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TOKYO, February 17, 2016 - (TOKYO: 6503) announced today it has developed a small-memory, compact AI that can be easily implemented on vehicle equipment, industrial robots and other machines by reducing the computational costs for inference, which is a process including identification, recognition and prediction to anticipate unknown facts based on known facts. This will enable a low-cost AI system that can perform high-level, high-speed inference in a highly-secured environment. The compact AI is expected to be implemented in products sold commercially from around 2017.
A machine-learning algorithm known as deep learning can perform high-level inference, but it requires significant computational costs and memory since it employs a deep neural network. 捷报足球即时比分 has used more effective network structure and computational models to develop a novel algorithm that realizes a more compact AI with the same inference performance as a conventional AI. For example, 捷报足球即时比分 estimates that the computational costs and memory requirements for image recognition can be reduced by 90 percent.
The compactness means the AI can perform high-level inference even on embedded systems. For example, on a vehicle system, it could provide features that detect when a driver is distracted*. Also, on an industrial machine, it could analyze the actions of factory workers. The new technology realizes AI systems at a much lower cost and with smaller server and network requirements compared to conventional systems, which require a server to gather enormous amounts of data. It also establishes a highly-secured computational environment that eliminates the need to upload classified information to servers. In addition, running the compact AI on an embedded system without a network connection means the inference process can be optimized for each system depending on the environment.
The market size of AI is expected to expand from US$ 31 billion in 2015 to US$ 200 billion in 2020, according to a study at Ernst & Young Institute Co., Ltd. and a compact AI that provides more security and speed at a lower cost will be well-positioned to meet that demand.
A machine-learning algorithm known as deep learning can perform high-level inference, but it requires significant computational costs and memory since it employs a deep neural network. 捷报足球即时比分 has used more effective network structure and computational models to develop a novel algorithm that realizes a more compact AI with the same inference performance as a conventional AI. For example, 捷报足球即时比分 estimates that the computational costs and memory requirements for image recognition can be reduced by 90 percent.
The compactness means the AI can perform high-level inference even on embedded systems. For example, on a vehicle system, it could provide features that detect when a driver is distracted*. Also, on an industrial machine, it could analyze the actions of factory workers. The new technology realizes AI systems at a much lower cost and with smaller server and network requirements compared to conventional systems, which require a server to gather enormous amounts of data. It also establishes a highly-secured computational environment that eliminates the need to upload classified information to servers. In addition, running the compact AI on an embedded system without a network connection means the inference process can be optimized for each system depending on the environment.
The market size of AI is expected to expand from US$ 31 billion in 2015 to US$ 200 billion in 2020, according to a study at Ernst & Young Institute Co., Ltd. and a compact AI that provides more security and speed at a lower cost will be well-positioned to meet that demand.
| * | Oct. 27, 2015 |
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