How AI works simple in layman's terms

 

AI, or Artificial Intelligence, works by enabling computers to "learn" from data and make decisions or predictions without needing explicit programming for every possible situation. Here’s a simple breakdown of how AI works in layman's terms:

AI isn't "thinking" like humans do. It’s more like a sophisticated pattern-matching tool that learns from data and gets better at making decisions, predictions, or recommendations over time based on that data.

1. Learning from Data

Imagine AI as a child learning from examples. Instead of being told everything step-by-step, the child is shown lots of examples to recognize patterns. For example, to teach an AI to recognize pictures of cats, you show it thousands of images of cats, and over time, it starts to figure out what makes a cat a cat (fur, whiskers, eyes, etc.).

This process is called training. The more data the AI sees, the better it becomes at recognizing patterns.

2. Making Predictions

Once an AI is trained, it can be tested on new data it hasn’t seen before. If the AI was trained to recognize cats, you can show it a new picture, and based on what it has learned, it can predict whether the picture contains a cat or not.

This is like how you would recognize a cat even if you’ve never seen that particular one before. The AI doesn't "know" what a cat is the way humans do, but it uses patterns from the training data to guess.

3. Improving Over Time

Just like a human can get better with practice, AI can improve the more it learns. This process is often called machine learning, where the AI keeps adjusting itself to get more accurate predictions or decisions.

For example, a recommendation system like Netflix can learn your preferences over time by watching your behavior (what shows you watch, how long you watch them) and then get better at suggesting movies or shows you'll like.

4. Different Types of AI

There are different kinds of AI, but most can be grouped into:

Rule-based AI: Follows predefined rules to solve problems. Like a calculator – it knows the rules of math but doesn't "learn."

Machine learning AI: Learns from examples and improves over time. Most of the modern AI systems (like Siri, Alexa, and self-driving cars) use this.

Deep learning AI: A more advanced form of machine learning that uses networks similar to the human brain (called neural networks) to solve very complex problems like image recognition or understanding language.

5. Mimicking Human Tasks

AI can mimic a variety of human tasks, such as:

Recognizing objects: Like identifying faces in photos.

Understanding language: Chatbots or virtual assistants like Siri or Google Assistant.

Making decisions: AI used in self-driving cars or medical diagnostics.

Best Popular Free AI tools

1. ChatGPT (OpenAI)

• Purpose: Conversational AI, language generation.
• Use Cases: Answering questions, generating text, brainstorming ideas, language translation, coding assistance.
• Platform: Web-based, also available through API.
• Strengths: Versatile, human-like text generation.

2. DALL-E 2 (OpenAI)

• Purpose: Image generation from text prompts.
• Use Cases: Creating unique images for design, marketing, and creative projects.
• Platform: Web-based, accessible through API.
• Strengths: Generates high-quality, creative images from descriptive text inputs.

3. DeepL

• Purpose: Language translation.
• Use Cases: Translating documents, websites, and text into different languages.
• Platform: Web-based, app, browser extensions.
• Strengths: High accuracy and nuanced translation quality.

4. Canva Magic Write

• Purpose: AI-powered content creation.
• Use Cases: Writing social media posts, blogs, emails, presentations.
• Platform: Canva's design platform.
• Strengths: Easy integration with design tools, ideal for marketers and content creators.

5. Runway ML

• Purpose: Video editing and AI tools for creatives.
• Use Cases: Video effects, image generation, text-to-image, object detection.
• Platform: Web-based.
• Strengths: User-friendly interface, wide range of creative AI tools.

6. Copy.ai

• Purpose: AI copywriting.
• Use Cases: Generating marketing copy, product descriptions, blog content.
• Platform: Web-based.
• Strengths: Tailored content generation for marketers, easy to use.

7. Lumen5

• Purpose: Video creation from text.
• Use Cases: Turning blog posts, articles, or any text into engaging videos.
• Platform: Web-based.
• Strengths: Simple, drag-and-drop interface, ideal for content marketers.

8. Hugging Face Transformers

• Purpose: Natural language processing and machine learning models.
• Use Cases: Sentiment analysis, text classification, translation, summarization.
• Platform: Open-source libraries for Python.
• Strengths: Extensive library of pre-trained models, strong community support.

9. Google Colab

• Purpose: Cloud-based Jupyter notebook environment.
• Use Cases: Running Python code, experimenting with machine learning models, data analysis.
• Platform: Web-based.
• Strengths: Free GPU access, collaborative features, supports many libraries.

10. Notion AI

• Purpose: Productivity and AI-assisted writing.
• Use Cases: Task management, writing assistance, note-taking, brainstorming.
• Platform: Web-based, app.
• Strengths: Seamlessly integrated into Notion’s workspace, assists with various writing tasks.

Getting started Spring AI with OpenAI Chat Model

 Problem statement :  develop spring boot API which connect's to Open AI and will give top 5

                                       personalities of any sports. Other than sports,

                                       if you give another details should display warning message.

We have different AI Chat models as mentioned below

• Chat Completion API

  • OpenAI Chat Completion (streaming and function-calling support)

  • Microsoft Azure Open AI Chat Completion (streaming and function-calling support)

  • Ollama Chat Completion

  • Hugging Face Chat Completion (no streaming support)

  • Google Vertex AI PaLM2 Chat Completion (no streaming support)

  • Google Vertex AI Gemini Chat Completion (streaming, multi-modality & function-calling support)

  • Amazon Bedrock

    • Cohere Chat Completion

    • Llama Chat Completion

    • Titan Chat Completion

    • Anthropic Chat Completion

    • Jurassic2 Chat Completion

  • MistralAI Chat Completion (streaming and function-calling support)

This is post will explain you about , how we can use Open AI model to build simple spring boot application

Step 1 :  Signup : https://platform.openai.com/signup

              generate key -  https://platform.openai.com/account/api-keys

Step 2 :           spring boot intializr : https://start.spring.io/

dependencies :  OpenAI and other related dependencies

                        Provide group  artifact and other required details

                       import project to IntelliJ

Step3 :          Write Controller 

package com.personal.openai;

import org.springframework.ai.chat.messages.Message;
import org.springframework.ai.chat.messages.SystemMessage;
import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.ai.openai.OpenAiChatModel;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;

@RestController
public class OpenAIController {


    @Autowired
    private OpenAiChatModel openAiChatModel;

    @GetMapping("/sports")
    public String getSportPersonaDetails(@RequestParam String name){
        var systemMessage = new SystemMessage("Your primary function is to provide details about sports," +
                " if anything else asks simply say that i can provide only sports details");
        String promptMessage =  String.format(" Could you please provide detailed information" +
                " on the top 5 sports persons currently in %s ?",name);
        Message message = new UserMessage(promptMessage);
        return openAiChatModel.call(message,systemMessage);

    }
}

Step 4: update the properties/yaml

spring.application.name=openai
spring.ai.openai.api-key=sk-proj-NNcDe2HRc1UUjcbIRkRsT3Blbk
spring.ai.openai.chat.enabled=true
spring.ai.openai.chat.options.model=gpt-3.5-turbo
spring.ai.openai.chat.options.temperature=0.7

Step 5 : Run the code and start the server

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <parent>
       <groupId>org.springframework.boot</groupId>
       <artifactId>spring-boot-starter-parent</artifactId>
       <version>3.2.5</version>
       <relativePath/> <!-- lookup parent from repository -->
    </parent>
    <groupId>com.personal</groupId>
    <artifactId>openai</artifactId>
    <version>0.0.1-SNAPSHOT</version>
    <name>openai</name>
    <description>Demo project for Spring Boot ai</description>
    <url/>
    <licenses>
       <license/>
    </licenses>
    <developers>
       <developer/>
    </developers>
    <scm>
       <connection/>
       <developerConnection/>
       <tag/>
       <url/>
    </scm>
    <properties>
       <java.version>17</java.version>
       <spring-ai.version>1.0.0-M1</spring-ai.version>
    </properties>
    <dependencies>
       <dependency>
          <groupId>org.springframework.boot</groupId>
          <artifactId>spring-boot-starter-web</artifactId>
       </dependency>
       <dependency>
          <groupId>org.springframework.ai</groupId>
          <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
       </dependency>

       <dependency>
          <groupId>org.projectlombok</groupId>
          <artifactId>lombok</artifactId>
          <optional>true</optional>
       </dependency>
       <dependency>
          <groupId>org.springframework.boot</groupId>
          <artifactId>spring-boot-starter-test</artifactId>
          <scope>test</scope>
       </dependency>
    </dependencies>
    <dependencyManagement>
       <dependencies>
          <dependency>
             <groupId>org.springframework.ai</groupId>
             <artifactId>spring-ai-bom</artifactId>
             <version>${spring-ai.version}</version>
             <type>pom</type>
             <scope>import</scope>
          </dependency>
       </dependencies>
    </dependencyManagement>

    <build>
       <plugins>
          <plugin>
             <groupId>org.springframework.boot</groupId>
             <artifactId>spring-boot-maven-plugin</artifactId>
             <configuration>
                <excludes>
                   <exclude>
                      <groupId>org.projectlombok</groupId>
                      <artifactId>lombok</artifactId>
                   </exclude>
                </excludes>
             </configuration>
          </plugin>
       </plugins>
    </build>
    <repositories>
       <repository>
          <id>spring-milestones</id>
          <name>Spring Milestones</name>
          <url>https://repo.spring.io/milestone</url>
          <snapshots>
             <enabled>false</enabled>
          </snapshots>
       </repository>
    </repositories>

</project>

 

Step 6:  Output :   http://localhost:8080/sports?name=cricket

              http://localhost:8080/sports?name=tollywood

              

This how we can work on any Models. 

        

How do you foster collaboration between development, operations, and other teams in a Java project?

 

Fostering collaboration between development, operations, and other teams in a Java project requires a combination of communication, tools, and processes. Here are some strategies to help facilitate collaboration:

Establish clear communication channels: Use tools like Slack, Microsoft Teams, or similar platforms for real-time communication. Also, have regular meetings, such as daily stand-ups or weekly sync-ups, to keep everyone aligned and informed about project progress.

Encourage cross-functional teams: Form cross-functional teams comprising members from development, operations, QA, and other relevant departments. This helps in sharing knowledge and understanding each other's perspectives.

Implement DevOps practices: Embrace DevOps principles to bridge the gap between development and operations. Automate processes such as continuous integration, continuous delivery, and infrastructure provisioning to streamline collaboration and reduce bottlenecks.

Adopt Agile methodologies: Agile methodologies like Scrum or Kanban promote collaboration through iterative development cycles, regular feedback, and close collaboration between team members.

Use version control and collaboration tools: Leverage version control systems like Git along with collaboration platforms like GitHub or Bitbucket. These tools facilitate collaboration by allowing teams to work on the same codebase simultaneously and track changes effectively.

Provide training and knowledge sharing: Organize workshops, brown bag sessions, or knowledge-sharing sessions to educate team members about each other's roles, technologies, and best practices. Encourage learning and cross-training to build a more cohesive team.

Promote a culture of transparency and trust: Foster an environment where team members feel comfortable sharing their ideas, concerns, and feedback openly. Encourage transparency in decision-making processes and foster trust among team members.

Establish shared goals and metrics: Define common goals and metrics that align with both development and operations objectives. This encourages teams to collaborate towards shared outcomes and promotes a sense of collective responsibility.

Implement collaborative tools for project management: Utilize project management tools like Jira, Trello, or Asana to track tasks, assign responsibilities, and monitor progress collaboratively. Ensure that these tools are accessible to all team members and updated regularly.

Celebrate successes and learn from failures: Recognize and celebrate achievements as a team, whether it's delivering a successful release or overcoming a challenging issue. Similarly, encourage a blame-free culture where failures are seen as learning opportunities, and teams work together to identify solutions and prevent recurrence.

By implementing these strategies, you can foster a culture of collaboration and cooperation among development, operations, and other teams in a Java project, ultimately leading to improved efficiency, quality, and innovation.