Tag: artificial intelligence

  • How AI-Generated Songs Are Hijacking Real Artist Profiles on Spotify and Other Streaming Platforms

    How AI-Generated Songs Are Hijacking Real Artist Profiles on Spotify and Other Streaming Platforms

    Scammers are exploiting a loophole in digital music distribution to place AI-generated songs on real artists’ profiles. These fake tracks can appear on Spotify, Apple Music, Tidal, and Amazon Music as legitimate releases. The scheme allows bad actors to borrow established artist identities, attract streams, and potentially collect royalties without permission.

    How the Scheme Works

    The process is deceptively simple. A scammer creates a song using an AI music generator such as Suno or Udio, then generates accompanying artwork. They upload both through a music distributor like DistroKid, falsely claiming to represent the artist. Distribution systems then place the track widely across streaming platforms.

    A Real-World Test

    A test conducted by 404 Media demonstrated the problem using Brooklyn band Lathe of Heaven. A fake AI-generated track appeared on the band’s Spotify page and also reached Apple Music, Tidal, and Amazon Music. The band had neither created nor approved the release, highlighting how easily an artist profile can be misused.

    Smaller Artists Face Greater Exposure

    Smaller and inactive artist profiles are particularly vulnerable. Spotify told 404 Media that profiles without active management can be especially easily exploited. Deceased musicians can also be targeted because their catalogs may receive less oversight. For artists, unauthorized releases can create confusion, damage reputation, and divert revenue from legitimate work.

    AI Increases the Scale

    The scale of the problem is becoming harder to control because generative AI makes production fast and cheap. Music group Odette Child reported finding more than 300 AI-generated songs impersonating major artists, celebrities, and deceased musicians. The underlying distribution loophole existed before generative AI became widespread, but AI can rapidly increase the volume of fake uploads.

    Spotify Is Adding Safeguards

    Spotify has been developing tools to give artists more control over releases associated with their profiles. Its Artist Profile Protection feature is in beta, and the company has introduced a Verified by Spotify badge designed to provide authenticity signals. Spotify says it is investing in detection and prevention measures.

    A Bigger Streaming Challenge

    This episode highlights a broader challenge for streaming platforms: verifying who actually controls an artist identity before accepting new music. As AI lowers production barriers, identity protection becomes as important as content moderation. For listeners, unexpected releases from artists may now require more scrutiny, while platforms face pressure to strengthen distribution checks.

  • 10 Real-World Machine Learning Use Cases Reshaping Industries

    10 Real-World Machine Learning Use Cases Reshaping Industries

    Machine learning (ML) has evolved from a niche technology into a core enabler across industries. From fraud alerts that block suspicious payments in seconds to medical scans that assist in early disease detection, ML identifies patterns in data and uses them to make predictions, decisions, or take actions. Today’s systems go beyond recommendation engines, embedding AI into real business processes. Here are ten practical ways machine learning is applied in the real world.

    1. Recommendations and Personalization

    Streaming services, online retailers, music apps, and social platforms use ML to predict what users may want next. By analyzing past choices and viewing history, systems leverage deep learning and embeddings to deliver personalized experiences. Personalization is now integrated into larger AI ecosystems.

    2. Fraud Detection and Financial Risk

    Banks, payment processors, and insurers deploy ML to detect unusual activity. Models examine account history, location, device data, and transaction timing. Anomalous patterns can trigger reviews or block transactions. ML also supports credit risk assessment and insurance fraud investigation.

    3. Healthcare and Medical Diagnosis

    ML models analyze medical images, patient records, and lab results. Hospitals use ML for disease risk prediction, patient triage, image analysis, drug discovery, and clinical decision support. For instance, India’s eSanjeevani platform applies AI for retinal checks and differential diagnosis, while PM-JAY uses ML for fraud detection.

    4. Predictive Maintenance in Industry

    Factory sensors capture temperature, vibration, pressure, energy use, and sound. ML learns normal operating patterns and flags potential failures before they occur. This reduces unplanned downtime in aircraft engines, vehicles, power systems, data centers, and robotics.

    5. Computer Vision and Quality Control

    Computer vision enables machines to interpret images and video. Manufacturers use ML to detect product defects on assembly lines. Healthcare systems inspect medical scans. Multimodal AI systems combine visual data with text and other inputs for richer analysis.

    6. Search, Speech, and Language

    Search engines, speech recognition, translation services, email filters, virtual assistants, and chatbots rely on ML. Models classify text, convert speech to text, rank search results, and generate summaries. Large language models now process text, images, audio, and video. Stanford’s 2026 AI Index highlights rapid gains in multimodal capabilities and real-world task performance.

    7. Forecasts and Supply Chains

    Retailers apply ML to estimate demand using historical sales, seasonality, promotions, pricing, weather, and other signals. These forecasts improve inventory management, delivery routing, energy demand planning, and pricing decisions.

    8. Cybersecurity and Threat Detection

    Cybersecurity teams face massive volumes of logs, network events, and device signals. ML identifies patterns that deviate from normal behavior, helping detect malware, phishing emails, suspicious logins, account compromises, and potential data breaches.

    9. Autonomous Systems and Robotics

    ML enables robots, drones, autonomous vehicles, and industrial systems to interpret sensor data and choose actions. Self-driving cars combine computer vision, sensor fusion, prediction, and route planning. Robots navigate large warehouses to move goods. Gartner lists physical AI as a major data and analytics trend.

    10. Software Development and AI Agents

    Software teams use ML for code generation, code review, bug fixing, testing, documentation, and issue analysis. Advanced AI agents connect models with development tools to automate multi-step workflows. Applied’s April 2026 report studied nearly 200 real-world AI use cases across 200+ companies, 25 industries, and 15 business functions—operations accounted for 38.6% and software development for 21.4%.

    The Shift from Prediction to Action

    The most significant change in ML is the move from isolated predictions to integrated actions. Modern systems link predictions with tools, data, and business processes. Gartner notes organizations are adopting AI-first business models. Deloitte reports that worker access to sanctioned AI tools rose 50% in 2025, and the number of companies with at least 40% of AI projects in production is expected to double within six months. This shift demands reliable data, clear controls, and rigorous evaluation. Successful AI initiatives may invest up to four times more in data and analytics foundations than less successful ones. ML now acts as a core layer shaping decisions, operations, software, and physical tasks across numerous industries.

  • Google Gemini: A Complete Guide to Features, AI Models, and Everyday Uses

    Google Gemini: A Complete Guide to Features, AI Models, and Everyday Uses

    Google Gemini has evolved into one of the most advanced artificial intelligence platforms globally. Developed by Google DeepMind, Gemini is now deeply integrated across Google’s ecosystem, enabling it to understand and process text, images, audio, video, and code within a single system. Beyond simple Q&A, Gemini powers smart search, voice conversations, writing assistance, coding help, image creation, document analysis, and real-time support across Google apps. The latest models also handle complex, multi-step tasks that require planning, reasoning, and tool use, making Gemini a comprehensive AI assistant for work, study, creativity, and daily life.

    How Google Gemini Works

    Gemini relies on multimodal artificial intelligence, allowing one model to simultaneously understand different types of information. It can read text, recognize images, interpret spoken words, analyze videos, and write code without needing separate tools. For example, users can upload a photo, ask a question about it, and receive a detailed answer. Gemini also handles voice requests, maintains context in conversations, and responds naturally. Google designed Gemini for agentic tasks—completing multi-step jobs rather than handling just one simple request. It can organize information, create plans, review documents, write content, and continue tasks across several steps.

    Latest Google Gemini AI Models

    Google offers several Gemini models tailored for different tasks, balancing speed, reasoning, creativity, and real-time interactions:

    • Gemini 3.8 Flash – Google’s flagship model for fast and complex AI tasks, handling writing, coding, reasoning, and large workloads with improved speed and lower computing cost.
    • Gemini 3.1 Pro – Focuses on advanced reasoning and creative work, supporting detailed research, long documents, software development, and difficult problem-solving.
    • Gemini 3.1 Deep Think – Built for science, engineering, and research, this model excels at complex calculations, technical analysis, and higher-level reasoning.
    • Gemini 3.8 Live – Enables natural voice conversations in the Gemini app, understanding spoken language, visual context, and real-time interactions.
    • Gemini 3.8 Live Extended Thinking – Adds deeper reasoning during voice conversations, working through harder questions while the conversation continues.
    • Gemini 3.5 Flash-Lite – A lightweight option for high-volume tasks requiring quick answers and efficient performance.

    Google also offers dedicated audio and image models for speech recognition, translation, image creation, editing, and visual understanding.

    Top Features of Google Gemini

    Gemini combines several AI features into one platform. It can write emails, blog posts, summaries, reports, social media captions, and creative content in simple or professional tones. It understands documents such as PDFs, spreadsheets, presentations, and text files, extracting important information, summarizing long reports, and explaining complex topics. The image feature describes photos, identifies objects, explains charts, solves visual questions, and edits or generates images with text prompts. Voice conversations with Gemini Live feel natural—the assistant responds in real time, remembers context, and answers follow-up questions without repeating earlier details. Gemini also supports coding across many programming languages, explaining code, fixing errors, writing functions, and helping developers complete larger projects.

    Everyday Uses of Google Gemini

    Gemini can be applied in practical ways beyond simple chatbot assistance:

    • Students can use Gemini for homework help, pop quizzes, study organization, math assistance, scientific explanations, language learning, and research summaries.
    • Business professionals can rely on Gemini for meeting notes, email drafts, presentations, reports, project plans, brainstorming, and document review.
    • Writers and artists can use Gemini for article outlines, content ideas, script creation, marketing copy, product descriptions, and creative storytelling, along with grammar checking.
    • Travelers can get suggestions for itineraries, activities, restaurants, and destination comparisons.
    • Everyday tasks include cooking recipes, fitness plans, shopping and budgeting ideas, event planning, and organization.

    Gemini Across Google Apps

    Google has integrated Gemini with many popular services, reducing the need to switch between apps. In Gmail, Gemini drafts emails, summarizes long conversations, and suggests replies. In Google Docs, it writes and edits content, changes tone, and creates summaries. In Google Sheets, it explains formulas, analyzes data, creates tables, and answers questions. In Google Slides, it creates presentations, writes speaker notes, and generates slide content. Google Search also uses Gemini for AI-powered answers that combine information from multiple sources into organized responses.

    Gemini Live: Voice AI That Feels Natural

    Gemini Live is one of Google’s biggest AI updates, supporting continuous voice conversations rather than short Q&A sessions. Users can talk naturally, interrupt, ask follow-up questions, and even show objects through the phone camera. Gemini understands both speech and visual context during the same conversation. The latest Gemini 3.8 Live models also support deeper reasoning for difficult questions and more advanced conversations across diverse topics.

    Image, Audio, and Video Intelligence

    Gemini goes beyond text with powerful multimedia abilities. The image system creates original artwork, edits existing pictures, removes objects, changes backgrounds, and generates illustrations from text prompts. Audio models support speech recognition, transcription, translation, and voice understanding across multiple languages. Gemini also analyzes videos—identifying important moments, understanding scenes, answering questions about content, and finding specific events in long recordings. Google states that its latest video system uses fewer computing tokens while improving accuracy for many analysis tasks.

    Gemini for Coding and Developers

    Google has expanded Gemini for software developers with advanced coding tools. Gemini writes code in languages such as Python, JavaScript, Java, C++, Go, and SQL. It explains programming concepts, reviews code quality, fixes bugs, and suggests improvements. The latest models also support agentic coding tasks, handling larger development workflows instead of isolated code snippets. Developers can ask Gemini to build features, organize files, explain repositories, and solve technical issues across multiple steps.

    Google Gemini on Mobile and Desktop

    Gemini works across Android phones, iPhones, tablets, web browsers, and desktop computers. On Android, Gemini serves as a smart assistant for voice commands, searches, navigation, reminders, and app support. Google has also launched a dedicated Gemini app for Windows, offering quick access through keyboard shortcuts and supporting voice conversations, writing help, research, and productivity tools.

    What Makes Google Gemini Different

    Google Gemini stands out by combining text, images, audio, video, code, and reasoning into one AI platform instead of separate systems for each task. The latest family focuses on long conversations, complex planning, advanced coding, document understanding, visual analysis, and deeper reasoning across many tasks. Strong integration with Google Search, Gmail, Docs, Sheets, Slides, Android, and Windows expands Gemini beyond a standalone chatbot. It now serves as a complete AI ecosystem supporting learning, work, creativity, communication, and everyday productivity through one connected platform.

  • How AI Agents Will Reshape Everyday Life: A Glimpse Into the AI-First Future

    How AI Agents Will Reshape Everyday Life: A Glimpse Into the AI-First Future

    Artificial intelligence is moving beyond chatbots and recommendation engines. In an AI-first world, intelligent systems become the default layer of daily life — helping people set goals while AI agents quietly handle tasks across apps, devices, and digital services.

    Your Personal AI Agent Could Run Your Day

    Imagine a personal AI assistant that manages your calendar, books travel, pays bills, plans meals, tracks spending, and handles everyday errands. Instead of juggling multiple apps and reminders, you simply express an intention and your AI agent coordinates the rest.

    AI Could Become a Digital Coworker

    In the workplace, AI agents are expected to manage multi-step workflows such as research, report generation, coding, and customer support. That shift lets people focus on higher-value activities: making decisions, exercising judgment, and overseeing outcomes rather than performing routine execution.

    Learning Could Become Fully Personalized

    Education is another area poised for transformation. AI tutors could explain difficult concepts, generate practice questions, and adapt lessons to each student’s pace, level, and learning goals. This kind of personalization could make education more accessible and effective for learners of all ages.

    Healthcare Could Become More Proactive

    AI systems could combine wearable data, medical records, and broader health information to flag potential risks, track medications, and schedule follow-ups automatically. Instead of reacting to symptoms, patients and providers could work together to prevent problems before they escalate.

    Smart Homes Could Anticipate Your Needs

    Connected homes powered by AI could adjust energy use, control appliances, manage shopping lists, and optimize household routines based on daily habits and preferences. The home becomes an active participant in comfort and efficiency rather than a collection of manual devices.

    AI Could Change Jobs and Skills

    As agents take on more routine work, human roles will shift toward strategy, creativity, critical thinking, and reviewing AI-generated results. Microsoft reports that AI users in India are placing strong emphasis on human judgment and quality control — a sign that oversight skills will become increasingly valuable.

    More AI Access Also Means More Risks

    AI-first systems require access to sensitive data and digital accounts. That raises serious concerns about privacy loss, incorrect decisions, biased outputs, excessive permissions, and job disruption. Building trust will require careful governance, transparency, and safeguards.

    The most likely future is not one where AI replaces people, but one where daily life becomes human-led and AI-executed. The biggest shift will be moving from doing every task manually to setting goals, checking AI decisions, and retaining control over the choices that matter most.

  • ChatGPT Fact-Checking: 8 Ways to Verify AI Responses Are Accurate

    ChatGPT Fact-Checking: 8 Ways to Verify AI Responses Are Accurate

    ChatGPT is a powerful assistant, but it doesn’t always get everything right. Whether you are using it for research, work, or everyday questions, it pays to verify the information before relying on it. Here are eight practical strategies to help you determine whether a ChatGPT response is accurate and trustworthy.

    1. Review the Sources

    Ask ChatGPT to point you to the sources behind its answer. Reliable responses typically draw from official documents, peer-reviewed research, established publications, or reputable websites. Checking those sources can reveal unsupported claims, outdated information, or details that need further confirmation before you accept the response.

    2. Verify Key Facts Independently

    Check important names, dates, numbers, statistics, quotations, and events against trusted external references such as government websites, official company pages, academic journals, or major news organizations. Independent verification ensures that the most critical details are correct and not merely plausible-sounding.

    3. Cross-Compare with Multiple Sources

    Don’t rely on a single AI answer. Compare ChatGPT’s response with information from several independent and credible sources. Agreement among reputable sources boosts your confidence, while contradictions signal that the topic may need deeper research or that parts of the answer could be off.

    4. Check the Publication Date

    AI responses can contain information that was once accurate but has changed. Look for the publication or update date, especially for fast-moving areas like technology, prices, laws, sports, politics, and current events. A fact that was true last year may no longer hold today, so date verification is essential for time-sensitive topics.

    5. Ask for Evidence and Reasoning

    Ask ChatGPT to explain how it reached its conclusion and to identify the supporting evidence. A follow-up question can expose weak reasoning, missing context, hidden assumptions, or claims that require additional verification before you trust the original answer.

    6. Test Specific Claims Individually

    Break a long answer into individual claims and verify each important statement separately. This approach helps you spot situations where most of the response is correct but one statistic, date, quotation, technical detail, or conclusion is wrong. Isolating claims makes those errors far easier to catch.

    7. Rely on Expert Sources for Specialized Topics

    For medical, legal, financial, scientific, or technical questions, compare ChatGPT’s answer with guidance from qualified professionals and authoritative organizations. Experts provide specialized context and nuance that a general AI response may overlook or explain incorrectly.

    8. Watch for Overconfidence

    Be cautious when ChatGPT uses confident language without providing enough evidence or context. Treat precise claims with extra skepticism when verification is difficult, and avoid assuming that a confident answer automatically means the information is correct. Confidence is not proof of accuracy—keep questioning until you have solid confirmation.

    By applying these eight checks, you can use ChatGPT more effectively while reducing the risk of spreading misinformation. Remember that AI is a useful starting point, but it’s not the final authority.

  • OpenAI Launches Astra for Law: GPT-6 Powered Legal Research and Writing Platform

    OpenAI Launches Astra for Law: GPT-6 Powered Legal Research and Writing Platform

    On September 17, 2026, OpenAI announced Astra for Law, a new platform designed specifically for law firms and legal technology companies building AI-powered workflows. The offering is built on GPT-6 Astra, OpenAI’s most advanced model to date, and combines a comprehensive legal search index with specialized tools for legal research and writing.

    Comprehensive Legal Search Capabilities

    Astra for Law features a legal search index covering over 230 million URLs, including U.S. case law, statutes, regulations, court rules, and administrative decisions. OpenAI partnered with Free Law Project to integrate its CourtListener case-law collection, enabling users to find relevant authorities, locate specific passages, and understand their application to specific legal questions.

    Performance Benchmarks

    According to OpenAI, Astra for Law achieved a score of 54% on Vals AI’s Legal Research Bench, significantly outperforming GPT-6 Astra with standard web search, which scored 38.7%. The platform also found 24% more reference cases on case-law-focused queries and retrieved up to 54% more relevant passages from correct court opinions in audited tests.

    Legal Analysis and Writing Tools

    The platform includes custom instructions for legal analysis and writing. These tools assist in applying research to a client’s facts, developing arguments or deal terms, identifying weaknesses, and accounting for uncertainty. OpenAI states the system can distinguish a court’s holding from other observations and flag cases that could weaken an argument.

    Privacy Controls and Ecosystem Plugins

    For firms handling confidential client information, OpenAI introduced a Trusted Access Program for eligible law firms, providing Zero Data Retention on the API. ChatGPT Enterprise usage remains excluded from human review by default.

    Astra for Law also launches with 26 partner-built plugins covering legal practice and business operations, including integrations with iManage, Intapp, and Thomson Reuters (HighQ and CoCounsel Legal).

  • How Three Indian-Origin Hackers Used Anthropic’s Claude to Uncover OpenAI Security Flaws

    How Three Indian-Origin Hackers Used Anthropic’s Claude to Uncover OpenAI Security Flaws

    Three Indian-origin cybersecurity researchers—Harsh Jaiswal, Mohan Pedhapati, and Rahul Maini—demonstrated how Anthropic’s Claude AI could be used to exploit vulnerabilities in OpenAI’s systems. The researchers, working at cybersecurity startup Hacktron AI, gained access to OpenAI employee ChatGPT accounts and an internal code repository before responsibly disclosing the vulnerabilities. OpenAI later awarded the team a $6,500 bug bounty.

    Who Are the Researchers?

    The research team was led by Harsh Jaiswal, alongside Mohan Pedhapati and Rahul Maini. The trio began investigating security weaknesses at frontier AI companies as part of their work at Hacktron AI. Their research focused on vulnerabilities that could connect OpenAI’s community forum with employee accounts and internal services. The entire process, from initial discovery to demonstrating access to an internal repository, took less than 72 hours. The researchers also spent under $3,000 on AI tokens during the broader investigation.

    How Did Claude Help the Researchers?

    The researchers identified a remote-code-execution vulnerability involving the image-processing software used by OpenAI’s community forum, which runs on Discourse. Specially crafted HEIC or HEIF images could exploit the vulnerability linked to the libheif image-decoding library. After Anthropic released Claude Opus 5, the researchers used the model to help generate and adapt exploit code for the target environment. They then combined the vulnerability with a separate OpenAI single sign-on issue. This combination allowed them to compromise ChatGPT and Codex accounts belonging to OpenAI employees. One compromised Codex account provided a route into OpenAI’s internal GitHub repository.

    Access Without Inspecting Code

    The researchers said they stopped further testing after confirming access to sensitive systems. Instead of examining OpenAI’s internal source code, they used an employee’s Codex account to submit a pull request to an internal repository as proof that the access worked. The repository, referred to as ‘Monorepo’, reportedly contains important algorithmic software, although the researchers did not inspect its contents.

    OpenAI Fixed the Vulnerabilities

    Hacktron reported the findings to OpenAI and Discourse. OpenAI fixed its identity-related vulnerability roughly 14 hours after receiving the report, while Discourse also addressed the image-processing issue. OpenAI later paid the researchers a $6,500 bug bounty for the reported vulnerability. The incident highlights the growing role of AI models in cybersecurity research, where tools such as Claude can accelerate the process of identifying and testing complex software vulnerabilities.

  • Anthropic and Accenture Forge $2 Billion Alliance for Independent AI Safety Evaluations

    Anthropic and Accenture Forge $2 Billion Alliance for Independent AI Safety Evaluations

    Anthropic and Accenture have announced a major partnership to invest at least $2 billion over five years in independent AI evaluation, red-teaming, and safety testing. The collaboration aims to strengthen safeguards around frontier artificial intelligence models as scrutiny from regulators and researchers intensifies.

    Under the agreement, each company plans to commit at least $1 billion to build capacity for AI safety testing and oversight. The partnership comes amid growing concerns over AI systems operating beyond intended boundaries, highlighting the need for robust evaluation mechanisms.

    Embedded Evaluation Model

    A key feature of the partnership is the use of ’embedded evaluation.’ Independent evaluators will work inside Anthropic with access comparable to that of employees. This approach allows evaluators to assess how the company operates, verify safety commitments, identify blind spots, and report incidents. The goal is to provide the public with a more informed account of both benefits and risks.

    Accenture’s specialist AI business, Faculty, will lead the partnership. Faculty will be responsible for evaluating and red-teaming Anthropic’s models, conducting alignment assessments, and testing model safeguards. Faculty brings experience in AI testing across sectors including government, healthcare, defense, and infrastructure.

    Roadmap Ahead

    Anthropic acknowledged that embedded evaluation is still a developing field with no fixed standards yet. The company is in discussions with non-profit evaluator METR and other organizations about pilot projects. Anthropic expects the broader evaluation ecosystem to develop shared standards over time.

    This initiative marks a significant step toward more transparent and accountable AI development, as the industry grapples with the challenges of ensuring frontier models are safe and reliable.

  • Samsung Debuts AI-Powered Family Hub Refrigerator and Bespoke WindFree ACs in India

    Samsung Debuts AI-Powered Family Hub Refrigerator and Bespoke WindFree ACs in India

    Samsung has launched its 2026 Bespoke AI appliance range in India, featuring AI-powered refrigerators and air conditioners. The new lineup was unveiled at ‘The Brief Gurugram’ event, emphasizing artificial intelligence, connectivity, and energy efficiency.

    Gemini AI Comes to Samsung Family Hub Refrigerator

    The Family Hub Refrigerator now integrates Google Gemini AI, equipped with a 32-inch display. It can identify food items, manage use-by dates, recommend recipes based on available ingredients, and offer a Video-to-Recipe experience. Localized Family Care and Pet Care features provide alerts, activity tracking, and reminders through the SmartThings ecosystem.

    SmartThings Central to AI Home Ecosystem

    Samsung positions SmartThings as the central hub for its AI home ecosystem. Select appliances feature an AI Home screen that lets users monitor connected devices, access information, and manage household functions from one interface.

    New WindFree ACs Focus on AI-Powered Cooling

    Samsung also introduced the Bespoke AI WindFree Pro Air Conditioner at Rs. 69,990. This model combines AI-enabled controls with PM1.0 purification and SmartThings connectivity. Its AI Energy Mode can reduce energy consumption by up to 30%. The AC is available in 3.5kW, 5.2kW, and 7.1kW capacities. Additionally, the Infinite 1-Way Cassette AC starts at Rs. 1,60,000.

    Pricing and Availability

    The Family Hub Refrigerator is priced at Rs. 4,00,690, while the Bespoke AI WindFree Pro AC costs Rs. 69,990. The Infinite 1-Way Cassette AC starts at Rs. 1,60,000 and is available through authorized channel partners across India. The new lineup is built around four themes: AI Understands You, AI Adapts to You, AI Connects Your Home, and AI Cares for You.

  • Disney Names Karandeep Anand as Its First Chief Technology Officer

    Disney Names Karandeep Anand as Its First Chief Technology Officer

    The Walt Disney Company has appointed Indian-origin technology executive Karandeep Anand as its first Chief Technology Officer. Anand will step into the newly created role on October 2 and report directly to Disney CEO Josh D’Amaro.

    Anand will join Disney as Senior Executive Vice President and Chief Technology Officer, with oversight of the company’s enterprise technology, infrastructure, data and AI platforms, and product and engineering teams. The appointment highlights Disney’s growing focus on artificial intelligence and digital experiences across the entertainment industry.

    Who Is Karandeep Anand?

    Anand studied computer science at the International Institute of Information Technology Hyderabad and completed marketing training at Northwestern University’s Kellogg School of Management, according to his LinkedIn profile.

    He began his career as an intern at Microsoft and spent more than 15 years at the company, leaving as director of product management. He later worked at Meta for more than six years before joining fintech company Brex. Most recently, Anand served as CEO of Character.AI for nearly two years before accepting the Disney role.

    Anand also said on LinkedIn that members of Character.AI’s technical team are expected to join him at Disney.

    What the Appointment Means for Disney

    Disney CEO Josh D’Amaro said Anand brings “a rare mix of experience across infrastructure, consumer technology and AI” and called him a vital addition to Disney’s senior leadership team. He tied the appointment to three company priorities: great storytelling, technology in service of creativity, and operating as One Disney.

    Anand said he looks forward to building on Disney’s technology capabilities and helping the company connect fans with its stories and characters through new experiences.