Machine learning is going mobile

#artificialintelligence

Machine learning--the process by which computers can get better at performing tasks through exposure to data, rather than through explicit programming--requires massive computational power, the kind usually found in clusters of energy-guzzling, cloud-based computer servers outfitted with specialized processors. But an emerging trend promises to bring the power of machine learning to mobile devices that may lack or have only intermittent online connectivity. This will give rise to machines that sense, perceive, learn from, and respond to their environment and their users, enabling the emergence of new product categories, reshaping how businesses engage with customers, and transforming how work gets done across industries. Emerging technologies rarely get as big a publicity boost as machine learning recently saw, when Google software defeated one of the world's top players of Go, one of the most complex board games ever created, in a best-of-five series of matches.6 The international headlines confirmed that machine learning--the process by which fresh data can teach computers to better perform tasks--is one of the hottest domains within the field of artificial intelligence, and that this cognitive technology is progressing rapidly.7 Neural networks--computer models designed to mimic aspects of the human brain's structure and function, with elements representing neurons and their interconnections--are an increasingly popular way of implementing machine learning.


Machine Learning is Going Mobile

#artificialintelligence

An emerging trend promises to bring the power of machine learning to mobile devices, opening the door to a plethora of valuable new applications. Machine learning--the process by which computers can get better at performing tasks through exposure to data, rather than through explicit programming--requires massive computational power, the kind usually found in clusters of energy-guzzling, cloud-based computer servers outfitted with specialized processors. But recent developments may enable machine learning to be embedded into mobile devices, thus greatly expanding applications for its use and providing new opportunities for marketers. Neural networks--computer models designed to mimic aspects of the human brain's structure and function, with elements representing neurons and their interconnections--are an increasingly popular way of implementing machine learning. They are particularly well suited for performing perceptual tasks such as computer vision and speech recognition.


Machine learning is going mobile

#artificialintelligence

Machine learning--the process by which computers can get better at performing tasks through exposure to data, rather than through explicit programming--requires massive computational power, the kind usually found in clusters of energy-guzzling, cloud-based computer servers outfitted with specialized processors. But an emerging trend promises to bring the power of machine learning to mobile devices that may lack or have only intermittent online connectivity. This will give rise to machines that sense, perceive, learn from, and respond to their environment and their users, enabling the emergence of new product categories, reshaping how businesses engage with customers, and transforming how work gets done across industries. Google has introduced language translation software, using small neural networks optimized for mobile phones, which can perform well without an Internet connection.1 Lenovo announced a mobile phone that uses multiple sensors, high-speed image processing hardware, and specialized Google software to support capabilities such as indoor wayfinding, precision measuring, and augmented reality even when offline.2 NVIDIA, a maker of graphics processing technology, introduced an embeddable module for computer vision applications in devices such as drones and autonomous vehicles that the company says consumes one-tenth the power of a competing offering.3 Qualcomm introduced a new processor and software platform that support machine learning tasks such as image classification, speech recognition, and anomaly detection without a connection to a network.4