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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.

Get Customization in the report as per your requirements:- https://datamintelligence.com/customize/intercom-devices-market

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?

Contact Us –Company Name: DataM IntelligenceContact Person: Sai KiranEmail: Sai.k@datamintelligence.comPhone: +1 877 441 4866Website: https://www.datamintelligence.com

About Us –DataM Intelligence is a Market Research and Consulting firm that provides end-to-end business solutions to organizations from Research to Consulting. We, at DataM Intelligence, leverage our top trademark trends, insights and developments to emancipate swift and astute solutions to clients like you. We encompass a multitude of syndicate reports and customized reports with a robust methodology.Our research database features countless statistics and in-depth analyses across a wide range of 6300+ reports in 40+ domains creating business solutions for more than 200+ companies across 50+ countries; catering to the key business research needs that influence the growth trajectory of our vast clientele.

This release was published on openPR.

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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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.

None

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.

None

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.

None

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.

None

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.

None

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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Chainlink Powers 3 Major Use Cases Under the Monetary Authority of Singapore’s Project Guardian | Chainlink Blog

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Chainlink Powers 3 Major Use Cases Under the Monetary Authority of Singapore’s Project Guardian | Chainlink Blog


Table of Contents

Chainlink Powers 3 Major Use Cases Under the Monetary Authority of Singapore’s Project Guardian

Swift, UBS Asset Management, and Chainlink Bridge Tokenized Assets With Existing Payment Systems

SBI Digital Markets, UBS Asset Management, and Chainlink Unlock Automated Fund Administration and Transfer Agency

ADDX, ANZ, and Chainlink Introduce Privacy-Enabled Cross-Chain, Cross-Border Connectivity for Tokenized Commercial Paper 

Paving the Way For Production-Level Blockchain Use Cases in Banking and Capital Markets

Chainlink in the News



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Dogecoin Peaks at 43 Cents on Continued Election Glee – Decrypt

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Dogecoin Peaks at 43 Cents on Continued Election Glee – Decrypt



Dogecoin (DOGE) ballooned to $0.4398 on crypto exchange Binance on Tuesday, marking its highest value in over three years.

The Shiba Inu-themed meme coin saw a massive 143.2% rise in the past week, significantly outpacing Bitcoin’s (BTC) 28.2% gain during the same period, as per CoinGecko data.

The rally follows Donald Trump’s election win, boosting optimism for speculative assets such as Dogecoin as traders bet on pro-crypto policies. The challenge now is for DOGE to sustain this momentum while attracting long-term institutional interest.

“With the election overhang now behind us, the rally in crypto markets appears sustainable, buoyed by positive forward signals from the U.S.,” Julien Auchecorne, head of Auros Ventures, told Decrypt. “However, uncertainties remain, particularly around altcoins. We’re closely watching if alts will continue trailing Bitcoin or if they’ll begin to outperform, potentially drawing back retail interest.”

Elon Musk, the self-proclaimed “Dogefather,” fueled even more buzz last month by suggesting that, if appointed by Trump, he could lead a “Department of Government Efficiency” (D.O.G.E.) with the ambitious goal of slashing $2 trillion or more from the federal budget.

The nod to the acronym “D.O.G.E” sent Dogecoin enthusiasts into a frenzy, pushing the coin to new heights. Musk’s endorsement—whether serious or in jest—often leads to spikes in DOGE’s price.

Despite the excitement around Dogecoin, Auchecorne points to some structural challenges in the altcoin market.

“The structure of altcoin launches is under pressure, especially as institutional incentives still heavily favor de-risking at launch and redirecting capital afterward,” he said. “Protocols are now adjusting strategies to retain institutional participation post-launch, taking a more measured approach to their target fully diluted valuations (FDVs).”

For example, Aptos—a network founded by ex-Meta employees—saw rapid sell-offs in 2022 from early institutional investors following its launch, causing steep post-launch declines. In contrast, protocols such as Arbitrum are adopting gradual token releases to retain institutional engagement and achieve more stable growth.

Yet, questions remain about the sustainability of the DOGE rally.

“As this dynamic evolves, we expect capital flows to be selectively drawn to projects that can sustainably balance institutional needs with strong market appeal,” Auchechorne noted.

Amid the retail-driven frenzy, Canada-based Spirit Blockchain Capital has said it wants to mirror MicroStrategy’s Bitcoin accumulation plan to build large reserves of Dogecoin.

Spirit’s approach includes launching exchange-traded products (ETPs) and payment gateways, seeking to institutionalize Dogecoin’s use case beyond its meme origins.

While Dogecoin remains below its all-time high of $0.73 in 2021, the recent surge indicates that it’s far from losing its appeal. The original meme coin now the sixth-largest crypto by market cap, ahead of Circle’s stabelcoin USDC and Ripple (XRP), shows CoinGecko data.

Edited by Stacy Elliott.

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How Phi-3-Vision-128K Enhances Document Processing with AI-Powered OCR

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How Phi-3-Vision-128K Enhances Document Processing with AI-Powered OCR


In the evolving landscape of artificial intelligence, the development of multimodal models is reshaping how we interact with and process data. One of the most groundbreaking innovations in this space is the Phi-3-Vision-128K-Instruct model—a cutting-edge, open multimodal AI system that integrates visual and textual information. Designed for tasks like Optical Character Recognition (OCR), document extraction, and comprehensive image understanding, Phi-3-Vision-128K-Instruct has the potential to revolutionize document processing, from PDFs to complex charts and diagrams.

In this article, we will explore the model’s architecture, primary applications, and technical setup and explore how it can simplify tasks like AI-driven document extraction, OCR, and PDF parsing.

What is Phi-3-Vision-128K-Instruct?

Phi-3-Vision-128K-Instruct is a state-of-the-art multimodal AI model in the Phi-3 model family. Its key strength lies in its ability to process textual and visual data, making it highly suitable for complex tasks requiring simultaneous interpretation of text and images. With a context length of 128,000 tokens, this model can handle large-scale document processing, from scanned documents to intricate tables and charts.

Trained on 500 billion tokens, including a mix of synthetic and curated real-world data, the Phi-3-Vision-128K-Instruct model utilizes 4.2 billion parameters. Its architecture includes an image encoder, a connector, a projector, and the Phi-3 Mini language model, all working together to create a powerful yet lightweight AI capable of efficiently performing advanced tasks.

Core Applications of Phi-3-Vision-128K-Instruct

Phi-3-Vision-128K-Instruct’s versatility makes it worthwhile across a range of domains. Its key applications include:

1. Document Extraction and OCR

The model excels in transforming images of text, like scanned documents, into editable digital formats. Whether it’s a simple PDF or a complex layout with tables and charts, Phi-3-Vision-128K-Instruct can accurately extract the content, making it a valuable tool for digitizing and automating document workflows.

2. General Image Understanding

Beyond text, the model can parse visual content, recognize objects, interpret scenes, and extract useful information from images. This ability makes it suitable for a wide array of image-processing tasks.

3. Efficiency in Memory and Compute-Constrained Environments

Phi-3-Vision-128K-Instruct is designed to work efficiently in environments with limited computational resources, ensuring high performance without excessive demands on memory or processing power.

4. Real-Time Applications

The model can reduce latency, making it an excellent choice for real-time applications, such as live data feeds, chat-based assistants, and streaming content analysis.

Getting Started with Phi-3-Vision-128K-Instruct

To harness the power of this model, you’ll need to set up your development environment. Phi-3-Vision-128K-Instruct is integrated into the Hugging Face transformers library, version 4.40.2. Make sure your environment has the following packages installed:

# Required Packages
flash_attn==2.5.8
numpy==1.24.4
Pillow==10.3.0
Requests==2.31.0
torch==2.3.0
torchvision==0.18.0
transformers==4.40.2

To load the model, update your transformers library and install it directly from the source:

pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers

Once set up, you can begin using the model for AI-powered document extraction and text generation.

Example Code for Loading Phi-3-Vision-128K-Instruct

Here’s a basic example in Python for initializing and making predictions using Phi-3-Vision-128K-Instruct:

from PIL import Image
import requests
from transformers import AutoModelForCausalLM, AutoProcessor

class Phi3VisionModel:
def __init__(self, model_id=”microsoft/Phi-3-vision-128k-instruct”, device=”cuda”):
self.model_id = model_id
self.device = device
self.model = self.load_model()
self.processor = self.load_processor()

def load_model(self):
return AutoModelForCausalLM.from_pretrained(
self.model_id,
device_map=”auto”,
torch_dtype=”auto”,
trust_remote_code=True
).to(self.device)

def load_processor(self):
return AutoProcessor.from_pretrained(self.model_id, trust_remote_code=True)

def predict(self, image_url, prompt):
image = Image.open(requests.get(image_url, stream=True).raw)
prompt_template = f”<|user|>\n<|image_1|>\n{prompt}<|end|>\n<|assistant|>\n”
inputs = self.processor(prompt_template, [image], return_tensors=”pt”).to(self.device)
output_ids = self.model.generate(**inputs, max_new_tokens=500)
return self.processor.batch_decode(output_ids, skip_special_tokens=True)[0]

phi_model = Phi3VisionModel()
image_url = “https://example.com/sample_image.png”
prompt = “Extract the data in json format.”
response = phi_model.predict(image_url, prompt)
print(“Response:”, response)

Testing OCR Capabilities with Real-World Documents

We ran experiments with various types of scanned documents to test the model’s OCR capabilities. For example, we used a scanned Utopian passport and a Dutch passport, each with different levels of clarity and complexity.

Example 1: Utopian Passport

The model could extract detailed text from a high-quality image, including name, nationality, and passport number.

Output:

{
“Surname”: “ERIKSSON”,
“Given names”: “ANNA MARIA”,
“Passport Number”: “L898902C3”,
“Date of Birth”: “12 AUG 74”,
“Nationality”: “UTOPIAN”,
“Date of Issue”: “16 APR 07”,
“Date of Expiry”: “15 APR 12”
}

Example 2: Dutch Passport

The model handled this well-structured document effortlessly, extracting all the necessary details accurately.

The Architecture and Training Behind Phi-3-Vision-128K-Instruct

Phi-3-Vision-128K-Instruct stands out because it can process long-form content thanks to its extensive context window of 128,000 tokens. It combines a robust image encoder with a high-performing language model, enabling seamless visual and textual data integration.

The model was trained on a dataset that included both synthetic and real-world data, focusing on a wide range of tasks such as mathematical reasoning, common sense, and general knowledge. This versatility makes it ideal for a variety of real-world applications.

Performance Benchmarks

Phi-3-Vision-128K-Instruct has achieved impressive results on several benchmarks, particularly in multimodal tasks. Some of its highlights include:

The model scored 81.4% on the ChartQA benchmark and 76.7% on AI2D, making it one of the top performers in these categories.

Why AI-Powered OCR Matters for Businesses

AI-driven document extraction and OCR are transformative for businesses. By automating tasks such as PDF parsing, invoice processing, and data entry, businesses can streamline operations, save time, and reduce errors. Models like Phi-3-Vision-128K-Instruct are indispensable tools for digitizing physical records, automating workflows, and improving productivity.

Responsible AI and Safety Considerations

While Phi-3-Vision-128K-Instruct is a powerful tool, it is essential to be mindful of its limitations. The model may produce biased or inaccurate results, especially in sensitive areas such as healthcare or legal contexts. Developers should implement additional safety measures, like verification layers when using the model for high-stakes applications.

Future Directions: Fine-Tuning the Model

Phi-3-Vision-128K-Instruct supports fine-tuning, allowing developers to adapt the model for specific tasks, such as enhanced OCR or specialized document classification. The Phi-3 Cookbook provides fine-tuning recipes, making extending the model’s capabilities for particular use cases easy.

Conclusion

Phi-3-Vision-128K-Instruct represents the next leap forward in AI-powered document processing. With its sophisticated architecture and powerful OCR capabilities, it is poised to revolutionize the way we handle document extraction, image understanding, and multimodal data processing.

As AI advances, models like Phi-3-Vision-128K-Instruct are leading the charge in making document processing more efficient, accurate, and accessible. The future of AI-powered OCR and document extraction is bright, and this model is at the forefront of that transformation.

FAQs

1. What is the main advantage of Phi-3-Vision-128K-Instruct in OCR? Phi-3-Vision-128K-Instruct can process both text and images simultaneously, making it highly effective for complex document extraction tasks like OCR with tables and charts.

2. Can Phi-3-Vision-128K-Instruct handle real-time applications? Yes, it is optimized for low-latency tasks, making it suitable for real-time applications like live data feeds and chat assistants.

3. Is fine-tuning supported by Phi-3-Vision-128K-Instruct? Absolutely. The model supports fine-tuning, allowing it to be customized for specific tasks such as document classification or improved OCR accuracy.

4. How does the model perform with complex documents? The model has been tested on benchmarks like ChartQA and AI2D, where it demonstrated strong performance in understanding and extracting data from complex documents.

5. What are the responsible use considerations for this model? Developers should be aware of potential biases and limitations, particularly in high-risk applications such as healthcare or legal advice. Additional verification and filtering layers are recommended.



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The Future of DeFi: Predicting the Next Frontier of Financial Freedom – Web3oclock

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The Future of DeFi: Predicting the Next Frontier of Financial Freedom – Web3oclock


Predictions and trends for the DeFi space 

Technological advancements shaping DeFi

The long-term outlook for decentralized finance

Growth and Evolution of DeFi

Trends and Predictions in DeFi:

Technological Advancements Shaping DeFi:

Long-Term Outlook for Decentralized Finance:

1. Mainstream Adoption and Global Financial Inclusion:

2. Integration with Central Bank Digital Currencies (CBDCs):



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Czech Republic’s Aovotice.cz Lauded By JazzJoyandRoy.com | Web3Wire

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Czech Republic’s Aovotice.cz Lauded By JazzJoyandRoy.com | Web3Wire


Czech Republic’s Aovotice.cz Lauded By JazzJoyandRoy.com | Web3Wire

Famous artwork created for Jazz Joy and Roy Global Radio by Kathryn Diane Gray

When Roy O’dell Gray worked morning drive on now-defunct WNWZ News Radio in Richmond, Virginia many moons before founding Jazz Joy and Roy Global Radio in 2007, he got a phone call from ‘Soul Train’ announcer Sid McCoy, thanking Gray for playing one of McCoy’s syndicated programs. McCoy’s gesture is the main reason that Gray tries to make a public “thank you” statement when independent websites drive new listeners to JazzJoyandRoy.com.

Aovotice.cz features a searchable “Jazz Joy and Roy” article by journalist Martin Kozminsky that has won Gray’s gratitude.

Gray says, “If a Sid McCoy was able to stop what he was doing to thank me, surely I can hit the multitasking pause button on giving my wife Kathy a four hour full-body massage under a warm blanket with one hand while running the over one hundred JazzJoyandRoy.com sister sites that power both the Jazz Joy and Roy Global Radio Network and the Bible Ball Inc charity with the other, all while doing thousands a multiplication math problems in my head to practice remaining clear-eyed during competitive tennis matches…to acknowledge Aovotice.cz and their readers in The Czech Republic and around the globe.”

In Gray’s eyes, “Global broadcasting is a tough business in which joy is fueled by sometimes surprising listener subjectivity, and you can bet your proverbial bottom dollar that, for every listener that writes to Request@JazzJoyandRoy.com to tell you a song is sensational, a little due diligence can find 5 listeners who will tell you the song is the worse thing since unsliced bread. With Aovotice.cz, the subjective ball bounced in our favor.”

Jazz Joy and Roy Global RadioMarital Relations Music Radio JJ&RComedy Song Global Radio JJ&RModern Country Global Radio JJ&RClassic Country Global Radio JJ&RChristian Global Radio JJ&RRoll The Rock Radio JJ&RCrossover Jazz Global Radio JJ&R10334 W. Peoria Ave.Sun City, AZ 85351Press Contact: Barbie BensonBarbieBenson@JazzJoyandRoy.com

Underwritten by Bible Ball Inc™, a nonprofit organization, Jazz Joy and Roy Global Radio’s JazzJoyandRoy.com operates over 100 sister sites and 7 global music radio stations, plus bonus stations, which support Bible Ball Inc initiatives to train volunteer Bible Ball Inc staff to play competitive amateur sports games like tennis and golf against amateur athletes who are awardedfree Bibles, sports lessons, tees, hats and a free 15 minute onsite post-game Bible study and refreshments…for participating.  For more information visit BibleBall.org. Jazz Joy and Roy Global Radio, the network credited, along with The View and Stevie Wonder, with the rise to superstardom of singer/actor Andra Day of “Rise Up” and “Billie Holiday” fame…is always looking for your help tracking down the following types of stories and more:

1. A portrait of a blind, bald author who has written extensively about doing business in Norway, Singapore or Japan.2. A day in the life of an irrepressible heart attack survivor who almost died, but never stops pursuing excellence in the fashionable socks industry.3. A look into the life of an elderly pastor who owns over 80 pets and is on a crusade to reduce Church gossip globally.4. A profile of an extremely tall person who has no arms, but owns multiple corporations and manages to hug scores of people with compassion.5. A full feature story on a business that employs fascinating family members in 40 or more countries..6. An interview with an individual who has collected more than a billion written prayer requests.

Write to: info@JazzJoyandRoy.com, info@BibleBall.org, Request@JazzJoyandRoy.com, PrayerRequest@JazzJoyandRoy.com, JesusLives@JazzJoyandRoy.com, ChurchAudioVisualPro@jazzjoyandroy.com, WhitePeopleHotline@JazzJoyandRoy.com, BlackPeopleHotline@JazzJoyandRoy.com, LatinaHotline@JazzJoyandRoy.com, AsianHotline@JazzJoyandRoy.com, JewishHotline@JazzJoyandRoy.com, GermanHotline@JazzJoyandRoy.com, ItalianHotline@JazzJoyandRoy.com, PhilippinoHotline@JazzJoyandRoy.com, PolishHotline@JazzJoyandRoy.com, GreekHotline@JazzJoyandRoy.com

This release was published on openPR.

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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