Abdulhakim Mainasara Sambawa’s Post

Definition of AI and Its Various Applications in Software Development. Artificial Intelligence is one of the oldest concepts, originating from ancient times and myths. However, it was not until the 20th century when old ideas and crude prototypes got practical realization. Many prominent mathematicians, logicians, and computer scientists joined their efforts to define the concepts of “reasoning” and “learning” in the context of computers. The works of Alan Turing, Arthur Samuel, Tom M. Mitchell, and many others created the base for the progress of AI and ML technologies and computer algorithms and programming languages in general. The generally recognized features that define AI are the ability to solve complex problems and the ability to generate information based on the source parameters. This way, researchers focus on the similarities between “human” thinking and “machine” thinking. Artificial Intelligence also should have the ability to learn, with human supervision or without it. Thanks to the rapid progress of AI and ML technologies, they have been successfully integrated into many key areas of modern life. For example, the bond between AI and software development turned into two-way beneficial cooperation. This means that Artificial Intelligence has two major layers of interaction with software engineering. It is an immensely useful tool that helps developers with numerous tasks. An AI paired with great computational power and data storage capabilities easily outperforms human abilities. It is an object of software developers’ labor. Software engineers are often tasked to create an AI for specific purposes or to integrate an existing AI into a solution that is being developed. Let’s take a look at both of these areas of interaction. The Applications of Artificial Intelligence in Software Engineering Software developers have mastered the art of training and customizing AI for various purposes. In general, state-of-the-art AI has unparalleled abilities for data mining, analysis, and classification. Those abilities also make Artificial Intelligence a great assistant capable of understanding requests and finding solutions. Here are a few examples of using AI technologies in software development: Examples of using artificial intelligence in software engineering Code Generation Modern AI can write code based on given parameters. The results, on average, are quite satisfactory but not enough to replace a skilled programmer. However, specialized AIs and learning models are getting progressively better. It is just a matter of time until they become on par with junior-level specialists. This pace of progress should be a clear signal for software engineers to keep improving their skills. The machine-written code still requires comprehensive checking from experienced specialists. #3MTT #MY3MTT #3MTTweeklyreflection #toughtleadership

You should actually be learning to code instead of starting a business. Here's why 👇👇👇 People always thought that AI would kill software engineering. “People won’t need to know how to code.” “What’s the point of learning it now?” “It doesn't matter. AI will do it for me.” But is that actually the case? AI really is changing the coding industry. 🔵 AI-powered coding assistants already have 1.3 Million customers ↳ Alreadyt used by major corporations like Goldman Sachcs & Ford 🔵 Copilot → Saves 1000s of hours/month across the industry ↳ Auto-completing code snippets ↳ Answering coding questions ↳ Software design & architecture But here is a hard fact: 👇 ❌ Copilot is NOT a substitute for human coders. Yes, it is making them dramatically more efficient, But robust QUALITY CHECKS are still needed. 🔴 Technology has Limitations: ↳ Potential copyright issues ↳ Buggy Suggestions ↳ Outdated Code Don’t get me wrong → It is rapidly improving -------------------------------------------------------------- 👇 👇 👇 👇 👇 👇 👇 👇 👇 👇 Are you still struggling with manual coding? Automate Tasks → Uplift Growth P.S. Repost ♻ this for your LinkedIn network

AI's Role in API Programming Artificial Intelligence (AI) is revolutionizing API (Application Programming Interface) programming by enhancing efficiency, security, and innovation. Here are some key ways AI is impacting this domain: 1. API Design and Development:  - Automated Code Generation: AI tools like OpenAI’s Codex can generate boilerplate code for APIs, significantly reducing development time and errors. They can understand high-level requirements and produce functional API code.  - Code Optimization: AI can analyze existing codebases to suggest optimizations and improvements, leading to more efficient and maintainable APIs. 2. Intelligent API Testing:  - Automated Testing: AI-powered tools can automate the creation of test cases, identifying edge cases and ensuring comprehensive coverage without manual intervention.  - Predictive Maintenance: By analyzing usage patterns, AI can predict potential failures or bottlenecks in APIs, allowing for proactive maintenance. 3. Enhanced Security:  - Threat Detection: AI algorithms can detect unusual patterns and anomalies in API usage, identifying potential security threats and preventing breaches.  - Security Recommendations: AI can recommend best practices for API security, such as appropriate authentication and authorization mechanisms, by learning from past vulnerabilities and attacks. 4. API Integration and Orchestration:  - Smart Integration: AI can automate the integration of disparate APIs, mapping data flows intelligently between systems and reducing the need for manual configuration.  - Dynamic Orchestration: AI can manage and optimize the interaction between multiple APIs, ensuring efficient data exchange and load balancing. 5. Personalization and Adaptation:  - Adaptive APIs: AI enables APIs to adapt their responses based on user behavior and preferences, offering personalized experiences and improving user satisfaction.  - Contextual Awareness: AI can make APIs context-aware, allowing them to understand and react to the context in which they are used, thereby improving relevance and accuracy. 6. Documentation and Learning:  - Automated Documentation: AI can generate and maintain up-to-date documentation for APIs by analyzing code changes, ensuring that documentation reflects the current state of the API.  - Learning from Interactions: AI can analyze how APIs are used, learning from interactions to suggest improvements and new features that align with user needs. Conclusion AI is transforming API programming by automating repetitive tasks, enhancing security, and enabling smarter integrations. As AI continues to evolve, its role in API development will likely expand, leading to more intelligent, efficient, and adaptable systems that meet the ever-growing demands of modern applications.

We have known, for at least since 35 years or so ago, when I took a course in “Computer AIded Software Engineering” (CASE), that Coding would be Augmented by AI tools. At that time folk thought that such tools would fully automate coding in a decade (an AI winter then overtook the exuberance that symbolism alone would deliver it, and the AI market went into a couple of decades long investment drought — there’s a lesson there in AI hype management, and ensuing threats of AI winters). But notwithstanding that, it appears that dream is likely to be fulfilled (maybe!). See… https://lnkd.in/eYFeHsdn I don’t expect human coders to be replaced so much as to evolve to be the Prompt Engineers and other new roles A/ The Future of Coding Jobs… https://lnkd.in/eiWyTWx4 As AI becomes more prevalent in the workplace, some coding jobs will likely be automated. However, there always will be a need for human coders. Instead, we are likely to see new coding jobs that require a different set of skills. For example, there will be greater demand for coders who can design and implement AI systems, integrate AI with existing software, and ensure the security and privacy of AI-powered applications. These jobs require combining technical and soft skills, such as creativity, critical thinking, and communication. Therefore, these are likely to remain unscathed.” B/ Tips for Programmers to Incorporate AI into Their Work As AI becomes more integrated into the coding world, programmers must adapt and learn new skills. Here are some ways programmers can incorporate AI into their work: 1. Learning AI programming languages & tools Coders can learn new programming languages and tools specifically designed for AI development, such as Python, TensorFlow, and PyTorch. 2. Collaborating with AI systems to enhance their code Programmers can work with AI systems to identify and fix bugs, optimise code, and improve the overall quality of their software. 3. Developing new applications & products using AI Coders can explore the possibilities of AI and develop innovative applications and products that leverage its capabilities

Is Artificial Intelligence the Future of Software Development? In recent years, we’ve seen an explosion in the field of artificial intelligence - does this hold the key to the future of software development? As innovative software developers, we're always looking for ways to push boundaries and drive progress. And with AI's ability to automate time-consuming coding tasks, reduce human error, and enhance creativity – it's easy to see why so many are captivated by its potential. However, it's important not to get swept up in the hype. Remember that while AI can certainly facilitate many aspects of software development, at its core it’s a tool – not a replacement for human ingenuity and expertise. ❗❗❗ As always, Active Logic is here to help! Reach out for more info! ❗❗❗

Advancements in AI, good✅ or bad🚫 for Software Engineers? The impact of AI on software engineering is multifaceted. While AI can significantly enhance productivity, innovation, and code quality, it also poses challenges such as potential job displacement, skill gaps, and ethical concerns. The advancements in AI have both positive and negative implications for software engineers. Here's a balanced view of how AI impacts the field: ✅Positive Impacts 🛠️ Enhanced Productivity: AI-powered tools can automate repetitive tasks, allowing software engineers to focus on more complex and creative aspects of their work. Code generation tools like GitHub Copilot can assist in writing code faster and reducing boilerplate. ✅ Improved Code Quality: AI can help identify bugs, vulnerabilities, and inefficiencies in the code. Tools like DeepCode and CodeGuru analyze codebases to provide suggestions for improvements, leading to higher quality software. 📚 Learning and Development: AI-driven personalized learning platforms can help software engineers upskill more effectively. Adaptive learning systems can tailor content to individual learning paces and styles. 💡 Innovation: AI opens up new possibilities for creating innovative products and solutions. Software engineers can leverage AI to develop applications in areas like natural language processing, computer vision, and predictive analytics. ⚙️ Efficiency in Testing: AI can automate and optimize testing processes, ensuring faster and more thorough testing cycles. This leads to quicker deployments and more robust software. 🚫Negative Impacts 🚫 Job Displacement: There is a concern that AI might replace some of the tasks traditionally done by software engineers, especially those involving routine coding and maintenance. This could lead to job displacement in some areas. 📉 Skill Gap: As AI technologies evolve, there is a need for software engineers to continually update their skills. Those who cannot keep up with the rapid pace of change might find it challenging to remain competitive. 🔗 Dependence on AI: Over-reliance on AI tools might lead to a decrease in fundamental coding skills. Software engineers may become dependent on AI for certain tasks, potentially impacting their ability to solve problems independently. ⚖️ Ethical and Security Concerns: The integration of AI in software development brings about ethical and security challenges. Engineers need to be vigilant about the ethical implications of AI applications and ensure robust security measures are in place. 🔍 Complexity in Debugging: AI-generated code can sometimes be difficult to understand and debug, especially if it’s not well-documented. This can add complexity to the software development process. Embracing AI as a tool rather than a replacement, and continually updating skills, can help software engineers leverage the benefits while mitigating the downsides. #AI #SoftwareEngineering #Technology #Productivity #Development #Security

As 2023 draws to a close, it is clear that "AI" has had a phenomenal impact on our day to day lives this year, and shows no signs of slowing down into 2024. AI seems to have been slightly slower to latch onto software development, but is this such a bad thing? Of course there are benefits: time can be saved, productivity boosted, and of course the high levels of accuracy are enough to make any CTO consider its viability. This also comes with cons, though; if these are not recognised and talked about quickly enough, irreversible damage could be done to the profession. Along with the positives, AI in software development also brings high initial purchase and implementation costs, dependence on algorithms, and arguably the most concerning: a lack of creativity. As great as AI is, it still cannot replace the creativity and critical thinking of a human developer. If AI takes over, this could see software devs out of work and looking for career changes. Behind this comes the collective loss of confidence from developers in the industry, which may take a long time to rebuild should the AI route not work out. What do you think about AI in software development; should it be kept away? Should it replace developers? Or should decisionmakers look to integrate AI into their dev teams, not to replace, but to assist developers by saving them time and eliminating mistakes in menial tasks? https://lnkd.in/eT6MQGEm

AI Revolution in Software Development Artificial Intelligence is transforming the software industry, from automated coding to predictive analytics. Here’s how AI is shaping the future of software development. 1. Automated Code Generation: Tools like GitHub Copilot use AI to suggest code snippets, significantly speeding up the development process. 2. Bug Detection and Correction: AI-driven tools can predict and fix bugs more efficiently than traditional methods, improving software quality. 3. Enhanced Testing: AI enhances testing procedures by automating test case generation and execution, which increases coverage and accuracy. 4. Predictive Analytics: AI analyzes project data to predict outcomes, helping teams make informed decisions about project management and resource allocation. 5. Personalized User Experiences: AI algorithms analyze user data to tailor software experiences, increasing user satisfaction and engagement. 6. Optimized Performance: AI tools monitor application performance in real time, predicting and managing potential bottlenecks before they affect users. 7. Advanced Security Features: AI-driven security systems can predict and mitigate potential threats faster than traditional software, enhancing data protection. 8. Natural Language Processing: AI can interpret human language, allowing developers to write queries or even code in their spoken language. 9. Smart Assistants in IDEs: Integrated Development Environments (IDEs) are becoming smarter with AI, providing real-time insights and suggestions to improve code efficiency. 10. Continuous Learning and Adaptation: AI systems continuously learn from new data, adapting to changes in user behavior or requirements without human intervention. AI is not just a tool; it’s becoming a fundamental part of how software is conceived, developed, and deployed. P.S. How do you see AI impacting your role in software development? ✏✏✏✏✏ 💻 Beginners-Friendly Coding Courses: Start Today www.codingdsa.com/courses/ 👉 Book a Trial Session Now: www.codingdsa.com/demo/ 🔄 Repost this post

Remote software development with Claude 3 and Gemma 2, two large language models. The former is from Anthropic while the latter is from Google. Objective: Develop an online variational Bayes algorithm for Latent Dirichlet Allocation in a Large Language Model Me: Gemma, Sketch out Python code for an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA) Gemma: This code provides a basic implementation of Online LDA using VB. You can modify and extend this code to suit your specific needs and data format.  Gemma provides code with example usage. Me: Claude, improve and comment on Gemma's code. Claude: To improve the provided Python code for an online variational Bayes algorithm for LDA, I'll make several enhancements focusing on efficiency, flexibility, and usability. Here's an improved version with comments explaining the changes: Key improvements and changes: 1. Implemented a more efficient document inference method (`infer_doc`) using variational inference. 2. Added a `fit` method to train the model on a corpus of documents using mini-batch learning. 3. Improved the `update_lambda` method to process mini-batches of documents more efficiently. 4. Added a `get_topic_words` method to easily retrieve the top words for each topic. 5. Used more appropriate hyperparameters (eta instead of beta) and added learning rate parameters (tau0 and kappa) for better control over the learning process. 6. Improved the initialization of variational parameters. 7. Added more comprehensive comments and docstrings for better code understanding. 8. Included an example usage section with synthetic data generation for demonstration purposes. This improved version is more efficient, easier to use, and follows the online variational Bayes algorithm for LDA more closely. It should perform better on large datasets and provide more flexibility in model training and interpretation. Me: Claude, comment on my code and suggest improvements.

Want to stay ahead in Programming Read this Now 👇👉 The coding industry is constantly evolving, and new trends emerge all the time. Here are five of the most important programming trends to watch: 👉 Artificial intelligence (AI) and machine learning (ML): AI and ML are transforming many industries, and the coding industry is no exception. Developers are using AI and ML to create new and innovative software applications, such as self-driving cars, facial recognition systems, and language translation tools. 👉 Cloud computing: Cloud computing is another major trend in the coding industry. More and more businesses are moving their applications and data to the cloud, and developers are needed to build and maintain these cloud-based applications. 👉 WebAssembly: WebAssembly is a new technology that allows developers to run code from different programming languages in the browser. This is opening up new possibilities for web development, such as the ability to create high-performance web applications and games. 👉 Low-code and no-code development: Low-code and no-code development platforms allow people to create software applications without having to write any code. This is making software development more accessible to people with all levels of technical expertise. 👉 Cybersecurity: Cybersecurity is a top priority for businesses of all sizes. Developers are needed to develop secure software applications and to protect against cyber attacks. These are just a few of the many game-changing programming trends that are emerging. By staying up-to-date on the latest trends and technologies, developers can position themselves for success in the workforce. What are your thoughts on these programming trends? What trends are you most excited about? #programmingtrends #2023 #gamechanger #developer #AI #ML #cloudcomputing #WebAssembly #lowcode #nocode #cybersecurity LinkedIn for Creators