Latest news with #collaborators
UAE Moments
2 days ago
- Entertainment
- UAE Moments
♌ Leo Daily Horoscope for July 3, 2025
Feeling like the main character today, Leo? You should. With the Sun lighting up your expressive side, today's energy pushes you to take the lead—but with grace. Whether you're chasing goals or love, the universe is giving you center stage. Just remember: your roar is powerful, but your heart is what truly steals the show. 💼 Career Your bold ideas are on fire, but slow your roll when pitching them. Collaborators may need a little more convincing. Give them time—they'll come around if you stay confident, not arrogant. 💘 Love You're glowing—and they're noticing. Whether you're single or taken, your energy is irresistible today. Just make sure it's not all about you. Listening can be just as attractive as talking (really). 🧠 Mental Health Your head's in the clouds with big dreams. That's great—but ground them with a little reflection. Journal. Meditate. Or call that wise friend who always brings you back to earth. 💪 Body You've got energy to burn, but don't overdo it. Channel your fire into a creative workout or dance session. Emotionally, things feel intense—journaling or talking things out with someone you trust can help you stay grounded.
Yahoo
23-06-2025
- Science
- Yahoo
Astronomy has a major data problem – simulating realistic images of the sky can help train algorithms
Professional astronomers don't make discoveries by looking through an eyepiece like you might with a backyard telescope. Instead, they collect digital images in massive cameras attached to large telescopes. Just as you might have an endless library of digital photos stored in your cellphone, many astronomers collect more photos than they would ever have the time to look at. Instead, astronomers like me look at some of the images, then build algorithms and later use computers to combine and analyze the rest. But how can we know that the algorithms we write will work, when we don't even have time to look at all the images? We can practice on some of the images, but one new way to build the best algorithms is to simulate some fake images as accurately as possible. With fake images, we can customize the exact properties of the objects in the image. That way, we can see if the algorithms we're training can uncover those properties correctly. My research group and collaborators have found that the best way to create fake but realistic astronomical images is to painstakingly simulate light and its interaction with everything it encounters. Light is composed of particles called photons, and we can simulate each photon. We wrote a publicly available code to do this called the photon simulator, or PhoSim. The goal of the PhoSim project is to create realistic fake images that help us understand where distortions in images from real telescopes come from. The fake images help us train programs that sort through images from real telescopes. And the results from studies using PhoSim can also help astronomers correct distortions and defects in their real telescope images. But first, why is there so much astronomy data in the first place? This is primarily due to the rise of dedicated survey telescopes. A survey telescope maps out a region on the sky rather than just pointing at specific objects. These observatories all have a large collecting area, a large field of view and a dedicated survey mode to collect as much light over a period of time as possible. Major surveys from the past two decades include the SDSS, Kepler, Blanco-DECam, Subaru HSC, TESS, ZTF and Euclid. The Vera Rubin Observatory in Chile has recently finished construction and will soon join those. Its survey begins soon after its official 'first look' event on June 23, 2025. It will have a particularly strong set of survey capabilities. The Rubin observatory can look at a region of the sky all at once that is several times larger than the full Moon, and it can survey the entire southern celestial hemisphere every few nights. A survey can shed light on practically every topic in astronomy. Some of the ambitious research questions include: making measurements about dark matter and dark energy, mapping the Milky Way's distribution of stars, finding asteroids in the solar system, building a three-dimensional map of galaxies in the universe, finding new planets outside the solar system and tracking millions of objects that change over time, including supernovas. All of these surveys create a massive data deluge. They generate tens of terabytes every night – that's millions to billions of pixels collected in seconds. In the extreme case of the Rubin observatory, if you spent all day long looking at images equivalent to the size of a 4K television screen for about one second each, you'd be looking at them 25 times too slow and you'd never keep up. At this rate, no individual human could ever look at all the images. But automated programs can process the data. Astronomers don't just survey an astronomical object like a planet, galaxy or supernova once, either. Often we measure the same object's size, shape, brightness and position in many different ways under many different conditions. But more measurements do come with more complications. For example, measurements taken under certain weather conditions or on one part of the camera may disagree with others at different locations or under different conditions. Astronomers can correct these errors – called systematics – with careful calibration or algorithms, but only if we understand the reason for the inconsistency between different measurements. That's where PhoSim comes in. Once corrected, we can use all the images and make more detailed measurements. To understand the origin of these systematics, we built PhoSim, which can simulate the propagation of light particles – photons – through the Earth's atmosphere and then into the telescope and camera. PhoSim simulates the atmosphere, including air turbulence, as well as distortions from the shape of the telescope's mirrors and the electrical properties of the sensors. The photons are propagated using a variety of physics that predict what photons do when they encounter the air and the telescope's mirrors and lenses. The simulation ends by collecting electrons that have been ejected by photons into a grid of pixels, to make an image. Representing the light as trillions of photons is computationally efficient and an application of the Monte Carlo method, which uses random sampling. Researchers used PhoSim to verify some aspects of the Rubin observatory's design and estimate how its images would look. The results are complex, but so far we've connected the variation in temperature across telescope mirrors directly to astigmatism – angular blurring – in the images. We've also studied how high-altitude turbulence in the atmosphere that can disturb light on its way to the telescope shifts the positions of stars and galaxies in the image and causes blurring patterns that correlate with the wind. We've demonstrated how the electric fields in telescope sensors – which are intended to be vertical – can get distorted and warp the images. Researchers can use these new results to correct their measurements and better take advantage of all the data that telescopes collect. Traditionally, astronomical analyses haven't worried about this level of detail, but the meticulous measurements with the current and future surveys will have to. Astronomers can make the most out of this deluge of data by using simulations to achieve a deeper level of understanding. This article is republished from The Conversation, a nonprofit, independent news organization bringing you facts and trustworthy analysis to help you make sense of our complex world. It was written by: John Peterson, Purdue University Read more: How the Hubble Space Telescope opened our eyes to the first galaxies of the universe Astronomy's 10-year wish list: Big money, bigger telescopes and the biggest questions in science Dark energy may have once been 'springier' than it is today − DESI cosmologists explain what their collaboration's new measurement says about the universe's history John Peterson does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
Geeky Gadgets
19-06-2025
- Business
- Geeky Gadgets
Learn How AI Agents Actually Work : Smarter Than You Think
What if machines could not only follow instructions but also think, adapt, and make decisions on their own? This isn't science fiction—it's the reality of AI agents, a new evolution in automation. Unlike traditional workflows that rigidly follow predefined steps, AI agents bring intelligence and flexibility to the table. Imagine a system that not only schedules your meetings but also reschedules them dynamically based on shifting priorities or external factors. This level of autonomy is reshaping industries, from content creation to customer support, and it's happening faster than most people realize. The AI Advantage uncover the mechanics behind how AI agents work and what makes them so fantastic. We'll explore their core components, how they differ from traditional automations, and the secret to their adaptability. Whether you're curious about building your own AI-driven systems or simply want to understand the technology powering modern workflows, this guide will demystify the process. By the end, you'll see why agents are more than just tools—they're collaborators in tackling complex challenges. The question is: how will you harness their potential? AI Agents vs Automations What Are Automations and AI Agents? Automations and AI agents serve distinct purposes, and recognizing their differences is critical for implementing the right solution in various scenarios. Automations: These are predefined workflows or pipelines that execute a fixed sequence of triggers and actions to achieve specific outcomes. For example, an automation might save email attachments to a designated cloud folder whenever a new email arrives. Automations are deterministic, meaning their behavior is predictable and consistent. These are predefined workflows or pipelines that execute a fixed sequence of triggers and actions to achieve specific outcomes. For example, an automation might save email attachments to a designated cloud folder whenever a new email arrives. Automations are deterministic, meaning their behavior is predictable and consistent. AI Agents: In contrast, AI agents are dynamic and adaptive. They incorporate capabilities such as reasoning, planning, memory, and autonomy to make decisions in real time. This enables them to handle more nuanced and complex tasks, such as responding to user feedback or adapting to changing contexts. The key distinction lies in adaptability: while automations follow rigid, predefined paths, AI agents adjust their actions based on the situation, making them more versatile for dynamic environments. Core Components of Workflows Workflows form the foundation of many automated systems, providing a structured approach to task execution. They consist of three primary components: Input: The data or event that initiates the workflow, such as a user query, a system notification, or an external trigger. The data or event that initiates the workflow, such as a user query, a system notification, or an external trigger. Processing Steps: The actions performed on the input, which may involve AI models like OpenAI's GPT to analyze data, generate responses, or perform calculations. The actions performed on the input, which may involve AI models like OpenAI's GPT to analyze data, generate responses, or perform calculations. Output: The final result of the workflow, such as a completed task, a generated report, or an actionable recommendation. To enhance workflows, integrating knowledge bases can provide dynamic context. For instance, web scraping can supply real-time information, while document uploads can offer domain-specific data. A practical example might involve a content creation workflow that uses AI to generate articles based on the latest industry trends. How AI Agents Actually Work in 2025 Watch this video on YouTube. Unlock more potential in AI agents by reading previous articles we have written. How AI Agents Build on Workflows AI agents elevate workflows by introducing intelligence and adaptability. They combine workflows, tools, and knowledge bases to perform tasks dynamically and respond to evolving circumstances. For example, an AI agent designed for content creation might: Use web scraping to gather up-to-date information on a specific topic. Generate a draft using an AI model like GPT, making sure relevance and accuracy. Send the completed draft to a recipient or publish it via an integrated platform. Unlike static workflows, AI agents can adjust their behavior based on user feedback, changing goals, or new data. This makes them particularly effective for tasks requiring flexibility, decision-making, and real-time adaptability. Real-World Applications of AI Agents AI agents are highly versatile and can be applied across a wide range of industries and use cases. Here are some practical examples: Content Creation: AI agents can generate articles, reports, or marketing materials by combining workflows with tools like web scraping and AI models. AI agents can generate articles, reports, or marketing materials by combining workflows with tools like web scraping and AI models. Programming Assistance: Developers can use AI agents to suggest code snippets, debug errors, or automate repetitive coding tasks. Developers can use AI agents to suggest code snippets, debug errors, or automate repetitive coding tasks. Research and Analysis: AI agents can gather, analyze, and summarize large datasets to provide actionable insights for decision-making. AI agents can gather, analyze, and summarize large datasets to provide actionable insights for decision-making. Customer Support: AI agents can handle customer inquiries, provide personalized responses, and escalate complex issues to human agents when necessary. AI agents can handle customer inquiries, provide personalized responses, and escalate complex issues to human agents when necessary. Task Automation: From scheduling meetings to managing inventory, AI agents can streamline operations and reduce manual effort. These applications illustrate how AI agents can enhance productivity, streamline processes, and tackle complex challenges across diverse domains. Workflows vs Agents: Key Differences The primary difference between workflows and AI agents lies in their adaptability and scope of application: Workflows: These follow fixed, deterministic paths with predictable outcomes, making them ideal for routine and repetitive tasks. These follow fixed, deterministic paths with predictable outcomes, making them ideal for routine and repetitive tasks. AI Agents: These are dynamic systems capable of making decisions and adjusting their actions based on context, user input, or changing objectives. This adaptability allows AI agents to handle tasks that are too complex or variable for traditional workflows, making them a powerful tool for addressing modern challenges. How to Build AI Agents: A Step-by-Step Guide Creating effective AI agents requires a structured approach that builds on foundational elements. Here's a step-by-step guide: Step 1: Start with Prompts: Develop clear and precise prompts to guide AI models like GPT in generating accurate and relevant outputs. These prompts serve as the foundation for effective communication with the AI. Develop clear and precise prompts to guide AI models like GPT in generating accurate and relevant outputs. These prompts serve as the foundation for effective communication with the AI. Step 2: Build Workflows: Design workflows that automate specific tasks by integrating triggers, actions, and outputs. This step ensures a structured approach to task execution. Design workflows that automate specific tasks by integrating triggers, actions, and outputs. This step ensures a structured approach to task execution. Step 3: Create AI Agents: Combine workflows, tools, and knowledge bases to build agents capable of dynamic decision-making and handling complex tasks. This integration enables the agent to adapt to changing circumstances and user needs. By following this progression, you can scale from simple automations to advanced AI-driven systems that address a wide range of challenges. Tools for Building AI Agents: Vector Shift Platforms like Vector Shift provide a comprehensive environment for designing and managing AI agents. Key features include: Integration with Knowledge Bases: Incorporate tools like web scraping and document uploads to provide dynamic, real-time context for tasks. Incorporate tools like web scraping and document uploads to provide dynamic, real-time context for tasks. Support for AI Models: Use advanced AI models like GPT to process data, generate content, and perform complex analyses. Use advanced AI models like GPT to process data, generate content, and perform complex analyses. Connectivity with External Tools: Seamlessly integrate with external platforms, such as Google search, email services, or project management tools, to enhance task execution. These platforms simplify the process of building and deploying agents, making them accessible even to users with limited technical expertise. By using such tools, you can focus on designing intelligent systems that deliver tangible results. Media Credit: The AI Advantage Filed Under: AI, Guides 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.
CNN
16-06-2025
- Politics
- CNN
Iran's Mossad paranoia grows, amid fears of Israeli spies wearing ‘masks, hats and sunglasses'
Iran has arrested dozens of people on suspicion of spying as fears grow in the Islamic Republic over the extent of its infiltration by Israel's Mossad intelligence service. Since Israeli strikes began Friday, 28 people in the capital have been arrested and accused of spying for Israel, while on Monday, one man arrested on that charge two years ago was hanged in what appeared to be a message to any would-be collaborator. The Iranian regime has also arrested scores of people across the country for allegedly sharing articles online 'in support of the Zionist regime' – accusing them of disrupting the 'psychological security of society' – including 60 people in Isfahan, where Israel claims to have targeted a nuclear site. The wave of arrests comes as Tehran reels from the revelation that Mossad operatives smuggled weapons into Iran before Israel's unprecedented attack and used them to target the country from within. So heightened have Iranian suspicions become since then that its Intelligence Ministry has been asking the public to report suspicious activity and issuing guidance on how to spot collaborators. One statement from the ministry urges people to be wary of strangers wearing masks or goggles, driving pickup trucks and carrying large bags or filming around military, industrial, or residential areas. Elsewhere, a poster published by the state-affiliated Nour News – which is close to Iran's security apparatus – singled out for suspicion people who wear 'masks, hats, and sunglasses, even at night' and those who receive 'frequent package deliveries by courier.' The poster asks people to report 'unusual sounds from inside the house, such as screaming, the sound of metal equipment, continuous banging' and 'houses with curtains drawn even during the day.' Another poster, attributed to the police and published on state media, advised landlords who had recently rented their homes to notify the police immediately. Meanwhile, journalists in Iran have told CNN they are prohibited from taking pictures on the street. The fears of Israeli penetration only amplify the anxieties felt by the increasingly isolated leadership of the Islamic Republic, which has been rocked in recent years by anti-regime protests sparked by the death of a young woman in the custody of the country's so-called morality police. The same force used to crack down on those protests, the Basij (a paramilitary wing of Iran's Revolutionary Guard) has been deployed in night patrols to increase 'surveillance' in the wake of the Israeli infiltration, according to Iran's state-controlled media. In a video statement Monday, Iran's chief of police Ahmad-Reza Radan urged 'traitors' to come forward, suggesting those who realized they had been 'deceived by the enemy' might receive more lenient treatment and be 'honored' by Iran – while those who were caught would be 'taught a lesson that the Zionist enemy is being given now.' The head of Iran's judiciary Gholam-Hossein Mohseni-Eje'i called for 'swift' punishment of those accused of collaborating with Israel. 'Let's say we have apprehended someone who is collaborating with (Israel), this matter under these war-like conditions … must be prosecuted swiftly and punished swiftly,' he said. The Iranian regime's rising paranoia comes as more details emerge of the Mossad operation that smuggled weapons into Iran ahead of the first strikes on Friday. According to Israeli officials, operatives established a base for launching explosive drones inside Iran, then used those drones to target missile launchers near Tehran. Precision weapons were also smuggled in, they say, and used to target surface-to-air missile systems, clearing the way for Israel's Air Force to carry out more than 100 strikes with upward of 200 aircraft in the early hours of Friday local time. Intelligence gathered by the Mossad in Iran also reportedly gave Israel's Air Force the ability to target senior Iranian commanders and scientists. Since then, according to Iranian media outlets, the government has seized equipment allegedly used during the Israeli operation – including 200 kilograms of explosives, several suicide drones, launchers and equipment used to manufacture the drones – in the city of Rey in Tehran province. A video published by the state-affiliated Fars News Agency showed a building with drone parts and other equipment.
Geeky Gadgets
11-06-2025
- Business
- Geeky Gadgets
Claude 4 Models & Claude Code Fundamentals : What You Need to Know
What if artificial intelligence could not only understand your most complex questions but also respond with the precision and nuance of a human expert? Enter the Claude 4 models—a new leap in AI technology that's redefining what's possible in natural language processing. With their ability to generate context-aware, human-like text and tackle intricate tasks across industries, these models are more than just tools; they're collaborators. Whether summarizing dense reports in seconds or crafting personalized learning experiences, Claude 4 models promise to transform the way we interact with technology. But what makes them so uniquely powerful? The answer lies in their sophisticated architecture and innovative design principles, which balance innovative advancements with practical adaptability. In this video, Tina Huang unpacks the core fundamentals of Claude 4 models, from their fantastic Claude Code architecture to the technical innovations that set them apart. You'll discover how these models achieve unparalleled contextual understanding, adapt seamlessly to diverse applications, and integrate effortlessly into existing workflows. Whether you're a tech enthusiast curious about the latest in AI or a professional exploring practical applications for your industry, this guide offers insights that are both accessible and deeply informative. As we explore the inner workings and real-world potential of Claude 4, one question lingers: how far can this technology take us in bridging the gap between human ingenuity and machine intelligence? Overview of Claude 4 Key Features of Claude 4 Models Claude 4 models distinguish themselves through their ability to process and generate human-like text with remarkable accuracy. Their design emphasizes advanced contextual understanding and adaptability, allowing them to tackle diverse tasks and complex queries effectively. Some of the standout features include: Contextual Understanding: These models excel at interpreting nuanced language patterns, making sure responses are both relevant and precise. These models excel at interpreting nuanced language patterns, making sure responses are both relevant and precise. Task Versatility: From summarizing extensive documents to generating creative content, Claude 4 models adapt seamlessly to a variety of use cases. From summarizing extensive documents to generating creative content, Claude 4 models adapt seamlessly to a variety of use cases. Industry Applicability: Their capabilities extend across sectors such as healthcare, finance, education, and more, showcasing their broad utility. For example, these models can summarize dense reports in seconds, craft engaging marketing content, or answer intricate technical questions with clarity. Their ability to adapt to specific domains highlights their versatility and practical value. The Claude Code Architecture At the core of Claude 4 models lies the Claude Code architecture, a robust framework that combines scalability with modularity. This architecture is built on transformer-based neural networks, making sure efficient processing of large datasets while maintaining high accuracy. The key architectural principles include: Modularity: The architecture allows for seamless updates and enhancements without disrupting existing functionalities, making sure long-term adaptability. The architecture allows for seamless updates and enhancements without disrupting existing functionalities, making sure long-term adaptability. Pre-Training and Fine-Tuning: Pre-training exposes the model to vast datasets to establish a foundational understanding of language, while fine-tuning tailors it to specific tasks or industries. Pre-training exposes the model to vast datasets to establish a foundational understanding of language, while fine-tuning tailors it to specific tasks or industries. Scalability: The design supports integration into diverse systems, making sure consistent performance across varying workloads and environments. This dual approach of pre-training and fine-tuning ensures that the models are both flexible and highly specialized, meeting the unique needs of different users and industries. The Claude Code architecture is a testament to the balance between innovation and practicality in AI design. Claude 4 Models & Claude Code Fundamentals Overview Watch this video on YouTube. Here are more detailed guides and articles that you may find helpful on Claude 4 models. Technical Advancements in Claude 4 Claude 4 models introduce several technical innovations that enhance their performance, efficiency, and usability. These advancements include: Improved Computational Efficiency: Optimized algorithms reduce processing time while maintaining high levels of accuracy, making the models faster and more reliable. Optimized algorithms reduce processing time while maintaining high levels of accuracy, making the models faster and more reliable. Enhanced Scalability: The models can handle larger datasets and more complex queries without compromising performance, making sure robust functionality in demanding scenarios. The models can handle larger datasets and more complex queries without compromising performance, making sure robust functionality in demanding scenarios. Refined Contextual Comprehension: Advanced NLP techniques improve the models' ability to understand and respond to nuanced inputs, making interactions more natural and intuitive. Advanced NLP techniques improve the models' ability to understand and respond to nuanced inputs, making interactions more natural and intuitive. Error-Handling Mechanisms: Real-time error detection and correction ensure reliable outputs, even in challenging or ambiguous situations. Real-time error detection and correction ensure reliable outputs, even in challenging or ambiguous situations. Reinforcement Learning: The models continuously improve by learning from user feedback, adapting to evolving needs and preferences over time. These innovations make Claude 4 models not only more efficient but also more adaptable to dynamic environments. Their ability to evolve ensures they remain relevant as user requirements and technological landscapes change. Watch this video on YouTube. Seamless System Integration A defining strength of Claude 4 models is their ease of integration into existing systems. Designed for compatibility, these models work seamlessly with APIs and cloud-based solutions, minimizing the need for extensive reconfiguration. The benefits of this seamless integration include: Ease of Deployment: Organizations can quickly incorporate the models into their workflows with minimal effort, reducing implementation time and costs. Organizations can quickly incorporate the models into their workflows with minimal effort, reducing implementation time and costs. Platform Compatibility: Claude 4 models are designed to function across various platforms, making sure broad accessibility and usability. Claude 4 models are designed to function across various platforms, making sure broad accessibility and usability. Customizable Applications: Businesses can tailor the models to specific use cases, enhancing operational efficiency and delivering targeted solutions. For instance, customer service platforms can use Claude 4 models to provide instant, accurate responses to inquiries, while educational tools can use their capabilities to create personalized learning experiences. This flexibility makes them a valuable asset across industries. Applications Across Industries The versatility of Claude 4 models is evident in their wide-ranging applications across multiple sectors. Some notable use cases include: Healthcare: Assisting in diagnosing medical conditions by analyzing patient data and providing evidence-based recommendations, improving patient outcomes. Assisting in diagnosing medical conditions by analyzing patient data and providing evidence-based recommendations, improving patient outcomes. Finance: Automating tasks such as fraud detection, risk assessment, and financial forecasting, enhancing decision-making processes. Automating tasks such as fraud detection, risk assessment, and financial forecasting, enhancing decision-making processes. Education: Developing personalized learning tools, creating interactive educational content, and improving the overall learning experience for students. Developing personalized learning tools, creating interactive educational content, and improving the overall learning experience for students. Creative Industries: Generating content, designing marketing strategies, and aiding in product development, fostering innovation and creativity. Their ability to replicate human creativity and adapt to specialized tasks makes them indispensable in both technical and creative fields. By addressing specific challenges and streamlining workflows, Claude 4 models empower organizations to achieve greater efficiency and innovation. Media Credit: Tina Huang 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.
