’re looking at hot dogs vs. legs (see the Not HotDog app for more). 

3. Powerful Online Search

Last year, 46.8% of the global population accessed the internet – by 2021, this figure is expected to grow to an incredible 53.7%. Google now processes over 40,000 search queries every second on average, which translates to over 3.5 billion searches per day and 1.2 trillion searches per year worldwide. Auto-suggestions within search engines can only improve when actual, live people are verifying accurate information and supplying it for AI training data in real-time. How can your business’ online search – either on your website or eCommerce platform – perform better with the help of human-trained AI? While machine learning algorithms can pull most of the weight in terms of training to become better, it takes the touch of a human-verified data to really perfect it.

TranscribeMe’s speech recognition engine offers the most powerful hybrid solutions for speech-to-text data. Our ability to produce the highest quality human-annotated and verified corpora training sets for machine learning and training, as well as customizations to fit your specific needs, makes TranscribeMe a leader in automated speech recognition. With thousands of voice-to-text experts around the world to assist in training speech recognition engines to be accurate and effective, we offer leading human-assisted machine learning services.

Ready to learn more? Want to see how expert human-assistance can improve your machine learning and AI systems?

Get in touch with our team today.