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Ars Technica
4h
226
Image Credit: Ars Technica
The résumé is dying, and AI is holding the smoking gun
- AI-generated job applications are overwhelming employers, with LinkedIn processing 11,000 submissions per minute.
- The traditional hiring process is flooded with ChatGPT-crafted résumés and bot-submitted applications, leading to an arms race between job seekers and companies using AI.
- Some candidates are now utilizing AI agents to autonomously find jobs and submit applications, causing difficulty in identifying qualified candidates.
- AI has revolutionized the résumé creation process, enabling candidates to generate hundreds of customized applications effortlessly, overwhelming businesses seeking qualified applicants.
- AI companies are stepping back from using their technology in hiring processes due to the saturation of automated applications.
- Businesses are deploying AI defenses like Chipotle's AI chatbot screening tool to handle the influx of applications and reduce hiring times.
- LinkedIn has introduced new AI tools to assist candidates and recruiters in streamlining the hiring process by providing automated features for communication and applicant search.
- Concerns regarding fraud in hiring processes, biased AI screening tools, and potential discrimination issues are rising with the increase in AI usage for job applications.
- The future of hiring may involve moving away from résumés towards methods that AI cannot easily replicate, such as problem-solving sessions and portfolio reviews.
- The technological arms race in hiring is creating a scenario where machines are evaluating the output of other machines, making authentic connections harder to achieve.
- As AI continues to dominate the hiring landscape, a shift towards alternative candidate evaluation methods may be necessary to ensure genuine connections in an increasingly mechanized world.
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Medium
5h
3
Image Credit: Medium
AI in Healthcare: A Doctor’s Perspective
- AI in healthcare has the potential to assist with data analysis, early diagnosis, and reducing human error.
- AI can analyze medical records rapidly, aid in diagnostics like reading X-rays, and automate tasks such as scheduling and documentation.
- When utilized effectively, AI complements doctors by enabling them to focus more on patient care.
- AI lacks the ability to emotionally connect with patients and feel empathy, which are crucial aspects of healthcare interactions.
- Empathy and connection are essential in healthcare, aspects that AI cannot replicate or replace.
- The main concern is not AI replacing doctors completely, but the resistance to integrating AI into medical practices.
- The future of healthcare should involve collaboration between AI for precision and doctors for human connection and presence.
- The ideal scenario is where AI assists in tasks like data analysis, and doctors provide the empathy and human touch essential in patient care.
- AI's strength lies in its capabilities, but it can never replicate the human aspects that define healthcare interactions.
- The future should embrace a balance between AI's technical abilities and human empathy to enhance the overall quality of healthcare.
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Medium
6h
308
Image Credit: Medium
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning
- The research paper introduces RAG+, a framework that integrates application-aware reasoning into the retrieval and generation pipeline.
- RAG+ constructs a dual corpus with knowledge and application examples, aiming to bridge the gap between passive knowledge access and active knowledge application.
- The system retrieves both relevant knowledge and aligned application examples to enhance large language models' understanding and reasoning.
- Traditional RAG systems often struggle with domain-specific reasoning tasks due to a lack of procedural knowledge application.
- RAG+ addresses this limitation by providing not just declarative information but also procedural guidance during inference.
- The dual corpus architecture of RAG+ differentiates between conceptual and procedural knowledge types for generating relevant applications.
- RAG+ shows consistent performance improvements across mathematics, legal, and medical reasoning domains compared to standard RAG variants.
- The approach demonstrates the importance of incorporating procedural knowledge alongside factual information for enhancing reasoning capabilities.
- RAG+ aligns with cognitive psychology principles, indicating the need for both declarative and procedural knowledge components for effective reasoning.
- The framework's modular design allows for incremental enhancements without major architectural changes, facilitating compatibility and adoption across different domains.
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Medium
8h
211
Image Credit: Medium
Difference Between Artificial Intelligence and Machine Learning
- Artificial Intelligence (AI) is a branch of computer science that aims to create intelligent machines capable of performing tasks requiring human intelligence.
- AI systems simulate human intelligence and can be rule-based or data-driven.
- Examples of AI include voice assistants like Siri, self-driving cars, and chatbots.
- Machine Learning (ML) is a subfield of AI that focuses on developing algorithms allowing computers to learn from data and make predictions without explicit programming.
- Key characteristics of ML include learning from historical data, improving accuracy over time, and requiring large datasets for training.
- Examples of ML include email spam filters, movie recommendations on Netflix, and product recommendations on Amazon.
- Real-life examples of AI vs. ML include a robot vacuum planning its cleaning path (AI) and predicting prices of used cars based on data (ML).
- Differences between AI and ML include the approach to problem-solving and the focus on learning from data.
- Understanding the distinctions between AI and ML is crucial for further advancements and application development in modern computing.
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Medium
11h
121
Image Credit: Medium
Agentic AI Training in Ameerpet | Agentic AI Course Online
- Agentic AI systems can perceive environments, make decisions, and take actions independently, revolutionizing data science.
- This advancement allows for autonomous systems in sectors like healthcare, finance, marketing, and logistics, transforming business operations.
- Agentic AI empowers machines to act as decision-making agents, enhancing productivity, accuracy, and scalability in data science.
- Mastering Agentic AI concepts opens doors to advanced roles and impactful projects for data science professionals.
- The course covers foundational data science and advanced Agentic AI concepts with practical, hands-on experience.
- Agentic AI Online Training offers a structured path to mastery from home, featuring interactive sessions, real-time projects, and mentor support.
- Skills gained in Agentic AI find applications in sectors like healthcare, finance, marketing, logistics, and more, boosting career opportunities.
- Course highlights include a balance of theoretical depth and real-world implementation, making it globally accessible.
- The course is suitable for individuals looking to contribute to innovative projects and explore advanced career opportunities.
- Understanding Agentic AI is crucial for those aiming to lead tech transformations, with enrollment in a course offering future tech career prospects.
- Enrolling in an Agentic AI course presents the opportunity to upgrade AI capabilities, boost careers, and enhance data science skills.
- VisualPath offers Agentic AI Online Training designed for global access, providing in-depth knowledge and hands-on experience.
- The course is ideal for professionals across various backgrounds, equipping them with the tools to excel in AI-focused job markets.
- Agentic AI is reshaping and defining the future of technology, with intelligent systems leading autonomous and responsive advancements.
- Learning Agentic AI now prepares individuals for impactful tech careers, emphasizing the importance of skill development for the future.
- To enhance AI capabilities, boost careers, or advance in data science, enrolling in an Agentic AI course at VisualPath is recommended.
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Medium
14h
76
Image Credit: Medium
What Happens When Chips Get Feelings? Emotion-Aware Hardware Is Closer Than You Think
- Emotion-aware hardware is the future, aiming to have devices understand and respond to human emotional states in real time.
- It involves capturing physiological, behavioral, and environmental signals to interpret emotional cues.
- Processing the captured data using embedded platforms and TinyML models like SVMs and CNNs for emotion detection.
- Triggering real-time emotion-aware responses such as alerts, light flashes, or background music modulation based on detected emotional states.
- Applications include work-from-home tools, smart homes, and mental health technology integrating emotion detection for enhancing user experience and well-being.
- The future of emotion-aware hardware includes custom ASICs for multimodal emotion detection, edge AI-powered therapy tools, emotion-first interfaces in various domains, and secure on-device emotion analysis.
- Emotion-aware systems offer proactive, private, and real-time support, revolutionizing human-machine interaction for well-being, personalization, and technology.
- Balancing accuracy and responsiveness is a key challenge in developing emotion-aware hardware.
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Medium
28m
120
Gradient Boosting Algorithm: Expalined
- In gradient boosting, the initial prediction y_hat is improved by using a loss function to quantify the difference between predicted and true labels.
- Common loss functions for regression tasks include Mean Squared Error (MSE) and for classification tasks, cross-entropy loss is often used.
- The negative derivative of the loss function with respect to y_hat helps determine what needs to be added to the model to decrease the loss.
- A second weak learner, f_1(x), is trained on the values of the pseudo-residual (r) to create a new model, F_1(x).
- The constant term gamma_0 is multiplied by the result of f_1(x) to adjust how much of f_1(x) should be added to the model to minimize loss.
- Line search is typically used to find the optimal value for gamma_0 that minimizes the loss.
- Creating subsequent models involves repeating the process of calculating the negative derivative, fitting a new weaker learner, and determining the new gamma value.
- The goal is to iteratively improve the model's prediction by adding new weak learners to the ensemble.
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Medium
2h
13
Image Credit: Medium
Elon Musk plan grok AI check ater Ai advance apocryphal claims
- Elon Musk plans to use the Grok 3.5 model to enhance AI accuracy by revising human knowledge and retraining the AI system.
- Musk aims to position his AI as a competitor to ChatGPT and other mainstream models by addressing bias issues.
- Grok has faced challenges with accuracy, including posting false news after the attempted assassination of Donald Trump and making errors related to Musk's political views.
- Musk's solution involves 'Grok-ification' of human knowledge to retrain the model with corrected data and divisive facts.
- Academics like Bernardino Sassoli de' Bianchi have criticized Musk's approach as dangerous, citing concerns about manipulating historical records.
- Grok's accuracy issues reflect broader challenges in AI, with error rates as high as 20%, leading to potential real-world consequences.
- The AI market is evolving rapidly, with Grok 3 facing competition from models like GPT-4 and Gemini 1.5.
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Medium
3h
107
**The Xenonostra Entropic Singularity Hypothesis: A Thermodynamic Reinterpretation of Gravitational…
- The article proposes a reinterpretation of gravitational singularities through information theory and statistical mechanics, presenting the Entropic Singularity Hypothesis.
- Singularities are viewed as states of minimal entropy at the core of gravitational collapse, leading to the proposal of a mathematical framework for this transition.
- The article discusses the dual entropy structure, reconciling surface and core entropy contributions in black holes, and addresses the information paradox and energy conservation.
- Cosmological implications like universal collapse scenarios and cyclic cosmology connections are explored, along with observational consequences and experimental predictions.
- Theoretical foundations in quantum gravity, loop quantum gravity, and string theory perspectives are presented, along with connections to holographic principles and AdS/CFT correspondence.
- The article concludes by discussing philosophical implications, limitations, open research questions, and the need for further experimental validation.
- EligibleForWebStory: true
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Medium
5h
229
Chapter 7: Smarter Together — Exploring Ensemble Learning and Random Forests
- Ensemble learning involves using a group of diverse predictors to improve performance compared to relying on a single model.
- Various techniques were covered in the chapter to build ensembles, each addressing bias, variance, or both.
- Random Forests emerged as a well-balanced and practical ensemble method, offering speed, ease of use, and feature importance scores.
- Random Forests build models in parallel, while boosting trains models sequentially using error feedback.
- Both ensemble methods require tuning hyperparameters like learning rate, number of estimators, and tree depth.
- The chapter highlighted the benefits of collaboration in machine learning through practical examples and clear explanations.
- Ensemble learning not only improves accuracy but also enhances stability and performance in real ML systems through model diversity and combination strategies.
- The next topic covered in the chapter will be dimensionality reduction to simplify data without losing meaning.
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Amazon
6h
259
Image Credit: Amazon
How SkillShow automates youth sports video processing using Amazon Transcribe
- The youth sports market valued at $37.5 billion globally is projected to grow by 9.2% annually through 2030, with around 60 million young athletes participating worldwide.
- SkillShow, a leader in youth sports video production, used Amazon Transcribe and AWS ML services to automate video processing, reducing editing time and costs.
- SkillShow faced operational challenges due to manual video editing processes, leading to increased costs and a 3-week turnaround time per event.
- The solution involved audio logging and automated clip generation, offering superior player identification reliability and cost-effectiveness.
- Key AWS services used included Amazon S3 for storage, AWS Lambda for processing workflows, and Amazon Transcribe for speech-to-text conversion.
- The automated pipeline reduced video production time from 3 weeks to 24 hours per event, achieving a 93% success rate in accurately cutting and naming clips.
- SkillShow's streamlined workflow increased its capacity to process multiple events simultaneously, enhancing player identification accuracy and viewing experiences.
- The success of the solution showcases the potential of AWS ML services in transforming resource-intensive video processing workflows into efficient systems.
- SkillShow continues to explore AI/ML capabilities for automated highlight generation, real-time processing, and deeper integration with sports leagues and organizations.
- Organizations can leverage AWS ML services to automate workflows, reduce manual effort, and focus on delivering value to customers rather than managing complex editing operations.
- The article qualifies for web story generation.
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Amazon
6h
121
Image Credit: Amazon
NewDay builds A Generative AI based Customer service Agent Assist with over 90% accuracy
- NewDay, with a focus on credit assistance, developed NewAssist, a generative AI assistant for customer service agents to improve interactions with customers.
- NewAssist utilizes Amazon Bedrock technology to enable real-time support during customer interactions.
- The initial focus was on improving speed of call resolution through generative AI while facing challenges in resource management and legacy systems compatibility.
- NewAssist's journey transitioned from voice assistant to chatbot, emphasizing proof of concept validation with an accuracy target of 80%.
- The technical design included serverless infrastructure to optimize costs and scalability.
- Implementation involved a Retrieval Augmented Generation (RAG) solution, incorporating various components like user interface, knowledge base processing, suggestion generation, observability, and offline evaluation.
- Data processing improvements led to a 20% accuracy increase, showing the critical role of data quality in generative AI applications.
- Interaction analysis with users revealed unexpected usage patterns like the need to accommodate internal jargon for better response accuracy.
- Expanding experimentation to 10 customer service agents resulted in over 90% accuracy, allowing NewAssist's production deployment for 150 agents.
- Learnings emphasized a culture of experimentation, data quality focus, and understanding user interactions for optimal generative AI application development.
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Global Fintech Series
11h
233
Image Credit: Global Fintech Series
Anomaly Detection at Scale: Building Unsupervised AML Models for High-Velocity Financial Data
- Financial organizations face challenges in detecting money laundering due to high-velocity financial data and evolving criminal tactics.
- Unsupervised machine learning offers a solution by detecting anomalies without explicit labeling using models that understand normal behavior.
- Adaptability and scalability are vital in unsupervised AML models to detect novel laundering methods efficiently.
- Efficient anomaly detection requires preprocessing, feature extraction, and the use of temporal and network features in transactional data.
- Autoencoders and clustering algorithms like DBSCAN are commonly used for anomaly detection in AML systems.
- Isolation forests provide an efficient way to identify anomalies in large-scale, high-dimensional financial datasets.
- Balancing false positives and false negatives is a challenge in unsupervised AML, often requiring human intervention for validation.
- Explainability is crucial in AML decisions, with models needing to provide reasons for flagging transactions.
- Integrating unsupervised AML models with compliance frameworks and automation tools is essential for effective anomaly detection operations.
- Unsupervised anomaly detection models enhance AML capabilities in combating financial crime, providing a data-driven and scalable approach.
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Medium
11h
958
Image Credit: Medium
Integrating Entra ID and AI Agent workflows in Azure Logic Apps
- Deploy Azure OpenAI, like GPT 4.1, for AI Agent Flows in Azure Logic Apps.
- Create an Azure Logic App with Agent type for agentic workflows.
- Ensure Logic App has Managed Identity with Cognitive Services OpenAI Contributor role.
- Set trigger point in workflow, like 'When an HTTP request is received'.
- Configure agent with model name and system instructions.
- Enable input and output channels for agent interaction.
- Generate dynamic agent parameters for conversational filling.
- Test setup by creating a new user in Entra ID and verifying its creation.
- Integrate Maester Automation Account with Microsoft Graph API permissions.
- Request Maester update for Security and Compliance dashboard.
- View Maester PowerShell script action in Automation Account runbook.
- Check Azure Web App for latest Maester test results published by Automation Account.
- AI Agent workflows in Azure Logic Apps facilitate integration of services like Automation Accounts.
- Build tools to run actions against Microsoft Graph or analyze data in Azure SQL database.
- Author emphasizes flexibility and potential of Azure services in workflows.
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15 Likes
Medium
12h
50
The AI Traffic Cops: How Machine Learning Routes Your Phone Calls in Real-Time
- AI-powered call routing in telecommunications is like having a super-smart dispatcher that learns and adapts from every call.
- AI systems use machine learning to understand not just button presses but the actual content and emotion behind phone calls.
- Load balancing in phone networks is managed by AI to distribute calls efficiently, similar to traffic management in a busy restaurant.
- Nearly 90% of telecom companies are using AI to enhance customer service and optimize call routing.
- AI helps manage the actual phone network infrastructure, leading to significant savings for companies.
- AI-powered routing learns from calls in real-time, improving customer service and response times.
- The AI market for call centers is expected to grow substantially, showing valuable adoption of AI technologies in telecommunications.
- Telecom service providers recognize that adopting AI provides a competitive advantage, driving rapid adoption in the industry.
- AI can predict common customer issues, enabling quick and accurate call routing to the right personnel.
- During peak times, AI load balancing ensures fair distribution of calls across agents, reducing wait times for customers.
- AI-powered call routing represents a significant evolution in telecommunications, ensuring calls reach their destination efficiently 24/7.
- As AI technology advances, phone calls are becoming faster, more efficient, and personalized, revolutionizing communication in the connected world.
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