Source: Deep Learning on Medium
2020 Trending: Deep Learning AI Market to give a boost to retail, healthcare, automotive, BSFI and telecom Industries
The Deep Learning AI market is poised to grow at an excess of 30% CAGR during the forecast period from 2019–2023.In terms of investments in the field of artificial intelligence & deep learning, the U.S. has overtaken China to grab the topmost spot as a venture capital investment hub.
Deep learning is an integral subset of machine learning that has networks capable of learning from unstructured data. Several companies across industries such as retail, healthcare, aerospace & defense, and BFSI, among others, are joining hands with software development companies to use deep learning technology, for saving cost and time and for improving overall efficiency. Healthcare companies such as Bayer A.G., Eli Lilly & Company, Amgen Corporation, and AstraZeneca Plc are strengthening their internal research team and collaborating with software development companies to improve the drug discovery process.
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The impact of deep learning AI across industries such as retail, healthcare, automotive, telecom, and BFSI is expected to intensify in the next 5 years. The U.S. attracted the most investment in 2018, accounting for more than 50% of total investments in AI across the globe. The investment figure in the country is expected to grow significantly in the coming years. The growth in investments in the U.S. is fueled by companies such as Cruise Automation, Zymergen, Dataminr with the total investment reaching approximately US$ 14 Bn in 2019. On the other hand, China attracted investment worth US$ 7.4 Bn in the same year.
Despite complexities, major corporations are focusing on implementing deep learning AI technologies in their processes. Deep learning AI is used for a wide range of applications such as cybersecurity, predictive maintenance, autonomous vehicles, and threat monitoring, among others. Cyber-attacks have witnessed a steep increase over the past few years across industries. Supply chain attacks increased by approximately 78% in 2018 and data breaches have increased by up to 4 times since 2016. Such threats have led both the public and private sector to invest heavily in defense and cyber security. This has led to an exponential rise in demand for deep learning AI, which in turn, has created a positive impact on the entire ecosystem of artificial intelligence. Cybersecurity companies have witnessed sales growth by approximately 15% over the past 3 years, with annual spending amounting to more than US$ 120 Bn.
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Deep Learning: Segmentation
Deep Learning AI is analyzed by Components like Processors, Memory, Storage, FPGA, ASIC, Modules also by Services & Solutions like Smartphone & Tablet, Wearable, Workstation System, Medical Devices, Pharma/Biopharma, Smart Modules, Imaging Systems, Smart Trackers, Smart Meters, Others.
Deep learning AI Market is also analyzed by Application like Predictive Maintenance/Self Diagnostics, Fraud Reduction, Cybersecurity, Network Security, Network Optimization, Customer Analytics, Virtual Assistance, Network Operations & Monitoring Management, Security & Surveillance, Recommendation Engine, Predictive Merchandising, Inventory Management, Others.
Deep learning AI Market is also analyzed by End-users like Logistics, Healthcare, Transportation, Automotive, Retail, BFSI, Telecom & Consumer Electronics, Oil & Gas, Others
News: Fall in the value of cryptocurrencies
The fall in the value of cryptocurrencies acted as a boon for the GPU manufacturers, as prices of GPU’s witnessed a sharp decline after the first quarter of 2018. GPU prices of NVIDIA Corporation and AMD Inc. witnessed a dramatic increase by up to 50% in the first quarter of 2018 due to a boom in cryptocurrencies. This put a lot of pressure on manufacturers and consumers alike, as both were struggling to contend with the high prices of GPUs. However, the value of cryptocurrencies witnessed a sharp decline from the second quarter of 2018, as nearly US$ 6 Bn was wiped out of the global market in a single day. This eased up the road for GPU manufacturers and they resumed their research & development activities to upgrade their hardware.
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