What is today’s beat?

• J1 Assistant - only released in China 🤔 

• Google study on Graphs (GNN) for reasoning

• Tutorials, and some links for CES 2025 press conferences.

  
  
  Your FREE newsletter
   share
  to show support
  

🎯 RELEASES 🎯

Matter.ai
J1 Assistant Launches Only to the Chinese

Synopsis

Matter.ai introduces J1 Assistant, an AI-driven personal assistant featuring voice input and integration with multiple AI models like Google and ChatGPT. Currently, only the Android version is available, and only to Chinese IP address!

Core Observations

1. Voice Input Integration: Users can initiate voice commands to perform tasks such as web searches, AI model queries, or creating memos, enhancing hands-free interaction.

2. AI Model Integration: The assistant interfaces with proprietary Jarvis AI, Google, and ChatGPT, providing diverse and accurate responses tailored to user needs.

3. Ripple Touch Interface: Introduces a unique interaction design called "Ripple Touch," offering an intuitive and engaging user experience.

Broader Context

The launch of J1 Assistant signifies a notable advancement in AI-driven personal assistants, integrating multiple AI models into a single platform. This development could influence the evolution of AI assistants, offering users a more versatile and efficient tool. However, its initial exclusivity to Android devices and specific models may limit its immediate user base.

View more here

Google
Study Reveals Transformer's Enhanced Graph Reasoning Capabilities

Synopsis

Google's recent research on transformer models highlights their surprising efficiency and capability in graph-based reasoning tasks, traditionally dominated by specialised models like Graph Neural Networks (GNNs). This development provides insights into the general-purpose nature of transformers, offering new perspectives on model selection for graph-based tasks and enhancing AI’s ability to tackle problems such as connectivity and cycle detection within graphs.

Core Observations

1. Transformer Efficiency in Graph Tasks: The study reveals that transformers can efficiently solve graph problems like connectivity and shortest path with fewer parameters compared to GNNs.

2. Long-Range Dependency Analysis: Transformers outperform GNNs in tasks that involve long-range dependencies, showcasing their potential in complex graph structures.

3. Innovative Representation of Graph Tasks: Google introduces a new representational hierarchy for graph reasoning, providing a more structured understanding of how transformers handle various graph-based problems.

Broader Context

This research shifts the focus on choosing the right architecture for graph-related tasks. By demonstrating transformers' strength in handling graph algorithms typically suited for GNNs, it opens avenues for more versatile AI applications. Reasoning is the hot topic right now, and here are a few of the current, and common methods of solving reasoning with LLMS that it challenges:

• Chain-of-Thought (CoT) Reasoning: Breaks tasks into step-by-step processes, improving complex reasoning through intermediate steps for logical problem-solving.

• Few-Shot and Zero-Shot Learning: Models reason about tasks with minimal or no examples, generalizing to new tasks by leveraging prior knowledge.

• Attention Mechanisms & Self-Attention: Focuses on relevant parts of input data to model long-range dependencies and improve reasoning across different text parts.

• Symbolic Reasoning & Neural-Symbolic Systems: Combines neural networks with symbolic logic, enabling explicit, rule-based reasoning for structured tasks.

Read the full article here

Trending

• GenAI report with Dataiku and Databricks

• Modal, AI infrastructure, is SOC compliant

• Felix Hill, A senior staff researcher at Google DeepMind passes. Read his blog about the stress and mental health of a researcher.

We are 100% free!

And with your support, we create more FREE content!

Please share us with a friend!

⚙️ BUILDERS BYTES ⚙️

What will you learn today?

Today’s tutorial is with LangChain and ChromaDB to make a rag pipeline. In this tutorial, we will explore how to build an LLM-based agent that retrieves documents, grades their relevance to a user question, and generates or rewrites responses based on the documents retrieved. We'll also learn how to integrate various tools within the LangChain ecosystem.

Key Takeaways

1. Document Retrieval: Set up a retriever to fetch relevant web documents using LangChain's WebBaseLoader and Chroma vectorstore.

2. Decision Functions: Define the agent's actions to generate or rewrite responses based on document relevance.

3. Tool Integration: Bind external tools to LLMs for enhanced functionality, such as document retrieval and response generation.

View the tutorial here

⭐️ ⭐️⭐️⭐️⭐️
Like these tutorials?
👉️ Star out repo to show support
⭐️⭐️⭐️⭐️⭐️⭐️

Do you have a product in AI and would like to contribute?
👉️ email us: [email protected] 

Is there something you’d like to see in this section?
👉️ share your feedback

Trending

• Code: Khoj - your local AI assistant

• Code: LiteLM, Call all LLM APIs using the OpenAI format - Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc

• Paper Don’t Do RAG, do CAG - Cache Augmented Retrieval

🤩 COMMUNITY 🤩

EVENTS

CES is the most powerful tech event in the world — the proving ground for breakthrough technologies and global innovators and starts with some press conferences tonight and others tomorrow. Here are a few links to press conferences, which should be making major releases in robotics.

• Sony, Toyota, Samsung, Nvidia,

LEARNING

DataCamp is a solid provider of online courses and resources to learn data science. Here are a few free introductory courses to keep your skills sharp!

• Large Language Models (LLMs) Concepts

• Large Language Models for Business

• Introduction to LLMs in Python

THANK YOU

Found something cool?
Want something different?

Our Mission at AlphaWise

AlphaWise strives to cultivate a vibrant and informed community of AI enthusiasts, developers, and researchers. Our goal is to share valuable insights into AI, academic research, and software that brings it to life. We focus on bringing you the most relevant content, from groundbreaking research and technical articles to expert opinions to curated community resources. 

Looking to connect with us?

We actively seek to get involved in community with events, talks, and activities. Email us at [email protected] 

Looking to promote your company, product, service, or event?