Now let's move on to machine learning which is a subset of artificial intelligence. It provides computers with the ability to learn without being explicitly programmed and utilize predictive analytics to forecast outcomes and also to assess the probability of future predictive events.
Machine learning has the ability to identify risk and to identify opportunities for businesses by using cognitive computing techniques, and it supports much greater efficiency. It can understand and can respond to human sentiments and emotions as well. Machine learning has gotten beyond the capabilities of predictive analytics and beyond the capabilities of big data analytics.
It also surpasses in some ways human capabilities by thinking independently and making its own evaluations and its own conclusions. If we look to the machine learning development, we see that it's accelerating very quickly, and we will discuss now the cause of this rapid advancement.
One such cause is the recent explosion of big data. Data is everywhere, and it is expanding from a number of sources. You can think about the sources for text, for images, for digitized documents and for internet devices. Everything is connected right now.
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These sources make it easier for machines to train and to learn and as a result this enables more accurate predictions, more insights, more recommendations, and it drives business innovation and experimentation. Training big models and sophisticated models requires a lot of input data to determine weights for a lot of variables.
It's the processing power and training big models within meaningful amount of time. It's also step wise improvement in the underlying machine learning and deep learning algorithms which enable us to train much, much deeper models essentially from scratch and standing on the shoulders of giants and then looking back makes you wonder why we haven't been able to train these deeper models already in the late 90s or early 2000s.
It's because of a lot of experimentation was required to figure out the exact algorithms, the initialization, the weight functions to make training deeper models from scratch actually viable. Today we are at a juncture point with these great trends. Data processing power and improved algorithms have come together creating a virtuous cycle creating new potential from machine learning and artificial intelligence.
This brings us to the third topic of discussion today which will focus on the business benefits of machine learning. Machine learning technology has enabled businesses to work with unstructured data like text and images and videos. On the other hand we have natural language recognition. It's also abbreviated as NLR, and this is the ability of a computer to understand the human language.
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Companies are using this natural language recognition as well to strengthen their analytic foundation and to better understand the needs of their clients to achieve a more expansive operational environment. Also, if you look for images, machine learning is used to determine if an image has certain objects. An example could be facial recognition, and we know that for example, Facebook is using this.
It has a nine layer algorithm. It is a deep learning algorithm where they find the face and image. They analyze it, and they compare it against all the known faces, and then they make a prediction based upon these factors, and they can do it this way with an accuracy of 97.5% which is remarkable I think.
Machine learning can also recognize objects in videos, and this enables developers to build applications that can automatically extract information from a video. This is also being further developed into an API. There are numerous benefits for businesses who utilize machine learning, and it definitely enables faster decision making. It can automate processes which facilitate better outcomes.