GeeksforGeeks

Summer Training 

As a Software Engineer with a focus on Full-Stack Web Development and a strong passion for AI/ML, I specialize in turning raw data into actionable insights and building scalable, user-centric applications. I am proficient in a wide range of technologies, including JavaScript, React, Node.js, Python, Django, SQL, TensorFlow, PyTorch, Matplotlib, Seaborn, NLTK, SpaCy, OpenCV, and more. My expertise in machine learning, data visualization, and web development enables me to deliver comprehensive solutions that bridge the gap between technical complexity and business needs. Whether it’s building robust web applications or leveraging AI to solve real-world challenges, I am driven by a commitment to creating innovative, impactful solutions. I firmly believe that data, when harnessed effectively, has the power to revolutionize industries. I am always exploring new technologies, collaborating with like-minded professionals, and staying up-to-date with the latest industry trends...

🚀Big news from OpenAI!
ChatGPT Go plan is now free for 12 months starting Nov 4, 2025 — offering access to GPT-5, faster image generation, longer memory, and more.
Perfect time to explore advanced AI tools without any cost!

Link : https://lnkd.in/gjDQSGDB

#AI #ChatGPT #OpenAI #Productivity #Innovation #AITools #ArtificialIntelligence #GPT5 #AIForEveryone #Technology #GenerativeAI #LLMs #LargeLanguageModels #ChatGPTGo #Transformer #GenAI #DeepLearning #AIIsEverywhere #AIResearch #AIRevolution #AIIsTheFuture #AIIsThePresent

Kaif Islam

OpenAI

ChatGPT

MERN Stack || Machine Learning || Deep Learning (Neural Networks) || Generative AI || Natural Language Processing || Computer Vision || Android Development || Problem Solving Skills || Effective Communication Skills

Excited to share my latest project: an AI-powered Image-to-Text Extraction Android Application built using Google ML Kit.
Combining AI and Android development, this project showcases how machine learning can enhance mobile experiences through intelligent text recognition.

#AndroidDevelopment #Android #Google #Application #Google #MLKits #MachineLearning #Developer #SoftwareEngineer #AI #ArtificialIntelligence

Grateful to the Lovely Professional University Alumni Association for the thoughtful birthday wishes! Proud to be part of such a supportive community. 🎂🎓 

#LPU #Alumni #Gratitude #LovelyProfessionalUniversity

Lovely Professional University

Day 3 - Types of AI Agents:-
AI agents exist along a spectrum of complexity and capability. Understanding the different types helps us recognize which kind of agent is most suitable for various tasks and what we can reasonably expect from each.

1. Simple Reflex Agents:-
Think: “If this happens → do that.”
How they work: They react instantly based on current input, without considering history or context.
Example: A thermostat — if the room gets cold, it turns the heater on.

2. Model-Based Agents:-
Think: “I remember what happened before.”
How they work: They keep track of past actions or world states to make smarter decisions.
Example: A robot vacuum that remembers where it has already cleaned.

3. Goal-Based Agents:-
Think: “What action will help me reach my goal?”
How they work: They plan and make decisions based on specific goals they want to achieve.
Example: A GPS navigation system finding the best route to your destination.

4. Utility-Based Agents:-
Think: “Which option gives me the best result?”
How they work: They compare outcomes and choose the most satisfying one — not just any goal, but the best one.
Example: A ride-hailing app choosing the fastest and cheapest route for you.

5. Learning Agents:-
Think: “I’ll learn and improve over time.”
How they work: They learn from experience to get better at tasks.
Example: Netflix’s recommendation system is improving based on what you watch.

In Short:-
Reflex → Reacts
Model-Based → Remembers
Goal-Based → Plans
Utility-Based → Optimizes
Learning → Improves

Day 2 - How AI Agents Actually Work?
Understanding how AI agents function requires grasping a fundamental concept called the agent loop. This is the continuous cycle that agents follow as they work toward their goals: perceive the current situation, think about what to do next, act by taking a specific step, observe the results of that action, and then repeat the process. This cycle continues until the agent determines it has completed the task or needs human assistance to proceed further.

Let’s walk through a concrete example to see this in action.
* Imagine we ask an AI agent to help plan a vacation to Tokyo for next month.
* The agent first perceives our request and understands the goal, which is to find flights, accommodation, and create an itinerary within our constraints.
* In the thinking phase, it determines that the first logical step is to search for available flights during our preferred dates. It then acts by using a flight search tool to query available options.
* After executing this action, it observes the results, perhaps discovering that direct flights are expensive but connecting flights offer significant savings.
* Based on this observation, the agent thinks again and decides to present both options while noting the trade-offs.
* It might then move to the next step, searching for hotels near popular attractions or transit hubs, again observing the results and adapting its recommendations based on what it finds.

This brings us to a crucial aspect of modern AI agents, which is their ability to use tools.

An agent isn’t limited to just processing information and generating text responses. It can invoke various tools to extend its capabilities. These tools might include web search engines to find current information, APIs to check flight prices or weather forecasts, calculators for complex computations, code interpreters to analyze data, or databases to store and retrieve information. When the vacation planning agent needs current flight prices, it can use a flight search tool. When it needs to know the weather in Tokyo next month, it uses a weather API. The agent decides which tools to use and when to use them based on what it’s trying to accomplish.

This observation and adaptation phase is what separates agents from simple automated scripts.

After each action, the agent examines the results and adjusts its strategy accordingly. If a hotel search returns no available rooms in our preferred neighborhood, the agent doesn’t just give up or blindly continue with its original plan. Instead, it observes this obstacle and adapts, perhaps expanding the search to nearby areas or adjusting the date range slightly. This feedback loop allows agents to handle unexpected situations, work around problems, and refine their approach iteratively until they achieve the desired outcome or determine that the goal cannot be reached with available resources.

Day 5 - Model-Based Agents :-
Model-based agents represent a significant step up in sophistication because they maintain an internal representation of the world that they cannot directly perceive. This internal model helps them make better decisions even when they lack complete information about their environment.

Consider a robot vacuum cleaner navigating a home. It cannot see the entire floor layout at once, but as it moves around, it builds a mental map of where furniture is located, which areas it has already cleaned, and where obstacles exist. This internal model allows it to plan efficient cleaning routes and avoid repeatedly bumping into the same obstacles.

In the context of AI assistants, a model-based agent might keep track of what information it has already gathered during a conversation, what tools it has tried, and what the current state of a multi-step task is. This memory of past states is crucial for maintaining coherence across extended interactions and making informed decisions based on accumulated knowledge rather than just the immediate present.

Google has announced Project Suncatcher, an experimental plan to run AI data centers in space using solar-powered satellites equipped with Tensor Processing Units (TPUs). The project explores whether machine learning workloads could be processed directly in orbit, where solar panels are up to eight times more efficient than on Earth.

Here’s what’s planned:

AI in Orbit: Solar-powered satellites in low Earth orbit would handle machine learning tasks and communicate through optical links.

Radiation Testing: Google’s Trillium v6e TPUs survived radiation levels nearly three times higher than expected for a five-year mission.

High-Speed Networking: Lab tests reached 1.6 terabits per second transmission using optical transceivers.

Partnership With Planet Labs: Two prototype satellites are set to launch by early 2027 to test TPU performance and data transfer in orbit.

#Google #AI #ArtificialIntelligence #MachineLearning #DeepLearning #ResearchPaper #Research #GoogleDeepmind #TPUs #Technology #Innovation #AIIsTheFuture #AIIsThePresent #AIForEveryone #AIIsEverywhere

Google has announced Project Suncatcher.

I’m happy to share this AI-Powered Image to Text Extraction Android Application project I worked on. Check it out here: https://lnkd.in/gAhGSpGD

#AndroidApplication #Kotlin #MLKit #Google #GoolgeMLKit #App #Application #Project #Innovation #Technology #TechIndustry #TechCompany #MNCs #ArtificialIntelligence #GenAI #MachineLearning #DeepLearning #AgenticAI #AI #AIIsTheFuture #AIIsEverywhere #AITrending

Day 4 - Simple Reflex Agents:-
Simple reflex agents are the most basic type, operating on straightforward condition-action rules. These agents perceive the current state of their environment and respond with predetermined actions based on pattern matching.

Think of a thermostat. When the temperature drops below the set threshold, it turns on the heating. However, when the temperature rises above another threshold, it turns off the heating. In the software world, a basic customer service chatbot that recognizes keywords like “refund” and responds with a preset message about the refund policy is a simple reflex agent. These agents are fast, predictable, and easy to build.

However, they’re severely limited because they have no memory of past interactions and cannot handle situations their designers didn’t explicitly anticipate. They work well for repetitive, well-defined scenarios but struggle with tasks that require nuance or context.

Excited to attend “The Future of AI: Build Agents That Work” a joint event by Databricks and OpenAI  featuring Sam Altman and Ali Ghodsi.

The session will explore how AI agents can transition from research to real-world deployment with reliability, scalability, and quality.

Looking forward to insights on how Agent Bricks can accelerate production-ready AI systems.

#AI #OpenAI #Databricks #GenerativeAI #Innovation #MachineLearning #ChatGPT #GPT  #ArtificialIntelligence #DeepLearning #LLMs

databricks.com

The Future of AI: Build Agents That Work

Databricks

Exploring the future of AI through A Compact Guide to Large Language Models — a great overview of how LLMs have evolved, their real-world use cases, and what’s next in the age of generative AI.
From GPT to open source innovation, this guide breaks down complex ideas into clear insights. A must-read for anyone building with or learning about AI.

#LLMs #LargeLanguageModels #ChatGPT #AI #ArtificialIntelligence #MachineLearning #DeepLearning #AgenticAI #AIAgent #MCP #AIIsTheFuture #AIIsEverywhere

A Compact Guide to LLMs

Exploring The Big Book of GenAI by Databricks — a fantastic guide to mastering production-ready Generative AI.
5 ways to harness your data for powerful GenAI applications.

#GenerativeAI #Databricks #AI #GenAI #LLMs #LargeLanguageModels #DeepLearning #ChatGPT #ML #DL #MachineLearning

Generative AI 

Day 1 - What are AI Agents?
Imagine asking a digital assistant to help plan a weekend trip. Instead of simply searching for flights when we type “flights to Paris,” it understands our broader goal. It checks flight options, compares hotel prices in different neighborhoods, looks up the weather forecast, and even suggests popular restaurants near our hotel. It remembers if we had mentioned a tight budget earlier in the conversation and adjusts its recommendations accordingly.
This is an AI agent at work, a system that doesn’t just respond to commands but actively works toward accomplishing our goals.

The Fundamentals of AI Agents:-
At its core, an AI agent is a software system that can perceive its environment, make decisions, and take actions to achieve specific goals with some degree of independence. The word “agent” is key here. Just as a travel agent acts on our behalf to find flights and negotiate deals, an AI agent acts on behalf of users or systems to accomplish tasks without needing constant guidance for every single step.
The distinction between traditional programs and AI agents comes down to how they operate.
Traditional software follows a predetermined path. When we use a spreadsheet program, it performs calculations exactly as we specify through formulas. When we click a button in an application, it executes a fixed sequence of operations. The behavior is entirely predictable because every scenario has been explicitly programmed.
AI agents, however, can navigate uncertain situations and make choices based on their observations. They don’t just execute instructions, but figure out what needs to be done and how to do it.
This capability has become particularly powerful with the emergence of large language models. These models, trained on vast amounts of text data, have developed remarkable abilities to understand natural language, reason about problems, and generate human-like responses. When we combine these language models with the ability to use tools and take actions, we get modern AI agents. The language model serves as the “brain” that processes information, makes decisions, and determines the best course of action. It can break down complex requests, understand context and nuance, and communicate naturally with both humans and other systems.

Four key characteristics define what makes something an AI agent:
First is autonomy, or the ability to operate without constant human intervention and make decisions independently.
Second is reactivity or responding appropriately to changes in the environment and new information.
Third is proactiveness, which is about taking initiative to achieve goals rather than just reacting to immediate stimuli.
Fourth is social ability, or the capacity to interact with other agents or humans through some form of communication.

#AI #ArtificialIntelligence #AIAgent #AgenticAI #MachineLearning #AITrend #AIForEveryone #DeepLearning #GenerativeAI #LLMs #Developer #Programmer #API

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