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12.10 Conclusion and Future Research

AI blockchain enabled distributed autonomous energy organizations may help to increase the energy efficiency, cyber security, and resilience of the electricity infrastructure. These are timely goals as we modernize the US power grida complex system of systems that requires secure and reliable communications and a more trustworthy global supply chain. While blockchain, AI, and IoT are creating a buzz right now, many challenges remain to be overcome to realize the full potential of these innovative technological solutions. A lot of news and media coverage of blockchain today falsely suggests that it is a panacea for all that ails usclimate change, cyber security, and volatile financial systems. There is similar hysteria around AI, with articles suggesting that the robots are coming, and that AI will take all of our jobs. While these new technologies are disruptive in their own way and create some exciting new opportunities, many challenges remain. Several fundamental policy, regulatory, and scientific challenges exist before blockchain realizes its full disruptive potential.

Future research should continue to explore the challenges related to blockchain and distributed ledger technology. Applying AI blockchain to modernizing the electricity infrastructure also requires speed, agility, and affordable technology. AI-enhanced algorithms are expensive and often require prodigious data sets that must be broken down into a code that makes sense. However, a lot of noise (distracting data) is being collected and exchanged in the electricity infrastructure, making it difficult to identify cyber anomalies. When there is a lot of disparate data being exchanged at subzero-second speeds, it is difficult to determine the cause of the anomaly, such as a software glitch, cyber-attack, weather event, or hybrid cyber-physical event. It can be very difficult to determine what normal looks like and set the accurate baseline that is needed to detect anomalies. Developing an AI blockchainenhanced grid requires that the data be broken into observable patterns, which is very challenging from a cyber perspective when threats are complex, nonlinear, and evolving.

Applying blockchain to modernizing and securing the electricity infrastructure presents several cyber-security challenges that should be further examined in future research. For example, Ethereum-based smart contracts provide the ability for anyone to write electronic code that can be executed in a blockchain. If an energy producer or consumer agrees to buy or sell renewable energy from a neighbor for an agreed-upon price, it can be captured in a blockchain-based smart contract. AI could help to increase efficiency by automating the auction to include other bidders and sellers in a more efficient and dynamic waythis would require a lot more data and analysis to recognize the discernable patterns that inform the AI algorithm of the smart contracts performance. Increased automation, however, will also require that the code of the blockchain is more resilient to cyber-attacks. Previously, Ethereum was shown to have several vulnerabilities that may undermine the trustworthiness of this transaction mechanism. Vulnerabilities in the code have been exploited in at least three multimillion dollar cyber incidents. In June 2016 DAO was hackedits smart contract code was exploited, and approximately $50 million dollars were extracted. In July 2017 code in an Ethereum wallet was exploited to extract $30 million dollars of cryptocurrency. In January 2018 hackers stole roughly 58 billion yen ($532.6 million) from a Tokyo-based cryptocurrency exchange, Coincheck, Inc. The latter incident highlighted the need for increased security and regulatory protection for cryptocurrencies and other blockchain applications. The Coincheck hack appears to have exploited vulnerabilities in a hot wallet, which is a cryptocurrency wallet that is connected to the internet. In contrast, cold wallets, such as Trezor and Ledger Nano S, are cryptocurrency wallets that are stored offline.

Despite being a centralized currency, Coincheck was a cryptocurrency exchange with a single point of failure. However, the blockchain shared ledger of the account may potentially be able to tag and follow the stolen coins and identify any account that receives them (Fadilpai & Garlick, 2017). Storing prodigious data sets that are constantly growing in a blockchain can also create potential latency or bloat in the chain, requiring large amounts of memory. Requirements for Ethereum-based smart contracts have grown over time and the block takes a longer time to process. For time-sensitive energy transactions, this situation may create speed, scale, and cost issues if the smart contract is not designed properly. Certainly, future research is needed to develop, validate, and verify a more secure approach.

Finally, future research should examine the functional requirements and potential barriers for applying blockchain to make energy organizations more distributed, autonomous, and secure. For example, even if some intermediaries are replaced in the energy sector, a schedule and forecast still need to be submitted to the transmission system operator for the electricity infrastructure to be reliable. Another challenge is incorporating individual blockchain consumers into a balancing group and having them comply with market reliability and requirements as well as submit accurate demand forecasts to the network operator. Managing a balancing group is not a trivial task and this approach could potentially increase the costs of managing the blockchain. To avoid costly disruptions, blockchain autonomous data exchanges, such as demand forecasts from the consumer to the network operator, will need to be stress tested for security and reliability before being deployed at scale. In considering all of these innovative applications, as well as the many associated challenges, future research is needed to develop, validate, and verify AI blockchain enabled DAEOs.

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Artificial Intelligence - an overview | ScienceDirect Topics

RESEARCH TRIANGLE PARK, N.C.--(BUSINESS WIRE)--Today, Lenovo (HKSE: 992) (ADR: LNVGY) Infrastructure Solutions Group (ISG) announces the expansion of the Lenovo ThinkEdge portfolio with the introduction of the new ThinkEdge SE450 server, delivering an artificial intelligence (AI) platform directly at the edge to accelerate business insights. The ThinkEdge SE450 advances intelligent edge capabilities with best-in-class, AI-ready technology that provides faster insights and leading computing performance to more environments, accelerating real-time decision making at the edge and unleashing full business potential.

As companies of all sizes continue to work on solving real-world challenges, they require powerful infrastructure solutions to help generate faster insights that inform competitive business strategies, directly at edge sites, said Charles Ferland, Vice President and General Manager, Edge Computing and Communication Service Providers at Lenovo ISG. With the ThinkEdge SE450 server and in collaboration with our broad ecosystem of partners, Lenovo is delivering on the promise of AI at the edge, whether its enabling greater connectivity for smart cities to detect and respond to traffic accidents or addressing predictive maintenance needs on the manufacturing line.

Accelerate Business Insights at the Edge

Edge computing is at the heart of digital transformation for many industries as they seek to optimize how to process data directly at the point of origin. Gartner estimates that 75 percent of enterprise-generated data will be processed at the edge by 2025 and 80 percent of enterprise IoT projects will incorporate AI by 2022. Lenovo customers are using edge-driven data sources for immediate decision making on factory floors, retail shelves, city streets and telecommunication mobile sites. Lenovos complete ThinkEdge portfolio goes beyond the data center to deliver the ultimate edge computing power experience.

Expanding our cloud to on-premise enables faster data processing while adding resiliency, performance and enhanced user experiences. As an early testing partner, our current deployment of Lenovos ThinkEdge SE450 server is hosting a 5G network delivered on edge sites and introducing new edge applications to enterprises, said Khaled Al Suwaidi, Vice President Fixed and Mobile Core at Etisalat. It gives us a compact, ruggedized platform with the necessary performance to host our telecom infrastructure and deliver applications, such as e-learning, to users.

Enhance Performance, Scalability and Security

Designed to stretch the limitations of server locations, Lenovos ThinkEdge SE450 delivers real-time insights with enhanced compute power and flexible deployment capabilities that can support multiple AI workloads while allowing customers to scale. It meets the demands of a wide variety of critical workloads with a unique, quieter go-anywhere form factor, featuring a shorter depth that allows it to be easily installed in space constrained locations. The GPU-rich server is purpose-built to meet the requirements of vertically specific edge environments, with a ruggedized design that withstands a wider operating temperature, as well as high dust, shock and vibration for harsh settings. As one of the first NVIDIA-Certified Edge systems, Lenovos ThinkEdge SE450 leverages NVIDIA GPUs for enterprise and industrial AI at the edge applications, providing maximum accelerated performance.

Security at the edge is crucial and Lenovo enables businesses to navigate the edge-to-cloud frontier confidently, using resilient, better secured infrastructure solutions that are designed to mitigate security risks and data threats. The ThinkEdge portfolio provides a variety of connectivity and security options that are easily deployed and more securely managed in todays remote environments, including a new locking bezel to help prevent unauthorized access and robust security features to better protect data.

The ThinkEdge SE450 is built on the latest 3rd Gen Intel Xeon Scalable processor with Intel Deep Learning Boost technologies, featuring all-flash storage for running AI and analytics at the edge and optimized for delivering intelligence. It has been verified by Intel as an Intel Select Solution for vRAN. This pre-validated solution takes the guesswork out of the evaluation and procurement process by meeting strictly defined hardware and software configuration requirements and rigorous system-wide performance benchmarks to speed deployment and lower risk for communications service providers.

Our collaboration with Lenovo helps enterprises across many sectors drive business value through network transformation and edge computing, said Jeni Panhorst, Vice President and General Manager of the Network & Edge Platforms Division at Intel. Resilient and flexible edge servers built with 3rd Gen Intel Xeon Scalable processors provide enhanced performance enabling the delivery of innovative AI-driven services where customers will expect them.

Edge site locations are often unmanned and hard to reach; therefore, the ThinkEdge SE450 is automatically installed and managed with Lenovo Open Cloud Automation (LOC-A) and easily configured with Lenovo XClarity Orchestrator software. Remote access to the server, via a completely out-of-band wired or wireless access, avoids any unnecessary trip to the edge locations.

AI-Ready Solutions at the Edge

Through an agile hardware development approach with partners and customers, the Lenovo ThinkEdge SE450 is the culmination of multiple prototypes, with live trials running real workloads in telecommunication, retail and smart city settings. The ThinkEdge SE450 AI-ready server is designed specifically for enabling a vast ecosystem of partners to make it easier for customers to deploy these edge solutions. As enterprises build out their hybrid infrastructures from the cloud to the edge, it is the perfect extension for the on-premise cloud currently supporting Microsoft, NVIDIA, Red Hat and VMware technologies.

Providing a complete portfolio of Edge servers, AI-ready storage and solutions, Lenovo offerings are also available as-a-Service through Lenovo TruScale, which easily extends workloads from the edge to the cloud in a consumption-based model.

Learn more here about this artificial intelligence edge solution.

LENOVO, THINKEDGE, TRUSCALE and XCLARITY are trademarks of Lenovo. Intel is a trademark of Intel Corporation or its subsidiaries in the U.S. and/or other countries. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. NVIDIA is a trademark of NVIDIA Corporation. Inc. VMware is a trademark of VMware, Inc. All other trademarks are the property of their respective owners. 2021 Lenovo. All rights reserved.

About Lenovo

Lenovo (HKSE: 992) (ADR: LNVGY) is a US$60 billion revenue Fortune Global 500 company serving customers in 180 markets around the world. Focused on a bold vision to deliver smarter technology for all, we are developing world-changing technologies that power (through devices and infrastructure) and empower (through solutions, services, and software) millions of customers every day and together create a more inclusive, trustworthy, and sustainable digital society for everyone, everywhere. To find out more visit https://www.lenovo.com, and read about the latest news via our StoryHub.

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Lenovo Delivers Artificial Intelligence at the Edge to Drive Business Transformation - Business Wire

HOUSTON--(BUSINESS WIRE)--Cognitive Space announced the highlights of a very successful year in its mission to dramatically improve the way we monitor the Earth for economic, environmental, and national security understanding. The company helps organizations fly their satellites with new tools for New Space - providing satellite operators and space infrastructure companies with sophisticated SaaS services for optimizing revenue and performance yield, forecasting future capacity, and orchestrating collection management as satellite constellations grow and scale.

The New Space economy is attracting massive investment and is growing exponentially. Space will be filled with thousands of new commercial satellites, said Scott Herman, CEO of Cognitive Space. But building out the required ground architecture is a major hurdle for New Space companies and usually represents a significant monetary investment, a multi-year time commitment, and major execution risk as they build their business. Cognitive Space provides a blueprint and an operational capability that de-risks and accelerates their buildout schedule, controls costs, and then optimizes their ongoing operations to power their business vision.

2021 Highlights for Cognitive Space:

$5.5M in Investment Capital raised Cognitive Space started the year with a $1.5M pre-seed raise, followed in November with the closing of a $4M Series Seed led by Grit Ventures of Menlo Park. Additional investors include Argon Ventures, Techstars, UltraTech Capital Partners, Cultivation Capital, Glasswing Ventures, Gutbrain Ventures, PBJ Capital, SpaceFund, and Deep Ventures. Outside counsel for the transaction were Covington and Burling LLP. As a result of this 2021 fundraising effort, Cognitive Space enters 2022 with $5.5M in funds ready to apply towards commercial product development and company growth.

Billions of investment dollars are flowing into the New Space economy. There is an unmet imperative for cost-effective, scalable, and business-savvy constellation operations, commented Jennifer Gill Roberts, Managing Partner at Grit Ventures. We believe Cognitive Space's AI-driven approach to maximizing constellation revenue and performance yield gives their customers a significant competitive advantage in this emerging market for Space-based services.

New and expanded US Government contracts Cognitive Space continued its work with several US Government agencies, including the US Space Force, the Air Force Research Lab (AFRL), the National Geospatial Intelligence Agency (NGA), and other members of the national security community. In these engagements Cognitive Space focused on concept development and rapid prototyping for topics such as orchestrated collection management, hybrid space architecture, and global monitoring. Of particular note, Cognitive Space was selected as a winner of the Space Force Pitch Day competition, resulting in a $1.7M contract for exploring new approaches to satellite operations using Artificial Intelligence.

Cognitive Space also supported several US Government exercises, including RIMPAC, Northern Edge, and Joint Warrior. The company orchestrated collection opportunities across multiple commercial and government suppliers of satellite remote sensing. Cognitive Space provided the US Government with insight into the emerging wave of commercial remote sensing capabilities, helping them understand the impact of these capabilities on future operations, tradecraft, tools, and procurement methods.

Commercial Sales Traction This summer, Cognitive Space introduced its SaaS-based platform for autonomous and dynamic satellite operations to a growing set of commercial satellite operators and space infrastructure companies. The platform revolutionizes satellite operations with the power of artificial intelligence for mission management, collections planning, and communications link coordination. The suite is available in versions tailored for startups, growth, and enterprise-class customers in the New Space domain.

Strong Revenue growth Cognitive Space continues to dramatically increase its year-over-year revenue with new contracts, solid bookings, and a dense opportunity pipeline going into 2022.

Accelerator Wins Cognitive Space was competitively selected for several startup accelerators, including the Amazon Web Services (AWS) Seraphim Space Accelerator and the NGA Startup Accelerator. As one of 10 companies chosen by AWS out of a field of approximately 200 startups, Cognitive Space received $100,000 in cloud infrastructure credits, AWS Cloud training and support, mentorship, and additional business development resources including opportunities to speak with space-savvy venture investors. With the NGA Accelerator, Cognitive Space has been working with government analysts on a pilot project exploring the role of future commercial satellite capabilities for facility monitoring and pattern-of-life analytics in real-world scenarios.

Building the best team in AI-driven Satellite Operations Cognitive Space continues to recruit aggressively for an expanding team of AI/ML scientists and mathematicians, satellite and aerospace engineers, full-stack and frontend/backend developers, system architects, and Cloud DevOps engineers. In 2021, the company also made strategic additions to the executive team by recruiting senior industry veterans Scott Herman (as CEO) and Hanna Steplewska (as VP, Business Development & Operations). Scott and Hanna bring deep experience in Space Operations, Satellite Remote Sensing, Geospatial Analytics, and National Security and a comprehensive understanding of the New Space ecosystem.

About Cognitive Space

More information about Cognitive Space can be found at http://www.cognitivespace.com.

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Cognitive Space 2021 Recap Momentum in Artificial Intelligence for Satellite Operations - Business Wire

New York, Dec. 08, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Healthcare Artificial Intelligence (AI) Market - Global Outlook & Forecast 2021-2026" - https://www.reportlinker.com/p06188649/?utm_source=GNW 5 billion by 2026, growing at a CAGR of 46.21%.

Several pharmaceutical companies are implementing innovative technologies to boost their growth in the global healthcare industry. Collaboration of GSK with Exscientia identified a small compound for targeted therapeutics and its characteristics towards the specific target using the AI platform. AI is becoming an incredible platform in the pharmaceutical industry. For instance, Novartis announced Microsoft as a strategic partner in AI and data science to set up an AI innovation lab. Since the last year, over 50+ companies have got machine learning and AI algorithms approvals. During the COVID-19 pandemic, AI played a significant role in the healthcare industry. An analytics study by Accenture combined with clinical applications demonstrated the potential of AI to reduce approximately USD 150 billion per annum by 2026 in the US healthcare system.

The following factors are likely to contribute to the growth of the healthcare artificial intelligence market during the forecast period:

Increase in patient volume & complexities associated with data fueling demand for AI. The shrinking operational workforce in healthcare facilities propelling the need for AI. Technological advancement & innovations in AI influencing end-users in the market. Rising Investment in advanced drug discovery & development process augmenting the adoption of AI.

KEY HIGHLIGHTS

The healthcare providers segment accounted for the largest market share with around 48% compared to others in 2020. According to the research, Arizton estimated that APAC would witness the highest growth in the healthcare artificial intelligence (AI) market during the forecast period.

The study considers a detailed scenario of the present healthcare artificial intelligence market and its market dynamics for the period 2021?2026. It covers a detailed overview of several market growth enablers, restraints, and trends. The report offers both the demand and supply aspects of the market. It profiles and examines leading companies and other prominent ones operating in the market.

HEALTHCARE ARTIFICIAL INTELLIGENCE (AI) MARKET SEGMENTATIONThis research report includes a detailed segmentation by Category Application Technology End-user Geography

HEALTHCARE ARTIFICIAL INTELLIGENCE (AI) MARKET SEGMENTS

The software industry provides numerous services to the healthcare industry. With the advent of medical software, human errors are minimized in the global medical market. Using advanced software in healthcare enables the clinical experts to expertise their practices. Software providers are gaining huge opportunities in the healthcare artificial intelligence (AI) market. Software development companies are constantly striving to improve the industry and bring innovations.

Market Segmentation by Application Hospital Workflow Management Medical Imaging and Diagnosis Drug Discovery and Precision Medicine Patient Management

Market Segmentation by Technology Machine Learning Querying Method Natural Language Processing Others

Market Segmentation by End-users Healthcare Providers Pharma-biotech and medical devices companies Payers Others

GEOGRAPHICAL ANALYSISUS is the major revenue generator of the healthcare artificial intelligence (AI) market across the North American region. In North America, the potential increase in AI GDP is compounded by tremendous opportunities to adopt more productive technologies.

Market Segmentation by Geography

North Americao USo Canada Europeo Germanyo UKo Franceo Italyo Spaino Nordic Countries APACo Chinao Indiao Japan, Australiao South Koreao Rest of APAC Latin Americao Brazilo Mexicoo Argentina Middle East & Africao Turkeyo Saudi Arabiao South Africao UAE

VENDOR ANALYSISGiant players are focusing on pursuing organic growth strategies to enhance their product portfolio in the healthcare artificial intelligence (AI) market. Several initiatives by the players will complement growth strategies, which are gaining traction among end-users in the market. Rising growth of startups collaborating with key vendors in promoting their artificial intelligence in healthcare applications creating heavy competition in the market.

Prominent Vendors

Google IBM (International Business Machines) Intel Corporation Medtronic Microsoft Corporation Nvidia Corporation Siemens Healthineers

Other Prominent Vendors

Arterys Caption Health Enlitic Catalia Health General Vision Philips Stryker Shimadzu Recursion Pharmaceuticals GE Healthcare Remedy Medical Subtle Medical Netbase Quid Biosymetrics Sensely InformAI Bioclinica Owkin Binah.AI Oncora Medical Qure.AI Technologies Lunit Caresyntax Anju software Imagia Cybernetics Deep Genomics Welltok Inc. MDLive MaxQ AI Qventus Workfusion

KEY QUESTIONS ANSWERED:

1. How big is the healthcare artificial intelligence (AI) market?2. Which region has the highest share in the healthcare artificial intelligence market?3. Who are the key players in the healthcare AI market?4. What are the latest market trends in the healthcare artificial intelligence market?5. What is the use of AI in the healthcare market?Read the full report: https://www.reportlinker.com/p06188649/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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The healthcare artificial intelligence market is expected to reach USD 44.5 billion by 2026 - GlobeNewswire

Take a look at how AI can impact the NFL and what changes will it bring to the table.

Nowadays, technology is advancing at a very fast pace which opens up new opportunities for sports to improve the game. The sports industry in North America alone is worth more than $80 billion, and with the use of technology, the sports industry can grow even more.

This means that the sports industry has both the impetus and the means to leverage all of the power of the evolving AI market just to make the game safer, more exciting, make marketing more targeted and impactful, and sports more appealing to the wider market.

In the past couple of years, weve seen many sports all around the world that started using artificial intelligence and data learning, and the NFL is no exception. Even though we still havent scratched the surface of AIs potential, most professionals think that this is the right path for most sports including the NFL.

In todays article we will take a look at how AI can impact the NFL and what changes will it bring to the table.

Robotic coaches are not far away from reality, and soon most teams will rely on AI coaches to improve athletes skills.

For example, imagine an offensive line coach. He has 10 players in front of him, and he is analyzing the situation for arms strength to speed-from-standing focusing on one player at a time.

Now imagine the same situation with a robotic AI coach. He can analyze and watch all players simultaneously, and instantly crunch speed numbers of each arm rotation of every player who throws the ball.

Nowadays, data learning helps coaches make wiser decisions when it comes to improving each athletes skills, but in the future, this process will be autonomous.

Back in the day, player health and safety werent the first priority for teams, but nowadays, the situation is much different. Teams are now focusing more on their abilities, strength, mental and physical health.

In fact, this was the leagues first attempt at using machine learning and AI to improve the health of athletes and prevent injuries. The partnership between NFL and AWS resulted in the creation of the Digital Athlete, which is a computer simulation model designed to replicate infinite scenarios within the game environment, including outside environmental factors and variations by position.

All of this data will help teams improve treatment and rehabilitation of injuries in the short term, and possibly help predict and prevent injuries.

Wearable technology isnt something new. However, theyve grown more powerful which means they can collect more data during the game and in training sessions which helps coaches determine which segment should athletes focus on improving.

On top of that, wearable tech alongside AI can also help reduce the risk of injuries. One torn muscle or overextended knee caused by working too hard before warming up can mean loss of millions to the team, and possibly the championship title.

Another important wearable tech for the NFL is the helmets fitted with multiple nodes that detect impact to the skull during play. This will help manufacturers create better and safer helmets by analyzing the force that went to their heads during collisions.

Referees have one of the toughest jobs in sports, and since they are only human, we cant expect them to be right all of the time. However, with the use of technology, the days of bad calls may soon be at an end.

Nowadays, referees have the help of wearables, nano sensors, even artificial intelligence just to help reduce referee errors. AI technology can analyze movement through multiple cameras on the field and make an accurate in-game decision in seconds. This will not only make decisions more accurate but also improve the pace of the game, which is something that the fans would love.

Can you imagine fans having some kind of input in a game? It would be awesome to see how fans will interact with NFL games in the future since the AI possibilities are limitless.

Nowadays, most sports use the power of social media and share their opinion about the game like determining a fans favorite player of the day. AI can improve the accuracy of TwinSpires guide on NFL odds and make betting more and more a game of skill instead of luck.

However, thanks to AI and machine learning fans can interact with sports even more. For example, you can watch the game in a stadium and through digital devices, you can check out each players data and possibly share your thoughts about their performance. This will intensify the relationship between the NFL and fans and make the sport even more desirable for the wider audience.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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How NFL Can Implement Artificial Intelligence To Improve The Game - Analytics Insight