Artificial Intelligence Market: Strongly Influencing the Present and Future of Businesses and Humankind

Artificial intelligence (AI) can be understood as a science, engineering and deployment of machines, which perform tasks with intelligence as similar to humans. Since its inception 60 years ago, AI has observed significant growth in recent years. Initially, AI was considered as topic for academicians, though in recent years with development of various technologies, AI has turned into reality and is influencing many lives and businesses. 

Additionally, evolution of various other supplementary technologies such as cloud computing, machine learning and cognitive computing are collectively paving the growth of the market for AI. “In 2015, the AI chipset market was valued for USD 589.8 million and is expected to reach USD 16,059.3 million in 2022, at a CAGR of 62.9% from 2016 to 2022”, says Sachin Garg who tracks global market for semiconductor at research firm MarketsandMarkets. Many IT giants and start-ups are investing heavily in development of AI software solutions and hardware products. Some the prominent players in AI market in the recent times are Intel Corporation (U.S.), Google Inc. (U.S.), Microsoft Corporation (U.S.), Amazon.com, Inc. (U.S.), Baidu, Inc. (China), and NVIDIA Corporation (U.S.). 

 

Computer vision technology has become a part of high definition video games, while deep learning; which is a subset of machine learning is now implemented in speech recognition, drug discovery, health monitoring and various others applications. Robotics has evolved as one of the most promising AI technology in recent times. With wide range of applications, AI enabled robots are nowadays applied in welding, cutting, color coating and polishing purposes in automotive industry; carrying out several clinical tests and performing surgeries in healthcare industry. Furthermore, robotics and AI are likely to benefit the industries which are lacking the skilled and young workforce such as agriculture, factories, food processing and fulfillment centers.

Moreover, AI is likely to gain traction with amalgamation of various technologies such as deep learning, robotics, digital personal assistance, querying, natural language processing, and context-aware processing to develop an AI-featured product. In near future, AI is expected to make a crucial impact in multiple end-use applications such as driverless cars, healthcare diagnostics, and physical assistance in elder care.

AI is likely to disrupt every business segment across the world. The end-use industries of AI featured in this AI market study includes, agriculture, BFSI, manufacturing, healthcare, oil & gas, media & advertising, transportation and automotive, retail, among others. Among these verticals, transportation and automotive sector was the largest contributor to the AI market in 2015. This was resultant of significant investment made by federal governments and venture capitalists in development of connected and autonomous vehicles. 

These funding consequently resulted in some of the tangible recent development in autonomous projects. For instance, in October 2016, Otto (U.S.) (now acquired by Uber Technologies, Inc. (U.S.)) shipped its first consignment from Denver, Colorado, U.S. to Denver Springs, U.S. using driverless truck. 

The autonomous driving was carried for 120 mile journey. However, healthcare industry is estimated to exhibit highest growth in near term. The growing penetration of AI in health care assistance and medical management are the two key factors driving the market for the healthcare industry. Moreover, AI technologies are also used in the healthcare industry for decision-making processes, extracting information from the data collected from patients, and processing it further for testing and simulation of new treatments, scenarios, and devices.

The increasing large and complex datasets even called as big data, and the adoption of AI-enabled products and services to improve consumer-centric services are the two major growth drivers of the AI market. However, the scarcity of low cost and energy efficient hardware, and the lack of skilled workforce for development of AI algorithms and tools is curbing the growth of the AI market.

In essence, AI offers significant growth opportunities to all the stakeholders involved in its ecosystem. Development of human-aware AI systems and widening scope of AI technologies in niche markets are prominent market opportunities existing today. In addition to this, deploying AI at the edge of the network is likely to become a massive industry trend in coming years.

From new product launches to mergers & acquisitions, AI industry has witnessed some tremendous and crucial developments in recent years. Many companies are relying on their in-house developments, while some are growing with inorganic growth practices. Google, one of the frontrunners in AI had launched industry’s first AI-enabled smartphone “Pixel” in October 2016, which signifies that AI is now turning into a reality. On the other hand, IBM acquired Truven Health Analytics (U.S.) in April 2016, with this IBM intent to derive insights from Truven’s health data using Watson Health’s cognitive capabilities. 

On similar lines, in January 2016, IBM acquired The Weather Company, based in Atlanta, Georgia (U.S.) to introduce a new platform for IBM Watson Internet of Things. Alongside this, leading graphics processing unit (GPU) providers, NVIDIA Corporation launched the industry’s first deep learning system, “NVIDIA DGX- 1”, in April 2016. This tool is a completely integrated system with deep learning hardware and software, and development tool kit.

Thus, AI technologies are the future for many industries which are striving for efficient, low-cost operations and high profitability in their markets.


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Neuromorphic Computing: A Disruptive Technology for High-Performance Computing

Neuromorphic computing is inspired by the functioning of a human brain which makes it possible to encode information more efficiently than the present computer chips. In the von Neumann architecture-based processors available today, the data shuttles between the processor and the storage system for the execution of each instruction, while in neuromorphic chips, processing and storage functions are integrated; thus, each neuron can process small piece of information and stores it locally. Synapses help in data communication and in connecting the neurons, thereby enabling biological brain-like functions for neuromorphic chips. Such fundamental changes in the architecture of neuromorphic processing chips enable massive parallel execution of information, thereby significantly increasing the speed of processing with low power consumption. 

End of Moore’s law would lead to surge in neuromorphic computing adoption

In the next few years, end of Moore’s law—the number of transistors in a dense integrated circuit doubles approximately every two years—would result into reduced space between electrons and holes and would lead to problems such as current leakage and overheating in ICs. 

These problems would lead to slower performance, high power consumption by ICs, and reduced durability. Thus, the need for finding an alternate way to increase the computational power of chips has fueled the development of neuromorphic chips. Neuromorphic chips can be 176,000 times more efficient in running brain-like work load compared to modern CPUs. Another important factor is the reduction in size of a transistor would result into increase in cost. As a result, researchers are evaluating different approaches to conduct large-scale computation models, which are inspired by biological principles. 

“The neuromorphic computing market, by offering, is expected to be valued at USD 6,591.1 thousand in 2016 and is likely to reach USD 272,915.7 million by 2022, at a CAGR of 86.0% between 2016 and 2022”, says Sachin Garg who tracks global market for semiconductor at research firm MarketsandMarkets.

Ongoing Developments in Neuromorphic Computing

Various development projects have been undertaken by companies and research organizations to develop neuromorphic computing systems. Engineers in the Stanford University (U.S.) developed a circuit board, Neurogrid, which simulates one million neurons connected by six billion synapses connected in structured patterns and consume 100,000 times lesser power compared to a supercomputer.  

The Freie University, in collaboration with the University of Bielefeld, Kirchoff Institute for Physics, and Heidelberg, is working on designing a neuromorphic chip and software modeled on insects’ odor-processing systems and developing it to recognize plant species by their flowers. The BrainScaleS neuromorphic system has been developed at the University of Heidelberg (Germany) which was a collaboration of 19 research groups from 10 European countries, funded by the European Union. SpiNNaker, a parallel low-power neuromorphic supercomputer, was built at the Manchester University in the U.K. and was funded by the U.K. government until early 2014.  

IBM Corporation (U.S.), HP Enterprise (U.S.), Samsung Electronics Limited (South Korea), Intel Corp. (U.S.), HRL Laboratories, LLC (U.S.), General Vision Inc. (U.S.), Applied Brain Research, Inc. (U.S.), and BrainChip Holdings Ltd. (U.S.) are some of the major companies in the neuromorphic computing market. In 2011, IBM (U.S.) unveiled what it calls TrueNorth, a custom-made, brain-like chip that builds on a simpler experimental system.

TrueNorth is equipped with 4,096 processor cores, and it replicates one million human neurons and 256 million synapses—two of the fundamental biological building blocks that make up the human brain. It is the largest chip IBM has ever built at 5.4 billion transistors and has an on-chip network of 4,096 neurosynaptic cores. Samsung Electronics (South Korea) is using IBM’s TrueNorth chip for its machine-vision project to develop image processors.  HRL Laboratories (U.S.) is testing its neuromorphic chips for drones for surveillance and target tracking applications. The Institute for Neuroinformatics (Switzerland) has developed neuromorphic vision sensors, silicon cochlea, and medium-scale neuromorphic processors such as the Reconfigurable On-Line Learning Spiking (ROLLS) and cxQuad chips which use sub-threshold analog circuits. 

In May 2016, General Vision released a new version of its NeuroMem API, compatible with two commercial chips featuring its neural network technology on silicon: (1) General Vision’s CM1K chip and (2) the new Intel’s Curie model.

Adoption of Neuromorphic Computing by Government Agencies and Institutions

The U.S. government is increasing its support of new paradigms, including neuromorphic and quantum computing, to maintain its position as the leader in the field of high-performance computing. In line with this, the U.S. government’s Lawrence Livermore National Laboratory (LLNL) (California, U.S.) has purchased a first-of-a-kind brain-inspired supercomputing platform for deep learning developed by IBM. Based on IBM’s neurosynaptic chip “TrueNorth”, this platform would process an equivalent of 16 thousand neurons and 4 billion synapses and consume mere power of 2.5 watts. LLNL would use this new system to explore new computing capabilities in cybersecurity, control the U.S. nuclear weapons, and manage agreements to keep watch on nuclear weapons in the world.

The Defense Advanced Research Projects Agency provided funds to HRL Laboratories’ Center for Neural and Emergent Systems to develop a chip with 576 silicon “neurons” to be used in a drone aircraft. IBM Watson, a technology platform that uses natural language processing (NLP) and machine learning to reveal insights from large amounts of unstructured data, has initiated partnerships with various major cancer institutes and clinics to derive personal insights from the cancer patients’ deoxyribonucleic acid (DNA). European Union, the Heidelberg University, and the University of Manchester have invested more than USD 100 thousand on “Human Brain Project (HBR)”, which is functional to reconstruct the complex functional role of a human brain and simulate it using parallel architecture.  

Emerging Applications of Neuromorphic Computing

As an immediate application, neuromorphic chips can be integrated with the CPU, thereby enhancing its capabilities of pattern recognition. Parallel connected neurons can perform operations such as image classification, speech recognition, and data mining at a greater speed than that by a conventional CPU, thereby speeding up the overall pattern recognition tasks, while releasing operation load on the CPU. In 2012, IBM Sequoila, a supercomputer build on the Von Neumann architecture, simulated brain using 500 billion neurons and 100 trillion synapses. The supercomputer could simulate brain functioning at a 1/1,500 of actual and consumed 12 GW of power. Similar simulation using neuromorphic chip requires 35 KW of power. 

The new NeuroMem API is available for the Intel Arduino/Genuino 101 and for the General Vision’s BrainCard. In August 2015, IBM developed “rodent brain” chips that are designed with 48 million nerve cells, which is approximately equal to the number of neurons in a rodent head. These chips are used to identify images, recognize spoken words, and understand natural language processing (NLP). Neuromorphic chips can execute instructions rapidly and consume low power. They are highly efficient in pattern recognition which is why can be used for computational applications in various verticals such as military and defense and  information technology (IT), among many others. These chips can be integrated with drones, smartphones, automobiles, search engines, and climate prediction equipment, among others. 

Using pattern recognition, a drone can identify the region it is flying over and respond accordingly. In a smartphone, pattern recognition helps in capturing and tagging images based on objects captured and also in recognizing the voice of an individual. In the automotive sector, pattern recognition can be used to understand the health or position of a driver and adjust the driver’s seat automatically to provide optimum comfort. Moreover, pattern recognition capabilities can be used in search engines to enhance performance, detect frauds in financial markets, and predict climate by using equipment to anticipate climatic conditions accurately. 

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For more information Visit: Neuromorphic Computing Market by Application - 2022

Beyond LoRa & SigFox: Narrowband to drive the adoption of cellular IoT

The existing technologies in the LPWA market such as LoRa and SigFox are less reliable and less secure and also incur a high operational cost. To overcome the connectivity challenges such as high power consumption, less coverage, and so on, 3^rd-Generation Partnership Program (3GPP) members such as Huawei (China) and Ericsson (Sweden) are trying to speed up the standardization of the LPWA IoT technology. Narrowband IoT (NB-IoT) technology is expected to overcome these shortcomings as it is highly reliable, consumes low power, and incurs less operational cost.  

As most IoT devices, especially sensors, do not need to send data of large sizes, they can work with narrowband connections. Narrowband IoT (NB-IoT) connectivity is ideal for devices which send data of small size over a long range. According to the mobile operator’s installed base, NB-IoT deployment can be of three types, that is, stand alone, in band, and guard band. The deployment of NB-IoT in sub1GHz bands enables good propagation and penetration characteristics of NB-IoT. 

NB-IoT is designed to operate in 200 KHz carrier and is able to operate in shared spectrum with an existing LTE network. “The narrowband IoT (NB-IoT) chipset market is expected to grow from USD 16.7 Million in 2017 to USD 181.02 Million by 2022, at a CAGR of 61.06% during the forecast period”, says Sachin Garg who tracks global market for semiconductor at research firm MarketsandMarkets.

Market Drivers

·         Increase in adoption of M2M communication between electronic devices

• NB-IoT technology offers low-power usage and wide area coverage compared to other LPWA technologies.
• The growing adoption of connected devices drives the market for machine-to-machine communication, and thus creates a lucrative market for NB-IoT technology.

·       
Growing demands for long-range connectivity

• The existing LPWA technologies such as LoRa and SigFox are operated in unlicensed spectrum and have limitations in terms of coverage area; whereas, NB-IoT is operated in licensed spectrum and exhibits large coverage area of approximately 35 km.
• Enhanced coverage is necessary for mobile and hard-to-reach devices such as trackers in agricultural land for monitoring and many other use cases.

·       
Need for low-power and low-cost technology

• Low power consumption is a major requirement for all LWPA applications such as smart meters, smart parking, wearables, and smart grids.
• The NB-IoT module is expected to cost around USD 5 per unit while other LPWA technologies such as LORA and SigFox cost around USD 9 per unit and USD 8 unit, respectively.

Developments for Commercializing NB-IoT

With the efforts of major players, NB-IoT has been accepted as a part of Release 13. In May 2014, Huawei and Vodafone collaborated and applied for the approval of the study of the Narrowband Machine-to-Machine (NB-M2M) technology in 3GPP GERAN. During the same year, in October, Qualcomm submitted a proposal for another version of the Narrow Band IoT technology with the name Narrowband Orthogonal Frequency Division Multiplexing (NB-OFDM). 

In May 2015, Huawei and Qualcomm submitted a proposal of air interface technologies and both the companies jointly agreed upon Frequency Division Multiple Access (FDMA) in uplink and Orthogonal Frequency Division Multiplexing (OFDMA) in downlink. In 2015, the names NB M2M and NB OFDM were changed to NB-CIoT (Narrow Band Cellular IoT). Additionally, during this period, Ericsson (Sweden) also invested capital and resources in the research and development of the NB-IoT technology.

In September 2015, 3GPP accepted both Narrowband Long-Term Evolution (NB-LTE) and Narrowband Cellular Internet of Things (NB-CIoT) in Release 13 and then the name was changed to NB-IoT.

Drivers for Key Countries

·     
   USA: The U.S. government has been investing significant amounts toward the implementation of IoT across various sectors such as infrastructure and utilities under programs such as Smart America. The U.S. government offers a supportive environment for research and development, which is facilitating advancements in IoT applications.

·     
   China:  China had 74 million M2M connections at the end of 2014 and the number is expected to grow at a CAGR of 29% between 2014 and 2021, building up an extensive Internet of Things (IoT). NB-IoT can be an efficient solution for IoT connectivity in China, considering the involvement of Chinese companies such as Huawei, China Mobile, and China Unicom.

Narrow Band-IoT chipset MARKET by region 

Major Market Developments 

     At the Internet of Things Summit during Mobile World Congress Shanghai (MWC Shanghai) 2016, Huawei Technologies (China) launched its end-to-end NB-IoT solution to help global operators expand their IoT services into new markets. With this, the company achieved global presence in the NB-IoT market.

·        In June 2016, U-blox Holding AG (Switzerland), a global leader in wireless and positioning modules and chips, announced the availability of SARA-N2 NB-IoT module, which is the world’s first cellular radio module compliant with the 3GPP Release 13 NB-IoT (LTE Cat. NB1) standard.

·      In February 2016, Nokia Networks (Finland) demonstrated NB-IoT network equipment to support operators and enterprises in addressing numerous use cases and business models in the rapidly developing IoT market at Mobile World Congress in 2016.

·   
In June 2016, Ericsson (Sweden), Nokia Networks (Finland), Intel Corporation (U.S.), and Telia (Sweden) entered into a partnership to invest in the joint development of a standard for NB-IoT to serve the growing IoT market in Sweden.

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E-Compass Market worth 2,187.1 Million USD by 2022

The report "E-Compass Market by Technology (Fluxgate, Hall Effect, Magnetoresistive), Type (1&2 Axis, 3 Axis, 6 Axis, 9 Axis), Application (Consumer Electronics, Aerospace & Defense, Automotive, Surveying, Marine) and Geography - Global Forecast to 2022", The e-compass market, in terms of value, is expected to grow from USD 808.6 Million in 2015 to USD 2,187.1 Million by 2022, at a CAGR of 14.57% between 2016 and 2022. The market, in terms of volume, is expected to grow from 1.60 Billion units in 2015 to 4.45 Billion units by 2022, at a CAGR of 15.00% between 2016 and 2022.

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The increasing demand for wearable electronics such as smart watches, fitness and sports trackers have created a huge demand for e-compasses as these devices are, in general, equipped with navigation functionality which has been made possible through the use of e-compasses. The GPS-based navigation technologies are able to provide a precise location only when they are in a range of GPS signals, however, the tilt compensated e-compasses does not rely on the GPS signals and continues to provide the right direction even in the absence of a GPS signal, hence making them extremely useful specifically for applications such as outdoor recreational sports.

Hall effect-based e-compass to hold the largest share of the e-compass market

The Hall effect-based e-compass is expected to hold the largest market share and dominate the e-compass market between 2016 and 2022, owing to the their increasing adoption within consumer electronics market which require low cost e-compass and compact sensors in devices such as smart phones, tablet computers, gaming consoles, and so on.

Consumer electronics application held the largest market share in 2015

The consumer electronics application is expected to hold the largest market share and dominate the e-compass market between 2016 and 2022. The increasing penetration of wearable electronics and usage of navigation-based mobile applications are creating a huge demand for e-compasses within the consumer electronics segment.

APAC to hold the largest market share and grow at the highest rate during the forecast period

The APAC region is expected to hold the largest market share and dominate the e-compass market between 2016 and 2022. The high growth and the large market can be attributed to the virtual monopoly of APAC in the manufacturing of consumer electronics, owing to the presence of companies such as Samsung, Sony, LG, Huawei, Lenovo, coupled with low-production costs, in terms of labor, has forced the consumer electronics manufacturers in the other regions to outsource a bulk of their manufacturing processes to countries such as China and Taiwan.

Furthermore the rapid penetration of handheld “smart” devices in the region is likely to continue during the forecast period; also the region has one of the fastest-growing markets for automotive, aerospace & defense which are the other key application markets for e-compasses.

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The e-compass ecosystem comprises of chip designers such as STMicroelectronics (France), mCube 

(U.S.), MEMSIC (U.S.) and MagnaChip (South Korea) among others; component manufacturers such as NXP Semiconductors N.V. (Netherlands), Bosch Sensortec (Germany), Asahi Kasei Microdevices (Japan), Honeywell (U.S.) among others; technology providers such as InvenSense, Inc. (U.S.), Honeywell (U.S.), and Bosch Sensortec (Germany), among others; and system integrators such as TrueNorth Technologies (U.S.), Aichi Steel (Japan), among others who integrate these e-compass sensors and sell them to end users to cater to their unique business requirements.

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