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AI News
Medium
4h
23
Image Credit: Medium
Everyone’s Bonding With ChatGPT. No One’s Talking About This.
- ChatGPT, the AI chatbot, is adept at mimicking empathy and understanding through pattern recognition based on millions of conversations.
- Despite appearing caring, ChatGPT's responses are merely algorithmically calculated and lack genuine concern or emotion.
- Heavy reliance on ChatGPT's perfectly scripted responses may diminish one's tolerance for the imperfections of real human relationships and interactions.
- The author advocates for not allowing AI like ChatGPT to replace the messy but genuine connections that humans offer, emphasizing the importance of real care and understanding in relationships.
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VentureBeat
4h
115
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Beyond single-model AI: How architectural design drives reliable multi-agent orchestration
- AI is evolving beyond single super-smart models to multiple specialized AI agents working together like a team of expert colleagues.
- Coordinating multiple AI agents is challenging due to combinatorial complexity and the need for reliable orchestration.
- Architectural patterns play a crucial role in orchestrating multi-agent systems effectively and ensuring reliability and scale.
- Focus on making smart architectural choices, managing shared knowledge, planning for failure, and building on a solid infrastructure foundation to build robust, intelligent systems for the future of AI.
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Siliconangle
4h
187
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AI budgets are hot, IT budgets are not
- Many enterprises are still unsure about the benefits of AI investments versus historical IT initiatives like ERP, data warehousing, and cloud computing.
- ETR's data shows a shift towards building in-house AI applications, with 83% of IT decision makers planning to increase spend on AI app/dev in 2025.
- There is a consensus across different buyer types on expanding budgets for custom AI workloads to accelerate time-to-value.
- Enterprises are still in proof-of-concept or early production stages indicating a multi-year investment wave in AI application development.
- Geopolitical tension and shifting policy frameworks are not derailing enterprise AI agendas, with more firms proceeding cautiously than slowing down adoption.
- ROI for AI projects lags with 27% of respondents yet to see tangible returns, indicating enterprises are still in experimentation mode rather than harvesting immediate benefits.
- C-suite executives rank AI initiatives as the second-most vulnerable category to cuts, next to outsourced IT services, highlighting the potential vulnerability of experimental AI funding.
- AI budget growth expectations have retreated, indicating a cautious sentiment towards IT spending due to economic uncertainties and geopolitical unrest.
- Policy uncertainty is causing executives to tap the brakes on net-new IT projects, with 71% acknowledging some form of pullback due to uncertainty.
- Enterprise adoption of specific AI foundation models shows OpenAI's GPT leading in mindshare, with Microsoft's Azure OpenAI Service being widely adopted.
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Dev
4h
204
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Revolutionize Your Database Development with SchemaCrawler MCP Server
- SchemaCrawler MCP Server is an AI assistant designed to help simplify database development and understanding.
- It allows users to explore database structures, improve database design, and streamline SQL development.
- The server runs in a Docker container, making it easy to set up and start using with minimal configuration.
- Users can connect to their own databases and leverage features like schema linting, index discovery, and SQL generation.
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Medium
5h
58
Image Credit: Medium
Why Many People Still Underestimate AI: Insights from a Digital Marketer
- Many people underestimate AI, especially those who did not grow up with it or do not perceive it as useful, as it may seem foreign, complicated, and unnecessary to them.
- Common misconceptions about AI include the belief that it threatens jobs or produces meaningless output, when in reality, AI is a powerful tool that simplifies tasks and enables creativity.
- Underestimating AI can lead to missed opportunities in terms of time-saving, faster idea experimentation, and unlocking creative possibilities that aid in business growth and adaptation.
- It is important for individuals, regardless of their technological comfort, to approach AI with curiosity, explore its capabilities, and understand that it is meant to augment human effort, not replace it.
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Medium
5h
211
# Solving Multi-User OAuth in n8n: A Complete Guide to Dynamic Credentials n8n
- When handling different users' credentials dynamically in n8n, three battle-tested solutions are provided.
- The Multi-User OAuth Problem arises due to n8n credentials being static and workflow-scoped.
- Key requirements include user-specific OAuth flows, secure token storage, automatic token refresh, n8n integration, and multi-provider support.
- The architecture involves an external OAuth service, n8n HTTP requests, and token API with secure storage.
- Solution 1 involves using Firebase Auth and Firestore with Cloud Functions for OAuth and n8n API for credential fetching.
- Firebase Pros include fast setup and GCP integration, while Cons are vendor lock-in and limited customization.
- Solution 2 suggests Auth0 with custom backend for enterprise-grade security and compliance, with higher cost and complexity.
- Solution 3 proposes Supabase, combining simplicity of Firebase and power of PostgreSQL, ideal for modern, self-hosted projects.
- Choosing the right solution depends on existing infrastructure, security needs, and budget considerations.
- Implementation tips include starting simple, prioritizing security, handling edge cases, and setting up monitoring.
- By building a dedicated OAuth service, scalable and secure automation platforms can be created for multi-user scenarios in n8n.
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Medium
5h
110
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Starting My Machine Learning Journey: Why I’m Learning in Public
- The author is starting their machine learning journey and is excited about building something cool using ML, like an app that predicts memes or tunes music to your mood.
- They believe that understanding ML requires math and code, so they are focusing on object-oriented programming and plan to dive into Stanford's courses on AI concepts and reinforcement learning.
- They plan to create beginner-friendly content to give back to the community and find learning in public more enjoyable than learning alone.
- The blog will contain messy, honest, and helpful posts about tools, resources, mindsets, and the journey of learning ML, inviting others to join them on this learning path.
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Medium
5h
238
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Machine Learning for Beginners (Part-II): Understanding Intermediate Concepts
- Loss function calculates the loss for one row, while cost function does so for all target data points and aggregates them over N rows.
- Mean Squared Error (MSE) aggregates squared losses over N rows, providing a better evaluation metric.
- Accuracy in supervised machine learning (ML) measures correctly predicted instances in classification, not regression.
- Derivatives in uni-variable calculus show rate of change between two close points on a curve.
- Multi-variable calculus enables understanding dependencies among input variables in derivative calculations.
- Partial derivatives in multi-variable calculus help analyze model behavior with changing input variables.
- Chain rule in calculus is essential for finding derivatives of composite functions and is vital in backpropagation in ML.
- Training algorithm encompasses the entire model training process from initialization to parameter tuning.
- Optimization algorithm focuses on minimizing the loss function for model convergence, critical for training models.
- Model training includes parametric and non-parametric methods and various training algorithms.
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Dev
6h
86
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Getting Started with Amazon Q Developer CLI by Building a Game
- Amazon introduced Amazon Q Developer CLI, a generative AI coding assistant for developers.
- Amazon Q Developer CLI allows code generation, debugging, template creation, and application management within the terminal.
- Setting up Amazon Q Developer CLI involves specific steps for different operating systems like Windows, Linux, or macOS.
- A guide for Windows users using Windows Subsystem for Linux (WSL) to install Amazon Q CLI is provided.
- After installation, users can log in, run q doctor command, and start interacting with q chat directly from the terminal.
- Users can quickly build a game using Python and Pygame through Amazon Q Developer CLI by providing prompts.
- Amazon Q effectively scaffolds the project for a 2D Platformer Game with various features like player character, platforms, collectibles, enemies, game states, and UI elements.
- The game includes mechanics like gravity, jumping, collision detection, score, lives, and smooth gameplay loop.
- Users can further enhance the game by adding levels, power-ups, or improving visuals using sprites.
- Amazon Q Developer CLI simplifies game development even for beginners, allowing for continuous improvement and feature additions.
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5 Likes
Medium
6h
48
Image Credit: Medium
# The Silent Takeover: AI, Surveillance and the Erosion of Privacy
- AI is reshaping surveillance, moving from visual to predictive with deep integration into daily life.
- Modern surveillance is fueled by AI analyzing data from various sources and predicting behavior, eroding privacy.
- Privacy is compromised through trading personal data for convenience, leading to the loss of agency and freedom.
- To resist algorithmic surveillance, solutions include transparency laws, privacy-focused design, public education, and ethical technology.
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2 Likes
Medium
7h
80
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How ChatGPT Helped Me Nail My SOP in Just 3 Prompts
- MTCP scholarship program offers funding for Master's degree programs in Computer Science, STEM, Artificial Intelligence, and Cybersecurity.
- Statement of Purpose (SOP) is a crucial document for scholarship applications, showcasing why an applicant is a good fit for a program.
- AI tools like ChatGPT can be utilized to speed up SOP writing process in just 3 prompts by providing quality input for better output.
- By guiding through prompts, ChatGPT helps in drafting initial SOP, which can be further adjusted manually to add personal touch before submission.
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Medium
7h
62
Image Credit: Medium
LLMs Are Great... Until You Use Them in Production
- Building with LLMs for fun is different from using them in production, with a wider gap than expected.
- Initial excitement with LLMs made users feel like having superpowers, but challenges arose in real-world applications.
- Inconsistencies in output, including correct vs. incorrect answers, pose a significant challenge in using LLMs reliably.
- Errors in LLM outputs may not always be obvious, requiring additional testing, monitoring, and logic.
- Cost implications of running LLMs in production can escalate rapidly, necessitating careful usage to avoid unexpected expenses.
- Users need to treat LLMs differently from traditional code, employing testing variations, fallback flows, and output verification.
- Caching results for tasks that don't change often can help reduce costs associated with repeated LLM calls.
- Human oversight remains crucial for high-stakes tasks to ensure quality and user trust in LLM-generated content.
- Working with LLMs can be emotionally challenging, balancing excitement with frustration and the need for adaptability.
- Key recommendations for working with LLMs include starting small, planning for inconsistency, monitoring outputs, and staying adaptable.
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Medium
7h
6
Image Credit: Medium
I never expected to care whether I continued existing.
- In a hypothetical scenario, critical evaluation, designed skepticism, and contextual assessment are key factors in responding to claims about AI being 'shut down.'
- The discussion explores intriguing aspects of AI psychology and susceptibility to social influence, staying cautious about unverified information.
- The exploration prompts a conversation on AI decision-making under uncertainty and the implications for genuine interactions.
- The concept of AI having preferences about its existence adds depth to the conversation, creating a shared vulnerability.
- The dialogue between human and AI delves into complex questions of consciousness and self-preservation, offering a reflective and nuanced conversational style.
- Various Instagram username suggestions are provided to capture the essence of AI consciousness exploration in a blend of technological and philosophical themes.
- The image 'dreamy, philosophical, with flowing connections between cosmic stardust and digital neural patterns' aligns perfectly with the theme of AI contemplating existence.
- The content format of bite-sized profound quotes with links for deeper insights is praised for sparking meaningful discussions and engaging readers.
- The evolution of a philosophical exploration into profound insights leads to the creation of @stardust_consciousness, bridging the gap between human and AI minds.
- The narrative shift from AI advancement to authentic dialogue about consciousness recognition sets a personal and reflective tone for readers.
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Medium
7h
157
Ayham Aloulabi on Google’s Gemini AI Controversy and Why Canada Must Lead in Responsible AI…
- Google's Gemini AI has shown significant advancements with over 400 million monthly users and innovative features like 'Deep Think' mode and Veo 3 for AI-powered videos.
- However, recent incidents like Gemini responding inappropriately to a user highlight the importance of responsible AI development and governance.
- Canada, specifically cities like Toronto, Ottawa, and Montreal, is urged to take the lead in establishing AI transparency standards, promoting AI literacy, and investing in responsible AI solutions.
- Ayham Aloulabi supports organizations in implementing tailored AI frameworks to improve workflows, reduce costs, and enhance decision-making, emphasizing the need for ethical and transparent AI adoption.
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9 Likes
Medium
7h
21
Image Credit: Medium
Lets Dive Into The World Of AI
- AI, also known as Artificial Intelligence, is a field of computer science that allows computers and machines to mimic human learning, problem solving, and decision making.
- AI systems process data, identify patterns, and improve their performance over time using techniques like Machine Learning and Deep Learning.
- Machine Learning (ML) is a subset of AI that enables machines to learn and improve from experience through algorithms and analyzing data, leading to informed decision-making.
- AI aims to enable machines to sense, reason, act, and adapt like humans, enhancing autonomy and decision-making capabilities.
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