Tag: artificial intelligence

  • Meta Muse: How Meta’s New AI Agent Moves from Answers to Action

    Meta Muse: How Meta’s New AI Agent Moves from Answers to Action

    Meta has unveiled Muse, an AI agent built not just to chat but to act. Unlike conventional virtual assistants that answer questions or generate text, Muse is designed to understand user goals and execute the steps needed to achieve them. This marks a significant shift from passive conversation to active task completion.

    AI That Takes Action

    Traditional AI assistants primarily generate responses, offer recommendations, or retrieve information. Muse represents a different philosophy: it interprets instructions, decides on the required sequence of actions, and performs tasks within digital environments. The goal is to reduce the manual steps users must take for everyday activities, making digital assistance more proactive and useful.

    From Answers to Execution

    Muse highlights the broader transition from conversational AI to agentic AI. Instead of stopping after delivering an answer, an agent can keep working toward a specific outcome. This means future AI systems could handle complex workflows, requiring less frequent guidance or supervision from the user.

    Meta’s AI Ambitions

    Muse fits squarely into Meta’s wider investment in artificial intelligence. The company has been embedding AI across its social platforms, consumer products, and research initiatives. Agentic systems could become a cornerstone of that strategy, enabling more interactive, responsive, and capable AI experiences.

    Understanding User Goals

    A key capability of Muse is its focus on understanding what users ultimately want to accomplish. Rather than treating each command in isolation, the agent is designed around completing broader objectives. This allows it to determine appropriate actions, adapt when circumstances change, and reduce unnecessary back-and-forth between users and digital systems.

    Potential Everyday Uses

    Agentic AI could eventually assist with tasks such as organizing information, completing digital workflows, managing routine chores, and navigating applications. The real value lies in reducing manual effort rather than merely generating another response. As these systems mature, their usefulness will hinge on reliability, permissions, privacy, and appropriate human oversight.

    The Next AI Shift

    Meta Muse represents a broader transition in artificial intelligence—from generating content to performing tasks. As AI agents become more capable, competition may increasingly focus on execution, reliability, and trust. The technology has the potential to reshape how people interact with software, letting AI handle more steps on their behalf.

  • AI vs. Human Intelligence: Key Strengths of Each and Why Collaboration Wins

    AI vs. Human Intelligence: Key Strengths of Each and Why Collaboration Wins

    Artificial intelligence continues to reshape the way we live and work, but comparing human abilities with machine capabilities shows that neither side is universally superior. People and AI excel in different areas, and the strongest outcomes usually come from combining the two.

    Where Humans Excel

    • Creativity and original thinking: Humans create original ideas by blending emotions, experiences, imagination, and personal perspective — qualities AI cannot genuinely replicate.
    • Emotional intelligence: People can read body language, tone, and personal context, which makes human judgment essential in sensitive or emotionally charged conversations.
    • Ethical reasoning: Humans can weigh values, social impact, personal responsibility, and real-world consequences when making difficult decisions.
    • Adaptability: Humans use common sense and past experiences to respond to sudden changes or incomplete information.

    Where AI Excels

    • Speed and scale: AI can process enormous amounts of information in seconds and complete repetitive tasks far faster than humans.
    • Data analysis: AI scans massive datasets, spots patterns, compares information, and surfaces useful insights without getting tired or distracted.
    • Consistency in repetitive work: AI can perform the same task thousands of times with steady performance, making it ideal for routine digital and office work.
    • 24/7 operation: AI systems do not need sleep and can handle requests, monitor systems, and process information around the clock.

    Why the Best Results Combine Both

    Neither humans nor AI can fully replace the other. The most effective outcomes come when human creativity, judgment, and empathy work alongside AI’s speed, scalability, and data-processing power.

  • Elon Musk Dismisses Former Anthropic Researcher’s AI Extinction Warning as a ‘Setup’

    Elon Musk Dismisses Former Anthropic Researcher’s AI Extinction Warning as a ‘Setup’

    Elon Musk publicly challenged a former Anthropic researcher who resigned and warned that artificial intelligence could pose an existential threat to humanity. Jacob Coxon, who claims to have spent three years working at both Anthropic and OpenAI, posted a series of messages on X expressing alarm over the rapid pace of AI development.

    Musk responded directly on the platform, calling the posts “seems like a setup” and later suggesting they appeared to be part of a coordinated “psy op.” The exchange unfolded over several hours through public posts on X, with both men trading remarks.

    In his initial posts, Coxon accused Anthropic and OpenAI of “racing straight to self-improving superintelligence and gambling with our lives.” He stated that people working on AI development believe the technology could kill people by the end of the decade and insisted his warning was not a “marketing stunt.”

    Coxon’s posts reportedly garnered over 120 million views within a day. Musk questioned the sudden reach, noting that he had never seen such attention on a new account with little prior activity. Reports also indicated that Coxon had spent only four months at Anthropic, leaving two months before his stock options would have vested.

    Coxon fired back at Musk on X, writing, “I’m real, and these are my real beliefs. You could ask your xAI researchers about me if you hadn’t fired them.” Billionaire investor Bill Ackman also weighed in, reacting to a post suggesting Coxon’s warning could be the start of a “sophisticated and well-funded PR operation” aimed at pushing AI regulation.

    The exchange comes amid growing scrutiny of safety practices at major AI labs. Anthropic recently stated it is prioritizing safety over speed whenever the two goals conflict, as both it and OpenAI prepare for high-profile public listings while competing to build increasingly powerful AI systems.

  • Why Artificial Intelligence Matters More Than Ever in 2026: Productivity, Science, and Policy Impacts

    Why Artificial Intelligence Matters More Than Ever in 2026: Productivity, Science, and Policy Impacts

    Key Takeaways

    • AI is becoming a major productivity tool, improving performance across software, customer support, advertising, and other structured tasks.
    • AI is reshaping economies and science, driving massive investment while helping researchers solve increasingly complex problems.
    • AI brings greater risks and responsibilities, making human oversight, cybersecurity, regulation, and responsible deployment increasingly important.

    Artificial intelligence now sits at the center of business, science, software, security, and public policy. Stanford’s 2026 AI Index reports that 88% of surveyed organizations use AI, while 70% use generative AI in at least one business function. Generative AI also reached 53% population adoption within three years—a faster path than the personal computer or the internet.

    AI Gives Businesses a New Productivity Tool

    AI has a clear effect on work that has set rules and clear results. Studies cited by Stanford report a 14% to 15% rise in customer support output, a 26% rise in software development output, and a 50% rise in ad output per worker. The gains vary by task, so human review still matters for deep judgment.

    AI agents add another layer to this change. These systems can handle several steps on a computer rather than only a reply. On the OSWorld test, agent accuracy rose from about 12% to 66.3%. That result still leaves a failure rate of about one in three attempts, which shows its limits.

    AI has Become a Major Economic Force

    Money also shows the scale of AI. Global corporate AI investment more than doubled in 2025. Private investment rose 127.5%, while generative AI investment rose more than 200%. The United States led private AI investment with USD 285.9 billion in 2025, compared with USD 12.4 billion in China. Stanford also reports more than USD 150 billion in annual capital outlay by Google in 2025, reflecting the high cost of AI data centers and compute systems.

    The labor market already shows clear effects. Employment for software developers aged 22 to 25 fell nearly 20% from 2024. One-third of surveyed organizations expect AI to reduce their workforce in the next year, although broad job losses have not yet appeared across the whole economy. The pattern points to new task and skill needs.

    AI Expands the Reach of Science

    AI now has a role in science, medicine, mathematics, and technology. The 2026 AI Index added a full science section that covers biology, chemistry, physics, and astronomy. Advanced models can solve difficult science and math tasks, while some models now meet or exceed human results on several expert tests. This gives researchers a new tool for data analysis and complex problem-solving.

    AI still has sharp limits. AI can win a gold medal-level result on the International Mathematical Olympiad yet read an analog clock correctly only 50.6% of the time, compared with 90.1% for humans. Such uneven skill shows why expert review remains essential. AI has great value when a task fits its strengths, but weak results can appear on simple tasks as well.

    AI Shapes National Policy and Security

    Governments now treat AI as a matter of economic power, security, and national policy. Stanford reports that national AI plans have expanded across many countries, while AI sovereignty has become a central policy goal. Countries seek local control over models, data, chips, and compute capacity. This makes AI part of wider competition across trade, technology, and national security.

    Security risks also rise as AI gains more power. Stanford recorded 362 documented AI incidents in 2025, up from 233 in 2024. AI can help security teams find threats, yet advanced models can also help attackers find software flaws and automate cyberattacks. Strong safeguards, human checks, and clear rules therefore matter as much as model capability.

    Regulation Gives AI a New Set of Rules

    AI now faces real legal duties in major markets. Under the European Union AI Act, some enforcement powers started on August 2, 2026. The rules cover prohibited AI practices, transparency duties for certain systems, and rules for general-purpose AI models. Legal compliance now joins product quality and business value in Europe.

    AI matters in 2026 not simply for its ability to produce text, images, or code. Its real importance comes from its effect on productivity, science, jobs, security, investment, and public policy at the same time. The strongest results will come from clear use cases, reliable systems, and careful human judgment. AI has reached a point where its limits still matter, yet its economic and social impact now matters far more.

    FAQs

    1. Why is artificial intelligence important in 2026?

    AI is important since it is influencing productivity, scientific research, employment, cybersecurity, investment, and government policy simultaneously.

    2. How is AI improving business productivity?

    AI can automate repetitive tasks, assist employees with complex workflows, and improve output in areas such as customer support, software development, and advertising.

    3. Is AI affecting employment in 2026?

    Yes. AI is changing the tasks and skills required for many jobs, with some roles experiencing employment declines while new AI-related opportunities emerge.

    4. Why is AI important for science and research?

    AI can analyze large datasets, assist with difficult scientific problems, and support researchers across fields such as biology, chemistry, physics, mathematics, and astronomy.

    5. Why does AI regulation matter?

    Regulation establishes legal requirements for AI systems, helps manage risks, and encourages safer and more transparent development and deployment of AI.

  • Top Tech Companies by Revenue for 2026: Amazon Tops $742B, NVIDIA Surges on AI

    Top Tech Companies by Revenue for 2026: Amazon Tops $742B, NVIDIA Surges on AI

    Amazon leads the 2026 ranking of the world’s largest technology companies by trailing-12-month (TTM) revenue with $742.77 billion, followed by Apple and Alphabet. The list highlights how artificial intelligence, cloud computing, e-commerce, and semiconductors drive massive scale. NVIDIA’s exceptional rise underscores AI’s commercial impact, while Chinese giants JD.com and Alibaba hold strong positions.

    Key Takeaways

    • Amazon tops the 2026 ranking with $742.77 billion in TTM revenue.
    • AI is reshaping tech: NVIDIA’s surge highlights the growing commercial impact of artificial intelligence and data-center demand.
    • Diverse markets drive scale: Retail, cloud, advertising, semiconductors, e-commerce, software, and consumer electronics power the world’s largest technology companies.

    Amazon

    Amazon ranks first with TTM revenue of $742.77 billion. Its business covers online retail, cloud services, ads, devices, and digital products. Fortune also placed Amazon at No. 1 in its 2026 Global 500, with $716.9 billion in fiscal-year 2025 revenue. That reach gives the company unusual scale across several major technology markets.

    Apple

    Apple ranks second with $451.44 billion in TTM revenue. The iPhone remains a central part of its business, while Mac, iPad, services, and other devices add more sales. Fortune placed Apple at No. 6 on the 2026 Global 500, with $416.2 billion in fiscal-year 2025 revenue.

    Alphabet

    Alphabet ranks third with $422.49 billion in TTM revenue. Google Search remains a major source of sales, while YouTube, cloud services, and other digital products add more value. Alphabet also has a major role in artificial intelligence through its products and research. Fortune placed Alphabet at No. 8, with $402.8 billion in fiscal-year 2025 revenue.

    Microsoft

    Microsoft ranks fourth with $318.27 billion in TTM revenue. Its business covers software, cloud services, enterprise tools, games, and artificial intelligence products. Azure produced $101.9 billion in revenue for the fiscal year ended June 2026. Microsoft also reported $281.7 billion in latest fiscal-year revenue. Cloud and AI now hold a major place in its business.

    Samsung Electronics

    Samsung Electronics ranks fifth with $270.94 billion in TTM revenue. Its product range covers smartphones, televisions, home appliances, displays, and semiconductors. This mix gives Samsung several major sources of sales. Fortune placed Samsung Electronics at No. 26 on its 2026 Global 500, with $234.7 billion in fiscal-year 2025 revenue.

    Foxconn

    Foxconn ranks sixth with $261.93 billion in TTM revenue. The company follows a different model from Apple or Microsoft. Foxconn serves as a major electronics manufacturer and supports global brands across the hardware supply chain.

    NVIDIA

    NVIDIA ranks seventh with $253.49 billion in TTM revenue. The company has seen an exceptional rise in sales from artificial intelligence hardware. Its graphics processors and data-center products support large AI systems. Fortune reported $215.9 billion in fiscal-year 2025 revenue, with a 65.5% rise. NVIDIA now holds a much larger place in the semiconductor market.

    Meta Platforms

    Meta Platforms ranks eighth with $214.96 billion in TTM revenue. The company owns Facebook, Instagram, and WhatsApp. Digital ads provide most of its revenue, while artificial intelligence and virtual reality shape its product strategy.

    JD.com

    JD.com ranks ninth with $187.94 billion in TTM revenue. The Chinese company has a major position in online retail and related digital services. Its scale places it ahead of many well-known US and European technology firms.

    Alibaba

    Alibaba ranks tenth with $145.39 billion in TTM revenue. Its business covers e-commerce, cloud services, digital platforms, and other internet products. Alibaba remains one of China’s most important technology companies.

    What the 2026 Data Shows

    The top ten list shows several major sources of technology revenue. Amazon leads with a huge retail and cloud base, while Apple and Alphabet stay above $400 billion. Microsoft sits above $300 billion, while Samsung Electronics and Foxconn each exceed $260 billion. NVIDIA now exceeds $250 billion, while Meta Platforms, JD.com, and Alibaba each exceed $100 billion.

    TTM revenue offers the most current view, while Fortune’s 2026 Global 500 uses fiscal-year 2025 revenue. The two measures should remain separate in a direct comparison. The data also shows how artificial intelligence, cloud services, semiconductors, e-commerce, and consumer devices contribute to the scale of the world’s largest technology companies.

    FAQs

    Which is the largest technology company by revenue in 2026?

    Amazon ranks first, with $742.77 billion in trailing-12-month (TTM) revenue.

    Which technology companies have more than $400 billion in TTM revenue?

    Amazon, Apple, and Alphabet all exceed $400 billion in TTM revenue.

    Why has NVIDIA’s revenue grown so rapidly?

    NVIDIA has benefited from exceptional demand for AI-focused GPUs, data-center hardware, and infrastructure supporting large-scale artificial intelligence systems.

    What is TTM revenue?

    TTM, or trailing-12-month revenue, measures a company’s revenue over its most recent 12-month period, providing a more current view than an older fiscal year.

    Is TTM revenue directly comparable with Fortune’s Global 500 figures?

    Not exactly. TTM figures reflect the latest 12 months, while Fortune’s 2026 Global 500 ranking uses fiscal-year 2025 revenue, so the measures should be kept separate.

  • 7 Best AI Presentation Tools to Create Stunning Slides in 2026

    7 Best AI Presentation Tools to Create Stunning Slides in 2026

    Creating compelling presentations no longer requires hours of manual design work. Thanks to artificial intelligence, you can now generate polished slides from simple prompts, rewrite content on the fly, and maintain consistent layouts with minimal effort. Below are seven of the top AI tools that can help you create professional presentations faster.

    1. Gamma

    Gamma allows you to create complete presentations from just a few prompts. Enter a topic, choose a style, and the platform generates a structured deck in minutes. It can rewrite content, adjust layouts, and add visuals, making it ideal when you need a refined presentation without starting from scratch.

    2. Canva

    Canva combines AI features with its extensive library of templates and design tools. Its AI can generate content, suggest layouts, create visuals, and improve existing slides. You can customize fonts, images, colors, and other elements, making it a practical choice for visually appealing presentations with creative flexibility.

    3. Microsoft PowerPoint (Copilot)

    Microsoft PowerPoint integrates AI through Copilot to simplify presentation creation. You can generate slides from prompts or existing documents, create content, and improve layouts. Since PowerPoint is widely used by businesses and students, its AI features speed up familiar workflows without requiring new software.

    4. Beautiful.ai

    Beautiful.ai focuses on automated design through intelligent templates. As you add or change content, the platform adjusts elements automatically, reducing manual formatting. It offers templates for business, marketing, education, and more, helping you create consistent, professional presentations with less effort.

    5. Tome

    Tome turns ideas and written prompts into visual presentations. It helps generate structures, written content, and visuals, which you can then edit to suit your needs. This tool is especially useful when you need to quickly develop a presentation but struggle with the initial structure and storytelling.

    6. SlidesAI

    SlidesAI converts written information into presentation slides, making it useful for students, professionals, and educators. Provide text or describe a topic, and the tool generates structured slides. Its integration with Google Slides lets you continue editing in a familiar environment without switching platforms.

    7. Plus AI

    Plus AI brings AI-powered presentation creation to tools like Google Slides and PowerPoint. It can generate presentations from prompts, rewrite content, and reorganize slides. This makes it a great choice for professionals who want AI assistance without changing their existing workflow, and it helps teams create and refine presentations faster.

  • Magisto Review: AI-Powered Video Editing Software – Features, Benefits, and Pricing

    Magisto Review: AI-Powered Video Editing Software – Features, Benefits, and Pricing

    Magisto is a video editing application powered by artificial intelligence (AI) that enables users to create professional-quality videos quickly and easily. Designed for novices and experts alike, the platform automates many aspects of video production while still offering manual editing options. Below is a comprehensive overview of Magisto, including its key features, benefits, challenges, and subscription details.

    General Information

    • Founded: 2009
    • Platform Support: Web, Android, iOS
    • Download: Click Here

    Main Features of Magisto

    • AI-driven video production, simplifying editing for beginners.
    • Automatic video generation within minutes.
    • Flexibility to manually edit AI-produced videos.
    • Cross-platform compatibility (web, Android, iOS).
    • Seamless publishing and sharing on social media platforms.
    • Theme customization, music library, text overlay, and transitions.
    • Cloud-based editing for easy access and collaboration.

    Benefits of Magisto

    • Makes video editing accessible to users with no prior experience.
    • Reduces production time dramatically thanks to AI automation.
    • Offers both automated and manual editing capabilities.
    • Works across multiple devices and operating systems.
    • Simplifies distribution to social media channels.

    Challenges of Magisto

    • The media import process may occasionally crash or experience issues.
    • Advanced editing features are locked behind paid subscriptions.
    • Free tier includes limited video duration and watermarked exports.
    • Manual editing tools are not as robust as dedicated professional editors.
    • Subscription cost may be prohibitive for infrequent users.

    Subscription Information

    Magisto offers both free and paid subscription plans. The free version provides basic AI-powered video editing but restricts video length and applies watermarks. Premium plans unlock longer videos, watermark removal, additional AI presets, stock media access, and extra Vimeo services depending on the chosen tier.

    Support Options

    Users can access a Help Center with articles, FAQs, tutorials, setup guides, and documentation. Customer support is available through online resources and email assistance for troubleshooting, subscription management, and technical guidance.

    Conclusion

    Magisto is a powerful AI video editing tool that lets content creators, marketers, and small businesses produce professional-looking videos automatically and quickly. Its combination of AI editing, customizable templates, cloud interface, and social media integration makes it a strong option. However, the need for a paid subscription to access advanced features and occasional import issues are notable drawbacks.

  • Pioneering Women in Tech: The Visionaries Who Shaped Computing, Spaceflight, and AI

    Pioneering Women in Tech: The Visionaries Who Shaped Computing, Spaceflight, and AI

    Throughout history, women have made groundbreaking contributions to technology, often overcoming significant barriers. From early programming to modern artificial intelligence, their work has laid the foundation for many of the innovations we rely on today. Here are seven women whose vision and determination changed the course of technology.

    Ada Lovelace: The First Computer Programmer

    Ada Lovelace is widely regarded as the world’s first computer programmer. In the 1840s, she wrote an algorithm for Charles Babbage’s Analytical Engine. Her notes also revealed her understanding that machines could handle more than just numbers. Her ideas arrived long before modern computers existed and helped shape the very concept of programming.

    Grace Hopper: Making Computers Accessible

    Grace Hopper played a major role in making computers easier to use. She helped develop COBOL, one of the earliest programming languages designed for business applications. Hopper also championed the idea of machine-independent programming languages. Her work made computing more practical and opened the field to a wider audience.

    Katherine Johnson: The Mathematician Behind NASA’s Missions

    Katherine Johnson was a mathematician whose calculations helped NASA send astronauts safely into space. She verified flight paths for missions including John Glenn’s historic orbital flight. Her work demonstrated the critical role of mathematics in space travel. Johnson later became an inspiration for women and minorities pursuing careers in STEM.

    Hedy Lamarr: From Hollywood to Wireless Communication

    Hedy Lamarr was not only a famous actress but also a brilliant inventor. During World War II, she and composer George Antheil developed a frequency-hopping communication system. This idea later became a foundation for technologies used in wireless communication. Her inventive work earned widespread recognition decades after it was created.

    Radia Perlman: The Mother of the Internet

    Radia Perlman transformed how computer networks communicate. She created the Spanning Tree Protocol, which enabled large networks to manage connections without creating endless loops. Her work became essential to Ethernet networking and helped make modern networks more reliable. Perlman’s contribution remains a key part of today’s connected world.

    Margaret Hamilton: Software Engineering for the Moon Landing

    Margaret Hamilton led the team that developed the onboard flight software for NASA’s Apollo missions. Her software was critical to the success of Apollo 11’s lunar landing. Hamilton also helped establish the discipline of software engineering and reliable computer systems. Her work showed how vital software could be to major missions.

    Fei-Fei Li: Advancing Artificial Intelligence

    Fei-Fei Li has been instrumental in the growth of modern artificial intelligence. She helped create ImageNet, a large visual database that became essential for training and testing image-recognition systems. Her research advanced computer vision and contributed to the rapid development of today’s AI technology.

  • How AI Detectors Actually Work: Methods, Accuracy, and Why You Shouldn’t Trust Them Completely

    How AI Detectors Actually Work: Methods, Accuracy, and Why You Shouldn’t Trust Them Completely

    AI detectors have become essential tools for educators, editors, and hiring managers who need to distinguish human-written text from machine-generated content. But how reliable are they really? This article explores the techniques behind AI detection, their real-world accuracy, and the critical limitations you need to understand.

    What AI Detectors Actually Measure

    AI detectors do not uncover a hidden signature embedded in text. Instead, they analyze statistical patterns—primarily perplexity and burstiness—and return a probability score, not a definitive verdict. Perplexity measures how predictable a sentence is to a language model; human writing tends to be less predictable. Burstiness captures the natural variation in sentence length. However, newer AI models have become better at mimicking these variations, making burstiness less reliable.

    Four Core Detection Techniques

    • Classifiers (e.g., GPTZero, Originality.ai): Trained on large datasets to recognize subtle patterns in word choice and structure.
    • Watermarking (e.g., Google DeepMind’s SynthID): Embeds a signal during generation, but only works if the AI system cooperates and editing can weaken it.
    • Provenance analysis: Examines document history, edit trails, and drafting behavior (e.g., Google Docs version history).
    • Text-based scanners (e.g., Turnitin’s AI detector): Analyze the submitted content directly without historical context.

    The Accuracy Problem

    No AI detector is 100% accurate. One widely cited study found that detectors flagged essays from non-native English speakers as AI-generated far more often than those from native speakers. Accuracy varies by dataset, AI model, and even the writer’s background. Paraphrasing or restructuring a passage can erase the original statistical fingerprint, causing many detectors to miss reworked AI content entirely.

    Why a Single Score Isn’t Enough

    Most real-world writing today involves some level of AI assistance. A single confidence score cannot fairly capture that blend. Institutions that treat detection scores as absolute proof risk penalizing honest authors while failing to catch cleverly rewritten machine text. The best approach is to use detection scores as a starting point, combined with contextual review, writing samples, and direct conversation.

    The Future of Trust in Writing

    As generation and detection technologies continue to evolve, the real challenge will shift from identifying machine text to redefining what trust means in written communication. Judgment, context, and human review will become more valuable than any single algorithmic score.

  • Meta Unveils Muse AI Agent: Automates Emails, Travel Bookings, and Daily Tasks with Secure Cloud VM

    Meta Unveils Muse AI Agent: Automates Emails, Travel Bookings, and Daily Tasks with Secure Cloud VM

    Meta has officially launched Muse, a personal AI agent designed to handle everyday chores like sending emails, booking travel, and completing multi-step tasks on behalf of users. The announcement was made on September 8, 2026, and the agent is now available in the United States through the Muse app and WhatsApp.

    Powered by Muse Spark, Meta’s most advanced model for agentic tasks, Muse operates as a conversational assistant that can go beyond simple chat. Users describe what they need—such as planning a dinner party or creating a grocery list from a saved Instagram recipe—and Muse builds a plan, opens a browser, fills in forms, and executes steps even after the user closes the app. It pauses to request approval before sending an email or making a purchase, ensuring the user remains in control at all times.

    Security and Privacy Features

    Security is a cornerstone of the Muse launch. The agent runs inside a Secure VM, a dedicated cloud computer isolated from other agents. User data and connected service credentials are stored in this environment. Users can choose which apps Muse can access, limit its actions, disconnect services, or instruct it to forget stored information entirely.

    Rollout and Future Plans

    Muse is currently limited to the US, with a broader rollout expected soon. Meta plans to integrate the assistant into its AI glasses lineup as the next major step. Most day-to-day functions will remain free, but the company has indicated that paid subscription plans will be introduced for users who want deeper access to advanced features. Exact dates for the glasses integration and subscription rollout have not been announced.

    Meta announced the launch on X, describing Muse as a personal AI agent that gets things done across every part of life. The company emphasized that the agent is built to help people complete real-world tasks with less effort, leveraging the power of Muse Spark.