AI/ML - Codemotion Magazine https://www.codemotion.com/magazine/ai-ml/ We code the future. Together Wed, 03 Jul 2024 15:03:51 +0000 en-US hourly 1 https://wordpress.org/?v=6.5.5 https://www.codemotion.com/magazine/wp-content/uploads/2019/11/cropped-favicon-150x150.png AI/ML - Codemotion Magazine https://www.codemotion.com/magazine/ai-ml/ 32 32 We made Claude go through a Real Tech Interview for a Senior Frontend Position https://www.codemotion.com/magazine/ai-ml/claude-tech-interview/ Wed, 03 Jul 2024 14:38:45 +0000 https://www.codemotion.com/magazine/?p=28546

In recent months, the tech world has been abuzz with talk of Claude 3.5 Sonnet by Anthropic. With claims of advanced language understanding and problem-solving capabilities, we couldn’t help but wonder: How would Claude fare in a real-world scenario? To satisfy our curiosity and put Claude to the test, we decided to simulate a tech… Read more

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In recent months, the tech world has been abuzz with talk of Claude 3.5 Sonnet by Anthropic. With claims of advanced language understanding and problem-solving capabilities, we couldn’t help but wonder: How would Claude fare in a real-world scenario?

To satisfy our curiosity and put Claude to the test, we decided to simulate a tech interview for a senior developer position (mainly working with React) at a software house with international clients. We crafted a series of questions that a candidate might face in an interview, covering technical expertise and soft skills.

Our virtual interview panel consisted of two key figures: a Chief Technology Officer (CTO) asking in-depth technical questions, and a non-technical HR representative exploring the candidate’s interpersonal and leadership abilities.

We instructed Claude to respond as if it were a real candidate in a live conversation, aiming to assess not just its technical knowledge but also its ability to communicate complex concepts clearly and professionally. We also asked it for simple code snippets as examples.

The questions were taken from real experiences shared on the web and we also improvised a bit.

In the following article, we’ll take you through Claude’s responses to questions ranging from the intricacies of single-page applications vs. Server-Side Rendering to strategies for performance optimization in React applications. We’ll also explore how Claude handled more open-ended questions about soft skills and working in an international environment.

Can Claude.ai pass the interview for a senior frontend developer position? Let’s find out.

Tech Interview with Claude: Senior Frontend Developer candidate

Question 1: What are the main differences between SPA and SSR?

SPAs load a single HTML page and dynamically update content as the user interacts with the app. They typically use JavaScript frameworks like React or Vue.js. The main advantages are smooth user experiences and reduced server load. However, they can have slower initial load times and potential SEO challenges.

SSR, on the other hand, generates the full HTML for each page on the server. This approach offers faster initial page loads and better SEO, as search engines can easily crawl the content. However, it may result in more server load and potentially slower page transitions.

Question 2: What are Web Vitals (LCP, FID, CLS)?

Web Vitals are a set of metrics that Google considers critical for user experience:

  • Largest Contentful Paint (LCP): Measures loading performance. It should occur within 2.5 seconds of when the page first starts loading.
  • First Input Delay (FID): Measures interactivity. Pages should have a FID of less than 100 milliseconds.
  • Cumulative Layout Shift (CLS): Measures visual stability. Pages should maintain a CLS of less than 0.1.

In the real world, these metrics guide developers in optimizing websites. For example, we might use techniques like lazy loading, code splitting, or optimizing assets to improve LCP. To enhance FID, we could minimize JavaScript execution time. For CLS, we’d ensure images and ads have reserved space to prevent layout shifts.

Is there a particular Web Vital you’d like to discuss further?

Question 3: How does React handle forms, and what are controlled components?

React handles forms through controlled components. In this approach:

  • Form elements like <input>, <textarea>, and <select> maintain their own state in vanilla HTML.
  • In React, we typically manage this state in the component’s state.
  • We use the value prop to set the element’s value and an onChange handler to update the state when the user types.

This gives React full control over the form elements, allowing for easy manipulation and validation of user input.

Here’s a simple example:

jsxCopy<code>function NameForm() {
  const [name, setName] = useState('');

  const handleSubmit = (event) => {
    event.preventDefault();
    console.log('Submitted name:', name);
  }

  return (
    <form onSubmit={handleSubmit}>
      <input 
        type="text" 
        value={name} 
        onChange={(e) => setName(e.target.value)} 
      />
      <button type="submit">Submit</button>
    </form>
  );
}</code>

Would you like me to explain any part of this concept in more detail?

4) How would you Build a React app that will interact with an MLB API to display a game schedule with players, scores, dates, and broadcast stations for each game?

To build a React application that interacts with an MLB API for schedules, I would approach it like this:

  1. Set up a new React project using Create React App or Next.js.
  2. Create components for different parts of the UI (e.g., Schedule, Game, Team).
  3. Use React Router for navigation if it’s a multi-page app.
  4. Utilize React hooks, particularly useState and useEffect, for state management and side effects.
  5. Use a library like Axios or fetch API to make HTTP requests to the MLB API.
  6. Implement error handling and loading states for API calls.
  7. Use React Context or Redux for global state management if needed.
  8. Implement responsive design for various screen sizes.

Here’s a basic example of how a component might look:

jsxCopy<code>import React, { useState, useEffect } from 'react';
import axios from 'axios';

function PlayoffSchedule() {
  const [games, setGames] = useState([]);
  const [loading, setLoading] = useState(true);
  const [error, setError] = useState(null);

  useEffect(() => {
    axios.get('MLB_API_ENDPOINT')
      .then(response => {
        setGames(response.data);
        setLoading(false);
      })
      .catch(err => {
        setError(err);
        setLoading(false);
      });
  }, []);

  if (loading) return <div>Loading...</div>;
  if (error) return <div>Error: {error.message}</div>;

  return (
    <div>
      {games.map(game => (
        <Game key={game.id} data={game} />
      ))}
    </div>
  );
}</code>

Would you like me to elaborate on any part of this approach?

Question 5: Yes, elaborate on your approach to performance optimization in this React application

For performance optimization in this React application, I would consider several strategies:

  1. Code splitting: Use React.lazy() and Suspense to load components only when needed.
  2. Memoization: Use React.memo, useMemo, and useCallback to prevent unnecessary re-renders.
  3. Virtual scrolling: For long lists, implement virtualization to render only visible items.
  4. Optimize images: Use appropriate formats, sizes, and lazy loading for images.
  5. Minimize network requests: Use GraphQL or implement request batching if possible.
  6. Implement caching: Cache API responses to reduce network requests.
  7. Use production builds: Ensure you’re using minified production builds in deployment.
  8. Optimize CSS: Use CSS-in-JS solutions or CSS Modules to reduce unused styles.
  9. Implement proper error boundaries to prevent entire app crashes.
  10. Use web workers for computationally intensive tasks to keep the main thread free.

Here’s a quick example of using React.memo and useCallback:

jsxCopy<code>import React, { useCallback, useState } from 'react';

const GameItem = React.memo(({ game, onSelectGame }) => {
  console.log('GameItem render');
  return (
    <div onClick={() => onSelectGame(game.id)}>
      {game.homeTeam} vs {game.awayTeam}
    </div>
  );
});

function GameList({ games }) {
  const [selectedGame, setSelectedGame] = useState(null);

  const handleSelectGame = useCallback((gameId) => {
    setSelectedGameId(gameId);
  }, []);

  return (
    <div>
      {games.map(game => (
        <GameItem 
          key={game.id} 
          game={game} 
          onSelectGame={handleSelectGame} 
        />
      ))}
    </div>
  );
}</code>

Would you like me to go into more depth on any of these optimization techniques?

The HR asks: Can you explain this last item to me in plain non-technical terms?

Certainly, I’d be happy to explain performance optimization in plain terms.

Performance optimization is like tuning up a car to make it run smoother and faster. Just as a well-tuned car uses less fuel and gets you to your destination quicker, a well-optimized website or app loads faster, responds more quickly to user actions, and uses less data and battery power.

In everyday terms, it’s about making sure that when someone uses our app:

  1. It opens quickly, without long waiting times.
  2. It responds immediately when they tap buttons or type something.
  3. It doesn’t drain their phone battery or use up all their mobile data.
  4. It works smoothly even if they have a slower internet connection.
  5. It doesn’t freeze or crash, even when dealing with lots of information.

Essentially, we’re trying to create the best possible experience for our users. We want our app to be like a helpful assistant – always ready, quick to respond, and not demanding too much from the user’s device or internet connection.

It’s similar to how a restaurant might optimize its service: ensuring quick seating, prompt order-taking, efficient food preparation, and swift delivery. The goal is to make the whole experience pleasant and efficient for the customer, just as we aim to do with our app for our users.

Does this explanation help clarify the concept?

The HR asks: Which soft skills do you think you’ll need in this senior position working with an international team for big clients around the world?

Thank you for that important question. In a senior position working with an international team and big global clients, I believe several key soft skills are crucial:

  1. Communication: Clear and effective communication is paramount, especially when working across different time zones and cultures. This includes being able to explain complex technical concepts in simple terms to non-technical stakeholders.
  2. Adaptability: The tech world changes rapidly, and working with diverse clients means adapting to different business needs and cultural norms. Being flexible and open to change is essential.
  3. Cultural sensitivity: Working with an international team and global clients requires an understanding and respect for cultural differences in communication styles, work practices, and business etiquette.
  4. Leadership: As a senior developer, I’d need to guide and mentor junior team members, which requires strong leadership skills.
  5. Time management: Balancing multiple projects across different time zones demands excellent time management and organizational skills.
  6. Empathy: Understanding the needs and perspectives of both team members and clients is crucial for building strong relationships and delivering solutions that truly meet their needs.
  7. Problem-solving: Being able to approach challenges creatively and find effective solutions, often under pressure, is a key skill in this role.
  8. Collaboration: The ability to work effectively in a team, fostering a positive and productive work environment, is vital in any senior position.
  9. Continuous learning: The tech field evolves rapidly, so a commitment to ongoing learning and professional development is crucial.
  10. Patience and stress management: Dealing with complex projects and diverse stakeholders can be stressful, so the ability to remain calm and patient under pressure is important.

These soft skills, combined with technical expertise, would allow me to effectively lead projects, manage relationships, and deliver high-quality results in an international, client-facing environment. Would you like me to elaborate on any of these skills or how I’ve applied them in my past experiences?


Do you think Claude is ready to become the new senior frontend developer in your company?

The post We made Claude go through a Real Tech Interview for a Senior Frontend Position appeared first on Codemotion Magazine.

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From Graphics to AI Powerhouse: The Rise of NVIDIA to The Top https://www.codemotion.com/magazine/ai-ml/from-graphics-to-ai-powerhouse-the-rise-of-nvidia-to-the-top/ Wed, 19 Jun 2024 14:36:44 +0000 https://www.codemotion.com/magazine/?p=28306

NVIDIA’s story is one of remarkable transformation, evolving from a graphics processing unit (GPU) creator to a dominant force in the tech industry. This timeline explores the key milestones, products, and innovations that fueled their meteoric rise. Additionally, as of June 2024, NVIDIA has become the world’s most valuable company, a testament to its strategic… Read more

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NVIDIA’s story is one of remarkable transformation, evolving from a graphics processing unit (GPU) creator to a dominant force in the tech industry. This timeline explores the key milestones, products, and innovations that fueled their meteoric rise. Additionally, as of June 2024, NVIDIA has become the world’s most valuable company, a testament to its strategic advancements and market influence.

The Rise of NVIDIA: A Timeline

1993: Genesis

Founded by Jen-Hsun Huang, Chris Malachowsky, and Curtis Priem, NVIDIA set its sights on revolutionizing computer graphics. Their vision was clear: to develop hardware that would bring cinematic-quality visuals to personal computers.

1998: The GeForce Revolution

The launch of the GeForce 256, the world’s first GPU with a dedicated transform and lighting engine, marked a turning point. It brought superior 3D graphics capabilities to personal computers, forever changing the gaming and design landscape. This innovation made real-time 3D graphics more accessible and laid the groundwork for the GPU to become a central component in modern computing.

2000s: Diversification and Domination

The rise of NVIDIA continued with the GeForce series, introducing features like programmable shaders and anti-aliasing, pushing the boundaries of visual fidelity. Their GPUs set new standards for performance and visual realism in video games.

However, NVIDIA’s ambitions extended beyond gaming. They developed GPUs for professional visualization and scientific computing. The Tesla line, introduced in 2008, specifically catered to this market, showcasing the GPU’s potential for general-purpose computing. This diversification demonstrated NVIDIA’s ability to adapt and thrive in multiple tech sectors.

The Age of AI and Accelerated Computing (2010s-Present)

This era witnessed NVIDIA’s transformation into an AI powerhouse. Here’s a deeper dive into their contributions:

Breaking Ground: GPUs and Deep Learning

Deep learning, a subfield of AI, relies on complex neural networks trained on massive datasets. These networks benefit tremendously from parallel processing, a core strength of GPUs. Unlike CPUs designed for sequential tasks, GPUs have thousands of cores that can handle computations simultaneously, significantly speeding up deep learning training.

CUDA: The Software Key

In 2006, NVIDIA introduced CUDA, a parallel programming framework specifically designed for their GPUs. CUDA allowed developers to leverage the GPU’s architecture for general-purpose computing, not just graphics. This opened the floodgates for AI researchers and engineers to utilize NVIDIA GPUs for deep learning tasks. CUDA’s introduction was a game-changer, making GPU-accelerated computing accessible to a broader audience.


Recommended article: Amazing and contradictory facts in the gaming industry


The Tesla Transformation

Originally designed for scientific computing, the Tesla line of GPUs underwent a significant shift. NVIDIA optimized them for deep learning workloads, incorporating features like high-bandwidth memory and tensor cores (specialized AI processing units) in later iterations. These advancements further solidified NVIDIA’s dominance in the AI hardware landscape. Tesla GPUs became synonymous with high-performance AI computing, driving breakthroughs in machine learning and data science.

Beyond Hardware: Democratizing AI

Recognizing the broader potential of AI, NVIDIA developed software tools and frameworks like NVIDIA CUDA X AI and RAPIDS. These tools simplify deep learning development, making it accessible to researchers and businesses without extensive hardware expertise. By lowering the barrier to entry, NVIDIA empowered a new wave of AI innovation across industries.

Building the AI Ecosystem

NVIDIA actively fosters the AI ecosystem through initiatives like NVIDIA DGX Systems, pre-configured supercomputers specifically designed for deep learning. They also host conferences like the GPU Technology Conference (GTC), providing a platform for researchers and developers to exchange ideas and accelerate AI advancements. These efforts have created a vibrant community of AI practitioners and have positioned NVIDIA as a central hub in the AI world.

Recent Milestone: World’s Most Valuable Company

In June 2024, NVIDIA’s market capitalization surpassed $3 trillion, making it the world’s most valuable company. This milestone is attributed to NVIDIA’s strategic focus on AI and its ability to capitalize on the growing demand for AI technologies. The company’s stock rose significantly, highlighting investor confidence in its future prospects. This achievement marks a significant shift in the tech industry, where NVIDIA now leads, followed closely by Microsoft and Apple according to different sources.

Conclusion

The rise of NVIDIA after its strategic shift towards AI, coupled with their powerful hardware and software solutions, has propelled them to the forefront of this transformative technology. As AI continues to evolve, NVIDIA’s influence and contributions will likely expand, solidifying their role as a tech titan.

NVIDIA’s journey from creating the first GPU to becoming a leader in AI and accelerated computing is a testament to their vision and adaptability.

The post From Graphics to AI Powerhouse: The Rise of NVIDIA to The Top appeared first on Codemotion Magazine.

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A Guide to Understanding Quality Image Annotation for Computer Vision https://www.codemotion.com/magazine/ai-ml/understanding-quality-image-annotation-for-computer-vision/ Tue, 18 Jun 2024 13:04:13 +0000 https://www.codemotion.com/magazine/?p=28284

Computer vision is at the heart of some of the most exciting emerging technologies today. Think about self-driving cars, conservational animal tagging, diagnostic imaging, or the ubiquity of facial recognition unlocking our phones; behind all of this innovation is computer vision.   Alas, computer vision requires image annotation which can be a painstaking, laborious task and… Read more

The post A Guide to Understanding Quality Image Annotation for Computer Vision appeared first on Codemotion Magazine.

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Computer vision is at the heart of some of the most exciting emerging technologies today. Think about self-driving cars, conservational animal tagging, diagnostic imaging, or the ubiquity of facial recognition unlocking our phones; behind all of this innovation is computer vision.  

Alas, computer vision requires image annotation which can be a painstaking, laborious task and pose serious problems if done improperly. This means there are fantastic opportunities arising in the tech professional landscape for developers who know about image annotation. 

This article dives into the nuts and bolts of image annotation, breaking down what it is, why it’s so important, and the challenges developers face in making it work. We’ll cover different types of annotation, essential skills for developers, and recommend the general best practices for image annotation whatever computer vision model you’re working on.

An Introduction to Computer Vision and Image Annotation

For the layperson, computer vision is an application of machine learning (ML) models that enables computers to understand the world around them through visual information – in a sense, it teaches them to see.

Unsurprisingly, this innovation is reshaping industries and transforming our interactions with technology. Its applications span a wide array of domains, each harnessing the power of visual data to drive groundbreaking advancements. This is one of the reasons why learning ML is better for your career than traditional programming.

Image sourced from scoop.market.us

The Increasing Applications of Computer Vision

  • Autonomous Vehicles: Autonomous vehicles perceive their surroundings by interpreting visual data from cameras and sensors. This is how tech is making autonomous cars safer than ever.
  • Facial Recognition: Facial recognition technology has become ubiquitous, powering security features on smartphones, surveillance systems, and authentication mechanisms. 
  • Medical Imaging: In the realm of healthcare, computer vision plays a pivotal role in revolutionizing medical imaging techniques. From X-rays and MRIs to CT scans and ultrasounds, algorithms analyze image data to aid in disease diagnosis and treatment. 
  • Retail and Inventory Management: By deploying cameras and image recognition algorithms, retailers can track products on shelves and monitor stock levels in real-time. Personalized shopping experiences, augmented reality fitting rooms, and AI-powered customer service have also come to play a big role in the retail landscape.
  • Conservation Efforts: Beyond commercial applications, computer vision contributes to vital conservation efforts offering a non-invasive solution to monitoring wildlife populations. Remote cameras equipped with image recognition technology enable researchers to gather valuable data for conservation initiatives. 

The Role of Image Annotation in Computer Vision

All of these applications of computer vision rely on ML algorithms sifting through vast data sets. Crucially, for this data to be understood, it must be high quality, clean, and properly annotated. Understanding techniques like web crawling vs web scraping are instrumental in collecting diverse and extensive datasets from various sources, which are then refined and annotated for use in training ML models.

Image annotation is the process of enriching raw visual data with metadata and tags. It’s what enables AI systems to understand and interpret images by adding contextual information, spatial references, and semantic labels. 

From drawing bounding boxes around objects to segmenting pixels and identifying key points, image annotation is how to bridge the gap between raw visual data and AI-driven insights. 

Different Types of Image Annotation

Image annotation is a broad umbrella term that encompasses a diverse array of types, techniques, and processes, each tailored to specific tasks and objectives. By leveraging these annotation techniques, developers can train robust ML models capable of accurately interpreting and analyzing visual data.

Let’s delve deeper into each annotation type to understand its role in computer vision applications:

 1. Bounding Boxes and Cuboids

Bounding boxes provide spatial annotations by enclosing objects within rectangular or cuboidal regions. This technique is essential for tasks like object detection and localization. 

In an image of a street scene from CCTV cameras, bounding boxes could be used to outline and identify vehicles, pedestrians, and traffic signs. This enables AI systems to recognize and analyze the spatial relationships between objects.

 2. Image Classification

Image classification involves associating entire images with predefined labels or categories. This technique trains models to recognize visual patterns and characteristics, enabling them to categorize images based on their content. 

For instance, in a dataset of shark images, each image may be labeled with the corresponding species (“Great White,” “Bronze Whaler,” “Shortfin Mako,” etc.) allowing AI systems to classify images accurately.
(Free to use image from Pexels)

 3. Lines, Splines, and Polylines

Annotating straight or curved lines aids in tasks such as road detection for autonomous vehicles and trajectory tracking for drones. In a satellite image of a road network, lines and splines can be used to delineate road boundaries, lanes, and intersections, providing crucial spatial information for navigation and path planning algorithms.

 4. Polygons and Semantic Segmentation

Polygons offer precise delineation of object boundaries within images, while semantic segmentation assigns categorical labels to individual pixels. This technique enables fine-grained scene understanding by identifying and categorizing objects and regions within images. 

Free to use image from Pexels

One use of polygons is in doctors analyzing medical imaging. These models can be used to outline tumors or anatomical structures. Semantic segmentation can also be used to differentiate between different tissue types or organs, aiding in disease diagnosis and treatment planning.

 6. Landmarking and Keypoint Annotation

Identifying keypoints or regions of interest within images facilitates tasks like facial recognition, pose estimation, and anatomical landmark detection. For instance, in a facial recognition system, keypoints can be used to identify and track facial features such as eyes, nose, and mouth, enabling accurate identification and analysis of facial expressions and gestures. 

Another thing: what is CCaaS’s, social media’s, and education’s shared characteristic? They’re all investigating ways to use AI emotion and sentiment analysis with computer vision to better understand the emotions of their target audiences. Landmarking and keypoint annotation plays a key part in this process too.

Challenges in Image Annotation

Essentially there is one challenge of image annotation: maintaining quality, consistency, and accuracy of annotation across vast data sets. Producing clean annotated data is the only goal of image annotation; however there are a number of practical obstacles in achieving this goal.

Among these challenges, the management of proprietary data under stringent legal frameworks becomes critical. The use of a digital contract can ensure that all parties adhere to agreed standards of data handling and annotation, protecting intellectual property and complying with privacy regulations.

To address these challenges, many organizations are turning to a sophisticated product dashboard that provides real-time insights into annotation quality and process efficiency, ensuring that standards are met across vast datasets.

As such, the challenge of maintaining quality can manifest in many different ways. These are the key trouble zones you’ll need to focus on:

  • Balancing Human and Automated Annotation – Achieving the right balance between human expertise and automated tools is crucial for optimizing annotation efficiency and accuracy. Leveraging automation for large-scale datasets while relying on human annotators for nuanced tasks ensures quality annotations.
  • Skills and Expertise – Image annotation requires technical proficiency with specialized tools and domain-specific knowledge. Providing adequate training and supporting annotators to achieve data science qualifications ensures consistency and accuracy in annotations.
  • Quality Control and Consistency – Image annotation requires a detailed plan with clear annotation instructions that are well communicated across dev teams. Implementing robust quality control measures like inter-annotator compliance audits, and consensus-based annotation, also helps maintain annotation accuracy and consistency across datasets.

Best Practices for Effective Image Annotation

When it comes to effective image annotation for computer vision projects, adopting best practices is essential to ensure accuracy, reliability, and efficiency. 

 1. Prioritize Human Annotation

Human annotation brings a level of expertise and contextual understanding that automated tools often lack. Human annotators can interpret complex visual data, recognize subtle nuances, and adapt to diverse annotation tasks. 

By prioritizing human annotation, developers can ensure high-quality annotations that meet the specific requirements of their projects. Additionally, human annotators can provide valuable insights and feedback, contributing to the overall success of the annotation process.

2. Annotate Images Completely

As a rule of thumb, the more care and attention you can invest in annotation, the better your ML model will fare. Annotating images completely doesn’t just mean annotating every image either. It means ensuring that bounding boxes are tightly configured, labels are clear and consistent, and most importantly, that all objects of interest are tagged. 

Failing to annotate all instances can hinder the model’s ability to accurately detect and classify objects. When annotating complex images with multiple objects, it’s essential to label all instances of each object category present in the image. 

In scenarios where objects are partially obscured or occluded by other objects or elements, it’s crucial to accurately annotate the visible portions of the occluded objects too. This ensures that the model learns to recognize and differentiate occluded objects correctly, enhancing its robustness in real-world scenarios where occlusions are common.

 3. Choose Reliable Annotation Tools

Selecting the right ML toolkit, specific annotation tools and platforms is paramount. Developers should evaluate annotation tools based on factors such as annotation types supported, scalability, ease of use, and collaboration features. 

Reliable annotation tools often offer advanced features such as version control, real-time collaboration, and quality assurance mechanisms, enhancing productivity and accuracy throughout the annotation process. The chosen tools should align with the project’s requirements and facilitate efficient annotation workflows.

It’s also important to plan for possible transitions between annotation tools, so it’s a good idea to have an application migration strategy in place and ensure your annotation tools provide the required features. 

Quality Image Annotation for Computer Vision: Conclusions

In conclusion, image annotation stands as a fundamental pillar of computer vision, enabling machines to perceive and understand the visual world. By understanding the diverse annotation techniques, addressing inherent challenges, and adopting best practices, developers can unlock the full potential of AI-powered image analysis.

The post A Guide to Understanding Quality Image Annotation for Computer Vision appeared first on Codemotion Magazine.

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ChatGPT 4o: A Comprehensive Overview for Developers https://www.codemotion.com/magazine/ai-ml/chatgpt-4o-a-comprehensive-overview-for-developers/ Tue, 14 May 2024 07:54:14 +0000 https://www.codemotion.com/magazine/?p=27679

OpenAI’s latest model, ChatGPT 4o, represents a significant advancement in AI technology, offering features that can notably enhance the developer experience. This article delves into the specifications and capabilities of ChatGPT 4o, particularly focusing on those aspects that are most relevant to code creation and writing. Key Enhancements in Natural Language Understanding ChatGPT 4o showcases… Read more

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OpenAI’s latest model, ChatGPT 4o, represents a significant advancement in AI technology, offering features that can notably enhance the developer experience. This article delves into the specifications and capabilities of ChatGPT 4o, particularly focusing on those aspects that are most relevant to code creation and writing.

Key Enhancements in Natural Language Understanding

ChatGPT 4o showcases substantial improvements in natural language processing (NLP). Trained on a larger and more diverse dataset, the model can now understand and generate text with greater accuracy and fluency.

Developer Benefits:

  • More accurate code documentation and generation
  • Enhanced ability to follow intricate coding instructions
  • Reliable and coherent responses in complex scenarios

These improvements mean developers can rely on ChatGPT 4o for more precise assistance in generating code snippets and documenting complex systems.

Superior Contextual Awareness

A notable feature of ChatGPT 4o is its enhanced contextual awareness. The model can maintain context over longer interactions, making it ideal for complex, multi-turn conversations.

Applications:

  • Extended debugging sessions
  • Detailed technical support
  • Development of sophisticated conversational agents

This improvement facilitates deeper exploration of issues and provides detailed guidance, helping developers navigate complex coding problems more effectively.

Advanced Integration Capabilities

ChatGPT 4o introduces advanced integration features, allowing embedding into various development environments and tools.

Integration Features:

  • Compatibility with popular IDEs like Visual Studio Code
  • Enhanced API support for multiple programming languages
  • Improved support for webhooks and other integration mechanisms

These capabilities enable developers to integrate ChatGPT 4o into their workflows more effectively, enhancing productivity and efficiency.

Expanded Knowledge Base

The model has access to an expanded knowledge base, which includes the latest programming languages, frameworks, libraries, and tools. This ensures that ChatGPT 4o provides up-to-date and relevant advice.

Use Cases:

  • Insights on emerging technologies
  • Recommendations for best practices
  • Assistance with new tools and frameworks

By staying current with technological advancements, ChatGPT 4o offers valuable support for developers exploring new areas.

Improved Code Generation and Debugging

ChatGPT 4o offers enhanced capabilities for code generation and debugging. The model can generate more accurate and efficient code snippets and identify potential issues more effectively.

Enhancements:

  • Accurate code generation with minimal manual tweaking
  • Detailed error explanations and suggestions for fixes
  • Interpretation of error messages, logs, and stack traces

These improvements streamline the coding process, reducing the time and effort required for debugging and development.

Collaboration and Learning

Beyond individual productivity, ChatGPT 4o supports collaboration and learning within development teams. It can act as a mentor, guiding less experienced developers through complex tasks.

Features:

  • On-demand assistance
  • Detailed explanations that are easy to understand
  • Shared understanding among team members

This capability is particularly valuable in remote work environments, where immediate access to expert guidance can enhance team cohesion and efficiency.

Security and Ethical Considerations

OpenAI has implemented robust security measures and ethical guidelines in ChatGPT 4o to minimize harmful outputs and ensure user data privacy.

Measures:

  • Enhanced data privacy protocols
  • Mechanisms to report misuse or unintended behavior

These measures underline OpenAI’s commitment to responsible AI deployment, ensuring that the technology is used safely and ethically.

Is ChatGPT Plus Still Worth It with 4o?

The introduction of ChatGPT 4o raises the question of whether the ChatGPT Plus subscription is still a worthwhile investment for developers. ChatGPT Plus offers access to GPT-4o’s enhanced features, including faster response times and priority access during peak times. For developers who frequently rely on AI for coding assistance, these benefits can translate into significant productivity gains.

Considerations:

  • Faster response times reduce waiting periods during code generation and debugging.
  • Priority access ensures availability even during high-demand periods.
  • The enhanced capabilities of GPT-4o, particularly in understanding and generating code, make it a valuable tool for complex development tasks.

Given these factors, developers who leverage AI in their daily workflows may find that the improvements in ChatGPT 4o justify the continued investment in a ChatGPT Plus subscription.

Evolution of OpenAI’s Language Models

To understand the significance of ChatGPT 4o, it’s useful to look at the evolution of OpenAI’s language models:

  • 2018: GPT-1 launched, demonstrating the potential of large-scale unsupervised language models. This initial model set the stage for future developments by showing the viability of generative pre-trained transformers.
  • 2019: GPT-2 was released with 1.5 billion parameters, significantly improving text generation capabilities. GPT-2’s ability to generate coherent and realistic text sequences marked a significant leap from GPT-1, showing substantial improvements in handling a wide range of language tasks.
  • 2020: GPT-3 launched, featuring 175 billion parameters, which represented a substantial leap in AI capabilities. GPT-3’s large parameter size enabled it to perform tasks with even greater fluency and versatility, making it a powerful tool for a wide range of applications, including coding and content creation.
  • 2023: GPT-4 was released, offering better reliability, creativity, and problem-solving skills. This model focused on enhancing the capabilities introduced by GPT-3, providing more accurate and context-aware responses.
  • May 2024: ChatGPT 4o was released, incorporating enhanced natural language understanding, contextual awareness, and integration features. This latest version builds on the strengths of its predecessors and includes specific improvements aimed at developers, such as more accurate code generation, better debugging assistance, and integration into various development tools and environments.

Conclusion

ChatGPT 4o represents a significant advancement in AI capabilities, offering enhanced natural language understanding, superior contextual awareness, advanced integration capabilities, and an expanded knowledge base. These improvements position it as a versatile and powerful tool for developers, potentially transforming the way they interact with AI.

While these advancements are promising, it is essential to remain objective and consider the practical implications and limitations of the technology, check alternatives and find the tool that’s right for you and your projects!

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Gamification Frameworks in AI: Strategies for Creating Engaging User Experiences https://www.codemotion.com/magazine/ai-ml/gamification-frameworks-in-ai-strategies/ Thu, 09 May 2024 10:26:01 +0000 https://www.codemotion.com/magazine/?p=27624

Gamification frameworks in AI employ gamification in several contexts through specialized systems. The AI structures provide the groundwork required for seamless provision and boosting user experience by guiding the design, implementing qualified activities, and augmenting structures.    Gamification frameworks in AI help to create strategies for engaging user experiences by involving gaming strategies in non-gaming platforms.… Read more

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Gamification frameworks in AI employ gamification in several contexts through specialized systems. The AI structures provide the groundwork required for seamless provision and boosting user experience by guiding the design, implementing qualified activities, and augmenting structures.   

Gamification frameworks in AI help to create strategies for engaging user experiences by involving gaming strategies in non-gaming platforms. You can use the systems in education, health supervision, personal productivity, the corporate sector, and several other areas. It increases readability and fosters a simple interface appropriate for even a layman. 

What’s the Gamification Framework in AI?

Gamification pertains to game design elements, game principles, usage, and application in non-game characteristics. This technique employs better user enhancement and engagement with a service. Multiple applications include various features, such as leaderboards, badges, line progress reports, rewards, and points accumulation. Developers add engaging features to tap users’ intrinsic motivation to elevate the user experience. 

Research Insights on Gamification Effectiveness

Insurtech

Even traditional industries such as insurance comprehend the requirement for gamification. Insurtech companies can see an improvement of 85% in their customer engagement by applying gamification frameworks and strategies.

Educational Applications

Gamification has shown significant impacts in educational settings, particularly in increasing student motivation. A meta-analysis highlights that gamification consistently improves motivation across various educational measures, with environments that promote collaboration and social interaction enhancing intrinsic motivation​ (Frontiers)​.

Programming Education

In programming education, the introduction of gamification elements like points and badges has been observed to significantly enhance student engagement. Studies comparing gamified systems with traditional teaching methods found that students exposed to gamification showed greater interest and participation in learning activities​ (SpringerOpen)​.

Science Education

Adaptive gamification, which tailors game elements to individual learning styles and preferences, has proven effective in motivating students in science education. This approach ensures that gamification aligns with educational goals, making learning more personalized and impactful​ (MDPI)​.

Gamification Learning and 8 Core Drives of Gamification 

Do you know how to employ gamification techniques, programming, modulation, and simulation, or how to build, train, and deploy AI models? You can utilize data science and machine learning tools, such as Tensorflow, MATLAB, Vertex AI, Unity, Amazon Sage Maker, Azure Machine Learning, and Google Cloud AutoML

You can also learn about 8-core drives for gamification:

  1. Epic Meaning & Calling: Inspires users by engaging them in a larger, meaningful story or goal.
  2. Development & Accomplishment: Focuses on users’ progress and achievements, providing satisfaction through challenges and accomplishments.
  3. Empowerment of Creativity & Feedback: Allows users to experiment and innovate within the system, offering feedback to guide improvements.
  4. Ownership & Possession: Encourages users to accumulate and customize elements within the platform, enhancing their attachment to the experience.
  5. Social Influence & Relatedness: Integrates social elements that foster interactions and competition or cooperation among users.
  6. Scarcity & Impatience: Creates a sense of urgency through limited-time offers or rare achievements, prompting quicker user action.
  7. Unpredictability & Curiosity: Keeps users engaged by incorporating elements of surprise and discovery.
  8. Loss & Avoidance: Motivates users by the desire to avoid negative outcomes, driving consistent engagement.

Challenges of Digital Gamification Frameworks 

Designers responsible for digital gamification frameworks come across multiple complications. They need to strike a cautious balance between the style, tone of the subject matter, and the amusement factor. If the designers don’t manage the gameplay, content, and rewards precisely, it can lead to the satisfaction of the consumers. 

Designers manage the degree of AI integration through the environment, graphics, and context. For instance, non-competitive and dialogue software might not be suitable for competitive corporate sectors or survival systems. There are certain requirements which ensure the concentration of users:

Autonomy 

User actions must be voluntary on any platform, and creators or designers shouldn’t institutionalize their preferences for consumers. Indicators that don’t push the players into distinct directions, but guide them through subtle prompts are much preferable. Giving control to the players makes them feel sovereignty and stability to boost user experience. 

Relatedness

Designers need to care that if any individual visits their platform, they will regard relevance highly. Hence, you should streamline the content and tone to project relevancy and loyalty to the audience. For instance, you might have played some games. You might have noticed that completing certain missions offers you additional rewards. 

In the same manner, Quora’s question-and-answer service rewards credits to its users while linking individuals with identical interests and preferences. This is how you can add relevance and replayability of the players to your services. 

Competence 

Competence keeps the patrons comfortable and autonomous. Products with indistinguishable features and typical elements, as well as those lacking distinctive elements, can exhaust the user’s interest. Henceforth, designers can expand the features to include high usability and minimize confusing components, including long texts and extensive descriptions. Rather, you can add symbols (heart for favorites, thumbs up for likability, and so on) or add progressive disclosure. 

How do Gamification Techniques Personalize Interactions?

AI tools and digital techniques can personalize interaction. You can align your services with users’ goals and system purposes through gamification tools. Tools like GPTs, Botsonic, Tidio, Jasper, Quillbot, Grammarly, GPTZero, Airgram, Play.ht, Divi AI, and SeamlessAI. 

These AI tools help create a compelling application with gamification and other aspects. The tools having open AI and neural works can employ various parameters to obtain human-friendly content. Until now, most of the tools use GPT 4 (Generative Pre-trained Transformer Generation 4). 

What Gamification Systems Pitfalls Designers Should Avoid?

If a system doesn’t utilize the gamification UX designs appropriately or overdo the AI integration, it can lead to several pitfalls. You should avoid the following decoys while adding AI frameworks to your applications: 

Manipulation

Gamification is all about making a service or app fun. It motivates users to have fun and use the platform frequently, and as a consequence, users spend hours on the application without any conscious judgment. 

Building a Game

While using the system, the users will focus more on building a game, earning points, and gathering rewards. In their pursuit of points, their concentration on the task they had to perform will lessen. Therefore, it can also serve as a distraction from the main task. 

Magic Paint 

If the UX design, color scheme, and graphics are subpar, it can’t make anything interesting. Hence, integrating an attention-grabbing design and color scheme is necessary to engage the users. Duolingo has a unique color scheme that applies to the magic paint concept. The color scheme of the language learning apps attracts the user’s minds and keeps them loosened up. 

How to Successfully Gamify the System Experience 

If you are a developer who wants to successfully gamify the system through AI, you need to research the consumers extensively. After deeply understanding the consumers’ needs and preferences, you can easily entice them to spend more time on your platform.

Here are some strategies for increasing user experience through gamification:

Understanding the Audience 

The first step to creating any design for the masses is to analyze their preferences. Later, the gamification elements were tailored precisely to their interests. Some users thrive in competitive surroundings, while others might seek personal games. Therefore, a system must have something for everyone and retain its audience appropriately. For instance, LinkedIn has systematic and distinct features which are self-explanatory. Consumers prefer such self-explanatory features and live progress bars. 

Align AI Features to User Goals 

Secondly, mechanics should analyze the user goals and integrate them into their systems through based points, challenges, and leaderboards. Combine storytelling that aligns with the user’s goals to establish a link with them. You can add characters or symbols to represent the story. Anime websites use characters from popular animes to direct their watchers. Likewise, you can add similar cartoons to add relevance. 

Tailoring the Structure for All Stakeholders

Designers need to tailor the structure of their application while balancing competitive elements with collaborative features to satisfy the stakeholders. It makes the gaming experience inclusive and engaging for everyone. 

Conclusion 

Incorporating gamification frameworks into AI-powered applications exemplifies a powerful strategy for creating engaging user experiences for multiple products and services. Through leveraging AI algorithms, businesses can personalize experiences, adapt gameplay, and encourage social interaction, resulting in competent engagement and user satisfaction.

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Tutorial: Easy Client-side AI Translator With Transformers.js – Codemotion https://www.codemotion.com/magazine/ai-ml/tutorial-easy-client-side-ai-translator-with-transformers-js-codemotion/ Tue, 07 May 2024 14:12:52 +0000 https://www.codemotion.com/magazine/?p=27570

Normally when we think of integrating an Artificial Intelligence model into a Web App, we immediately think of servers in Python or, even more often, cloud platform APIs. In this small guide, however, we want to explore a less trodden path: the possibility of including AI models directly in Web Apps for client-side use today… Read more

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Normally when we think of integrating an Artificial Intelligence model into a Web App, we immediately think of servers in Python or, even more often, cloud platform APIs. In this small guide, however, we want to explore a less trodden path: the possibility of including AI models directly in Web Apps for client-side use today thanks to JavaScript and the current Web Standards!

But before we launch into the fantasy and the code, let’s introduce you to the main characters of this story.

Meet the Transformers…

“Transformers” have revolutionized the field of natural language processing (NLP) and artificial intelligence: this architecture, based on the concept of “self-attention”, allows models to pay attention to different parts of a text simultaneously, thus capturing the relations between words even if they are distant in the sequence.

But what makes “Transformers” so special?

Unlike traditional sequential neural network models, such as recurrent neural networks (RNNs) or convolutional neural networks (CNNs), which process data sequentially, “Transformers” are able to process sequences in parallel, making processing much more efficient and faster.

Transformers.js – The Library to Run [Almost] All Models Available on Hugging Face in the Browser!

Among the many new tools and software related to AI, you might have missed a solution that combines the “Transformers” and the web world: it is the Transformers.js library!

This library allows you to run artificial intelligence models available in the Python world directly in your browser, thus opening up new possibilities in the field of web development and beyond.

With Transformers.js, you can easily incorporate powerful NLP models, such as GPT and BERT, but also Computer Vision tasks like image-to-text, depth-estimation, and much more, into your web applications, Node.js, or Electron!

All without the need to rely on external servers or cloud services since everything is executed inside the browser.

This feature, in addition to the obvious low infrastructure cost, introduces a complete level of privacy: sensitive data (such as health status or webcam images) may not leave the user’s device; indeed, after downloading the model, your web app will also work offline!

But how can we start using Transformers.js in our JavaScript projects? Keep reading to find out!

Now Let’s Experience the Thrill of Creating a True Web App Based on AI…

Here’s the most exciting part: putting into practice what we’ve discussed and creating a real web app based on artificial intelligence using Transformers.js!

In this tutorial, we will guide you step-by-step through the creation of a simple web application (an AI translator tool) that uses a “Transformers” based text generation model to translate sentences from Italian to English. You’ll see how easy it is to integrate Transformers.js into your project and start exploring the world of Artificial Intelligence in a creative and familiar way.

Prepare your favorite code editor and let’s tackle this simple AI translator step by step… like in a cooking recipe!

Practical Recipe for an AI Translator Integrated into a Web Page

For example, we will develop a simple translator that will use an AI model to translate from Italian to English with a click!

Requirements

You don’t need to know about AI, Python, or data science, just get by with vanilla JavaScript, HTML5, and CSS: the recipe is designed to be clear and accessible!

For example, we will not use any bundler but a simple /public folder to be served with your favorite web server.

Ingredients

For our web app, we will need three main ingredients:

A minimalist UI:

simple AI translator
  • A minimalist graphical interface for translation with an AI model
  • A textarea where the user will write the text to be translated
  • A button to start the translation
  • A textarea to display the translated output

A web worker (loaded as a module): will contain our AI model and allow us to query it without blocking the user interface of the main thread.

A simple application logic: will be responsible for tying together the UI, the web worker, and the translation process.

AI translator – Procedure

Preparing the UI

Let’s create a simple HTML page with textareas for input and output, along with a button to start the translation.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Web based AI Translator</title>
    <link rel="stylesheet" href="./style.css">
    <script type="module" src="./app.js"></script>
</head>
<body>
    <div id="container">
        <form id="translation-form">
            <!-- input to translate -->
            <textarea id="italian-text" rows="6" spellcheck="false" required disabled placeholder="Italian text"></textarea>
            <!-- button we will attach the event to translate -->
            <button id="translate-button" type="button" disabled>Loading model...</button>
            <!-- translated output (read-only) -->
            <textarea id="english-text" rows="6" spellcheck="false" placeholder="English text" readonly></textarea>
        </form>
    </div>
    <!-- include a progress bar to indicate when we are downloading the AI model (often > 50Mb) -->
    <div id="progress-bar"></div>
</body>
</html>

Preparing the User Experience

Now that we have our index.html ready, it’s time to sketch out our app.js!

We will limit ourselves to defining two functions to enable and disable interaction with the user interface (being careful to wait for the HTML page to load).

🔮 We will need it later when we are waiting for the results of long processes performed by the Web Worker.

document.addEventListener("DOMContentLoaded", () => {
    const translateButton = document.getElementById('translate-button');
    const italianTextarea = document.getElementById('italian-text');
    const englishTextarea = document.getElementById('english-text');
    const progressBar = document.getElementById('progress-bar');

    const disableUI = () => {
        italianTextarea.setAttribute('disabled', true);
        englishTextarea.setAttribute('disabled', true);
        translateButton.setAttribute('disabled', true);
        translateButton.innerText = 'Translating...'
    }

    const enableUI = () => {
        italianTextarea.removeAttribute('disabled');
        englishTextarea.removeAttribute('disabled');
        translateButton.removeAttribute('disabled');
        translateButton.innerText = 'Translate'
    }
    // More code follows...
});

Creating a Web Worker to Download and Run the AI Model

Since downloading the AI model files from Hugging Face can be lengthy and especially will be run by the user’s CPU, we must include a Web Worker in our project!

// Web Worker
import {
     pipeline,
     env
 } from "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.17.1";

env.allowLocalModels = false; // If using models from Huggingface, this must be set

var translator;
var task;
var model;

const progressCallback = (data) => {
    self.postMessage({
        status: 'downloading',
        result: data
    });
}

const updateCallback = (beams) => {
    const decodedText = translator.tokenizer.decode(beams[0].output_token_ids, {
        skip_special_tokens: true,
    })

    self.postMessage({
        status: 'update',
        result: decodedText
    });
}

const resultCallback = (output) => {
    self.postMessage({
        status: 'result',
        result: output
    })
}

self.addEventListener('message', async (event) => {
    const message = event.data;

    if (message.action == 'download') {
        task = message.task;
        model = message.model;

        translator = await pipeline(task, model, {
            progress_callback: progressCallback
        });

        self.postMessage({
            status: 'ready',
            task: task,
            model: model
        });

    } else if (message.action == 'translate') {
        const output = await translator(message.input, {
            // Secret ingredient
            ...message.generation,
            callback_function: updateCallback
        });

        resultCallback(output[0].translation_text);
    }
});

Adding the Web Worker to the App

Back in our app.js where:

  • We will load the worker.js file, taking care to pass the option type: “module” allowing us to load Translator.js as an ES6 module 👩‍🎤
  • We will define two functions to send two messages or actions to the web worker: download and translate
  • We will define what to do when our actions produce their respective results: ready when the model is ready and result when the result of the model’s execution is ready (text translated in this case)
  • We include the functions enableUI() and disableUI() to inform the user when the APP can interact and when not
  • Extra: the web worker also informs us of the progress in downloads and to make it clear that the APP is doing something by updating its property based on the percentage of download –progress-bar-percentage
var aiWorker = new Worker('worker.js', { type: "module" });

const translate = (text) => {
    disableUI(); // This will take some time...
    aiWorker.postMessage({
        action: 'translate',
        input: text
    });
}

const download = (model) => {
    disableUI(); // This will take some time...
    aiWorker.postMessage({
        action: 'download',
        task: 'translation',
        model: model
    });
}

aiWorker.addEventListener('message', (event) => {

     const { status, result } = event.data;

     // Web Worker is informing us that...
     
     if (status == 'downloading') {
         if (result.status == 'progress') {
             // animated progress bar for download
             progressBar.style.setProperty("--progress-bar-percentage", result.progress + "%");
         }

     }  else if (status == 'ready') {
         translateButton.addEventListener('click', () => {
             translate(italianTextarea.value);
         })

         enableUI();
     } else if (status == 'update') {
         // secret ingredient
         englishTextarea.value = result;
     } else if (status == 'result') {
         englishTextarea.value = result;
         enableUI();
         
     }
 })

 download('Xenova/opus-mt-it-en');

The Secret Ingredient

In the Web Worker and App comments, there are two points indicated as the secret ingredient… what is it?

When we create our “pipeline” we can add a callback that informs us of the model’s progress by giving us the partial output of the answer. This is an essential ingredient because otherwise, the user would not see the output until the end of the execution which can take even minutes.

Animation Showing the Words of Our Translator Coming Out One at a Time

From the user experience point of view, it will be essential to find interesting and creative ways to give people the illusion that our Artificial Intelligence is alive and thinking! 🎩✨🐇

By following these steps, you will be able to create a web app to translate text based on artificial intelligence!

Client-Side AI Model: Conclusions

There are many limitations to using this technology at this time: the heavy model files and the slowness of a simple CPU are certainly the most obvious! Moreover, many models cannot be run in the browser context.

However, I believe there are important aspects that make Transformers.js a great opportunity:

  • It is based on perhaps the most ubiquitous technology of the moment, the Transformers, and is also developed within Hugging Face… it’s really the JavaScript cousin of the Python library 🤗
  • You can make any model normally for Python compatible with a script (which transforms it into an optimized ONNX format): this means potentially having all models as soon as they come out!
  • The parameters of the Python models are the same in Transformers.js so the documentation, once you’ve learned the key concepts, is also valid for JavaScript programmers
  • Expands the ecosystem of AI models greatly, bringing them into PWAs, Node.js servers, and even AI-based Desktop Applications with ElectronJS… all well-documented even with use cases of React and other frameworks
  • Zero extra costs for external platforms and APIs
  • Its author, Xenova, seems to have very clear ideas and from this interview seems absolutely on the ball… and this seems to be a very promising aspect 🌟
  • But above all, we are all waiting for the real superhero of the story: WebGPU

But we will talk about this in future articles starting from the next in this series: Running a real Large Language Model (LLM) client-side in the browser!

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Prompt Engineer: Beyond the Buzzword https://www.codemotion.com/magazine/ai-ml/prompt-engineer-beyond-the-buzzword-ai-career/ Mon, 29 Apr 2024 14:36:00 +0000 https://www.codemotion.com/magazine/?p=27432

In the bustling world of tech, the emergence of “prompt engineer” as a professional title has sparked a mix of curiosity and skepticism. Is this role just another fleeting tech trend, or does it herald a significant shift in the tech job market? As artificial intelligence (AI) continues to evolve, the role of a prompt… Read more

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In the bustling world of tech, the emergence of “prompt engineer” as a professional title has sparked a mix of curiosity and skepticism. Is this role just another fleeting tech trend, or does it herald a significant shift in the tech job market? As artificial intelligence (AI) continues to evolve, the role of a prompt engineer is proving not just relevant, but essential, navigating the intersection between human ingenuity and machine learning (ML) capabilities.

Moreover, with the AI boom, prompt engineering is rapidly claiming the spotlight as possibly the hottest job in the tech industry, with salaries of up to 300,000 overtaking traditional giants like data scientists. But will this boom last?

Let’s delve into what this role entails, the skills it demands, and its increasing importance in the AI-driven ecosystem

According to Emanuele Fabbiani, Head of AI at Xtream, prompt engineering and the specific tools for the role can only grow in the immediate future: “All companies will have a big interest in adopting these tools, only that the big tech companies will provide the infrastructure while it will be up to the individual startups to create niche applications that meet the real needs of the market,” he explains.

The Essence of Prompt Engineering

Skills and Knowledge Requirements
Prompt engineering transcends traditional programming; it is an artful science requiring a nuanced blend of skills:

  • Technical Expertise: A solid grasp of AI model architectures and ML principles is foundational. Prompt engineers must understand how models interpret inputs and generate outputs.
  • Linguistic and Psychological Insight: This role demands a keen understanding of language and human psychology to craft prompts that effectively steer AI responses.
  • Problem-Solving Prowess: Each prompt is a mini-challenge, requiring the ability to deconstruct and simplify complex issues into clear, actionable inputs.

Recommended video: Giovanni Galloro & Fabrizio Pezzella – From code programming to prompt programming: use AI to improve your productivity

Loading the player...

Essential Tools for the Trade
Prompt engineers arm themselves with a variety of tools to sculpt and refine their prompts:

  • Development Platforms: Platforms like OpenAI’s Playground are crucial for real-time testing and refinement of prompts.
  • Analytics Software: Tools that analyze how models react to different prompts are indispensable for fine-tuning.
  • Version Control Systems: These systems are vital for managing prompt iterations, particularly in collaborative environments.

Industry Adoption and Career Prospects

Growing Demand in Major Companies
From tech behemoths like Google to dynamic AI startups, the demand for prompt engineers is surging as companies recognize the value of mastering AI interactions. This role is pivotal in various applications:

  • Customer Service Automation: Designing prompts that direct AI to manage customer queries with precision.
  • Content Creation: Leveraging AI for producing diverse content, from creative writing to code.
  • Educational Enhancements: Creating personalized educational tools through adaptive prompts.

The Rising Star of Tech Careers

Replacing Data Scientists as the Go-To Tech Job?
With the explosion of AI technology, prompt engineering is not just an emerging field—it’s a critical one. The role uniquely combines technical acumen with creative and strategic thinking. As AI becomes more ubiquitous, the ability to effectively communicate with and guide AI systems is becoming more crucial. In this light, prompt engineers may soon be as commonplace and sought-after in tech companies as data scientists once were, heralding a new era in career opportunities within the tech industry.

Conclusion
Far from being merely a buzzword, prompt engineering represents a burgeoning domain that marries human creativity with AI capabilities. As industries increasingly rely on AI, the role of the prompt engineer is set to grow not only in importance but also in necessity, securing its place as a fundamental and exciting career path in the future of technology.

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Is Devin Fake? The Dev Community Is Taking a Closer Look https://www.codemotion.com/magazine/ai-ml/is-devin-fake/ Mon, 22 Apr 2024 08:24:33 +0000 https://www.codemotion.com/magazine/?p=27194

Still under development, Devin has emerged as a groundbreaking tool, billed as the world’s first fully autonomous AI software engineer. Developed by Cognition AI, Devin promises to revolutionize the software development landscape by handling end-to-end software projects—from planning and coding to debugging and deployment—without human intervention. However, recent articles, discussions, and many viral videos have… Read more

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Still under development, Devin has emerged as a groundbreaking tool, billed as the world’s first fully autonomous AI software engineer. Developed by Cognition AI, Devin promises to revolutionize the software development landscape by handling end-to-end software projects—from planning and coding to debugging and deployment—without human intervention. However, recent articles, discussions, and many viral videos have cast doubts on these claims, stating the developers might have given Devin fake, or at least hyped capabilities.

Let’s take a look at some of Devin’s future capabilities according to the developers.

Overview of Devin

Devin is designed to function as an independent AI teammate, equipped with a suite of developer tools, including a shell, code editor, and a browser within a secure computing environment. It uses advanced AI to plan and execute complex engineering tasks, learn new technologies, identify and rectify bugs, and even train and fine-tune AI models. Its developers claim that it can autonomously build and deploy applications, making it more than just a coding assistant—it’s intended to be a full-fledged software engineer.

Promises Made by the Developers

Cognition AI has positioned Devin as a transformative tool in software engineering, capable of outperforming other AI models significantly. According to benchmarks shared by the company, Devin has a high issue resolution rate on the SWE-bench coding benchmark, showcasing its proficiency in real-world scenarios. The AI’s capability to operate autonomously and handle sophisticated tasks has been a major selling point, promising efficiency and innovation in software development.

Rising Skepticism: Devin Fake Claims

Despite these impressive credentials, skepticism has arisen within the developer community, fueled by a viral video and several articles that claim Devin’s abilities might have been overstated. Critics argue that while Devin demonstrates potential, the demonstrations and the data provided by Cognition AI may not entirely reflect its operational capabilities in everyday coding tasks. These critiques have gained traction on platforms like YouTube and Medium, where technical audiences often discuss and dissect new technologies.

The video in question, which has garnered significant attention, suggests that the tasks Devin is said to complete autonomously could have been pre-arranged or simplified to enhance the performance outcomes. This skepticism is echoed in several other articles and videos, where the authors question the transparency of the benchmarks used to evaluate Devin’s performance.

In the case of a video posted by user Internet of Bugs, the author shows that Devin wasn’t capable of completing a job in the Upwork platform as the developers claimed.

Community Response

The response from the developer community has been mixed. Some developers express enthusiasm about the potential of AI to reduce mundane coding tasks, thereby freeing up human developers for more complex and creative problems. Others, however, are cautious, pointing out the discrepancies between claimed capabilities and actual performance in uncontrolled environments. The debate extends to discussions about the ethical implications of AI in software development, including job displacement and the reliability of AI-produced code.

Conclusion

So is Devin fake? It’s still early to tell, but as AI continues to integrate more deeply into various sectors, the scrutiny of tools like Devin by the dev community is essential. While it represents a significant step forward in the field of AI-driven software development, the true extent of its capabilities remains a topic of debate. Whether Devin can truly fulfill the promises made by Cognition AI has yet to be seen in wider real-world applications. For now, the developer community remains watchful, eager to see how Devin evolves and how it might eventually impact the landscape of software engineering.

This ongoing debate underscores the importance of transparency and rigorous, independent testing of AI technologies before they are broadly accepted and implemented. As AI tools like Devin continue to develop, they must be accompanied by robust discourse and evaluation to ensure that they meet the high expectations set by both developers and the industries they aim to transform.

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The Slow Agony of Stack Overflow, or Not? https://www.codemotion.com/magazine/ai-ml/the-slow-agony-of-stack-overflow-or-not/ Thu, 04 Apr 2024 13:24:25 +0000 https://www.codemotion.com/magazine/?p=26943

Tough times for traditional intelligence. I know it’s undeniable if you just look around, but here our comparison is with artificial intelligence. “Developers? Developers are weak. The Blood of Numenor is nearly spent, its pride and dignity forgotten. It’s thanks to the devs that the Ring survives. I was there, Gandalf. I was there three… Read more

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Tough times for traditional intelligence. I know it’s undeniable if you just look around, but here our comparison is with artificial intelligence.

“Developers? Developers are weak. The Blood of Numenor is nearly spent, its pride and dignity forgotten. It’s thanks to the devs that the Ring survives. I was there, Gandalf. I was there three thousand years ago. I was there on the day the strength of the developers failed.”

An article with a Tolkien quote is denied to no one: Once the ring, or, if you prefer, generative AI, is found, why should a programmer give up the immense power it brings by going back to writing code, perhaps struggling to understand what a function does, or even going back to read that **** manual.

elrond meme stackoverflow

As I said, I was there three thousand years ago, though in computer years, it seems much more. I was programming in a time when the only resources were books, tomes of 500 or more pages that, despite being a freelancer, forced me to go to the office to consult them, being a digital nomad at the time would have meant buying a caravan with a trailer :-).

Then came the first forums, the first guides, those who used search engines better (Google was still one of many) could walk past the secretary’s desk feeling much taller. To write this article, I checked again, and some historical names have become something else or disappeared, following the technologies that have transformed or even become extinct. Esteemed people tell me that, if you dig deep, you can still find suggestions on how to call the Windows XP APIs from ASP through IIS.

Natural evolution led to specialized portals that gave me different feelings depending on the companies I worked for and the projects I was assigned, all generated conflicting feelings except one: Expert Exchange. Expert Exchange always made me feel poor.

There was a solution to almost anything; it let you read the first lines, which were usually full of the joy of those who had solved their problem, but then, if you weren’t subscribed, it blacked everything out. Those were years when asking your boss to pay for something like that was technically impossible, since credit cards were still for the few, and the most you could hope for in terms of training resources were pirate MSDN disks released 12 months prior.

Fortunately, however, Stack Overflow gradually emerged as the quintessential “lifebuoy” site. But calling it a site is a bit simplistic since there’s a very interesting company behind it. Officially, it was founded in 2008 by Jeff Atwood and Joel Spolsky. The idea for Stack Overflow came from both founders’ frustration with the difficulties of getting quick and effective answers to technical questions online.

Before Stack Overflow, online forums were often disorganized, poorly moderated, and hard to navigate. Often, answers to technical questions were scattered across numerous threads and not always reliable or weighed, making mistakes democratic and frustrating.

Jeff Atwood and Joel Spolsky envisioned Stack Overflow as a question-and-answer platform based on gamification principles and community moderation. The idea was to present suggested answers but also to have a self-sustaining endorsement system.

From the start, it attracted a growing user base thanks to its intuitive interface, the organized structure of questions and answers, and the effective community moderation system.

stack overflow ctrl+c ctrl+v

Looking back, things could only go in that direction with two founders of this caliber. Joel Spolsky, in particular, has always been a leading figure in the software world. Before Stack Overflow, he had an epic career in software development. He worked as a programmer at Microsoft, contributing to the development of products like Microsoft Excel and Microsoft Visual Basic, and later founded his software company, Fog Creek Software, which produced several successful applications, including Trello, an app that became a must when it comes to Agile methodologies and which I use even for shopping.

But beyond his entrepreneurial activity, Joel Spolsky is also known as the author of several books and for his blog “Joel on Software,” where he shared essays and reflections on software engineering, project management, and other related topics. In this book, he expresses a delivery inclination that would make a school in the years to come. His comparison between writing code and military training remains one of the most brilliant examples to describe the concept of effectiveness.

To this day, Stack Overflow remains the most effective tool, the main reference point for developers worldwide seeking answers to their technical questions. The platform offers a wide range of topics, from programming languages to web technologies, and hosts millions of questions and answers generated by the users themselves. In 2021, it received investments and grew to become a strategic asset worth some sacrifices.

Its acquisition by Prosus, the Dutch multinational known for acquiring the Chinese company Tencent, allowed the integration of Stack Overflow into its network of companies and platforms in the education and technology sector, offering a more complete and diversified range of services.

The exact figures have not been made public, but the industry press agreed that the deal was around two billion dollars, given the opportunity to leverage the vast user base and extensive technical expertise to further develop and improve its products and services.

In addition to being a vast source of knowledge for programmers and software developers of every experience level with millions of questions and answers on a wide range of topics, Stack Overflow is supported by an active community of programmers who contribute with questions, answers, and comments. This constant exchange of knowledge promotes the growth and professional development of developers.

Beyond technical questions and answers, it offers resources for professional development, such as career advice, programming best practices, and guidance on new technologies. Many companies also use it to recruit talent in the technology sector, as they can evaluate the skills and experience of potential candidates by viewing their questions, answers, and contributions.

It could have been a very convenient deal given all these activities and the user base.

However, returning to the present day, with AI Tools like Devin allowing even the creation of DevOps pipelines, does it still make sense to turn to Stack Overflow? Should we change all the try-catch constructs that went directly to search for errors on our site? Does the incredible drop in unique visitors trace an inexorable path?

https://observablehq.com/@ayhanfuat/the-fall-of-stack-overflow

the fall of stack overflow
The fall of stack overflows users (Source: https://observablehq.com/@ayhanfuat/the-fall-of-stack-overflow)

In my opinion, no!

It’s undeniable that generative AIs are currently seen as the tool that solves the world’s problems.

It’s hard to compete with them by proposing now-obsolete paradigms like a traditional search engine unless your business is based on the fact that a token on an AI burns the equivalent of a centuries-old oak. As the proverb goes, if you can’t beat them, join them: our friends have certainly not stood by idly, creating their OverflowAI, a generative AI made specifically to help devs solve their problems.
Here is an introductory video:

So far, it would be a matter of choosing for sympathy or convenience, considering that ChatGPT (and all its little brothers) cannot use data on Stack Overflow, except for a fee, and often the training methodology does not take into account the votes given to answers.

Therefore, it is likely that OverflowAI will be our preferred choice for writing particularly complex code where ChatGPT’s responses will be incomplete or cumbersome, or we might use it in conjunction with other AIs that have other strengths, balancing their results.

The fact remains that both the API ecosystem, the partnership with Google, and finally the possibility of having access to exclusive databases make OverflowAI among the most interesting tools for the Dev world.

And this until the next generative AI allows reading specifications directly from the client’s mind by conducting a Rorschach test…

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How to Debug ML Models to Boost Performance https://www.codemotion.com/magazine/ai-ml/how-to-debug-ml-models-to-boost-performance/ Tue, 26 Mar 2024 09:51:51 +0000 https://www.codemotion.com/magazine/?p=26751

Have you ever wondered whether your machine learning (ML) models are performing as well as they could be? There are plenty of factors that can impact their performance, skewing their results and negatively impacting the accuracy of their predictions. If you want to improve their performance, then ML model debugging is the way forward.  Read… Read more

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Have you ever wondered whether your machine learning (ML) models are performing as well as they could be? There are plenty of factors that can impact their performance, skewing their results and negatively impacting the accuracy of their predictions.

If you want to improve their performance, then ML model debugging is the way forward. 

Read on for a comprehensive overview of how to debug ML models, including common warning signs you should look for, and effective strategies you can employ.

What is ML Model Debugging?

As you’re probably aware, machine learning is a branch of artificial intelligence (AI) with many applications. Many organizations are adopting ML for a variety of use cases, from financial fraud prevention to part of their application modernization strategy.

Most common use cases for AI and ML, sourced from statista.com

Machine learning model debugging is the process of identifying and resolving issues, or ‘bugs’, within machine learning models that can affect their performance. 

The Unique Challenges of ML Model Debugging

Debugging is a crucial part of the traditional software development process. It’s used to diagnose problems, trace their causes, and implement fixes to make sure that the final product performs as well as possible.

When it comes to ML debugging, however, things get a bit more complicated -you’ll likely need somebody with a data engineer certificate to run the process.

Data extraction plays a vital role in ML model debugging, as it involves retrieving and preparing relevant data for analysis and troubleshooting.

To start with, poor quality results from an ML model don’t necessarily indicate the presence of a ‘bug’ in the traditional sense. There’s a lot of potential causes, such as poor data quality or hyperparameters being set to nonoptimal values. 

This means debugging ML models requires a deep understanding of the model, exploring a range of options, and running time-consuming experiments, leading to longer iteration cycles.


Read also: Top AI Certifications To Boost Your Career


Why debug machine learning models?

ML model debugging is crucial for a few reasons:

  • Ensuring reliability. Debugging ML models helps assure developers of their reliability, increasing confidence in their predictions.
  • Improving performance. Debugging can identify factors that negatively affect performance, enabling them to be fixed.
  • Reducing bias. Data interpretation allows you to identify biases in ML models, which helps developers to mitigate them, improving the model overall.
  • Saving time and resources. Bugs can be found earlier in development by carrying out consistent debugging, leading to more efficient iteration as time isn’t wasted on flawed models.

Common ML Model Bugs

Now that we understand the importance of ML model debugging, let’s examine some of the most common bugs that can affect the performance of ML models:

  • Data quality. ML models require high quality data to produce reliable results, meaning incomplete or inaccurate data can throw off the whole model.
  • Data leakage. Data from a test set or other source can be inadvertently included in the model’s training data, leading to compromised model reliability and skewed decision-making. This is known as data leakage.
  • Deployment issues. Deployed models can exhibit unexpected behavior if there are stark differences between the development and deployment environments.
  • Overfitting. Overfitting occurs when an ML model can give accurate predictions for training data, but not for new data. It’s caused when a model cannot generalize and fits too closely to the training dataset instead. This is usually due to the training data sample being too small or containing a lot of irrelevant information.
  • Underfitting. Underfitting occurs when a model is too simple to capture the underlying complexities of data. This leads to poor performance on the training data and test data.
Image sourced from commons.wikimedia.org, with edits to add English text.

5 Steps for Successful ML Model Debugging

Now that you have a better idea of what kind of bugs you might be looking for, it’s time to think about the best way to rectify them. Here are five key steps for successful ML model debugging.

  1. Define expected behavior

Your first step should be to clearly define the expected behavior and performance metrics for your machine learning model. By doing this you can establish a benchmark that you can evaluate your model’s performance against to detect errors.

  1. Identify the error

Unsurprisingly, it’s hard to fix a bug that you don’t know exists. You should evaluate your trained model on a validation set to assess its performance.

There are other ways to locate errors in ML models, too. Performance monitoring can signpost potential errors for investigation, as can gathering user feedback and looking for common pain points.

  1. Locate the problem

Once you’re aware that there’s an error to be fixed, you need to narrow down exactly where it’s occurring. 

You’ll first need to identify the datasets where the model performs poorly. How challenging this is will vary depending on the data model you’re using, (for our recommendation, see our Snowflake schema example). The more specific you can be, the greater chance you have of fixing the problem quickly. 

Try breaking down the problem to identify specific regions of data where the model exhibits particularly poor performance. For example, data quality issues such as missing values or shifting data values could be causing a problem.

  1. Analyze the error

Once you’ve located the error, you can begin to analyze it. This is important for identifying potential knock-on issues that could affect other areas of the model, allowing you to more accurately estimate the risk that the error poses.

  1. Attempt fixes and monitor

Once you’ve fully identified the error and its implications, it’s time to rectify it. Techniques such as cross-validation, regularization, or data augmentation can all be used to fix errors and improve model performance.

Iterative improvement should be used through the debugging process, adjusting model parameters or training procedures based on the results of error analysis. It’s important to continuously monitor the model’s performance even after you’ve fixed the error, making adjustments as necessary if problems persist.

Common ML Debugging Strategies

There’s no ‘one-size-fits-all’ approach to ML model debugging, partly due to the sheer number of different types of machine learning models. There are multiple strategies you can deploy to fix errors in your application. Here are some of the most effective approaches you can take.

Outsourcing Software: 

Outsourcing certain aspects of ML model debugging can also be a viable strategy, particularly for organizations lacking in-house expertise or resources. Outsourcing software and tapping into external expertise can provide fresh insights and accelerate the debugging process.

Residual analysis

Residual analysis is one of the most commonly used debugging methods among data scientists and developers. It involves assigning a numeric value to the difference between observed and predicted outcomes. 

The predicted value is subtracted from the observed value in order to calculate the residual. This is a useful method for determining exactly where a model is wrong, and for assessing its reliability when making predictions based on unseen data.

Image created by author

Data augmentation

Data is crucial when it comes to ML. Data augmentation is used to supplement small datasets or correct missing data, adding new training samples where gaps exist in the dataset. It’s common in ML problems to have a training dataset that doesn’t contain every possible answer or parameter of an algorithm; data augmentation allows us to fill those gaps to improve the accuracy and predictive power of the model.

Benchmark models

You can compare your model against a benchmark model in order to assess its performance. A benchmark model is a model that’s proven to be reliable, transparent, and easy to use and interpret.

Comparing your model’s performance against that of a benchmark allows you to easily see whether the new model offers significant improvements when it comes to predictive accuracy, or other relevant metrics.

Benchmark models are typically easy to implement, and don’t take up too much time. They can also be used to predict how an ML model will perform on different data classes. For instance, if you find that your ML model is overfitting on some data, a benchmark model can help you understand whether or not it’s because the data is special in some way.

Sensitivity analysis

Sensitivity analysis – sometimes referred to as “what if” analysis – can show how a model will react to unseen data by observing what it predicts from given data. It achieves this by estimating how sensitive a model is to changes in the input parameters from their nominal values.

Models can be sensitive to changes in multiple input variables. By using sensitivity analysis, you can determine which input variables are causing the problem by studying multiple variables in depth at once. 

Security audit

Machine learning models can be susceptible to attacks, but most commonly used assessment practices aren’t able to tell us if a model is safe or not.

Luckily, there are multiple ways to test the safety of ML models. It’s crucial to employ a cybersecurity framework that is capable of protecting your models from threats. 

By implementing robust security measures, and integrating AI into your business, you can ensure the integrity and reliability of your machine learning solutions.

All of your data, documentation, and information should only be accessible to authorized members. It’s also important to make routine backups, and  continually update all security measures to keep data secure. 

Model editing and monitoring

It’s crucial to keep monitoring and managing your machine learning models, even if they seem to be working fine. Things can drastically change in the blink of an eye. 

To track changes and identify where issues may have arisen, keep track of who trained the models, on what data, and when. Keeping records of all the datasets, inputs, and predictions is a big part of this. It’s always worth checking whether a model can be recalibrated and re-optimized.

Boost Your ML Models’ Performance by Debugging

Ensuring the optimal performance and reliability of machine learning models is crucial for their success, however you’re deploying them. ML model debugging is a core part of this. By systematically identifying and resolving issues, you can improve the accuracy, reliability, and fairness of your models, resulting in more trustworthy predictions.

There are a variety of ways to carry out ML model debugging, but the process should always be iterative to reflect the dynamic nature and continuous improvement cycle of the machine learning field. By embracing a systematic and multifaceted approach to debugging, developers can navigate the complexities of model development and deployment, unlocking the full potential of machine learning technologies in the process.

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