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👋 Hello ,
Welcome to another scintillating edition of AI_Distilled, featuring recent advancements in training and fine-tuning LLMs, GPT and AI models for enhanced business outcomes.
Let’s get started with this week’s news and analysis with an industry expert’s opinion.
“For me, the biggest opportunity we have is AI. Just like the cloud transformed every software category, we think AI is one such transformational shift. Whether it's in search or our Office software.” - Satya Nadella, CEO, Microsoft.
👉Apple Aims to Introduce Generative AI to iPhone and iPad in Late 2024:Tech analyst Jeff Pu suggests that Apple is planning to integrate generative AI into its devices, beginning as early as late 2024. Apple is expected to deploy a combination of cloud-based and on-device edge AI. This move is aimed at letting users automate complex tasks and enhance Siri's capabilities, possibly starting with iOS 18. Apple remains cautious about privacy and responsible use of AI, acknowledging potential biases and hallucinations.
👉 DALL·E 3 Unveiled for ChatGPT Plus and Enterprise Users: OpenAI has introduced DALL·E 3 in ChatGPT, offering advanced image generation capabilities for Plus and Enterprise users. This feature allows users to describe their desired images, and DALL·E 3 creates a selection of visuals for them to refine and iterate upon within the chat. OpenAI has incorporated safety measures to prevent the generation of harmful content. Moreover, they are researching a provenance classifier to identify AI-generated images.
👉 Universal Music Group Sues AI Company Anthropic Over Copyrighted Lyrics Distribution: Universal Music Group and music publishers have filed a lawsuit against Anthropic for distributing copyrighted lyrics through its AI model Claude 2. The complaint alleges that Claude 2 can generate lyrics closely resembling copyrighted songs without proper licensing, even when not explicitly prompted to do so. The music publishers claim that while other lyric distribution platforms pay to license lyrics, Anthropic omits essential copyright management information.
👉 Nvidia's Eureka AI Agent, Powered by GPT-4, Teaches Robots Complex Skills: Nvidia Research has introduced Eureka, an AI agent driven by GPT-4 from OpenAI, capable of autonomously training robots in intricate tasks. Eureka can independently craft reward algorithms and has successfully instructed robots in various activities, including pen-spinning tricks and opening drawers. It also published the Eureka library of AI algorithms, allowing experimentation with Nvidia Isaac Gym. This innovative work leverages the potential of LLMs and Nvidia's GPU-accelerated simulation technologies, marking a significant step in advancing reinforcement learning methods.
👉OpenAI in Talks for $86 Billion Valuation, Surpassing Leading Tech Firms:OpenAI, the company responsible for ChatGPT, is reportedly in discussions to offer its employees' shares at an astounding $86 billion valuation, surpassing tech giants like Stripe and Shein. This tender offer is in negotiation with potential investors, although final terms remain unconfirmed. With Microsoft holding a 49% stake, OpenAI is on its way to achieving an annual revenue of $1 billion. If this valuation holds, it would place OpenAI among the ranks of SpaceX and ByteDance, becoming one of the most valuable privately held firms globally.
👉 Mojo SDK Now Available for Mac: Unleashing AI Power on Apple Silicon: The Mojo SDK, which has seen considerable success on Linux systems, is now accessible for Mac users, specifically Apple Silicon devices. This development comes in response to user feedback and demand. The blog post outlines the steps for Mac users to get started with the Mojo SDK. Additionally, there's a Visual Studio Code extension for Mojo, offering a seamless development experience. The Mojo SDK's remarkable speed and performance on Mac, taking full advantage of hardware capabilities, is highlighted.
👉 Qualcomm Reveals Snapdragon Elite X Chip for AI-Enhanced Laptops:Qualcomm introduced the Snapdragon Elite X chip for Windows laptops, optimized for AI tasks like email summarization and text generation. Google, Meta, and Microsoft plan to use these features in their devices, envisioning a new era of "AI PCs." Qualcomm aims to rival Apple's chips, claiming superior performance and energy efficiency. With the ability to handle AI models with 13 billion parameters, this chip appeals to creators and businesses seeking AI capabilities.
Linear regression is one of the most widely known modeling techniques. Existing for more than 200 years, it has been explored from almost all possible angles. Linear regression assumes a linear relationship between the input variable (X) and the output variable (Y).
If we consider only one independent variable and one dependent variable, what we get is a simple linear regression. Consider the case of house price prediction, defined in the preceding section; the area of the house (A) is the independent variable, and the price (Y) of the house is the dependent variable. We import the necessary modules. It is a simple example, so we’ll be using only NumPy, pandas, and Matplotlib:
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import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import pandas as pd
Next, we generate random data with a linear relationship. To make it more realistic, we also add a random noise element. You can see the two variables (the cause, area, and the effect, price) follow a positive linear dependence:
#Generate a random data np.random.seed(0) area = 2.5 * np.random.randn(100) + 25 price = 25 * area + 5 + np.random.randint(20,50, size = len(area)) data = np.array([area, price]) data = pd.DataFrame(data = data.T, columns=['area','price']) plt.scatter(data['area'], data['price']) plt.show()
Now, we calculate the two regression coefficients using the equations we defined. You can see the result is very much near the linear relationship we have simulated:
W = sum(price*(area-np.mean(area))) / sum((area-np.mean(area))**2) b = np.mean(price) - W*np.mean(area) print("The regression coefficients are", W,b)
----------------------------------------------- The regression coefficients are 24.815544052284988 43.4989785533412
Let us now try predicting the new prices using the obtained weight and bias values:
y_pred = W * area + b
Next, we plot the predicted prices along with the actual price. You can see that predicted prices follow a linear relationship with the area:
📀 The Advantages of Small LLMs: Smaller LLMs are easier to debug and don't require specialized hardware, which is crucial in today's chip-demanding market. They are cost-effective to run, expanding their applicability. Additionally, they exhibit lower latency, making them suitable for low-latency environments and edge computing. Deploying small LLMs is more straightforward, and they can even be ensembled for improved performance.
📀 PyTorch Edge Unveils ExecuTorch for On-Device Inference: The PyTorch Edge team has introduced ExecuTorch, a solution that empowers on-device inference on mobile and edge devices with the support of industry leaders like Arm, Apple, and Qualcomm Innovation Center. ExecuTorch aims to address the fragmentation in the on-device AI ecosystem by offering extension points for third-party integration to accelerate ML models on specialized hardware.
📀AI-Boosted Software Development Journey: AI assistance simplifies design, code generation, debugging, and impact analysis, streamlining workflows and enhancing productivity. From idea to production, this post takes you through various stages of development, starting with collaborative design sessions aided by AI tools like Gmail's help me write and Google Lens. Duet AI for Google Cloud assists in code generation, error handling, and even test case creation. This AI assistance extends to operations, service health monitoring, and security.
📀 Scaling Reinforcement Learning with Cloud TPUs:Learn how Cloud TPUs are revolutionizing Reinforcement Learning by enhancing the training process for AI agents. This article explores the significant impact of TPUs on RL workloads, using the DeepPCB case as an example. Thanks to TPUs, DeepPCB achieved a remarkable 235x boost in throughput and a 90% reduction in training costs, significantly improving the quality of PCB routings. The Sebulba architecture, optimized for TPUs, is presented as a scalable solution for RL systems, offering reduced communication overhead, high parallelization, and improved scalability.
💡 Masterclass: AI/LLM Tutorials
🎯Building an IoT Sensor Network with AWS IoT Core and Amazon DocumentDB: Learn how to create an IoT sensor network solution for processing IoT sensor data via AWS IoT Core and storing it using Amazon DocumentDB (with MongoDB compatibility). This guide explores the dynamic nature of IoT data, making Amazon DocumentDB an ideal choice due to its support for flexible schemas and scalability for JSON workloads.
🎯 Building Conversational AI with Generative AI for Enhanced Employee Productivity: Learn how to develop a lifelike conversational AI agent using Google Cloud's generative AI capabilities. This AI agent can significantly improve employee productivity by helping them quickly find relevant information from internal and external sources. Leveraging Dialogflow and Google enterprise search, you can create a conversational AI experience that understands employee queries and provides them with precise answers.
🎯A Step-by-Step Guide to Utilizing Feast for Enhanced Product Recommendations:In this comprehensive guide, you will learn how to leverage Feast, a powerful ML feature store, to build effective product recommendation systems. Feast simplifies the storage, management, and serving of features for machine learning models, making it a valuable tool for organizations. This step-by-step tutorial will walk you through configuring Feast with BigQuery and Cloud Bigtable, generating features, ingesting data, and retrieving both offline and online features.
🎯 Constructing a Mini GPT-Style Model from Scratch: In this tutorial, you’ll explore model architecture, demonstrating training and inference processes. Know the essential components, such as data processing, vocabulary construction, and data transformation functions. Key concepts covered include tokens, vocabulary, text sequences, and vocabulary indices. The article also introduces the Self-Attention module, a crucial component of transformer-based models.
🎯 Deploy Embedding Models with Hugging Face Inference Endpoints: In contrast to LLMs, embedding models are smaller and faster for inference, which is valuable for updating models or improving fine-tuning. The post guides you through deploying open-source embedding models on Hugging Face Inference Endpoints. It also covers running large-scale batch requests. Learn about the benefits of Inference Endpoints, Text Embeddings Inference, and how to deploy models efficiently.
🚀 HackHub: Trending AI Tools
🔨xlang-ai/OpenAgents: Open platform with Data, Plugins, and Web Agents for data analysis, versatile tool integration, and web browsing, featuring a user-friendly chat interface.
🔨 AI-Citizen/SolidGPT: Technology business boosting framework allowing developers to interact with their code repository, ask code-related questions, and discuss requirements.
🔨SkalskiP/SoM: Unofficial implementation of Set-of-Mark (SoM) tools. Developers can use it by running Google Colab to work with this implementation, load images, and label objects of interest.
🔨 zjunlp/factchd: Code for detecting fact-conflicting hallucinations in text for developers to evaluate factuality within text produced by LLMs, aiding in the detection of factual errors and enhancing credibility in text generation.
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