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Bitcoin Strategic Reserve Could Happen. Why Not Dogecoin, Says Co-Founder – Decrypt

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Bitcoin Strategic Reserve Could Happen. Why Not Dogecoin, Says Co-Founder – Decrypt



Forget a Bitcoin reserve—what about a Dogecoin government stash? 

That’s the idea of the former-joke-now-serious-cryptocurrency’s founder, Billy Markus, who floated the idea on Twitter (aka X). 

“Why not national Dogecoin reserve tho [sic],” he wrote on the platform Friday.

Markus was responding to a post by prediction market Kalshi about bettors seeing an increasing chance of President-elect Donald Trump creating a strategic Bitcoin reserve.

Trump previously said that when in power, he would let the U.S. government buy up Bitcoin as a reserve asset. The U.S. already has assets like land, Treasuries, and gold in its reserves. 

Republican Senator Cynthia Lummis from Wyoming exclusively spoke to Decrypt this month about how such a plan would work. 

As Bitcoin gains momentum, floating such an idea isn’t as crazy as it sounds. With a market cap of $1.8 trillion, the orange coin is currently the seventh-biggest asset in the world, after this month overtaking both silver and Saudi Arabia’s petroleum and natural gas company, Saudi Aramco.

Dogecoin (DOGE), on the other hand, is the sixth-biggest cryptocurrency—and has a market cap of just $55 billion. The coin was created as a joke and is based on a popular Internet meme of a Shiba Inu dog. 

Still, it gained momentum in 2020-2021 after Tesla CEO and upcoming U.S. government employee Elon Musk constantly talked about how much he liked the virtual coin. The tech entrepreneur and world’s richest man has also (seemingly) seriously spoken about how the asset could be used for payments. 

And Wall Street analysts told Decrypt that it might not be long before the asset becomes available to traditional investors via an exchange-traded fund (ETF). Meanwhile, crypto analysts also told Decrypt that DOGE could easily smash through its all-time price record from 2021, with plenty of room to run. 

Things are suddenly getting very serious with the leading meme coin. But until a Bitcoin strategic reserve actually becomes reality, the notion of a Dogecoin one is likely to remain little more than a funny idea. Stranger things have happened in crypto, though.

Edited by Andrew Hayward

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Unveiling the Metaverse: Exploring the Bright Future of Virtual Reality and Digital Innovation – Web3oclock

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Unveiling the Metaverse: Exploring the Bright Future of Virtual Reality and Digital Innovation – Web3oclock


A Brief History of Metaverse

How does Metaverse Work? 

When is a Full-Fledged Metaverse Coming?

How is the Metaverse Accessed?

How do VR and the Metaverse Relate to Each Other?

How Does the Metaverse Fit into Web 3.0?

Differences Between Metaverse, VR, and AR

Metaverse Stock: Investment Opportunities in the Virtual World

What Does Gaming Have to Do with the Metaverse?

When is a Full-Fledged Metaverse Coming?

How is the Metaverse Accessed?

1. VR as a Gateway to the Metaverse:

2. Enhanced Immersion and Interaction:

3. Social Connectivity and Presence:

4. Gaming and Entertainment Experiences:

5. Building the Future of the Metaverse:

Differences Between Metaverse, VR, and AR:

CategoryMetaverseVirtual Reality (VR)Augmented Reality (AR)DefinitionA vast, interconnected network of virtual worlds and spaces where users can interact, socialize, create, and trade.A fully immersive digital environment experienced through VR headsets, isolating users from the physical world.A technology that overlays digital content onto the real-world environment, enhancing physical surroundings.ScopeEncompasses multiple technologies, including VR, AR, blockchain, social spaces, digital economies, and more.A component of the metaverse but can exist independently for games, simulations, and training.Often part of the metaverse but mainly focused on blending digital elements with the real world.Immersion LevelCan range from partial immersion (via mobile apps) to full immersion (via VR).Full immersion: users are completely “inside” a digital world and isolated from the physical surroundings.Partial immersion: users remain aware of and interact with the real world while seeing digital overlays.Access DevicesAccessible through VR headsets, AR glasses, desktops, smartphones, and other devices.Primarily accessed using VR headsets like Meta Quest, HTC Vive, PlayStation VR, etc.Accessed using AR glasses (Microsoft HoloLens), mobile apps (smartphones/tablets), etc.Primary Use CasesVirtual social interactions, digital asset trading (NFTs), gaming, remote collaboration, and creative content creation.Gaming, training simulations, virtual tours, and immersive learning experiences.Navigation, education, AR gaming (e.g., Pokémon Go), retail experiences, and training.InteractivityHigh interactivity involving user-driven economies, social connections, digital asset creation, and more.High interactivity within enclosed digital environments but typically focused on singular experiences.Moderate interactivity, with digital overlays responding to real-world movements and inputs.Presence of Social ElementsStrong social component, with virtual spaces for collaboration, communication, and shared experiences.Can have social interactions but often focused on individual immersive experiences.Limited social components; primarily a tool for enhancing individual experiences.Integration with BlockchainFrequently integrated with blockchain for decentralized ownership, NFTs, and digital currencies.Not inherently tied to blockchain, though some VR experiences may incorporate it.Rarely integrates with blockchain; focus is on enhancing real-world utility.User AvatarsUsers are represented by customizable avatars, often carrying digital identities across different platforms.Avatars may be used but are generally confined to specific VR apps or games.Generally not focused on avatars; digital overlays appear around the user instead.Example PlatformsDecentraland, Roblox, The Sandbox, Horizon Worlds, and more.Meta Quest experiences, Beat Saber, Half-Life: Alyx.Pokémon Go, Microsoft HoloLens apps, AR navigation apps.Relation to Physical WorldBlends virtual and physical worlds but allows users to fully immerse or partially participate.Detaches users completely from the physical world.Combines digital elements with the physical world; users remain aware of their surroundings.User IsolationVaries; some experiences are highly social, others are individual.High; users are cut off from the physical world during usage.Low; users interact with digital elements while being aware of their real-world context.Customization and Content CreationUser-generated content is a major focus; anyone can create, trade, or monetize virtual assets.Users can create or modify elements within specific VR experiences.Customization is limited to specific AR apps and overlays within real-world contexts.

Metaverse Challenges:

1. Technological Barriers:

2. Data Privacy and Security:

3. Legal and Regulatory Challenges:

4. User Experience and Accessibility:

5. Social and Psychological Effects:

6. Environmental Impact:

7. Economic Risks and Volatility:



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Exploring the Impact of Hype on Web3 and Emerging Technologies | Web3Wire

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Exploring the Impact of Hype on Web3 and Emerging Technologies | Web3Wire


“`html

The digital landscape is always evolving, taking leaps and bounds with each new innovation. Among the latest strides are Web3, the Metaverse, Artificial Intelligence (AI), and Virtual Reality (VR). While these technologies promise to reshape the future, there’s an ongoing debate about whether the overhype surrounding them is causing more harm than good.

Understanding the Hype Cycle

In the realm of technology, the hype cycle is a graphical representation of the maturity, adoption, and social application of technologies. This cycle has five phases:

Innovation Trigger: The initial phase where a breakthrough garners attention.Peak of Inflated Expectations: Early success stories generate over-enthusiasm and unrealistic expectations.Trough of Disillusionment: Technologies fail to meet expectations, leading to disappointment.Slope of Enlightenment: Some understand the benefits, leading to more pragmatic approaches.Plateau of Productivity: The true benefits and broad adoption are realized.

Web3 and emerging technologies are presently somewhere between the peak of inflated expectations and the trough of disillusionment, causing concerns about their real-world applicability and sustainability.

The Current State of Web3

Web3 represents the next generation of the internet, aiming for a decentralized web where users have more control over their data. Proponents argue that it could democratize online interactions, but critics point to the overhyped nature of the promises made. The challenges faced by Web3 include:

Complexity: New users often find decentralized platforms and technologies challenging to navigate.Security Concerns: The decentralized nature can expose systems to new vulnerabilities.Scalability Issues: Current blockchain technologies aren’t yet equipped to handle mass adoption smoothly.

The gap between vision and execution often leads to disillusionment, as the real-life implementation struggles to keep pace with ambitious ideas.

Metaverse: Beyond the Hype

The Metaverse is envisaged as a collective virtual shared space, merging physical and virtual realities. Large tech companies have invested billions, foreseeing a digital utopia. However, the hype has raised several questions:

Interoperability: Different platforms may struggle to work seamlessly together.Accessibility: High technology entry costs can limit widespread adoption.Social Implications: Concerns over addiction and mental health can’t be overlooked.

While the potential is vast, the current infrastructure and user base are still in their infancy, with many challenges to overcome before the Metaverse becomes mainstream.

The Role of AI and VR

Artificial Intelligence and Virtual Reality are integral to the advancement of Web3 and the Metaverse. They are at the very heart of enhancing user experience and providing sophisticated decision-making capabilities. Yet, the following are key factors shaped by the hype:

AI Ethics: Concerns over bias and decision-making transparency are prevalent in AI advancements.VR Realism: The promise of immersive experiences is often met with restricted sensory involvement.Integration Costs: Bringing AI and VR into mainstream use can be prohibitively expensive for many organizations.

Despite these challenges, AI and VR continue to gain traction, proving their worth in multiple industries and applications.

The Future of Hype-Driven Technologies

The excess hype has not killed Web3 and related technologies; instead, it highlights the growing pains often featured in transformative periods. For these technologies to thrive:

Realistic Expectations: Stakeholders must set achievable goals and timelines.User-Friendly Solutions: Ensuring technologies are accessible and intuitive for the average person.Collaborative Efforts: Different industries working together can lead to better integration and innovation.

Emerging technologies are in an evolutionary phase, where learning from failures and continued exploration is essential. Those who focus on delivering tangible value rather than just bold visions will likely lead the charge.

Conclusion

While the hype surrounding Web3, the Metaverse, AI, and VR may seem overwhelming, it serves as a crucial spark for innovation. By transitioning from a perspective driven by inflated expectations to one grounded in reality, we move closer to a tech-driven future that offers practical benefits.

As stakeholders adjust to this new perspective, they’ll cultivate a more sustainable and productive digital ecosystem, paving the way for technologies that will genuinely encapsulate advances for society.

“`

About Web3Wire Web3Wire – Information, news, press releases, events and research articles about Web3, Metaverse, Blockchain, Artificial Intelligence, Cryptocurrencies, Decentralized Finance, NFTs and Gaming. Visit Web3Wire for Web3 News and Events, Block3Wire for the latest Blockchain news and Meta3Wire to stay updated with Metaverse News.



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Local Installation Guide for OpenAI Whisper: Step-by-Step Instructions

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Local Installation Guide for OpenAI Whisper: Step-by-Step Instructions


OpenAI’s Whisper is a powerful and flexible speech recognition tool, and running it locally can offer control, efficiency, and cost savings by removing the need for external API calls. This guide walks you through everything from installation to transcription, providing a clear pathway for setting up Whisper on your system. Whether you’re transcribing interviews, creating captions, or automating workflows, this local setup will give you complete control over the process.

Step 1: Installing Whisper and Required Dependencies

To get started with Whisper, you’ll need to install both Whisper and some basic dependencies. Here’s how to do it:

1.1 Install Whisper

1.2 Install ffmpeg

ffmpeg is essential as it helps Whisper handle various audio formats by converting them into a readable format.

Step 2: Setting Up Your Environment

For Whisper to run smoothly, ensure that Python and pip are installed on your system.

2.1 Verify Python and pip Installation

2.2 Additional Tools for Windows

You might find it helpful to install Chocolatey, a package manager for Windows, if it’s not already installed. This can simplify the installation of other tools, such as ffmpeg.

Step 3: Transcribing Audio Files Locally

Whisper allows you to transcribe audio in multiple ways, either directly through the command line or by integrating it into Python scripts.

3.1 Transcribe Using Command Line

Navigate to the folder where your audio file is saved.

Enter the following command, replacing your_audio_file.mp3 with the actual file path:

whisper –model base –language en –task transcribe your_audio_file.mp3

The –model base option refers to the base model of Whisper. Larger models can improve accuracy but may require more resources.

3.2 Transcribe Using Python

You can also utilize Whisper directly in a Python script, which might be useful for developers building applications around Whisper.

Open your preferred Python editor and enter:

import whisper

model = whisper.load_model(“base”)
result = model.transcribe(“your_audio_file.mp3”)
print(result[“text”])

This script will load Whisper’s base model and output the transcribed text from the audio file specified.

Step 4: Important Considerations for Running Whisper Locally

Running Whisper locally is convenient, but there are some considerations for optimal performance:

4.1 System Resources

Whisper, particularly the larger models, can be resource-intensive. Ensure that your system has sufficient RAM and CPU capacity to handle the workload, especially if you plan to run multiple transcriptions or work with large audio files.

4.2 GPU Support

For faster processing, Whisper can take advantage of GPU support, which is especially useful when working with high-demand tasks or extensive transcription needs. If your system has a compatible GPU, this can reduce processing time significantly.

Conclusion

Following these steps, you can install and use OpenAI’s Whisper locally for audio transcription. This setup allows you to transcribe audio files quickly and efficiently without needing an internet connection or external API calls, providing full control over the transcription process and eliminating potential costs. Whisper’s flexibility and high-quality transcription make it a powerful tool for both personal and professional use cases.

FAQs

Is Whisper compatible with all operating systems?

Yes, Whisper can run on Windows, MacOS, and Linux. However, the installation commands for dependencies like ffmpeg may vary by system.

Can I use Whisper with non-English audio files?

Absolutely! Whisper supports multiple languages. You can specify the language in the command by modifying the –language option.

Is GPU usage mandatory for Whisper?

No, but it’s recommended for larger models or extensive transcription projects to speed up processing.

Does Whisper handle background noise well?

Whisper is robust but performs best with clear audio. Background noise may affect transcription accuracy, particularly with smaller models.

Can I transcribe live audio with Whisper?

Whisper is designed primarily for pre-recorded files, but with additional configurations, it can potentially handle live audio. However, this requires more advanced setup and a continuous data feed.



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ICP identity protocol DecideID to launch on Solana eliminating any KYC need for DeFi

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ICP identity protocol DecideID to launch on Solana eliminating any KYC need for DeFi



DecideAI has announced the integration of its biometric identity verification solution, DecideID, into the Solana blockchain, aiming to enhance security and trust within the ecosystem. This move introduces Proof-of-Personhood (PoP) capabilities to Solana, ensuring that users are verified as unique individuals without the need for traditional Know-Your-Customer procedures.

Raheel Govindji, founder and CEO of DecideAI, commented,

“DecideID is positioned to set a new standard in identity verification across the blockchain space. The Solana integration is just the beginning, and we anticipate a significant surge in user growth and adoption”

The integration is expected to address longstanding vulnerabilities in Solana’s airdrop ecosystem, which has previously been susceptible to Sybil attacks and botting. By verifying real users through AI-driven facial recognition and liveness detection technologies, DecideID aims to prevent fraudulent activities and ensure fair token distributions.

Solana developers will now have the opportunity to leverage DecideID’s identity verification tools to enhance the integrity of decentralized applications. This is particularly significant for DeFi projects, where ensuring that transactions are conducted by real and unique individuals adds a crucial layer of trust. The technology analyzes facial movement, depth, and micro-expressions to confirm user authenticity, utilizing Zero-Knowledge proofs to protect personal data during the verification process.

According to DecideAI, the integration will,

“Add a layer of trust in lending, staking, and yield farming by ensuring that only real and unique humans are behind transactions.

Additionally, DecideID’s AI facial recognition technology doesn’t require any Know-Your-Customer procedures or documentation, making it an easy and quick scan for users.”

The integration is currently in the testing phase and is expected to be fully launched before the end of the year. Upon release, Solana users will be able to link their wallets, participate in airdrops, and verify their personhood without extensive documentation. DecideAI is also in discussions with several Solana-based DeFi and NFT platforms that plan to adopt DecideID for enhanced security and fair governance participation.

This development is facilitated by the Internet Computer’s Chain Fusion technology, which allows for seamless protocol-level integration with other blockchains. The Internet Computer (ICP) acts as a powerful general-purpose blockchain and Web3 platform, enabling DecideID to extend its services beyond Solana with potential future integrations into networks like Ethereum.

DecideID reportedly has already verified over 14,000 unique users and aims to foster trust and accountability in decentralized applications. By preventing malicious activities and ensuring secure interactions, it contributes to a more transparent and fair blockchain environment.

Per the recent announcement, DecideAI’s mission extends beyond identity verification. It aims to reshape the landscape of large language models by prioritizing quality, collaboration, and ownership. Through its ecosystem comprising Decide Protocol, DecideID, and Decide Cortex, the company seeks to democratize access to AI resources while rewarding contributors and setting new standards for open-source collaboration.

Proof of personhood protocols on the rise

This integration places DecideID among notable Proof-of-Personhood initiatives such as World, Proof of Humanity, Idena, CorePass, and Anima Protocol.

World (formerly Worldcoin,) founded by Sam Altman, uses iris scans to generate unique identifiers for individuals, though it has faced privacy concerns over biometric data collection.

Proof of Humanity combines video verification with community endorsement to establish unique identities on the Ethereum blockchain. It aims to facilitate fair distribution in Universal Basic Income projects.

Idena employs AI-resistant tasks called “flips” to ensure each node in its network represents a unique human, enhancing decentralization and resistance to Sybil attacks.

CorePass, on the Core Blockchain network, allows users to verify and tokenize credentials such as passports, driving licenses, emails, and names. Data can then be sold to dApps, allowing users to realize the value of their data instead of giving it away for free.

Anima Protocol provides decentralized identity services on the BNB Chain, focusing on privacy while verifying user authenticity.

These projects, like DecideID, showcase how blockchain can address challenges such as fraudulent activities and fair resource distribution in digital ecosystems, but each takes a different approach to balance security, privacy, and user experience.

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Trump Taps Bitcoin Fan Matt Gaetz for Attorney General – Decrypt

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Trump Taps Bitcoin Fan Matt Gaetz for Attorney General – Decrypt



In a move that left both allies and opponents reeling, President-elect Donald Trump has tapped pro-crypto Florida Congressman Matt Gaetz to serve as the next Attorney General of the United States. It’s a move that suggests he’s doubling down on his pro-crypto agenda.

Known for his staunch advocacy of Bitcoin, Gaetz’s appointment is a clear signal that Trump’s second term will prioritize a pro-crypto agenda, even at the risk of deepening divisions within his own party.

Gaetz would “root out the systemic corruption at DOJ, and return the Department to its true mission of fighting Crime, and upholding our Democracy and Constitution,” Trump wrote on his Truth Social network on Wednesday.

With Bitcoin soaring to a record high of $93,477 following the announcement, the market is already responding positively to the prospect of a more supportive regulatory environment.

Gaetz’s nomination came following the Republican Party’s success in the 2024 elections, where pro-crypto candidates secured majorities in both the House and Senate.

However, the announcement of his nomination was met with gasps during a closed-door meeting of House Republicans, with several members reportedly expressing disbelief, as per an Axios report.

Reportedly, multiple senators expressing skepticism about his readiness for the position due to his confrontational approach and limited legal experience.

Gaetz has previously advocated for deregulating the crypto industry, pushing back against what he views as federal overreach by agencies like the SEC.

In June, Gaetz introduced legislation to allow Americans to pay their federal taxes using Bitcoin. The bill seeks to amend the Internal Revenue Code of 1986, enabling the IRS to accept Bitcoin for tax payments and enter into contracts for related services.

Despite the enthusiasm from the crypto sector, Gaetz faces a tough confirmation battle. His past legal troubles—including a federal investigation into allegations of sex trafficking, which ended without charges—are likely to be scrutinized during Senate hearings.

He was also under an ethics review in the House over allegations related to misconduct and inappropriate use of funds.

On Wednesday, House Speaker Mike Johnson confirmed that Gaetz had stepped down from his congressional seat, effectively halting the ethics probe, as investigations are limited to active members of Congress.

For the crypto industry, however, the mere prospect of having a pro-Bitcoin attorney general is already being seen as a significant win.

While Republicans have a majority, just a few dissenting votes could derail the nomination. Yet, Trump’s allies are pushing for a swift confirmation, saying Gaetz’s reformist agenda is essential for overhauling the DOJ’s approach to crypto regulations.

Edited by Stacy Elliott.

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A Comprehensive Guide to DeFi: Empowering Financial Freedom – Web3oclock

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A Comprehensive Guide to DeFi: Empowering Financial Freedom – Web3oclock


Core Concepts & Key Features of DeFi

Major DeFi Platforms and Tools

Benefits and Risks of DeFi

DeFi Use Cases and Applications

What is DeFi?

Who Invented DeFi? The History and Evolution:

E. Increased Security and Transparency

A. Uniswap:

Uniswap Labs Acquires by web3 o'clockUniswap Labs Acquires by web3 o'clock

B. Aave:

C. Compound:

D. Yearn Finance:

E. Synthetix:

F. MakerDAO:

G. Curve Finance:

DeFi Pulse and Its Significance:

DeFi PulseDeFi Pulse

Significance of DeFi Pulse:

DeFiDeFi
DeFiDeFi

Real-World Applications of DeFi:

Emerging Trends and the Future Outlook of DeFi:

Expert Opinions and Predictions:



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Linea Association unveils plan for decentralized governance with LINEA token

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Linea Association unveils plan for decentralized governance with LINEA token



At Devcon in Bangkok, the Linea Association announced its formation to oversee the development and governance of Linea’s open-source technology and ecosystem. The Swiss non-profit aims to decentralize the Linea Network—the zkEVM Layer-2 solution designed to scale Ethereum—by launching the LINEA token by the end of Q1 2025, enabling community-driven governance.

The Association’s mission includes supporting the growth of Linea Mainnet to build a fast, affordable, and secure network accessible worldwide. It plans to advance decentralization through new governance mechanisms and implement decentralized sequencing and proving. Empowering developers to create decentralized applications with enhanced user experiences and fostering strong, engaged communities are also key priorities.

Nicolas Liochon, founder of Linea and board member of the Linea Association, said.

“Decentralization is at the core of Linea’s vision. Linea must be owned and governed openly by all as a public good, just as Layer 1 Ethereum is.”

The governance structure will feature a Board of Directors, a General Assembly, an Executive Director, and a token governance body. The LINEA token will allow holders to participate in governance, with details on token design and utility to be shared before the token generation event. More than 1.3 million verified addresses have joined the network, reflecting Linea’s focus on organic community growth.

Since its mainnet launch in August 2023, Linea has processed over 230 million transactions, making it one of the fastest-growing zkEVMs on Ethereum. The ecosystem has also expanded to over 420 ecosystem partners. The technology is publicly available under the Apache license, allowing users to view, fork, and modify the code.

The Association operates independently of Consensys, aligning with CEO Joseph Lubin’s vision to decentralize core innovations progressively. Lubin said,

“As Consensys progresses toward decentralization, Linea represents a foundational step in our vision of creating a Network State for the emerging decentralized global economy.”

Linea has integrated long-term Ethereum contributors like Status, developers of the Nimbus client that secures 10% of Ethereum’s proof-of-stake network. The Association plans to decentralize core protocol development and governance further, ensuring social and technical alignment within the community.

The Swiss Association structure allows token holders to have governance over managing IP and a treasury supporting Linea’s mission. The focus remains on furthering the growth and development of the open-source LINEA technology and the Linea Network.

Linea aims to empower users and businesses to manage valuable on-chain data, including identity and property. Per the announcement, the Linea Association seeks to be a significant step toward decentralizing the network and fostering collaborative, transparent governance. The initiative aims to empower the global community to shape the future of Linea and contribute to the broader Ethereum ecosystem.

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Intercom Devices Market is expected to reach US$ 56.8 billion by 2031 – SAMSUNG ELECTRONICS CO., LTD., Panasonic Holdings Corporation, Schneider Intercom GmbH. | Web3Wire

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Intercom Devices Market is expected to reach US$ 56.8 billion by 2031 – SAMSUNG ELECTRONICS CO., LTD., Panasonic Holdings Corporation, Schneider Intercom GmbH. | Web3Wire


Intercom Devices Market is expected to reach US$ 56.8 billion by 2031 – SAMSUNG ELECTRONICS CO., LTD., Panasonic Holdings Corporation, Schneider Intercom GmbH. | Web3Wire

Intercom Devices Market

The Intercom Devices Market study by DataM Intelligence offer an in-depth analysis of the market, presenting insightful observations, statistics, historical data, and industry-validated market insights. The report delves into the competitive positioning of key companies, examining factors such as product offerings, pricing strategies, financial health, product portfolios, growth initiatives, and geographical reach.

Download a Free sample PDF (Use Corporate email ID to Get Higher Priority) at: – https://datamintelligence.com/download-sample/intercom-devices-market

What is the projected growth rate (CAGR) of the Global Intercom Devices market from 2024 to 2031, and what is the market value expected to change by 2031?

Global Intercom Devices Market reached US$ 22.9 billion in 2022 and is expected to reach US$ 56.8 billion by 2031, growing with a CAGR of 12.1% during the forecast period 2024-2031.

Intercom devices are communication systems used within buildings or specific areas to enable direct verbal communication between different rooms or sections. Commonly found in homes, offices, and public buildings, intercoms enhance security and convenience by allowing controlled, two-way communication. They are often integrated with security systems and may include audio and video functionalities. Intercoms support effective communication without requiring physical presence.

Key Developments:

❁ In 2023, Riedel Communications announced that In Concert Productions (ICP), a provider of advanced sound equipment and services, has integrated Riedel’s Emmy Award-winning Bolero wireless intercom system into its offerings. The Bolero system is renowned for its robust and reliable wireless communication capabilities, making it ideal for live event production and other demanding environments. This addition enables ICP to enhance communication efficiency for its clients, offering seamless and high-quality intercom solutions. The collaboration reflects both companies’ commitment to delivering state-of-the-art technology for dynamic event management and production.

List of the Key Players in the Intercom Devices Market:

SAMSUNG ELECTRONICS CO., LTD., Panasonic Holdings Corporation, Schneider Intercom GmbH, Hollyland, Godrej & Boyce Manufacturing Company Limited, Axis Communications AB, Alpha Communications, TCS TürControlSysteme AG, Riedel Communications and TOA Corporation.

Research Process:

Both primary and secondary data sources have been used in the global Intercom Devices Market research report. During the research process, a wide range of industry-affecting factors are examined, including governmental regulations, market conditions, competitive levels, historical data, market situation, technological advancements, upcoming developments, in related businesses, as well as market volatility, prospects, potential barriers, and challenges.

Segment Covered in the Intercom Devices Market:

By System: Wire, Wireless.

By Component: Hardware, Software and Services.

By Device: Door Entry Systems, Handheld Devices, Others.

By Technology: Analog, IP-Based.

By Communication: Push-To-Talk, Hands-Free.

By End-User: Commercial, Residential, Others.

Regional Breakout:

The global Intercom Devices Market report focuses on six major regions: North America, Latin America, Europe, Asia Pacific, the Middle East, and Africa.

✫ North America (U.S., Canada, Mexico)

✫ Europe (U.K., Italy, Germany, Russia, France, Spain, The Netherlands and Rest of Europe)

✫ Asia-Pacific (India, Japan, China, South Korea, Australia, Indonesia Rest of Asia Pacific)

✫ South America (Colombia, Brazil, Argentina, Rest of South America)

✫ Middle East & Africa (Saudi Arabia, U.A.E., South Africa, Rest of Middle East & Africa)

Get Year End Discounts on Premium Report:- https://www.datamintelligence.com/buy-now-page?report=intercom-devices-market

This Report Unveils:

✔ Go to Market Strategy- A roadmap to successfully product launch or service in the target market.

✔ Gain a clear picture of the market’s health and growth trajectory through neutral analysis.

✔ Deep Market Insights delve into development trends, competitor landscape, supply and demand dynamics, brand share & pricing analysis year-over-year growth patterns, and key players’ performance.

✔ Upon request, we can provide customized reports focusing on specific regions or countries, offering a granular view of their markets.

✔ Identify high-potential niche segments and regions poised for significant expansion.

✔ Analysis of Market Size (historical and forecast) Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) to understand the market’s overall size and your achievable market share.

✔ Gain a comprehensive understanding of the competitive landscape, including market share distribution, key players (innovators, startups, laggards, and pioneers), and their respective strengths and weaknesses.

**The full version of the report includes an in-depth analysis of emerging players and startups, which will provide valuable insights into the evolving market landscape and key strategies being adopted**

Chapter Outline:

Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by region, product type, application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.

Chapter 2: key insights, key emerging trends, etc.

Chapter 3: Manufacturers competitive analysis, detailed analysis of Intercom Devices manufacturers competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.

Chapter 4: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.

Chapter 5 & 6: Revenue of Intercom Devices in regional level and country level. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and market size of each country in the world.

Chapter 7: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the Intercom Devices market in different market segments.

Chapter 8: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the Intercom Devices market in different downstream markets.

Chapter 9: Analysis of industrial chain, including the upstream and downstream of the industry.

Chapter 10: The main points and conclusions of the report.

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Frequently Asked Questions

☞ What is the expected growth rate of the global Intercom Devices market for the forecast period?

☞ What are the key driving factors that are responsible to shape the fate of the Intercom Devices market during the forecast period?

☞ What will be the overall size of the market during the analysis period?

☞ What are the prominent market trends which influence the development of the Intercom Devices market across various regions?

☞ Who are the key market players and the market strategies that have helped them to secure the leading position in the global market?

☞ What are the challenges and threats that are likely to act as a barrier to the growth of the Intercom Devices market?

☞ What are the major opportunities that the companies can get to attain success in the world?

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L-Mul: The Breakthrough Algorithm for Energy-Efficient AI

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L-Mul: The Breakthrough Algorithm for Energy-Efficient AI


The development of artificial intelligence has brought about tremendous advancements in various fields, but running AI models can be incredibly resource-intensive, both financially and environmentally. AI models consume enormous amounts of electricity, and their energy demands are projected to grow as AI systems become more complex. For instance, in early 2023, running ChatGPT consumed around 564 MWh of electricity per day, equivalent to the daily energy usage of 18,000 U.S. households.

This vast consumption is largely due to AI models’ complex computations, especially floating-point operations in neural networks. These processes are inherently energy-hungry, involving heavy matrix operations and linear transformations. However, a revolutionary new algorithm promises to significantly reduce this energy load. It’s called L-Mul (Linear-Complexity Multiplication), and it could reshape the future of AI by making models faster and drastically more energy-efficient.

Let’s explore L-Mul, how it works, and what this means for the future of energy-efficient AI.

Why AI is Energy-Intensive

Neural networks are at the core of modern AI models, which use floating-point numbers to perform computations. These floating-point operations are essential for functions like matrix multiplications, which are critical to how neural networks process and transform data.

Neural networks typically use 32-bit and 16-bit floating-point numbers (known as FP32 and FP16) to handle the parameters, inputs, and outputs. However, floating-point multiplications are far more computationally expensive than basic integer operations. Specifically, multiplying two 32-bit floating-point numbers consumes approximately four times the energy required to add two FP32 numbers and 37 times more energy than adding two 32-bit integers.

Thus, floating-point operations present a significant energy bottleneck for AI models. Reducing the number of these floating-point multiplications without sacrificing performance can greatly enhance AI systems’ energy efficiency.

The Birth of L-Mul: An Energy-Saving Solution

This is where the L-Mul algorithm steps in. Developed by researchers and recently published on ArXiv, L-Mul simplifies floating-point multiplications by approximating them with integer additions. The key advantage? This algorithm can be seamlessly integrated into existing AI models, eliminating the need for fine-tuning and enabling substantial energy savings.

By replacing complex floating-point multiplications with much simpler integer additions, L-Mul achieves up to 95% energy reduction for element-wise tensor multiplications and saves up to 80% energy for dot product computations. This energy efficiency doesn’t come at the cost of accuracy either, making L-Mul a breakthrough for running AI models with minimal power consumption.

Understanding Floating-Point Operations

To better appreciate the impact of L-Mul, let’s take a closer look at the floating-point operations on which AI models rely. When you multiply two floating-point numbers, the process involves:

Exponent addition (O(e) complexity)

Mantissa multiplication (O(m²) complexity)

Rounding and normalization

The mantissa multiplication is the most resource-intensive part of this process, requiring significant computational power, which leads to high energy consumption. On the other hand, integer addition is far simpler and less energy-intensive, with a linear complexity of O(n), where n represents the bit size of the integers involved.

How L-Mul Works: Replacing Floating-Point Multiplications

The L-Mul algorithm simplifies this process by replacing floating-point mantissa multiplications with integer additions. Here’s how it works:

Two floating-point numbers (x and y) are represented by their mantissas (the fractional parts) and exponents.

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Instead of performing expensive mantissa multiplication, L-Mul uses integer additions to approximate the result.

If the mantissa sum exceeds 2, the carry is added directly to the exponent, skipping the need for normalization and rounding found in traditional floating-point multiplication.

This approach reduces the time complexity from O(m²) (for mantissa multiplication) to O(n), where n is the bit size of the floating-point number, making it far more efficient.

Precision vs. Computational Efficiency

In addition to being energy-efficient, L-Mul offers a high degree of precision. As AI models increasingly adopt 8-bit floating-point numbers (FP8) to reduce memory usage and computational cost, L-Mul shines as a highly effective alternative. FP8 has two common representations: FP8_e4m3 (more precise but with a smaller range) and FP8_e5m2 (less precise but with a larger range).

When compared to FP8, L-Mul outperforms in terms of both precision and computational efficiency. L-Mul offers greater precision than FP8_e4m3 while consuming fewer computational resources than FP8_e5m2, making it a superior alternative in many scenarios.

Real-World Applications of L-Mul

So, how does L-Mul perform in real-world AI tasks? Let’s break it down:

Transformer Models and LLMs

L-Mul can be directly applied to transformer models, particularly in the attention mechanism, where large-scale matrix multiplications occur. This application leads to up to 80% energy savings without sacrificing performance. No fine-tuning is required, which is a significant advantage.

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For instance, in large language models (LLMs) like Mistral-7b and Llama-3.1, L-Mul has been shown to outperform FP8 and Bfloat16, common floating-point formats used in transformers, across various benchmarks, including text-based and instruction-following tasks.

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GSM8k and Other Benchmarks

When evaluated on specific tasks like GSM8k, which tests models on grade-school math problems, L-Mul consistently outperformed FP8 in terms of accuracy and efficiency. This demonstrates that L-Mul can handle complex mathematical reasoning without requiring excessive computational power.

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Visual Question Answering (VQA) and Object Detection

In models like Llava-v1.5–7b, which are used for visual question answering and object hallucination, L-Mul again surpassed FP8 in both accuracy and computational efficiency, reaffirming its utility in multimodal tasks that require a combination of text and image processing.

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What the Future Holds for L-Mul

The ability to use L-Mul without fine-tuning and its remarkable energy savings means that it could become a key player in the future of AI development. It’s already clear that this algorithm can enhance the performance of models across multiple domains, from language processing to vision tasks, all while reducing the carbon footprint associated with AI computations.

The results are just as promising in models where fine-tuning is required. When tested on the Gemma2–2b-It model, L-Mul performed at the same level as FP8_e4m3, meaning that even fine-tuned models can maintain their accuracy while becoming more energy-efficient.

The future of AI is bright, but it also needs to be sustainable. With algorithms like L-Mul, we are on the path to creating smarter, faster, and greener AI systems.

Conclusion: A New Era of Energy-Efficient AI

The L-Mul algorithm represents a massive leap forward in developing energy-efficient AI. By replacing expensive floating-point multiplications with simpler integer additions, L-Mul reduces power consumption and improves computational efficiency and model performance across the board.

As AI advances and demands more computational power, solutions like L-Mul will be crucial for ensuring that progress does not come at an unsustainable cost to the environment.

Reference Reading

FAQs

What is L-Mul?

L-Mul stands for Linear-Complexity Multiplication, an algorithm that replaces floating-point multiplications with integer additions to improve energy efficiency in AI models.

How does L-Mul save energy in AI computations?

L-Mul simplifies the costly floating-point operations in neural networks, reducing energy consumption by up to 95% for tensor multiplications and 80% for dot products.

Does L-Mul affect the accuracy of AI models?

No, L-Mul maintains the accuracy of AI models while reducing their energy consumption, making it an ideal choice for energy-efficient AI systems.

Can L-Mul be integrated into existing AI models?

Yes, L-Mul can be seamlessly integrated into existing neural networks without any need for fine-tuning, making it a practical solution for enhancing energy efficiency.

How does L-Mul compare to FP8 in AI tasks?

L-Mul outperforms FP8 in both precision and computational efficiency, making it a superior alternative for many AI applications.



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