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👋 Hello,

🚀 Welcome to DataPro Newsletter #84!  

Dive into the dynamic world of data science and AI, where breakthroughs and trends shape our future.   

🔍 Highlights:  

Google's Genie   

Meta AI's Priority Sampling   

DeepMind's Hawk and Griffin   

CMU's OmniACT   

Qualcomm's GPTVQ   

Azure PyRIT   

Microsoft's ChunkAttention   

✨ Data Community Blogs:  

ML Workflow with Scikit-learn Pipelines   

Text Embeddings   

AI System Design   

Mixture of Thought LLM Cascades   

GNN with Pytorch Implementation  

Vertex AI MLOps Platform   

🏭 Industry Updates:  

Anthropic’s Claude 3 Sonnet in Amazon Bedrock    

Anthropic’s Claude 3 models in Vertex AI    

Microsoft’s Orca-Math   

Table Meets LLM  

OpenAI and Elon Musk   

📚 New in Packt Library:  

"Building AI Applications with ChatGPT APIs" by Martin Yanev   

DataPro Newsletter is not just a publication; it’s a comprehensive toolkit for anyone serious about mastering the ever-changing landscape of data and AI. Grab your copy and start transforming your data expertise today! 

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🔰 GitHub Finds: Any of These Repos in Your Toolbox?

🛠️ VAST-AI-Research/TripoSR: TripoSR, developed by Tripo AI and Stability AI, is an open-source model for fast 3D reconstruction from a single image. It outperforms others in speed and quality, generating 3D models in under 0.5 seconds on NVIDIA A100 GPUs. 

🛠️ facebookresearch/ViewDiff: ViewDiff creates consistent, high-quality images of 3D objects in real-world settings from multiple angles. 

🛠️ YubiaoYue/MedMamba: MedMamba, inspired by visual state space models, sets a new baseline for medical image classification, excelling across diverse datasets. 

🛠️ BAAI-Agents/Cradle: Cradle framework pioneers General Computer Control, enhancing agent capabilities for any task through reasoning and self-improvement. 

📚 Expert Insights from Packt Community

Building AI Applications with ChatGPT APIs - By Martin Yanev 

Setting Up the Code Bug Fixer Project 

Open PyCharm: Double-click on the PyCharm icon on your desktop or search for it in your applications folder to open it. 

On the PyCharm welcome screen, click on Create New Project or go to File | New Project. 

Choose the directory where you want to save your project. You can either create a new directory or select an existing one. 

Select the Python interpreter: Choose the version of Python you want to use for your project. 

Configure project settings: Give your project the name CodeBugFixer, and choose a project location. 

Once you’ve configured all the settings, click Create to create your new PyCharm project. 

After creating a new PyCharm project, the next step is to create the necessary files and folders for the CodeBugFixer project. 

Firstly, create two new Python files, called app.py and config.py, in the root directory of the project. The app.py file is where the main code for the CodeBugFixer app will be written, and the config.py file will contain any sensitive information such as API keys and passwords. 

Next, create a new folder called templates in the root directory of the project. This folder will contain the HTML templates that the Flask app will render. Inside the templates folder, create a new file called index.html. This file will contain the HTML code for the home page of the CodeBugFixer app. 

The project structure should look like the following: 

CodeBugFixer/ 
├── config.py 
├── app.py 
├── templates/ 
│   └── index.html 

By following these steps, you have created the necessary files and folders for your CodeBugFixer project in your PyCharm project. You can now start writing the code for your Flask app in the app.py file and the HTML code in the index.html file. 

Once you have the correct interpreter, you can open the terminal within PyCharm by going to View | Tool Windows | Terminal. Check your terminal and ensure that you can see the (venv) indicator to confirm that you are working within your virtual environment. This is an essential step to prevent conflicting package installations between projects and guarantee that you are using the correct set of dependencies. 

In the terminal window, you can install any necessary libraries as follows: 

(venv)$ pip install flask 
(venv)$ pip install openai 

Finally, in order to establish the foundation for utilizing the ChatGPT API in your CodeBugFixer app, you’ll need to add the following code to config.py and app.py: 

config.py 

API_KEY = <Your API Key> 

app.py 

from flask import Flask, request, render_template 
import openai 
import config 
app = Flask(__name__) 
# API Token 
openai.api_key = config.API_KEY 
@app.route("/") 
def index(): 
    return render_template("index.html") 
if __name__ == "__main__": 
    app.run() 

The config.py file will securely hold your OpenAI API key. Make sure to replace <Your API Key> with the actual API key that you obtained from OpenAI. 

Discover more insights from 'Building AI Applications with ChatGPT APIs' by Martin Yanev. Unlock access to the full book and a wealth of other titles with a 7-day free trial in the Packt Library. Start exploring today! 

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⚡ Tech Tidbits: Stay Wired to the Latest Industry Buzz! 

AWS ML Made Easy 

🌀 Anthropic’s Claude 3 Sonnet foundation model is now available in Amazon Bedrock: Amazon announced a collaboration with Anthropic to accelerate the development of Claude foundation models, making them accessible to AWS customers. Recently, Claude 3 was introduced, offering three models with varying levels of intelligence, speed, and cost. Claude 3 Sonnet is now available in Amazon Bedrock, providing faster speeds, increased steerability, and image-to-text vision capabilities. 

Mastering ML with Google 

🌀 Announcing Anthropic’s Claude 3 models in Google Cloud Vertex AI: Google Cloud is enhancing customer choice and innovation in Vertex AI with the addition of Anthropic's Claude 3, a new family of state-of-the-art AI models. These models, optimized for various enterprise applications, include the highly capable Claude 3 Opus, the balanced Claude 3 Sonnet, and the fast, compact Claude 3 Haiku. Customers can soon access all three models via API in Vertex AI Model Garden, starting with private preview access to Claude 3 Sonnet. The Claude 3 models offer improved reasoning, content creation, language fluency, and vision capabilities, enabling customers to focus on applications while benefiting from flexible scaling, cost optimization, and Google Cloud's security and compliance. 

Microsoft Research Insights

🌀 Orca-Math: Demonstrating the potential of SLMs with model specialization. The study on Orca and Orca 2 demonstrated how improved training methods can enhance the reasoning abilities of smaller language models, bringing them closer to larger models. Orca-Math, a 7 billion parameter model, specializes in solving math problems and outperforms larger models in this area. The research highlights the value of smaller models in specialized tasks and the potential of continual learning. The dataset and training procedure are available for further research. 

🌀 Table Meets LLM: Improving LLM understanding of structured data and exploring advanced prompting methods: This paper explores how large language models (LLMs) understand structured table data. It investigates effective prompts, inherent structured data detection, leveraging existing knowledge, and trade-offs among input designs for better understanding and utilization of table-based data in LLMs. 

OpenAI Updates 

🌀 OpenAI and Elon Musk: In a recent blog post, OpenAI shared its mission to ensure AGI benefits all of humanity, emphasizing the need for substantial resources. The post recounts disagreements with Elon Musk over funding and control, leading to his departure. OpenAI highlights its efforts to create widely available beneficial tools, such as GPT-4, and addresses ongoing legal disputes with Musk while reaffirming its commitment to its mission. 

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🔍 From Bits to BERT: Keeping Up with LLMs & GPTs 

🧞 Google’s Genie: Generative Interactive Environments. Genie introduces a new generative AI paradigm for creating interactive, playable environments from a single image prompt. It can generate virtual worlds from unseen images, including real-world photos or sketches. Trained on a large dataset of Internet videos without action labels, Genie learns fine-grained controls, identifying controllable parts of an observation and inferring consistent latent actions across different environments.  

🌀 Meta AI's Priority Sampling: Revolutionizing Machine Learning with Deterministic Code Generation. This research introduces Priority Sampling, a deterministic sampling technique for large language models that generates unique and confident code samples. It aims to improve code generation and optimization by providing a more structured and controllable exploration process, outperforming traditional sampling methods and enhancing model performance. 

🌀 Google DeepMind Launches Hawk and Griffin: Efficient Language Models with Advanced Attention Mechanisms. This paper introduces Hawk, an RNN with gated linear recurrences, and Griffin, a hybrid model combining gated linear recurrences and local attention. Hawk outperforms Mamba on downstream tasks, while Griffin matches Llama-2's performance with significantly less training data. Both models are hardware-efficient, with Griffin showing exceptional scalability and the ability to extrapolate on long sequences. The study also details efficient distributed training for large-scale models. 

🌀 CMU Unveils OmniACT: Groundbreaking AI Dataset for Measuring Program Execution Skills. OmniACT is a new dataset and benchmark designed to test if virtual agents can automate computer tasks by creating executable scripts. Initial tests show a significant gap between agent and human performance, highlighting the challenge and encouraging advancements in multimodal AI models. 

🌀 Qualcomm's GPTVQ: Speeding Up Large AI Networks with Vector Quantization. GPTVQ is a new fast method for post-training vector quantization of Large Language Models (LLMs), improving size vs. accuracy trade-offs. It uses column-wise quantization and updates with Hessian information, efficient codebook initialization, and further compression techniques. GPTVQ sets new standards in LLM quantization efficiency and latency, even on mobile CPUs.   

🌀 Azure PyRIT: Elevating ML Engineers with Python's Generative AI Risk Tool. PyRIT, a Python Risk Identification Tool for generative AI, automates AI Red Teaming tasks to assess the security of Language Model (LLM) endpoints. It employs proactive methods, categorizes risks, and offers detailed metrics, enabling researchers to mitigate potential risks in LLM deployment effectively. 

🌀 Microsoft Introduces ChunkAttention: Accelerating Self-Attention for LLMs! This research introduces ChunkAttention, a novel self-attention module for large language models (LLMs) that optimizes compute and memory operations by detecting shared prefixes in LLM requests. It breaks key/value tensors into chunks and uses a prefix tree to share them, speeding up the self-attention kernel by 3.2-4.8×. 

✨ On the Radar: Catch Up on What's Fresh

🌀 Streamline Your Machine Learning Workflow with Scikit-learn Pipelines: This blog explores the benefits of using Scikit-learn pipelines for simplifying machine learning workflows. It covers how pipelines can streamline preprocessing, modeling, hyperparameter tuning, and workflow organization, making code more efficient and maintaining consistency in data preprocessing. 

🌀 Do text embeddings perfectly encode text? The rapid advancement of generative AI has led to the widespread adoption of Retrieval Augmented Generation (RAG) systems, where AI retrieves relevant documents from a database to generate responses. This has given rise to vector databases, designed to store and search through embeddings, vector representations of documents. The paper "Text Embeddings Reveal as Much as Text" explores the security of embedding vectors, questioning whether they can be inverted back to text, posing challenges for privacy and information security. 

🌀 End to End AI Use Case-Driven System Design: This blog explores the complexities of AI system performance beyond TOPs (Tera Operations Per Second), focusing on real AI use cases. It dives into optimizing an AI system for an infinite zoom feature, emphasizing power efficiency through model and memory optimizations, dynamic power scaling, and specialized hardware accelerators. 

🌀 Navigating Cost-Complexity: Mixture of Thought LLM Cascades Illuminate a Path to Efficient Large Language Model Deployment: This post discusses how to significantly reduce costs while maintaining accuracy in utilizing Large Language Models (LLMs), crucial for various applications. It introduces a novel approach called Mixture of Thought (MoT) Cascades, employing a blend of weaker and stronger LLMs, along with innovative prompting techniques and consistency measurements.

🌀 Structure and Relationships: Graph Neural Networks and a Pytorch Implementation. This article introduces Graph Neural Networks (GNNs), a powerful method for modeling spatial and graphical structures in data, such as molecular structures, social networks, and city designs. It covers the mathematical description of GNNs, including graph convolution networks (GCNs) and graph attention networks (GATs), and provides a regression example using the PyTorch library. The article aims to make GNNs more accessible by explaining their principles and demonstrating their potential applications. 

🌀 Extensible and Customisable Vertex AI MLOps Platform: The article describes the development of an MLOps platform for scalable machine learning models on Vertex AI using Kubeflow pipelines. It aims to provide a modular, flexible, and integrated solution for building operationalized ML models, serving as an educational resource and foundation for teams. The platform addresses common challenges and emphasizes testing, configuration, and CI/CD orchestration. 

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