logo
Tesla awards boss Elon Musk $29bn in shares

Tesla awards boss Elon Musk $29bn in shares

BBC Newsa day ago
Tesla has granted its chief executive Elon Musk $29bn (£21.7bn) worth of its shares, in an attempt to keep the billionaire at the firm.The move comes after a US court struck down his 2018 pay package, worth more than $50bn, ruling that it was "unfair to shareholders".Musk has been appealing the decision made by the Delaware court in 2024 and on Monday Tesla told shareholders it was "confident" that the $29bn of shares "will incentivize Elon to remain at Tesla" especially as "the war for AI talent is intensifying".The award should boost Musk's voting power on the electric car company's board.
"It is imperative to retain and motivate our extraordinary talent, beginning with Elon", Tesla's board wrote on X, a platform owned by Musk, adding that "no one matches Elon's remarkable combination of leadership experience, technical expertise".The company said the billionaire had a "proven track record" in building "revolutionary and profitable businesses".Tech firms trying to assert themselves in the AI sector have been offering huge sums to workers at rivals in an effort to persuade them to join them and boost their development.Facebook founder Mark Zuckerberg was said to have recently tried to lure top developers from ChatGPT-creator OpenAI with million-dollar pay deals.Meanwhile Microsoft's AI division, headed up by former Google DeepMind co-founder Mustafa Suleyman, recently gained several new hires from Google's ranks.Tesla the company was at an "inflection point" and needed Musk's prowess as it pivots from being an electric vehicle firm to an AI and robotics focussed company.The company added that the share ward would be attractive for Musk "with other "demands on his time and attention".Musk's other roles include executive positions at xAI, Neuralink, and The Boring Company, which makes tunnels and other infrastructure in the US.He recently announced that he was stepping back from politics, after a stint as US President Donald Trump's advisor.
Additional reporting by Liv McMahon.
Orange background

Try Our AI Features

Explore what Daily8 AI can do for you:

Comments

No comments yet...

Related Articles

AI That Teaches Itself? Meet Google's Game-Changing MLE Star
AI That Teaches Itself? Meet Google's Game-Changing MLE Star

Geeky Gadgets

time2 minutes ago

  • Geeky Gadgets

AI That Teaches Itself? Meet Google's Game-Changing MLE Star

What if an AI could not only learn but also teach itself to improve, over and over again? Enter Google's new MLE Star, a self-improving machine learning engineering agent that's redefining the limits of artificial intelligence. With a jaw-dropping record of earning gold medals in 36% of Kaggle competitions it enters, this system doesn't just compete—it dominates. But what truly sets MLE Star apart isn't just its accolades; it's the way it continuously evolves, autonomously refining its methods and adapting to new challenges. This isn't just a leap forward for AI—it's a paradigm shift that could reshape how we think about problem-solving, innovation, and even the role of humans in machine learning. In this overview of MLE Star by Wes Roth, you'll uncover how this AI agent uses recursive self-improvement and iterative optimization to achieve unparalleled results. From its structured scaffolding system that pinpoints inefficiencies to its ability to adapt across industries like healthcare and business, MLE Star is more than a technological marvel—it's a glimpse into the future of AI-driven solutions. But with such fantastic potential comes pressing ethical questions: how do we ensure fairness, transparency, and accountability in systems that can outpace human oversight? As we delve into the mechanics and implications of MLE Star, one thing becomes clear: this is not just a tool—it's a challenge to rethink what AI can and should do. Google's MLE Star Overview What Makes MLE Star Unique MLE Star introduces a paradigm shift in AI by employing a dynamic, self-improving framework. Unlike traditional AI systems that rely on static models, MLE Star uses recursive self-improvement to analyze its outputs and refine its processes. This iterative methodology enables it to adapt and enhance its performance over time, making sure continuous optimization. Key features of MLE Star include: Recursive self-improvement: A capability that allows the system to refine its own performance autonomously. A capability that allows the system to refine its own performance autonomously. Iterative optimization: A step-by-step approach to improving solutions for greater accuracy and efficiency. A step-by-step approach to improving solutions for greater accuracy and efficiency. Interchangeable AI models: Flexibility to adapt across diverse tasks and applications. By integrating a structured scaffolding system, MLE Star systematically identifies areas for improvement, making sure impactful and precise results. This approach not only enhances its efficiency but also positions it as a versatile tool for solving complex problems. Proven Excellence in Kaggle Competitions MLE Star's capabilities have been tested and proven in real-world scenarios, particularly in Kaggle competitions, where it has delivered outstanding results. The agent has achieved medals in 63% of the competitions it participated in, with 36% of those being gold medals. Additionally, it has maintained a flawless 100% valid submission rate, a rare accomplishment that underscores its reliability and precision. These achievements highlight MLE Star's ability to handle intricate machine learning challenges with unparalleled accuracy. Its success in such competitive environments demonstrates its potential to transform AI research and development. Google Self Improving AI Agent Watch this video on YouTube. Uncover more insights about self-improving AI in previous articles we have written. Innovative Methodologies Behind MLE Star At the core of MLE Star's success lies its structured scaffolding system, which drives its ability to optimize and innovate. This system enables the agent to: Search for and evaluate existing AI models from online repositories, making sure access to the latest advancements. Refine these models through iterative improvements, enhancing their performance and applicability. Optimize specific components of code to maximize efficiency and accuracy. This targeted and systematic approach minimizes inefficiencies often associated with traditional AI systems. Furthermore, MLE Star's integration with advanced models, such as Google's Gemini 2.5 Pro, amplifies its capabilities, allowing it to deliver superior outcomes across a wide range of tasks. Applications Across Multiple Industries MLE Star's potential extends far beyond research, offering fantastic applications across various industries. Its ability to adapt and optimize makes it a valuable tool in addressing real-world challenges, including: Healthcare: Enhancing diagnostic accuracy, allowing personalized medicine, and improving patient outcomes. Enhancing diagnostic accuracy, allowing personalized medicine, and improving patient outcomes. Archaeology: Analyzing historical data to uncover patterns, insights, and previously unknown connections. Analyzing historical data to uncover patterns, insights, and previously unknown connections. Business: Streamlining operations, optimizing decision-making processes, and driving efficiency in resource management. These examples illustrate how MLE Star's advanced capabilities can drive innovation and efficiency, making it a versatile solution for diverse sectors. Ethical and Societal Considerations While MLE Star represents a significant technological breakthrough, it also raises critical ethical and societal questions. The automation of AI research and the potential for rapid, self-driven improvements bring challenges that must be addressed to ensure responsible development. Key considerations include: Fairness and transparency: Making sure that AI systems operate without bias and remain accountable to human oversight. Making sure that AI systems operate without bias and remain accountable to human oversight. Risk mitigation: Addressing concerns about the potential for an intelligence explosion and its implications. Addressing concerns about the potential for an intelligence explosion and its implications. Responsible innovation: Balancing performance improvements with ethical development practices to prioritize societal benefit. Proactively addressing these challenges is essential to ensure that advancements like MLE Star are deployed responsibly and equitably, fostering trust and maximizing their positive impact. Shaping the Future of AI MLE Star is a new achievement in machine learning engineering, showcasing the potential for AI systems to independently innovate and improve. Its success in competitive environments, such as Kaggle, and its ability to automate complex research processes highlight its fantastic potential. As industries increasingly adopt such advanced technologies, the implications for research, business, and society are profound. However, the ethical and societal challenges associated with these advancements must remain a priority. MLE Star is not just a technological milestone—it offers a glimpse into the future of AI and its role in shaping a more efficient, innovative, and interconnected world. Media Credit: Wes Roth Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.

LangExtract : Google's New Library for Simplifying Language Processing Tasks (NLP)
LangExtract : Google's New Library for Simplifying Language Processing Tasks (NLP)

Geeky Gadgets

time2 minutes ago

  • Geeky Gadgets

LangExtract : Google's New Library for Simplifying Language Processing Tasks (NLP)

What if you could simplify the complexities of natural language processing (NLP) without sacrificing accuracy or efficiency? For years, developers and researchers have wrestled with the steep learning curves and resource-intensive demands of traditional NLP tools. Enter Google's LangExtract—a new library that promises to redefine how we approach tasks like information extraction, sentiment analysis, and text classification. By using the power of large language models (LLMs) such as Gemini, LangExtract offers a streamlined, accessible, and highly adaptable solution to some of NLP's most persistent challenges. Whether you're a seasoned professional or a curious newcomer, this tool is poised to transform how we interact with language data. In this overview Sam Witteveen explores how LangExtract is reshaping the NLP landscape with its focus on efficiency and user-centric design. From its ability to process long-context data to its reliance on few-shot learning, LangExtract eliminates the need for extensive datasets and computational resources, making it a fantastic option for industries like finance, healthcare, and legal services. But what truly sets it apart? Is it the seamless integration into existing workflows, the reduced operational overhead, or the promise of high-quality results with minimal effort? As we unpack its features and applications, you'll discover why LangExtract is more than just another library—it's a bold step toward providing widespread access to advanced NLP capabilities. Overview of LangExtract Features How LangExtract Compares to Traditional NLP Tools Traditional NLP tools, such as those based on BERT, often require substantial fine-tuning, large datasets, and significant computational resources to achieve optimal performance. LangExtract eliminates much of this complexity by using the power of LLMs. With just a few well-crafted examples and prompts, users can achieve reliable and accurate results without the need for extensive training or resource-intensive processes. This makes LangExtract particularly appealing for production environments where time, cost, and efficiency are critical factors. Additionally, LangExtract's ability to process long-context data and generate structured outputs in formats like JSON ensures seamless integration into existing workflows. This flexibility allows users to experiment with different LLM versions, balancing performance and cost to meet specific project requirements. Google's New Library for NLP Tasks : LangExtract Watch this video on YouTube. Take a look at other insightful guides from our broad collection that might capture your interest in language processing. Practical Applications Across Industries The versatility of LangExtract makes it suitable for a wide range of real-world applications, including: Metadata Extraction: Processes large text corpora, such as news articles, legal documents, or financial reports, to extract valuable metadata efficiently. Processes large text corpora, such as news articles, legal documents, or financial reports, to extract valuable metadata efficiently. Training Dataset Creation: Assists the creation of specialized datasets for smaller models with minimal manual effort. Assists the creation of specialized datasets for smaller models with minimal manual effort. Automated Data Labeling: Streamlines the data labeling process, making it faster and more efficient for production environments. Its ability to handle extensive datasets and deliver accurate, structured outputs makes LangExtract an indispensable tool for industries that rely on precise and efficient information extraction, such as finance, healthcare, and legal services. Accessible and User-Friendly Design LangExtract prioritizes ease of use, offering a straightforward setup process that integrates seamlessly into existing workflows. By using widely used Python libraries and API keys, users can quickly implement LangExtract without requiring extensive technical expertise. Built-in visualization tools further enhance its usability, allowing users to analyze extracted data and refine their processes effectively. This focus on accessibility lowers the barrier to entry, making advanced NLP technologies available to a broader audience, including businesses, developers, and researchers. Whether you are a seasoned professional or new to NLP, LangExtract provides a practical and efficient solution for tackling complex language processing tasks. Advantages Over Conventional NLP Approaches LangExtract offers several distinct advantages compared to traditional NLP tools: Reduced Data Requirements: Eliminates the need for extensive data collection and model training, saving time and resources. Eliminates the need for extensive data collection and model training, saving time and resources. Operational Efficiency: Uses LLMs as a service, significantly reducing computational and resource overhead. Uses LLMs as a service, significantly reducing computational and resource overhead. User-Centric Design: Provides a polished and intuitive alternative to libraries like Prodigy and SpaCy, focusing on simplicity, scalability, and ease of use. By emphasizing efficiency, scalability, and user-friendliness, LangExtract enables users to achieve high-quality results with minimal effort. This makes it an ideal choice for both large-scale enterprise applications and specialized NLP projects. Media Credit: Sam Witteveen Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.

A Simple Guide to Starting Up in macOS Recovery
A Simple Guide to Starting Up in macOS Recovery

Geeky Gadgets

time2 minutes ago

  • Geeky Gadgets

A Simple Guide to Starting Up in macOS Recovery

macOS Recovery is a built-in utility available on every Mac, designed to help you troubleshoot and resolve system-related issues effectively. Whether you need to reinstall macOS, repair or erase your startup disk, or restore data from a Time Machine backup, this tool provides the essential resources to get your system back on track. The method to access macOS Recovery varies depending on whether your Mac uses Apple Silicon (M series chips) or an Intel processor. Below is a detailed guide to help you access and use macOS Recovery based on your Mac's hardware. The video below from Apple gives us more details. Watch this video on YouTube. Accessing macOS Recovery on Apple Silicon Macs If your Mac is powered by Apple's M series chips, accessing macOS Recovery is a simple and efficient process. Follow these steps to enter the recovery environment: Ensure your Mac is completely shut down. Press and hold the power button until the startup options screen or the 'Options' icon appears. icon appears. Select 'Options' and click 'Continue.' and click If prompted, choose your user account and enter the associated login credentials. Once you complete these steps, you'll enter the macOS Recovery environment. From here, you can access tools to reinstall macOS, repair your startup disk, or perform other recovery tasks. This streamlined process ensures that Apple Silicon Macs provide a user-friendly approach to system troubleshooting. Accessing macOS Recovery on Intel-Based Macs For Macs equipped with Intel processors, the process to access macOS Recovery differs slightly but remains straightforward. Follow these steps to start up from macOS Recovery: Shut down your Mac completely. Press the power button, then immediately press and hold the Command (⌘) + R keys. keys. Release the keys when the Apple logo or a spinning globe appears on the screen. If prompted, connect to a Wi-Fi network to access the internet. Select your user account and enter the login credentials, if required. After completing these steps, you'll gain access to the macOS Recovery utilities. The process is designed to ensure that Intel-based Macs provide the same level of functionality and troubleshooting capabilities as their Apple Silicon counterparts. Key macOS Recovery Utilities Once inside macOS Recovery, you'll have access to a range of tools designed to address various system issues. These utilities include: Reinstall macOS: Install a fresh copy of macOS while attempting to preserve your existing data, if possible. Install a fresh copy of macOS while attempting to preserve your existing data, if possible. Disk Utility: Diagnose and repair issues with your startup disk, or erase it if necessary to resolve persistent problems. Diagnose and repair issues with your startup disk, or erase it if necessary to resolve persistent problems. Restore from Time Machine Backup: Recover your data and system settings from a previously created backup, making sure minimal data loss. Recover your data and system settings from a previously created backup, making sure minimal data loss. Erase Startup Disk: Format your startup disk to prepare it for a clean installation of macOS, ideal for resolving severe software issues. Each of these utilities is tailored to specific troubleshooting needs, providing you with the tools necessary to restore your Mac to optimal performance. Whether you're addressing software glitches, disk errors, or preparing for a clean installation, macOS Recovery offers a comprehensive solution. Why macOS Recovery Matters macOS Recovery is a critical feature for maintaining your Mac's functionality and resolving technical challenges. It serves as a reliable safety net, allowing you to address issues such as software malfunctions, disk errors, or the need for a system reset. By understanding how to access macOS Recovery—whether on an Apple Silicon or Intel-based Mac—you can take proactive steps to troubleshoot problems and ensure your system remains in excellent condition. This utility is particularly valuable for users who rely on their Macs for work, education, or personal projects. The ability to quickly access recovery tools minimizes downtime and ensures that you can restore your system with minimal disruption. By familiarizing yourself with the steps outlined above, you'll be better prepared to handle unexpected issues and maintain your Mac's performance over time. Uncover more insights about macOS Recovery in the previous articles we have written. Source & Image Credit: Apple Support Filed Under: Apple, Guides, Laptops Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.

DOWNLOAD THE APP

Get Started Now: Download the App

Ready to dive into a world of global content with local flavor? Download Daily8 app today from your preferred app store and start exploring.
app-storeplay-store