Mediaboss Marketing

This is the fourth in a multi-part series on cryptography and the Domain Name System (DNS).

One of the "key" questions cryptographers have been asking for the past decade or more is what to do about the potential future development of a large-scale quantum computer.

If theory holds, a quantum computer could break established public-key algorithms including RSA and elliptic curve cryptography (ECC), building on Peter Shor's groundbreaking result from 1994.

This prospect has motivated research into new so-called "post-quantum" algorithms that are less vulnerable to quantum computing advances. These algorithms, once standardized, may well be added into the Domain Name System Security Extensions (DNSSEC) thus also adding another dimension to a cryptographer's perspective on the DNS.

(Caveat: Once again, the concepts I'm discussing in this post are topics we're studying in our long-term research program as we evaluate potential future applications of technology. They do not necessarily represent Verisign's plans or position on possible new products or services.)

The National Institute of Standards and Technology (NIST) started a Post-Quantum Cryptography project in 2016 to "specify one or more additional unclassified, publicly disclosed digital signature, public-key encryption, and key-establishment algorithms that are capable of protecting sensitive government information well into the foreseeable future, including after the advent of quantum computers."

Security protocols that NIST is targeting for these algorithms, according to its 2019 status report (Section 2.2.1), include: "Transport Layer Security (TLS), Secure Shell (SSH), Internet Key Exchange (IKE), Internet Protocol Security (IPsec), and Domain Name System Security Extensions (DNSSEC)."

The project is now in its third round, with seven finalists, including three digital signature algorithms, and eight alternates.

NIST's project timeline anticipates that the draft standards for the new post-quantum algorithms will be available between 2022 and 2024.

It will likely take several additional years for standards bodies such as the Internet Engineering Task (IETF) to incorporate the new algorithms into security protocols. Broad deployments of the upgraded protocols will likely take several years more.

Post-quantum algorithms can therefore be considered a long-term issue, not a near-term one. However, as with other long-term research, it's appropriate to draw attention to factors that need to be taken into account well ahead of time.

The three candidate digital signature algorithms in NIST's third round have one common characteristic: all of them have a key size or signature size (or both) that is much larger than for current algorithms.

Key and signature sizes are important operational considerations for DNSSEC because most of the DNS traffic exchanged with authoritative data servers is sent and received via the User Datagram Protocol (UDP), which has a limited response size.

Response size concerns were evident during the expansion of the root zone signing key (ZSK) from 1024-bit to 2048-bit RSA in 2016, and in the rollover of the root key signing key (KSK) in 2018. In the latter case, although the signature and key sizes didn't change, total response size was still an issue because responses during the rollover sometimes carried as many as four keys rather than the usual two.

Thanks to careful design and implementation, response sizes during these transitions generally stayed within typical UDP limits. Equally important, response sizes also appeared to have stayed within the Maximum Transmission Unit (MTU) of most networks involved, thereby also avoiding the risk of packet fragmentation. (You can check how well your network handles various DNSSEC response sizes with this tool developed by Verisign Labs.)

The larger sizes associated with certain post-quantum algorithms do not appear to be a significant issue either for TLS, according to one benchmarking study, or for public-key infrastructures, according to another report. However, a recently published study of post-quantum algorithms and DNSSEC observes that "DNSSEC is particularly challenging to transition" to the new algorithms.

Verisign Labs offers the following observations about DNSSEC-related queries that may help researchers to model DNSSEC impact:

A typical resolver that implements both DNSSEC validation and qname minimization will send a combination of queries to Verisign's root and top-level domain (TLD) servers.

Because the resolver is a validating resolver, these queries will all have the "DNSSEC OK" bit set, indicating that the resolver wants the DNSSEC signatures on the records.

The content of typical responses by Verisign's root and TLD servers to these queries are given in Table 1 below. (In the table, . are the final two labels of a domain name of interest, including the TLD and the second-level domain (SLD); record types involved include A, Name Server (NS), and DNSKEY.)

For an A or NS query, the typical response, when the domain of interest exists, includes a referral to another name server. If the domain supports DNSSEC, the response also includes a set of Delegation Signer (DS) records providing the hashes of each of the referred zone's KSKs the next link in the DNSSEC trust chain. When the domain of interest doesn't exist, the response includes one or more Next Secure (NSEC) or Next Secure 3 (NSEC3) records.

Researchers can estimate the effect of post-quantum algorithms on response size by replacing the sizes of the various RSA keys and signatures with those for their post-quantum counterparts. As discussed above, it is important to keep in mind that the number of keys returned may be larger during key rollovers.

Most of the queries from qname-minimizing, validating resolvers to the root and TLD name servers will be for A or NS records (the choice depends on the implementation of qname minimization, and has recently trended toward A). The signature size for a post-quantum algorithm, which affects all DNSSEC-related responses, will therefore generally have a much larger impact on average response size than will the key size, which affects only the DNSKEY responses.

Post-quantum algorithms are among the newest developments in cryptography. They add another dimension to a cryptographer's perspective on the DNS because of the possibility that these algorithms, or other variants, may be added to DNSSEC in the long term.

In my next post, I'll make the case for why the oldest post-quantum algorithm, hash-based signatures, could be a particularly good match for DNSSEC. I'll also share the results of some research at Verisign Labs into how the large signature sizes of hash-based signatures could potentially be overcome.

Read the previous posts in this six-part blog series:

The rest is here:
Securing the DNS in a Post-Quantum World: New DNSSEC Algorithms on the Horizon - CircleID

An IBM patent shows a hexagonal array of qubits in a quantum computer, arranged to minimize problems controlling the finicky data processing elements.

IBM secured 9,130 US patents in 2020, more than any other company as measured by an annual ranking, and this year quantum computing showed up as part of Big Blue's research effort. The company wouldn't disclose how many of the patents were related to quantum computing -- certainly fewer than the 2,300 it received for artificial intelligence work and 3,000 for cloud computing -- but it's clear the company sees them as key to the future of computing.

The IFI Claims patent monitoring service compiles the list annually, and IBM is a fixture at the top. The IBM Research division, with labs around the globe, has for decades invested in projects that are far away from commercialization. Even though the work doesn't always pay dividends, it's produced Nobel prizes and led to entire industries like hard drives, computer memory and database software.

Get the latest tech stories with CNET Daily News every weekday.

"A lot of the work we do in R&D really is not just about the number of patents, but a way of thinking," Jerry Chow, director of quantum hardware system development, said in an exclusive interview. "New ideas come out of it."

IFI's US patent list is dominated by computer technology companies. Second place went to Samsung with 6,415 patents, followed by Canon with 3,225, Microsoft with 2,905 and Intel with 2,867. Next on the list are Taiwan Semiconductor Manufacturing Corp., LG, Apple, Huawei and Qualcomm. The first non-computing company is Toyota, in 14th place.

Internationally, IBM ranked second to Samsung in patents for 2020, and industrial companies Bosch and General Electric cracked the top 10. Many patents are duplicative internationally since it's possible to file for a single patent in 153 countries.

Quantum computing holds the potential to tackle computing problems out of reach of conventional computers. During a time when it's getting harder to improve ordinary microprocessors, quantum computers could pioneer new high-tech materials for solar panels and batteries, improve chemical processes, speed up package delivery, make factories more efficient and lower financial risks for investors.

Industrywide, quantum computing is a top research priority, with dozens of companies investing millions of dollars even though most don't expect a payoff for years. The US government is bolstering that effort with a massive multilab research effort. It's even become a headline event at this year's CES, a conference that more typically focuses on new TVs, laptops and other consumer products.

"Tactical and strategic funding is critical" to quantum computing's success, said Hyperion Research analyst Bob Sorensen. That's because, unlike more mature technologies, there's not yet any virtuous cycle where profits from today's quantum computing products and services fund the development of tomorrow's more capable successors.

IBM has taken a strong early position in quantum computing, but it's too early to pick winners in the market, Sorensen added.

The long-term goal is what's called a fault tolerant quantum computer, one that uses error correction to keep calculations humming even when individual qubits, the data processing element at the heart of quantum computers, are perturbed. In the nearer term, some customers like financial services giant JPMorgan Chase, carmaker Daimler and aerospace company Airbus are investing in quantum computing work today with the hope that it'll pay off later.

Quantum computing is complicated to say the least, but a few patents illustrate what's going on in IBM's labs.

Patent No. 10,622,536 governs different lattices in which IBM lays out its qubits. Today's 27-qubit "Falcon" quantum computers use this approach, as do the newer 65-qubit "Hummingbird" machines and the much more powerful 1,121-qubit "Condor" systems due in 2023.

A close-up view of an IBM quantum computer. The processor is in the silver-colored cylinder.

IBM's lattices are designed to minimize "crosstalk," in which a control signal for one qubit ends up influencing others, too. That's key to IBM's ability to manufacture working quantum processors and will become more important as qubit counts increase, letting quantum computers tackle harder problems and incorporate error correction, Chow said.

Patent No. 10,810,665 governs a higher-level quantum computing application for assessing risk -- a key part of financial services companies figuring out how to invest money. The more complex the options being judged, the slower the computation, but the IBM approach still outpaces classical computers.

Patent No. 10,599,989 describes a way of speeding up some molecular simulations, a key potential promise of quantum computers, by finding symmetries in molecules that can reduce computational complexity.

Most customers will tap into the new technology throughquantum computing as a service. Because quantum computers typically must be supercooled to within a hair's breadth of absolute zero to avoid perturbing the qubits, and require spools of complicated wiring, most quantum computing customers are likely to tap into online services from companies like IBM, Google, Amazon and Microsoft that offer access to their own carefully managed machines.

See the original post:
Quantum computing research helps IBM win top spot in patent race - CNET

Finlands VTT Technical Research Centre has formed a strategic collaboration with tech startup IQM Group to build the countrys first quantum computer.

The VTT-IQM co-innovation partnership aims to deliver a 50-qubit machine by 2024, drawing on international quantum technology expertise to augment Finlands home-grown quantum capabilities.

The partnership combines VTTs expertise in supercomputing and networking systems with IQMs capacity to deliver a hardware stack for a quantum computer while working with VTT to integrate critical technologies.

The financing element of the project saw IQM launch a new series A funding round in November. The Helsinki-headquartered company raised 39m in new capital in the funding round, bringing to 71m the total amount raised by IQM for quantum computing-related research and development (R&D) project activities to date.

State-owned VTT is providing financing for the project in the form of grants totalling 20.7m from the Finnish government.

Micronova, a national research and development infrastructure resource operated jointly by VTT and Aalto University, will provide the clean room environment to build the quantum computer and associated components at a dedicated facility at Espoo, southwest of Helsinki. The build will use Micronovas specialised input and micro- and nanotechnology expertise to guide the project.

The project marks the latest phase in cooperation between VTT and Aalto University. The two partners are also involved in a joint venture to develop a new detector for measuring energy quana. As measuring the energy of qubits lies at the core of how quantum computers operate, the detector project has the potential to become a game-changer in quantum technology.

IQMs collaborative role with VTT emerged following an international public tender process. All partners expect to see robust advances in the quantum computing project in 2021, said Jan Goetz, CEO of IQM.

This project is extremely prestigious for us, said Goetz. We will be collaborating with leading experts from VTT, so this brings a great opportunity to work together in ways that help build the future of quantum technologies.

Finlands plan to build a 50-qubit machine stacks up reasonably well in terms of ambition and scope, compared with projects being run by global tech giants Google and IBM.

In 2019, Google disclosed that it had used its 53-qubit quantum computer to perform a calculation on an unidentified unique abstract problem that took 200 seconds to accomplish. Google, which hopes to build a one million-qubit quantum computer within 10 years, estimated that it would have taken the worlds most powerful supercomputer, at the time, 10,000 years to resolve and complete the same calculation.

For its part, IBM is engaged in a milestone project to build a quantum computer comprising 1,000 qubits by 2023. IBMs largest current quantum computer contains 65 qubits.

The VTT-IQM project will proceed in three stages. The first will involve the construction of a five-qubit computer by the year of 2021. The project will then be scaled up in 2022, parallel with enhancement of support infrastructure, to deliver the target 50-qubit machine in 2023.

Our focus is more on how effectively we use the qubits, rather than the number, said Goetz. We expect, that by 2024, we will be in a place where there is a high likelihood of simulating several real-world problems and start finding solutions with a quantum computer.

For instance, conducting quantum material simulations for chemistry applications such as molecule design for new drugs, or the discovery of chemical reaction processes to achieve superior battery and fertiliser production.

The Finnish governments direct funding of the project is driven by a broader mission to further elevate the countrys reputation as a European tech hub and computing superpower, said Mika Lintil, Finlands economic affairs minister.

We want Finland to harness its potential to become the European leader in quantum technologies, he added. By having this resource, we can explore the opportunities that quantum computing presents to Finnish and European businesses. We see quantum computing as a dynamic tool to drive competitiveness across the whole of the European Union.

Within VTT, the quantum computing project will run parallel with connected areas of application, including quantum sensors and quantum-encryption algorithms. Quantum sensors are becoming increasingly important tools in medical imaging and diagnostics, while quantum-encryption algorithms are being deployed more widely to protect information networks.

Quantum computing-specific applications have the capacity to empower businesses to answer complex problems in chemistry and physics that cannot be solved by current supercomputers, said VTT CEO Antti Vasara.

Investing in disruptive technologies like quantum computing means we are investing in our future ability to solve global problems and create sustainable growth, he said. Its a machine that has immense real-world applications that can make the impossible possible. It can be used to simulate or calculate how materials or medicinal drugs work at the atomic level.

In the future, quantum technologies will play a significant role in the accelerated development and delivery of new and critical vaccines.

Finlands advance into quantum computing will further enhance Helsinkis status as a Nordic and European hub for world-leading innovative ecosystems dedicated to new technologies.

The project will also bolster IQMs capacity to build Europes largest industrial quantum hardware team to support projects across Europe, said Goetz.

IQM established a strategic presence in Germany in 2020, following the German governments commitment to invest 2bn in a project to build two quantum computers.

We are witnessing a boost in deep-tech funding in Europe, said Goetz. Startups like us need access to three channels of funding to ensure healthy growth. We need research grants to stimulate new key innovations and equity investments to grow the company. We also require early adoption through acquisitions supported by the government. This combination of funding enables us to pool risk and create a new industry.

IQMs initial startup funding included a 3.3m grant from Business Finland, the governments innovation financing vehicle, in addition to 15m equity investment from the EIC (European Innovation Council) Accelerator programme.

The 71m harvested by IQM in 2020 ranks among the highest capital fund raising rounds by a European deep-tech startup in such a short period.

Continued here:
Tech partnership to drive Finlands quantum computing project - ComputerWeekly.com

The year 2020 was profoundly challenging for citizens, companies, and governments around the world. As covid-19 spread, requiring far-reaching health and safety restrictions, artificial intelligence (AI) applications played a crucial role in saving lives and fostering economic resilience. Research and development (R&D) to enhance core AI capabilities, from autonomous driving and natural language processing to quantum computing, continued unabated.

Baidu was at the forefront of many important AI breakthroughs in 2020. This article outlines five significant advances with implications for combating covid-19 as well as transforming the future of our economies and society.

The trendand why it matters. It typically takes years, if not decades, to develop a new vaccine. But by March 2020, vaccine candidates to fight covid-19 were already undergoing human tests, just three months after the first reported cases. The record speed of vaccine development was partly thanks to AI models that helped researchers analyze vast amounts of data about coronavirus.

There are tens of thousands of subcomponents to the outer proteins of a virus. Machine learning models can sort through this blizzard of data and predict which subcomponents are the most immunogenici.e., capable of producing an immune responseand thereby guide researchers in designing targeted vaccines. The use of AI in vaccine development may revolutionize the way all vaccines are created in the future.

Baidus innovations. In February, Baidu opened its LinearFold AI algorithm for scientific and medical teams working to fight the virus. LinearFold predicts the secondary structure of the ribonucleic acid (RNA) sequence of a virusand does so significantly faster than traditional RNA folding algorithms. LinearFold was able to predict the secondary structure of the SARS-CoV-2 RNAsequence in only 27 seconds, 120 times faster than other methods. This is significant, because the key breakthrough of covid-19 vaccines has been the development of messenger RNA (mRNA) vaccines. Instead of conventional approaches, which insert a small portion of a virus to trigger a human immune response, mRNA teaches cells how to make a protein that can prompt an immune response, which greatly shortens the time span involved in development and approval.

To support mRNA vaccine development, Baidu later developed and released an AI algorithm for optimizing mRNA sequence design called LinearDesign, which aims to solve the problem of unstable and unproductive mRNA sequences in candidate vaccines.

In addition to opening up access to LinearFold and LinearDesign for researchers around the world, Baidu also formed a strategic partnership with the National Institute for Viral Disease Control and Prevention, part of the Chinese Center for Disease Control and Prevention. Following an outbreak at Beijings Xinfadi market in June, Baidus AI technology allowed authorities to complete genome sequencing of the coronavirus strain within 10 hours, helping curb the outbreak. In December, Baidu unveiled PaddleHelix, a machine learning-based bio-computing framework aimed at facilitating the development of vaccine design, drug discovery, and precision medicine.

The trendand why it matters. Autonomous driving technology continued to mature in 2020, with the industrys leading companies testing driverless cars and opening up robotaxi services to the public in various cities. Fully automated driving, which enables rides without a human safety driver on board, will be necessary for the scalability and commercialization of autonomous driving.

Baidus innovations. Over the past year, Baidu launched the Apollo Go Robotaxi service in the Chinese cities of Changsha, Cangzhou, and Beijingincluding in busy commercial areasbecoming the only company in China to start robotaxi trial operations in multiple cities.

These developments are a result of Baidus continuous innovation in developing AI systems that can safely control a vehicle in complex road conditions and solve the majority of possible issues on the road, independent of a human driver.

At Baidu World 2020, its annual technology conference, Baidu also demonstrated its fully automated driving capabilitywhere the AI system drives independently without an in-vehicle safety driver. To support fully automated driving, Baidu developed the 5G Remote Driving Service, a safety measure whereby remote human operators can take control of a vehicle in the event of an exceptional emergency. Baidus achievement of fully automated driving, and the rollout of its robotaxis, suggests a positive outlook for the commercialization of the technology in the near future.

The trendand why it matters. In 2020, natural language systems became significantly more advanced at processing aspects of human language like sentiment and intent, generating language that aligns with human speaking and writing patterns, and even visual understanding, meaning the capability to express understanding about an image through language. These natural language models are powering more accurate search results and more sophisticated chatbots and virtual assistants, leading to better user experiences and creating value for businesses.

Baidus innovations. Baidu released a new multiflow sequence framework for language generation called ERNIE-GEN. By training the model to predict semantically complete blocks of text, ERNIE-GEN performs at an elite level across a range of language generation tasks, including dialogue engagement, question generation, and abstractive summarization.

Baidus vision-language model ERNIE-ViL also achieved significant progress in visual understanding, ranking first on the VCR leaderboard, a dataset of 290,000 questions built by the University of Washington and the Allen Institute for AI, that aims to test visual understanding ability. ERNIE-ViL also achieved state-of-the-art performance on five vision-language downstream tasks. Visual understanding lays the foundation for computer systems to physically interact in everyday scenes, as it involves both understanding visual content and expressing it through language. It will be crucial for improving the quality of human-machine interaction.

The trendand why it matters. Quantum computing made significant inroads in 2020, including the Jiuzhang computers achievement of quantum supremacy. This carries significance for AI, since quantum computing has the potential to supercharge AI applications compared to binary-based classical computers. For example, quantum computing could be used to run a generative machine learning model through a larger dataset than a classical computer can process, thus making the model more accurate and useful in real-world settings. Advanced technologies such as deep learning algorithms are also playing an increasingly critical role in the development of quantum computing research.

Baidus innovations. Baidu achieved a number of technical breakthroughs in 2020 that promise to bridge AI and quantum computing. In May, Baidu launched Paddle Quantum, a quantum machine learning development toolkit that can help scientists and developers quickly build and train quantum neural network models and provide advanced quantum computing applications. The open-source toolkit both supports developers building quantum AI applications, and helps deep learning enthusiasts develop quantum computing. In September, Baidu entered cloud-based quantum computing with the launch of Quantum Leaf, which provides quantum development kits such as QCompute, and can shorten the life cycle of quantum programming and help realize a closed-loop quantum tool chain.

The trendand why it matters. AI hardware continued to develop in 2020, with the launch of several AI chips customized for specialized tasks. While an ordinary processor is capable of supporting AI tasks, AI-specific processors are modified with particular systems that can optimize performance for tasks like deep learning. As AI applications become more widespread, any increase in performance or reduction in cost can unlock more value for companies that operate a wide network of data centers for commercial cloud services, and can facilitate the companys internal operations.

Baidus innovations. At Baidu World 2020, the company offered a glimpse into its next-generation AI processor, the Kunlun 2, which it plans to put into mass production in early 2021. The chip uses 7 nanometer (nm) processing technology and its maximum computational capability is over three times that of the previous generation, the Kunlun 1. The Kunlun chips are characterized by high performance, low cost, and high flexibility, which can support a broad range of AI applications and scenarios, helping foster greater AI adoption and reducing usage costs. More than 20,000 Kunlun 1 chips have now been deployed to support Baidus search engine and Baidu Cloud partners since they launched in 2018, empowering industrial manufacturing, smart cities, smart transportation, and other fields.

This content was produced by Baidu. It was not written by MIT Technology Reviews editorial staff.

Go here to read the rest:
These five AI developments will shape 2021 and beyond - MIT Technology Review

Pune, Jan. 18, 2021 (GLOBE NEWSWIRE) -- The global artificial intelligence (AI) chipsets market size is expected to reach USD 108.85 billion by 2027, exhibiting a CAGR of 38.9% during the forecast period. The increasing implementation of 3D technology along with neural networks & deep learning technologies will promote the healthy growth of the market during the forecast period, states Fortune Business Insights, in a report, titled Artificial Intelligence (AI) Chipsets Market Size, Share & COVID-19 Impact Analysis, By Chipset Type (Graphics Processing Unit (GPU), Field Programmable Gate Arrays (FPGAs), Application-specific Integrated Circuit (ASIC) and Others), By Application (Natural Language Processing (NLP), Robotic Process Automation (RPA), Machine Learning, Computer Vision and Others), By Computing Technology (Cloud Computing and Edge Computing), By Function (Training and Inference), By Industry (Consumer Electronics, Healthcare, BFSI, IT & Telecom, Manufacturing, Automotive, Retail, and Others), and Regional Forecast, 2020-2027. The market size stood at USD 8.14 billion in 2019.

Click here to get the short-term and long-term impact of COVID-19 on this market.

Please visit: https://www.fortunebusinessinsights.com/artificial-intelligence-ai-chipsets-market-104500#

The whole world is battling with the novel coronavirus, leaving numerous industries distraught. The authorities of several countries have initiated lockdown to prevent the spread of this deadly virus. Such plans have caused disturbances in the production and supply chain. But, with time and resolution, we will be able to combat this stern time and get back to normality. Our well-revised reports will help companies to receive in-depth information about the present scenario of every market so that you can adopt the necessary strategies accordingly.

The report on artificial intelligence (AI) chipsets market accentuates:

All-inclusive analysis of the marketDynamic insights into the segmentsExtensive data about dominant regionsKey information about prominent players Latest developments Market drivers and restraintsCOVID-19 Influence

Request a Sample Copy of Report: https://www.fortunebusinessinsights.com/enquiry/request-sample-pdf/artificial-intelligence-ai-chipsets-market-104500

Market Driver:

Increasing Cognizance about Quantum Computing to Promote Growth

The growing adoption of quantum computing technology to solve complex problems and perform analytical calculations will spur opportunities for the market. For instance, Google LLC's Sycamore quantum computer is currently the fastest computer that can perform a specific task in around 200 seconds. Quantum computers are enabled with technologies such as artificial intelligence, machine learning, computer vision, big data, AR/VR, and others. The growing knowledge about quantum computing will spur demand for AI chipsets, in turn, aiding the market growth. Quantum computing is used in various functions such as fraud detection, risk management, portfolio optimization, and applications where instant data response is required. The growing adoption of risk management solutions among organizations will contribute positively to market growth in the near future.

Popularity of AI-based Solutions to Boost Market During Coronavirus

The production of AI chipsets has been greatly affected by the coronavirus. According to the index of industrial production (IIP) data, in 2020, the manufacturing sector production registered a decline of 11.1% in July, as covid-19 lockdown slows down the manufacturing process. However, the demand for such chipsets has improved immensely during the pandemic because of the adoption of AI among various industries. Various industries such as automotive, manufacturing, and others have implemented AI solutions to ease up processes. Besides, the focus on advanced AI-based solutions by prominent players will aid the market amid coronavirus. For instance, in May 2020, Nvidia Corporation expanded its EGX Edge AI platform by introducing new products called the EGX Jetson Xavier NX and EGX A100.

Ask for Customization: https://www.fortunebusinessinsights.com/enquiry/customization/artificial-intelligence-ai-chipsets-market-104500

Regional Analysis:

Emergence of Startup Companies to Propel Market in Asia Pacific

Asia Pacific is expected to dominate the global market owing to the developing economies such as South Korea, India, China. The growing acceptance of AI-based solutions will foster healthy growth of the market in the region. The government of Singapore has created an AI Ethics Advisory Council as a part of its AI Strategy to deploy AI applications across various industries in 2018. The strong startup ecosystem is expected to further drive the market in Asia Pacific. Europe is expected to hold the largest share in the global market owing to the presence of AI solution providers in the European countries. The growing focus on R&D investments coupled with the adoption of AI technologies will consequently bolster the growth of the market in Europe. The Middle East and Africa is expected to grow rapidly during the forecast period owing to the smart city initiatives in the region.

Key Development:

July 2019: MediaTek Inc. announced the launch of its new AI chipset - "MTK i700" that is featured with high-speed edge AI computation for rapid image recognition, AR applications, smart homes, stores, and factories, etc.

The Report Lists the Key Companies in the Artificial Intelligence (AI) Chipsets Market:

Quick Buy- AI Chipset Market: https://www.fortunebusinessinsights.com/checkout-page/104500

Major Table of Content:

TOC Continued..

Speak To Our Analyst- https://www.fortunebusinessinsights.com/enquiry/speak-to-analyst/artificial-intelligence-ai-chipsets-market-104500

Have a Look at Related Research Insights:

Customer Experience Management Market Size, Share & Industry Analysis, By Component (Solution, Services), By Deployment (On-Premises, Cloud), By Organization Size (SMEs, Large Enterprises), By Touchpoint (Telephone, Email, Web, Social Media, and Others), By End-User (BFSI, Retail, IT and Telecommunications, Healthcare, Manufacturing, Government, Travel and Transportation and Others) and Regional Forecast, 2019-2026

Customer Data Platform Market Size, Share & COVID-19 Impact Analysis, By Component (Platform and Services), By Deployment (Cloud, On-premises), By Type (Access, Analytics, and Campaign) By Industry (Banking, Financial Services and Insurance (BFSI), Retail and ecommerce, Information Technology (IT) and Telecom, Media and Entertainment, Travel and Hospitality, Healthcare, and Others (Government and Education)), and Regional Forecast, 2020-2027

Professional Service Automation Software Market Size, Share & Industry Analysis, By Application (Consulting Firms, Marketing and Communication Firms, IT Firms, Architecture and Construction Firms, Research Firms, Accounting Firms, Others), By Deployment Type (Cloud, On-premise) and Regional Forecast, 2019-2026

Artificial Intelligence (AI) Market Size, Share and Industry Analysis By Component (Hardware, Software, Services), By Technology (Computer Vision, Machine Learning, Natural Language Processing, Others), By Industry Vertical (BFSI, Healthcare, Manufacturing, Retail, IT & Telecom, Government, Others) and Regional Forecast, 2019-2026

Digital Payment Market Size, Share & Industry Analysis, By Payment Type (Mobile Payment, Online Banking, Point of Sale, Digital Wallet), By Industry (Media & Entertainment, Retail, BFSI, Automotive, Medical & Healthcare, Transportation, Consumer Electronics, Others), and Regional Forecast, 2019-2026

About Us:

Fortune Business Insightsoffers expert corporate analysis and accurate data, helping organizations of all sizes make timely decisions. We tailor innovative solutions for our clients, assisting them address challenges distinct to their businesses. Our goal is to empower our clients with holistic market intelligence, giving a granular overview of the market they are operating in.

Our reports contain a unique mix of tangible insights and qualitative analysis to help companies achieve sustainable growth. Our team of experienced analysts and consultants use industry-leading research tools and techniques to compile comprehensive market studies, interspersed with relevant data.

At Fortune Business Insights, we aim at highlighting the most lucrative growth opportunities for our clients. We therefore offer recommendations, making it easier for them to navigate through technological and market-related changes. Our consulting services are designed to help organizations identify hidden opportunities and understand prevailing competitive challenges.

Contact Us:Fortune Business Insights Pvt. Ltd.308, Supreme Headquarters,Survey No. 36, Baner,Pune-Bangalore Highway,Pune- 411045, Maharashtra,India.Phone:US: +1-424-253-0390UK: +44-2071-939123APAC: +91-744-740-1245Email:sales@fortunebusinessinsights.comFortune Business InsightsLinkedIn|Twitter|Blogs

Read Press Release https://www.fortunebusinessinsights.com/press-release/global-artificial-intelligence-ai-chipsets-market-10357

Read more from the original source:
Artificial Intelligence (AI) Chipsets Market to Reach USD 108.85 Billion by 2027; Rising Adoption of Deep Learning Technologies to Aid Expansion,...