Exploring Prospects For Machine Learning in Digital Revolution - Sundiata Tech


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Thursday, August 10, 2017

Exploring Prospects For Machine Learning in Digital Revolution

In 2016, Intel acquired Nervana, a machine learning industry leader and a platform for machine intelligence in hardware engineering, systems software, machine learning, and cloud.

Pradeep Dubey, Intel Fellow and Director of Parallel Computing Lab (PCL), and Amir Khosrowshahi, Co-founder, Nervana systems, noted in a report on; Scaling to Meet the Growing Needs of Artificial Intelligence (AI), that Nervana’s goal is to build a platform for machine intelligence. This means using computers to create and process large datasets and make inferences on them. The goal is to accelerate the process by optimizing Deep Learning and other algorithms.

However, the purpose of machine learning is to provide solutions for human problems. The following are some places the Nervana machine learning platform can immediately provide these solutions:

Healthcare – Medical imaging is one of the biggest areas. Volumetric imaging with MRIs and CT scans, even single images can produce issues. Some static medical images are as much as 200,000 pixels per side; one image alone can be larger than the benchmark dataset. So the computer problems are enormous and we must be able to efficiently scale.

Agriculture – Genomic problems and climate modeling, as well as robot vegetable harvesters, which selectively harvest crops, are used. These require special needs of scaling at the edge as well as in the cloud where you need low-latency inference.

Finance – There are many use cases here, such as the vast IT problems a financial institution would have. Exchanges for various kinds of financial instruments traded in different ways can use Deep Learning to better focus trading times and methods. These also are used to anticipate potential fraud to protect the exchange against adverse events.

Automotive – Speech recognition, driver assist, automated driving. These all have massive datasets, collecting even more data. The scale is massive, requiring a full solution that is not only processing on the edge in the car but also at the data center.



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