Matthew Miller, Author at Techbest - Top Tech Reviews In Australia - Page 21 of 92

HUAWEI FreeBuds Pro 4 Wireless Earbuds Review


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HUAWEI FreeBuds Pro 4, Wireless Earbuds, Real Sound with Dual Driver, Stable and Clear Calls, Dynamic Smart ANC, Compatible with iOS&Android, Noise Cancelling, White

Skullcandy Indy Evo Wireless Earphones Review


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Skullcandy Indy Evo Wireless Earphones, Black

Xiaomi Redmi Buds 6 Pro Active Noise Cancelling Review


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Xiaomi Redmi Buds 6 Pro Active Noise Cancelling up to 55dB Advanced Triple Driver LDAC Compatible High Resolution Audio Wireless Up to 36 Hours Music Playback Google Fast Pair Space Black

HUAWEI FreeClip Wireless Bluetooth Headphones Review


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HUAWEI FreeClip, Rose Gold, Wireless Bluetooth Headphones, Innovative Aesthetic Design, Featherlight Use

sajawass Wireless Earbuds Review


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sajawass Wireless Earbuds, 75H Playtime Bluetooth 5.3 Headphones, IP7 Waterproof for Running, Wireless in-Ear Earphones for iOS/Android

Google DeepMind Implements AI Tool to Address Software Weaknesses


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Google DeepMind’s CodeMender AI Agent Addresses Software Vulnerabilities

Google DeepMind’s CodeMender AI Agent Addresses Software Vulnerabilities

Google DeepMind addresses software vulnerabilities with AI agent

Brief Overview

  • Google DeepMind unveils CodeMender, an AI solution for spotting and correcting software vulnerabilities.
  • In just six months, CodeMender has provided 72 security enhancements to open-source software.
  • The AI tool utilizes Gemini Deep Think models to independently diagnose and resolve intricate coding challenges.
  • Human experts review the AI-generated patches prior to final implementation.
  • Google invites input from open-source maintainers to further improve the tool.

CodeMender: A Significant Step in AI-Enhanced Security

Google DeepMind’s newest creation, CodeMender, signifies a major leap in applying artificial intelligence to tackle software vulnerabilities. This AI agent is engineered to instantaneously detect and rectify security flaws, empowering developers in the relentless effort to secure codebases.

Self-Sufficient Debugging with Gemini Deep Think Models

CodeMender employs the latest Gemini Deep Think models to facilitate self-sufficient debugging and resolution of intricate vulnerabilities. These models provide the AI with analytical tools to implement effective code modifications, which are subsequently validated automatically to avert regressions and new troubles.

Collaboration Between Humans and AI in Code Security

Although CodeMender autonomously detects and proposes patches, these AI-created solutions undergo human scrutiny before they are enacted. This cooperative model guarantees that the patches not only remedy the problem but also conform to style standards, making them more comprehensible for human developers to review and endorse.

Influence on Open-Source Initiatives

In the last six months, CodeMender has made 72 security contributions to open-source projects, managing codebases as large as 4.5 million lines. Remarkably, the tool has pinpointed and rectified complex problems like memory heap buffer overflows and improved the libwebp library by implementing bounds checks to avert buffer overflows.

Upcoming Developments and AI in Security

Google intends to solicit feedback from open-source project maintainers to further hone CodeMender prior to its broader deployment. Additionally, DeepMind is gearing up to release comprehensive technical documents on the tool. The role of AI in security is expanding, evident from other DeepMind projects such as the Big Sleep tool and Google’s initiatives for AI-driven ransomware detection in the Workspace productivity suite.

Conclusion

Google DeepMind’s CodeMender serves as an AI-driven solution aimed at autonomously identifying and patching software vulnerabilities. By utilizing state-of-the-art AI models paired with a human review process, CodeMender has already made notable contributions to open-source initiatives, highlighting AI’s potential in bolstering software security.

Questions & Answers

Q: What is CodeMender?

A: CodeMender is an AI solution created by Google DeepMind to autonomously locate and correct security vulnerabilities in software code.

Q: How does CodeMender operate?

A: CodeMender harnesses Gemini Deep Think models to independently debug code and recommend patches, which are subjected to human review before execution.

Q: What impact has CodeMender made thus far?

A: In half a year, CodeMender has delivered 72 security fixes to open-source projects and tackled intricate issues such as memory heap buffer overflows.

Q: How does Google plan to enhance CodeMender?

A: Google will collect feedback from open-source maintainers and develop thorough technical documents to refine CodeMender further before a wider release.

Q: How does CodeMender ensure the reliability of its patches?

A: Patches proposed by CodeMender undergo review by human developers to ensure compliance with style guidelines and to prevent the introduction of new issues.

Soundcore V20i Open-Ear Headphones Review


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Soundcore V20i by Anker Open-Ear Headphones, Adjustable Ear Hooks, Ultra-Comfort, Snug Fit, Powerful Sound, Clear Calls, IP55, LED Lights, 36H Playtime, Bluetooth 5.4 Earbuds, Multipoint Connection

IPL TECH Infinity Pods Bluetooth 5.3 Wireless Earbuds Review


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IPL TECH Infinity Pods Bluetooth 5.3 Wireless Earbuds – Touch Control, HiFi Sound, Mic, Long Battery

LinkedIn Initiates Legal Proceedings Against Suspected Data Scrapers Utilizing Millions of Counterfeit Accounts


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Quick Read

  • LinkedIn has launched a legal suit against ProAPIs and Netswift for supposedly generating fake accounts for the purpose of data scraping.
  • The firms are accused of utilizing millions of fraudulent accounts to gain access to restricted and confidential LinkedIn information.
  • LinkedIn aims to prevent these companies from reaching its platform and demands the eradication of the scraped data.
  • This lawsuit emphasizes LinkedIn’s continuous struggle against data scraping, including a prior case against HiQ Labs.
  • The legal proceedings highlight the difficulties in safeguarding user data in today’s digital landscape.

Legal Proceedings Against Data Scrapers

LinkedIn, a subsidiary of Microsoft, has begun legal action against ProAPIs, a company based in Delaware, and Netswift, a Pakistani entity, for allegedly maintaining millions of fake accounts. These accounts are claimed to be used for extensive data scraping on the professional networking site.

Details of the Accusations

The lawsuit, submitted in the Northern District of California, asserts that ProAPIs and its affiliates operate a vast array of fake accounts. These accounts reportedly gather data from LinkedIn member profiles, company information, and user posts before being identified by LinkedIn’s security protocols.

LinkedIn claims that ProAPIs generated thousands of email accounts to create these fake identities using fictitious names and stock photos as profile images. Despite LinkedIn’s attempts to shut them down, the defendants allegedly continued to generate hundreds or thousands of new accounts each day.

Effects on LinkedIn’s Infrastructure

The data scraping activities have imposed an “unreasonable load” on LinkedIn’s servers, burdening resources disproportionally compared to genuine users. This has compelled LinkedIn to expand its server capacity, resulting in considerable time and financial expenditure to address the situation.

LinkedIn’s Requests and Legal Precedents

LinkedIn is pursuing a jury trial and a permanent injunction to stop the defendants from accessing its site. The platform demands that all collected data be eliminated, that customers who bought this data be informed, and that damages are compensated, which may include potential punitive damages.

LinkedIn’s user agreement categorically forbids scraping and the creation of fake accounts, stipulations which the defendants acknowledged. This case follows LinkedIn’s earlier legal triumph over HiQ Labs, a company that employed comparable scraping methods.

Summary

LinkedIn’s legal action against alleged data scrapers underscores the persistent struggle to maintain user privacy and data fidelity. By taking a stand against such actions, LinkedIn intends to protect its platform and users from unauthorized data harvesting.

Q&A Section

Q: Which companies are implicated in the lawsuit?

A: The lawsuit involves ProAPIs, a US-based company, and Netswift, a Pakistani firm, alongside individual defendant Rehmat Alam.

Q: What is LinkedIn accusing these firms of?

A: LinkedIn accuses them of generating millions of fraudulent accounts to illegally scrape user data from the platform.

Q: How does LinkedIn identify and manage fake accounts?

A: LinkedIn employs technical measures to identify and manage fake accounts, though some manage to collect data before they are terminated.

Q: What are the possible repercussions for the defendants?

A: If convicted, the defendants could face a permanent restraining order, the destruction of scraped data, customer notifications, and damage payments.

Q: How has LinkedIn approached similar cases previously?

A: LinkedIn successfully resolved a legal dispute against HiQ Labs, a company involved in similar data scraping practices.

Q: Why is data scraping a significant issue for LinkedIn?

A: Data scraping can result in unauthorized utilization of user data, which may expose users to spam, fraud, and privacy violations.