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Welcome to the weekly AI Newsletter, where I provide actionable ideas and tips to assist you in your job and business.

Before we start, share this week’s updates with a friend or a colleague:

Today at a Glance:

• most familiar application of LLM to generate code to create software
• Generative AI Usecase: Code generation with Large Language Models
• AI Weekly news and updates, Favorite Tip Of The Week & Things to Know
• Open Tech Talk Podcast, the latest episode on Transforming Text to Audio: The Future of AI in Content Creation

Increase Coding Efficiency with LLM-Powered Code Generation

Code Generation is one of the top use cases from large language models (LLMs) and is on the radar for creating code in the most famous programming languages. The latest developments are helping to put this use case on the priority list for large enterprises and how quickly they can bring it to the IT ecosystem.

The release of Claude 3.5 Sonnet from Anthropic, especially the enhancements in independent writing, editing, and executing code with sophisticated reasoning and troubleshooting capabilities, caught my attention. So, I tried it out; here is my experience of the last few days, where I took a use case of developing a web app for a repository of Gen AI use cases,

This was my prompt:

Based on this prompt, I have the structure below.

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Based on this structure, I started by passing the prompts in natural language to LLM, and here is the complete code for MVP, which took less than 30 minutes to complete.

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And here is the output

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This is undoubtedly one of the most common use cases being explored. By leveraging the power of LLMs for code generation, developers can enhance productivity, ensure code quality, and make coding accessible to a broader audience. This technology streamlines the development process, enabling faster and more efficient software creation.

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Let’s go through the benchmark released along with the Claude 3.5 Sonnet version.

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Source: Claude 3.5 Sonnet

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Code Generation uses large language models (LLMs) to automatically generate programming code based on natural language descriptions or prompts. This application helps developers quickly create code snippets, functions, or entire programs by leveraging LLM language-understanding capabilities.

• The developer describes the desired functionality or a specific coding task in natural language. This could include the type of programming language, the objective of the code, and any particular constraints or requirements.
• Example: “Generate a Python function that takes a list of numbers and returns the list sorted in ascending order.”
• The LLM processes the input prompt, leveraging its extensive training data to understand the context and requirements. It then generates the corresponding code snippet that meets the specified criteria.
• The generated code is presented to the developer, who can review, test, and integrate it into their project. The developer can also provide feedback or request further modifications to refine the code.

Benefits for the Enterprises:

• Increased Productivity: LLMs can significantly reduce the time required to write code by automating routine and repetitive coding tasks. This allows developers to focus on more complex and creative aspects of software development.
  • Example: A developer can quickly generate boilerplate code for setting up a web server or database connection, speeding up the initial setup phase of a project.
• Enhanced Accuracy and Consistency: Using LLMs minimizes the risk of human error, and the generated code is consistent with best practices and standards. This ensures higher quality and more reliable code.
  • Example: Generating standardized functions for data validation, ensuring all input data meets required formats and constraints.
• Accessibility for Non-Experts: LLMs make coding more accessible to individuals who may not have advanced programming skills. By describing their requirements in natural language, non-experts can generate functional code snippets without extensive coding knowledge.
  • Example: A data analyst with limited programming experience can generate scripts for data analysis tasks by simply describing their needs to the LLM.

Weekly News & Updates…

Last week’s AI breakthroughs marked another leap forward in the tech revolution.

1. Meta LLM Compiler is a series of models based on Meta Code Llama, with enhanced code optimization and compiler functions. These models can simulate the compiler, predict the best passes for code size, and disassemble code. They can also be customized for new optimizations and compiler tasks. Link​
2. Voiceover Studio enables you to create video voiceovers and podcasts with multiple speakers and sound effects in one seamless workflow. Upload a video and add your dialogue and sound effects. Link​
3. Gen-3 Alpha is Runway’s new base model for video generation. It is trained jointly on videos and images and will power Runway’s Text to Video, Image to Video, and Text to Image tools, as well as existing control modes. Link​
4. Microsoft released a new model called Florence-2, a versatile vision foundation model for various vision and vision-language tasks. It excels in captioning, object detection, segmentation, and OCR, and is boosted by the FLD-5B dataset with 5.4 billion annotations. Florence-2 uses a prompt-based learning approach for efficient and flexible task handling. Link​
5. Open AI is a content Partnership with TIME​

The Cloud: the backbone of the AI revolution

• ​AI-Powered Oracle Clinical Digital Assistant Transforms Interactions Between Practitioners and Patients
• Cohere’s Command R Model Series Is Now Available on OCI​
• To power Grok for xAI, Dell Technologies, Nvidia, and xAI have joined hands to build an AI factory. Link​
• Roblox gaming platform: Thinking Outside the Blox: How Roblox Is Using Generative AI to Enhance User Experiences​

Gen AI Use Case of the Week:

Generative AI use cases for Tech folks:

Code Generation with Large Language Models

Business Challenges:

Time-Consuming Manual Coding: Writing code manually is slow and labor-intensive, especially for repetitive tasks.

Human Error: Manual coding increases the risk of errors, which can lead to bugs and inefficiencies.

Skill Gaps: Non-experts or less experienced developers may need help to write complex code, slowing down project timelines.

AI Solution Description:

How This Will Be Done with LLMs:

1. Input Prompt: Users provide natural language descriptions of the desired code functionality.
2. Processing: The LLM processes the input and understands the context and requirements.
3. Output: The LLM generates accurate and functional code snippets based on the input prompt, which users can then integrate into their projects.

Expected Impact/Business Outcome:

Revenue: Faster development cycles lead to quicker product launches, increasing revenue.

User Experience: Enhanced productivity and reduced errors improve the overall software quality, improving user satisfaction.

Operations: Streamlined coding processes allow developers to focus on more strategic tasks, optimizing resource utilization.

Process: Automating routine coding tasks reduces the workload and stress on development teams, improving morale and efficiency.

Cost: Reducing the time spent on manual coding lowers development costs and minimizes expenses related to fixing bugs and errors.

Required Data Sources:

• Code Repositories: Existing codebases to help the model learn coding patterns and best practices.
• Natural Language Descriptions: Examples of prompts and descriptions used to generate code
• Technical Documentation: Guides and references that outline coding standards and requirements.

Strategic Fit and Impact Rating:

Strategic Fit: High

Impact Rating: High

This use case aligns well with organizations aiming to enhance their software development capabilities. Implementing LLMs for code generation can significantly boost efficiency, reduce costs, and improve the quality of software products, providing a solid competitive advantage.

Favorite Tip Of The Week:

Here’s my favorite resource of the week.

• Go through this latest survey on the Large Language Models for Code Generation

Source: https://arxiv.org/abs/2406.00515​

Potential of AI

• Coding by the LLM is getting better and better; the conversation started by Andrew Ng

source: x​

Things to Know…

A proposal for implementing the AI Act’s training data transparency requirement for GPAI by Open Future. Transparency in AI training data is crucial for

Source: Open Future

understanding how these models work and improving accountability in AI development. Unfortunately, opacity in training data is often used to protect AI-developing companies, affecting copyright holders and others seeking to understand AI models.

This brief’s blueprint for the template, developed with experts from various sectors, outlines a practical summary and documentation of training data.

The Opportunity…

Podcast:

• This week’s Open Tech Talks episode 138 is “Transforming Text to Audio: The Future of AI in Content Creation with Ian Harris.” Ian Harris, Founder of Pulse Podcasts, has started his unique idea to help others capitalize on AI advancements.

Apple | Spotify | Youtube

Courses to attend:

• A course on AI and NLP from Hugging Face on YouTube​
• Stanford CS224N: Natural Language Processing with Deep Learning on Youtube​

Events:

• ​GITEX GLOBAL, Oct 14-18, 2024, Dubai, UAE
• ​EUROPEAN Conference on Artificial Intelligence, Oct 19-24, 2024 Santiago de Compostela

Tech and Tools…

• ​Semantic Kernel is an SDK that integrates Large Language Models (LLMs) such as OpenAI, Azure OpenAI, and Hugging Face with traditional programming languages like C#, Python, and Java. This integration is achieved by enabling the definition of plugins that can be easily chained together in just a few lines of code.
• ​NVIDIA NeMo Framework is a scalable and cloud-native generative AI framework designed for researchers and PyTorch developers working on Large Language Models (LLMs), Multimodal Models (MMs), Automatic Speech Recognition (ASR), Text to Speech (TTS), and Computer Vision (CV) domains. Its purpose is to help you efficiently create, customize, and deploy new generative AI models by leveraging existing code and pre-trained model checkpoints.

Data Sets…

• ​The 3D Poses​ in the Wild dataset” has accurate 3D poses for evaluation. Researchers at the Stanford Vision and Learning Lab are also using the 3DPW dataset for a Social Motion Forecasting Challenge (SoMoF).
• ​CommonsenseQA is a new multiple-choice question-answering dataset requiring different commonsense knowledge types to predict the correct answers. It contains 12,102 questions with one correct answer and four distractor answers.

Other Technology News

Want to stay on the cutting edge?

Here’s what else is happening in Information Technology you should know about:

• Ilya Sutskever, OpenAI’s former chief scientist, launches new AI company Safe Superintelligence (SSI) as reported by TechCrunch​
• Perplexity’s grand theft AI reported by The Verge on misinformation from the Spamy AI blog posts

AI First Community to Learn & Share…

Have a question or need some assistance with your AI project, or maybe you want to be part of the thriving community to learn AI together,

​Click here to join the AI Tech Circle – It’s your Community on Discord​

Download 100+ Gen AI use cases:

click above to get a collection of use cases

That’s it!

As always, thanks for reading.

Hit reply and let me know what you found most helpful this week – I’d love to hear from you!

Until next week,

Kashif Manzoor