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Global Artificial Intelligence Chips Market 2017-2021: Top Drivers and Forecasts by TechnavioTechnavio market research analysts forecast the global artificial intelligence (AI) chips market to grow at a CAGR of more than 54% during the forecast period, according to their latest report. This Smart News Release features multimedia. View the full release here: http://www.businesswire.com/news/home/20170717005538/en/ 
Technavio has published a new report on the global artificial intelligence (AI) chips market from 2017-2021. (Graphic: Business Wire) The market study covers the present scenario and growth prospects of the global artificial intelligence (AI) chips market for 2017-2021. The report also lists GPUs, ASIC, FPGAs, and CPUs as the four major product segments. According to Raghu Raj Singh (News - Alert), a lead analyst at Technavio for embedded systems research, "The high growth rate of hardware is due to the increasing need for hardware platforms with high computing power, which helps run algorithms for deep learning. The growing competition between startups and established players is leading the development of new AI products, both for hardware and software platforms that run deep learning programs and algorithms." This report is available at a USD 1,000 discount for a limited time only: View market snapshot before purchasing Buy 1 Technavio report and get the second for 50% off. Buy 2 Technavio reports and get the third for free. Technavio analysts highlight the following three market drivers that are contributing to the growth of the global AI chips market:
Looking for moreinformation on this market? Request a free sample report Technavio's sample reports are free of charge and contain multiple sections of the report including the market size and forecast, drivers, challenges, trends, and more. Heavy investment by companies in designing their own chips AI is not only about software, but it also requires hardware to support different applications. Many companies are investing heavily in developing their chips that are designed for AI development. For instance, Google (News - Alert) designed TPU, an ASIC that is specific to neural networks. It is a network of software and hardware that can learn individual tasks by analyzing large amounts of data. A challenge for ASIC is that it can perform only one function well. If another function is required, then it needs redesigning of the chip. The TPU contains a set of instructions that can help developers make changes to the existing codes and also develop new algorithms. Another vendor that has already invested USD 2 billion during 2010-2016 in the R&D of AI chip is NVIDIA (News - Alert). In April 2017, NVIDIA developed a chip called Tesla P100, which is designed to provide more power in case of deep learning. These chips have more than 150 billion transistors, making it the world's largest chip. Tesla P100 has a neural network that can learn the data 12 times faster than the other chips of NVIDIA. Increasing implementation of AI in robotics Robotics is all about creating efficient and intelligent robots. Robotics involves the use of computer-controlled mechanical devices to perform specific tasks that are hazardous or tedious for humans. "The contributions of AI in robotics include decision making, human-robot interaction, learning, perception, and reasoning. AI uses qualitative data to recognize the shapes of the objects, their ontologies, and their relationships for connecting the shapes with object names. The use of qualitative data helps in faster processing and automatic tagging," says Raghu. AI eliminates or reduces the risk to human life in many applications. Powerful AI software is used to develop high-precision capabilities for robots, which makes them free from human control and results in increased productivity. Such AI software is incorporated on chips that use neural networks. When a robot interacts with the real world, it gathers data through its sensors, and the neural networks compare these inputs with desired outputs. Therefore, the effectiveness of robots lies in the accuracy of the coding about the real world. Use of AI in cyber security Cyber threats are increasing in frequency and complexity. Cyber attackers are using automation technologies to carry out these attacks. Many organizations use manual efforts to prevent threats by analyzing internal security findings and then combining that with the external threat information. Such traditional methods take weeks or months to detect intrusions, and attackers can take advantage of this time lapse to extract data. Organizations are now using AI for their day-to-day operations to counter cyber-attacks. Darktrace, a UK-based company, uses machine learning to track cyber-attacks. It has developed a system called Antigena, which uses the AI capabilities. Antigena has AI chips that are pre-programmed to automatically respond and takes actions to neutralize a threat as soon as a threat is identified. It acts as a digital antibody by stopping the devices or connections and reducing the speed within the network, thereby protecting the business operations. Browse Related Reports:
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