Accuracy is one of the major concerns in the healthcare sector. Machine learning have the capabilities to provide more accurate diagnosis and healthcare services, which in turn has augmented demand for machine learning in healthcare sector. For instance, diagnosis of diabetic eye disease requires frequent examination of pictures at the back of an eye by the specialist. The features in the image helps to identify sensitivity of disease, which in turn, indicates fluid leakage and bleeding. Moreover, in 2016, Google has developed a deep learning algorithm, which analyze images and provides training to the system by using a data set of 128,000 images. Thus, the system diagnose the disease with a level of accuracy similar to human ophthalmologists. On similar lines, Google researchers are developing a deep learning algorithm for early diagnosis of skin cancer and breast cancer.
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Machine learning is a branch of artificial intelligence that enables machines to learn directly from data, experience, and examples. By permitting computers to execute specific tasks smartly, machine learning allows computers to carry complex processes by learning from examples or data, rather than following pre-programmed rules. Increasing volume of data being generated across industry verticals creates an exhaustive repository for machines to learn from, something that is further backed by rapid strides made in processing power of computers, in turn enhancing the analytical capabilities of machine learning systems.
Increasing advancements in technology leading to higher accuracy of systems fueling market growth
People interact with various systems, which are based on machine learning such as recommender systems, voice recognition systems, and image recognition systems. Rapid advancement in technology in image recognition system has increased the accuracy of the system, which has fueled the demand for machine learning in various systems. For instance, in image labeling challenge, the accuracy of machine learning was 72% in 2010 and it reached to 96% in 2015. The ability of machines to process large volumes of data and to use the data for prediction have made the machine learning a key tool in various applications such as BFSI, healthcare etc.
Integration of machine learning in robotics has fueled growth of the machine learning market
Rampant advancements in robotic industry has created various innovations in robots with the integration of sensor technologies and materials. The advancements in machine learning have increased the capabilities of robots to contribute in applications such as drones and autonomous vehicles. Moreover, the increasing demand for advance robotic system in various verticals such as automotive, electronics, food and beverages, healthcare etc has fueled the market growth. According to International Federation of Robots, in 2016, around 294,000 units of industrial robots were deployed across the globe. For example: In 2016, Fanuc, a Japan-based company, announced development of a robot with deep reinforcement learning technique, which enables the robot to train itself over a very short time duration.
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