PITTSBURGH, Oct. 11, 2024 /PRNewswire/ — “I wanted to create a convenient way to mark your page in a book and highlight various lines or passages,” said an inventor, from Sunnyside, N.Y., “so I invented the MARK IT. My bookmark design offers immediate access to a highlighter when reading.”

The patent-pending invention provides an improved design for a bookmark. In doing so, it ensures that a highlighter is readily available when needed. As a result, it allows the user to easily mark the page as they read, and it eliminates the need to find a separate highlighter. The invention features a two-in-one design that is easy to use so it is ideal for avid readers, students, workers, etc. Additionally, a prototype model and technical drawings are available upon request.

The original design was submitted to the Long Island sales office of InventHelp. It is currently available for licensing or sale to manufacturers or marketers. For more information, write Dept. 23-LJD-408, InventHelp, 100 Beecham Drive, Suite 110, Pittsburgh, PA 15205-9801, or call (412) 288-1300 ext. 1368. Learn more about InventHelp’s Invention Submission Services at http://www.InventHelp.com.

View original content to download multimedia:https://www.prnewswire.com/news-releases/inventhelp-inventor-develops-modified-bookmarkhighlighter-ljd-408-302272508.html

SOURCE InventHelp

A new resource from Info-Tech Research Group offers a strategic framework for evaluating large language models (LLMs) in the service providers industry based on cognitive, interactive, and ethical benchmarks. By equipping IT leaders with the tools to balance functionality, cost, and ethical considerations, the firm’s blueprint will help mitigate risks such as vendor lock-in and hidden costs to foster more informed decision-making and drive innovation within the industry.

TORONTO, Oct. 11, 2024 /PRNewswire/ – As managed service providers (MSPs) and technology firms evolve to meet new challenges in AI adoption, selecting the right large language model (LLM) has become increasingly complex. Info-Tech Research Group addresses these challenges with its newly published blueprint, Leverage Metrics and Benchmarks to Evaluate LLMs. This research-backed resource equips executives, including CIOs transitioning to CTO roles and senior leaders in operations and quality assurance, with a strategic framework and essential tools for evaluating large language models (LLMs). By using specific metrics and benchmarks, the resource ensures that the selection process aligns with their unique needs and business objectives.

“The AI services industry is quickly entering a period of LLM commoditization. Businesses will soon face challenges in not only adopting generative AI technology but also navigating an evolving marketplace where the most performant and cost-effective option is not obvious,” says Justin St-Maurice, principal research director at Info-Tech Research Group. “While ChatGPT is a serious contender and a disruptor, it should not be a default product choice. Nor should OpenAI be a single go-to vendor.”

In its resource, Info-Tech outlines the significant challenges service providers encounter when selecting the right LLM from a wide range of options. Each LLM offers distinct functionalities and unique value propositions, adding layers of complexity to the evaluation process. Furthermore, the firm advises that as major providers seek to recoup their investments, hidden costs associated with operating LLMs are emerging, raising concerns about vendor lock-in and escalating expenses. The difficulty in translating LLM benchmarks into practical performance metrics can further complicate the task of identifying the most suitable model for specific organizational needs.

“Navigating this evolving landscape in LLM selection requires a partnership between business and technology leaders,” explains St-Maurice. “Technologists will need to work with the business to buy, customize, or build models that address specific gaps and deliver specific value, all while balancing and optimizing tangible costs and measurable efficiencies against specific performance requirements.”

The firm’s blueprint further emphasizes the importance of selecting LLMs based on their capabilities and performance, especially for IT leaders in the service provider industry. This approach not only helps mitigate the risk of vendor lock-in but also ensures that organizations find the right balance between cost and performance, which is crucial for long-term success.

In Leverage Metrics and Benchmarks to Evaluate LLMs, Info-Tech recommends that technology leaders evaluate LLMs using the following key metrics and benchmarks:

Cognitive Benchmarks: Assess the model’s reasoning, comprehension, and problem-solving skills as well as its ability to apply knowledge in various contexts. This approach helps ensure that the LLM can handle complex tasks and adapt to a wide range of scenarios.Interactive Benchmarks: Evaluate how effectively the model engages in dialogue, follows instructions, and maintains contextual understanding across interactions. This method is crucial for delivering an intuitive, seamless experience for end users, especially in service-oriented environments.Ethical Benchmarks: Examine the model’s fairness, safety, and ability to detect bias. The LLM should adhere to ethical guidelines and responsible AI principles, ensuring it operates in a transparent and secure way.

Info-Tech’s insights and advisory for IT leaders in the service provider industry provides actionable tools to navigate the increasingly complex AI landscape. By leveraging the firm’s solution library within the newly published blueprint, IT leaders can explore innovative concepts and applications of LLM technology, driving both operational efficiency and creative innovation.

For exclusive and timely commentary from Justin St-Maurice, an expert in technology services, and access to the complete Leverage Metrics and Benchmarks to Evaluate LLMs blueprint, please contact pr@infotech.com.

About Info-Tech Research Group
Info-Tech Research Group is one of the world’s leading research and advisory firms, proudly serving over 30,000 IT and HR professionals. The company produces unbiased, highly relevant research and provides advisory services to help leaders make strategic, timely, and well-informed decisions. For nearly 30 years, Info-Tech has partnered closely with teams to provide them with everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.

To learn more about Info-Tech’s divisions, visit McLean & Company for HR research and advisory services and SoftwareReviews for software buying insights.

Media professionals can register for unrestricted access to research across IT, HR, and software and hundreds of industry analysts through the firm’s Media Insiders program. To gain access, contact pr@infotech.com.

For information about Info-Tech Research Group or to access the latest research, visit infotech.com and connect via LinkedIn and X.

View original content to download multimedia:https://www.prnewswire.com/news-releases/optimizing-ai-for-service-providers-info-tech-research-group-details-the-importance-of-strategic-llm-selection-302274184.html

SOURCE Info-Tech Research Group

BEIJING, Oct. 11, 2024 /PRNewswire/ — WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (“WiMi” or the “Company”), a leading global Hologram Augmented Reality (“AR”) Technology provider, today announced a federated learning framework based on layered and sharded blockchain technology, which can solve multiple key issues in federated learning, including information interaction, data security and privacy protection, computational efficiency, and system scalability by combining layered and sharded blockchain technologies to achieve more efficient and secure data collaboration.

In the federated learning framework based on layered and sharded blockchain technology, the IoT network is finely divided into a multi-layer structure, and each layer is subdivided into multiple shards, aiming to optimize the information interaction and processing efficiency. The strategy of multiple layers and multiple shards enables the communication between nodes to be restricted to the same shard, which significantly reduces the complexity of information interaction and greatly reduces the global communication cost. And the sharding mechanism ensures that each shard can execute local training tasks independently and in parallel, accelerating the overall learning process. At the same time, cross-shard data exchange is performed only when the model parameters are updated, which not only ensures the training efficiency, but also further strengthens the data security and privacy protection.

In response to the abnormal or malicious behavior that may occur in federated learning, WiMi has developed a highly adaptive consensus algorithm. The algorithm is able to accurately identify and reject abnormal models, effectively resist interference caused by malicious or erroneous data, and ensure the accuracy and reliability of learning results. The application of blockchain technology records the transaction details of every model update, provides an untampered audit log, enhances system transparency, and establishes a foundation of trust among participants.

With the help of encryption and distributed ledger technology, WiMi’s federated learning framework ensures the security of data during transmission and storage, effectively guarding against data leakage and tampering. Distributed ledger uses cryptographic techniques to protect the security and integrity of data, such as hash functions, public and private key encryption, and other techniques. These techniques prevent problems such as data tampering, forgery, and theft. In addition, data privacy can be further protected by restricting user access to data through smart contracts or other permission control mechanisms.

The sharding and parallel processing mechanism greatly improves computational efficiency and reduces latency, which is particularly suitable for real-time learning scenarios of large-scale IoT devices. The flexible layering and sharding design enables the system to seamlessly adapt to all kinds of network environments from small LANs to global scale. This design not only improves the scalability of the system, but also enables it to be flexibly deployed and operated in different network environments to meet diverse needs.

The federated learning framework builds an efficient, secure, and scalable IoT learning platform through layered and sharded technologies, adaptive consensus algorithms, encryption and distributed ledger technologies, and flexible computing architectures, laying a solid foundation for future large-scale machine learning applications. The federated learning framework based on layered and sharded blockchain not only overcomes the limitations of traditional federated learning, but also creates a brand-new path to safer and more efficient data collaboration, which is a profound insight and layout for future smart life. Whether it is smart home, smart city, or Industry 4.0, federated learning technology based on layered and sharded blockchain shows broad application prospects, and is expected to promote the digital transformation of all industries, and build a smarter, safer, and more efficient future society. In the era of the Internet of Everything, WiMi will also continue to explore and practice, leading the way to a new era of smarter, safer and more efficient data collaboration.

About WIMI Hologram Cloud

WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.

Safe Harbor Statements

This press release contains “forward-looking statements” within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as “will,” “expects,” “anticipates,” “future,” “intends,” “plans,” “believes,” “estimates,” and similar statements. Statements that are not historical facts, including statements about the Company’s beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company’s strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the US Securities and Exchange Commission (“SEC”) on Forms 20−F and 6−K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward−looking statement, including but not limited to the following: the Company’s goals and strategies; the Company’s future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company’s expectations regarding demand for and market acceptance of its products and services.

Further information regarding these and other risks is included in the Company’s annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.

View original content:https://www.prnewswire.com/news-releases/wimi-announced-a-federated-learning-framework-based-on-layered-and-sharded-blockchain-technology-302274111.html

SOURCE WiMi Hologram Cloud Inc.