A naukri.com initiative
AI News
Semiengineering
1d
242
Image Credit: Semiengineering
The Rise Of Generative AI On The Edge
- AI and ML have evolved with the rise of transformers and Generative AI (GenAI), enabling more accurate and context-aware models for complex tasks.
- CNNs, RNNs, and transformers have transformed AI, with transformers using attention mechanisms for better data processing and performance.
- Generative AI leverages transformers to generate new data like text and images and powers applications such as ChatGPT and DALL-E.
- Deploying GenAI on edge devices offers advantages in real-time processing, privacy, and security, catering to applications like autonomous vehicles.
- Challenges in deploying GenAI on edge devices include computational complexity, data requirements, limited connectivity, power consumption, and hardware constraints.
- Model optimization, software support, and addressing security and privacy concerns are crucial for effective deployment of GenAI on edge devices.
- Selecting the right processor, such as Neural Processing Units (NPUs), is essential for balancing performance and efficiency in running GenAI models on edge devices.
- Overcoming challenges in computational complexity and power consumption will pave the way for seamless integration of AI on edge devices, driving innovation.
Read Full Article
14 Likes
Medium
1d
56
Image Credit: Medium
Case Study: Enhancing Spotify User Engagement with a Mood Slider Feature
- Spotify aims to enhance user engagement with a Mood Slider feature.
- The feature is designed to address the emotional aspect of music discovery by allowing users to set their current mood.
- Through a lightweight interface, users can select the emotion they are feeling, which then influences song recommendations.
- The implementation of the Mood Slider is considered an MVP approach, delivering immediate value and deepening user engagement.
Read Full Article
3 Likes
Semiengineering
1d
310
Image Credit: Semiengineering
How Physical AI Is Redefining The Automotive Industry
- Physical AI is reshaping the automotive industry by enhancing autonomous systems' interaction with the physical world.
- It enables cars to navigate traffic, detect hazards, and optimize driving conditions for safety and comfort.
- AI automates and enhances vehicle design processes, fostering innovation through tools like geometric deep learning and GANs.
- AI integration in mechanical design allows for predictive simulations and adjustments, saving time and costs in development.
- Physical AI in autonomous vehicles elevates driving experiences with real-time decision-making and adaptive features.
- Safety challenges in autonomous vehicles are addressed by training AI systems in diverse safety-critical scenarios.
- AI enhances manufacturing processes by improving precision, efficiency, and quality assurance in automotive production.
- The synergy of physical AI with core automotive technologies drives innovation in electric vehicles, autonomous navigation, and V2X communication systems.
- Cadence plays a key role in automotive evolution by providing design platforms and tools for driving safety, digital cockpits, electric vehicles, and more.
- Physical AI bridges the gap between digital intelligence and real-world applications, offering transformation in mobility and vehicle design.
Read Full Article
18 Likes
Medium
1d
223
How AI is Transforming Venture Capital: Evaluating Startup Investments and Predicting Success
- AI-driven tools are being used to analyze vast amounts of data to predict the success of startups.
- Platforms like SignalFire and EQT Ventures automate data analysis to evaluate market potential and financial health.
- AI tools assess founder experience and team dynamics to determine a startup's potential for success.
- The future of venture capital involves integrating AI into investment strategies and leveraging it for data-driven insights and decision-making.
Read Full Article
13 Likes
Medium
1d
94
Image Credit: Medium
How to Write A Humanized Article Using ChatGPT
- Inject your own personality and style when using AI-generated content. Treat the AI as a collaborative tool.
- Provide context and specify the desired tone when prompting ChatGPT.
- Use adjectives to describe the desired tone and style of the content.
- Rewrite and revise the AI-generated text to match your own natural tone and voice.
Read Full Article
5 Likes
Medium
1d
163
Image Credit: Medium
The Art and Science of Software Testing in 2025: A Journey Through Innovation
- In 2025, software testing is at the forefront of technological innovations, blending art and science to ensure robust and revolutionary digital experiences.
- AI is transforming software testing by automating tasks, reducing manual effort, but also necessitating human-AI collaboration to ensure fairness and accuracy.
- Shift-Left and Shift-Right testing methodologies have become key pillars of QA workflows, proactively identifying and addressing defects throughout the development lifecycle.
- Cybersecurity testing is essential in 2025, with tools like OWASP ZAP and AI simulations playing a crucial role in predicting and preventing threats.
- The rise of low-code/no-code testing platforms is democratizing testing, allowing non-technical users to participate in crafting automated tests.
- Sustainability is reshaping software testing practices, with cloud-based testing and AI optimization contributing to greener and more efficient testing environments.
- Hyper-automation, integrating AI, machine learning, and RPA, is streamlining testing processes and enhancing quality assurance.
- Testing in the era of IoT requires specialized frameworks and cloud-based device farms to ensure interoperability, security, and performance across diverse devices.
- Ethical considerations are paramount in software testing, focusing on fairness and privacy in AI-driven applications and complying with regulations like GDPR.
- In 2025, quantum computing introduces new testing challenges, requiring testers to adapt to quantum-specific methods and probabilistic outcomes.
Read Full Article
9 Likes
Digitaltrends
1d
356
Image Credit: Digitaltrends
Microsoft’s Bing adds a Copolit Search mode to rival Google AI Search
- Microsoft has rolled out a new Copilot Search option for Bing search engine, in competition with Google's AI Search mode.
- To access Bing's Copilot Search, users can visit Bing Search or use the shortcut 'bing.com/copilotsearch?q=(QUERY)'. It is available on both mobile and desktop.
- Copilot Search provides AI-generated answers displayed as a wall of text or curated answer with numbered bulleted lists and sections.
- Users should double-check with the source material as AI search tools may occasionally provide inaccurate results.
Read Full Article
21 Likes
Medium
1d
129
Image Credit: Medium
Runway Gen-4: A New Era in AI Video Creation
- Runway's Gen-4 is an advanced AI video generation model that ensures consistency in characters and scenes.
- The model provides high-quality and realistic simulations, allowing the incorporation of real objects in digital environments.
- Applications of Runway Gen-4 include filmmaking, marketing, advertising, and education.
- Experts believe that this technology enhances creativity rather than replacing it, expanding the horizons of imagination.
Read Full Article
7 Likes
Medium
1d
7
Image Credit: Medium
The Burnt Toast Theory: What It Is and What Science Has to Say About It
- The Burnt Toast Theory is the idea that small events can have significant consequences.
- It gained popularity on social media platforms like Twitter and TikTok.
- Science does not provide much evidence to support the theory.
- The Burnt Toast Theory can be seen as a fun way to cope with life's annoyances and offer a positive outlook.
Read Full Article
Like
Dev
1d
284
Image Credit: Dev
Common Challenges of Financial Software Application Testing
- Financial software application testing is crucial due to the increasing reliance on online banking and financial apps, with the market projected to reach $24.4 billion by 2026.
- Key considerations for testing financial applications include impact analysis, functional testing, security testing, performance testing, compliance and regulatory testing, and test automation.
- Common challenges in financial application testing include security across platforms, managing multiple applications and operating systems, complex financial instrument management, compliance with regulatory norms, technology updates, time constraints, data migration challenges, and real-time transaction testing.
- Security testing is vital in financial software due to the sensitivity of information processed, making compliance with industry security standards essential.
- Testing teams face the challenge of ensuring financial software works seamlessly across various devices and operating systems through cross-platform testing.
- Complex financial applications require thorough testing to ensure that internal processes and third-party tools do not interfere with one another.
- Adhering to new regulatory norms, managing frequent technology updates, and meeting time constraints for testing are additional challenges in financial application testing.
- Efforts to address challenges in financial software testing include automated testing processes, rigorous data migration testing, and ensuring real-time transaction reliability.
- Analyzing financial software applications accurately and efficiently is crucial for building user trust and meeting industry standards, requiring active problem-solving approaches.
- Enrolling in financial analysis courses can enhance the ability to assess financial applications effectively and ensure they meet industry standards.
Read Full Article
17 Likes
Dev
1d
163
Image Credit: Dev
AI-Driven Testing: The Future of Software Quality Assurance
- AI-driven testing refers to the use of machine learning algorithms and AI-powered tools to automate and optimize software testing processes.
- AI-powered testing brings benefits such as self-healing test scripts, predictive analytics for defect detection, and autonomous bug detection.
- Key benefits of AI-driven testing include faster test execution, improved accuracy, and better test coverage.
- Popular AI-driven testing tools include Keploy, Testim, Applitools, and Mabl.
Read Full Article
9 Likes
Medium
1d
30
Image Credit: Medium
Python Generators for Data Loading in Machine Learning Projects
- Generators allow developers to process data lazily, streaming it as needed rather than loading everything into RAM, which is particularly useful for handling large datasets.
- An iterator in Python is an object that allows us to traverse through a sequence one element at a time without loading the entire sequence into memory.
- Generators are a type of iterator in Python that simplify the process of creating iterators by using the 'yield' keyword to produce values lazily.
- Generators are beneficial in handling large datasets and can be used for scenarios like loading and preprocessing image datasets without frameworks like TensorFlow or PyTorch.
Read Full Article
1 Like
Medium
1d
341
Image Credit: Medium
AI Jobs Boom In India
- AI job postings in India have surged by 42% over the past two years, totaling 253,000 positions.
- The demand for skilled AI professionals in India is outpacing the available talent, with job openings expected to be 1.5 to 2 times the talent by 2027.
- AI is becoming integral across industries, with companies like JPMorgan reporting a 20% efficiency boost among software engineers using AI coding assistants.
- Continuous learning is essential to thrive in the AI job market.
Read Full Article
20 Likes
Medium
1d
300
Image Credit: Medium
Building Confidence: Tips and Tricks to Unlock Your Full Potential
- Confidence is the faith you have in yourself, your abilities, and your judgment.
- Confidence is essential for achieving success in all areas of life.
- Practical tips to build confidence include practicing positive self-talk, focusing on strengths, setting achievable goals, and taking care of yourself.
Read Full Article
18 Likes
Medium
1d
224
Image Credit: Medium
AI Selfie Style or Privacy Minefield? Concerns Grow Over ChatGPT’s Ghibli Art Generator
- Critics are raising concerns over the data usage and privacy implications of ChatGPT's Ghibli art generator.
- OpenAI reassures users that their data is not used to train models without explicit consent.
- Transparency in data handling is in high demand due to the integration of AI tools in daily life.
- Users are reminded to be cautious when using AI-generated content features and to understand privacy settings.
Read Full Article
13 Likes
For uninterrupted reading, download the app