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In recent years, there have been enormous leaps in the quality of a wide range of technologies. We now have speech recognition functions on our smartphones and computers, which many companies have adapted, for instance, Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa, as well as the many voice- responsive features of Google.

 

According to Chinese search giant Baidu, customers have tripled their use of speech interface applications in the past 18 months. In addition to the advances in voice recognition, the advances in image recognition have also evolved a tremendous amount. Medical start-ups have claimed that they will soon be able to use computers to read MRIs, X-Rays, and CT scans more accurately than radiologists. This will result in the ability to diagnose cancer earlier and less invasively, as well as accelerating the search for life saving pharmaceuticals. Better image recognition is crucial in order to achieve improvements in robotics, self-driving cars, and autonomous drones. All of these breakthroughs have been made possible through artificial intelligence (AI) techniques, also known as deep learning.

 

The most fascinating aspect of AI is that no human being is capable of programming a computer to perform any of the actions described above, instead programmers have fed a learning algorithm, exposed it to terabytes of data to train it (which could include thousands of images or years’ worth of speech samples), and have allowed the computer to establish how to recognize the desired sentences, objects or words. To sum this up, these computers can now teach themselves, which is essentially revolutionizing the IT industry immensely.

 

The following diagram demonstrates the most significant areas of research involved in the creation of AI.

 

 

 

Deep learning is what is driving today’s artificial intelligence explosion. AI has been around for decades, however, due to recent advancements in artificial neural networks, AI has exploded. Artificial neural networks are inspired by the biology of the human brain, these machine learning models learn through algorithms and analyzing massive amounts of data, then can perform a specific task. Deep learning breaks down tasks in ways that make all kinds of machine assists seem possible. These advancements in neural networks have been the result of many breakthroughs, one being reinventing image recognition. Today, image recognition by machines trained via deep learning are in many cases better than humans, ranging from identifying indicators for cancer in blood to identifying tumors in MRI scans.

 

Deep learning and artificial intelligence will have a profound impact on the future as it continues to progress. Big data has been a primary driver for AI advancements, these technologies can collect and organize an immense amount of information in order to collect insights and make predictions that are predominantly beyond the capabilities of manual processing. In addition to increasing organizational efficiency, it can also dramatically reduce the likelihood that a critical mistake will be made. Artificial intelligence can distinguish if irregular patterns, for example, fraud or spam, and alert companies in real time about these suspicious activities. AI machines can also be trained to handle incoming customer calls, reducing costs. Artificial intelligence can even be used to scan the database and search the web for potential customers that inhibit the same buying patterns as existing customers. It is only a matter time before AI will become a commonplace in everyday life.

            

Reference:

ITware is currenlty working on a camera streaming solution for the Android mobile devices, where one of the main profiles of the project is artifical intelligence. Our customer uses AI to analyze and quantify the content in the video streams.