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• 9 chatbots for you to try

• Meta’s Large Model Concept

• What is Model Context Protocol?

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Article
9 chatbots for you to try

Synopsis

In 2024, AI chatbots have significantly advanced, becoming integral tools across various industries. Their enhanced capabilities in natural language processing, reasoning, and multimodal interactions have improved user engagement and operational efficiency. The global chatbot market is projected to grow from $7.01 billion in 2024 to $20.81 billion by 2029, indicating their increasing relevance and mainstream adoption. So let’s look at some that you can use!

Core Observations

1. Meta AI: integrated into Meta's platforms like Facebook or messenger.

2. ChatGPT (OpenAI): The most common, enough said! It excels in generating human-like text and summarization but lacks in personalized writing tasks.
   

3. Google Gemini: Similar to ChatGPT, but also has Google Labs with a full suite of other products that integrates with all things google.
   

4. Microsoft Copilot: Integrated into Microsoft's suite, Copilot assists with tasks across applications.
   

5. Claude (Anthropic): Emphasizing ethical AI interactions, Claude focuses on providing safe and reliable conversational experiences and is particularly popular in the developer community.
   

6. Perplexity AI: Specializes in real-time web browsing, offering users up-to-date information and contextually relevant responses.
   

7. Poe: Focuses on creative content generation, assisting users in writing and brainstorming processes.
   

8. Grok (xAI): Developed by xAI, Grok-2 is among the top AI chatbots, comparable to OpenAI's GPT-4, and is integrated into the X platform. It’s free on X.
   

9. HuggingChat: An open-source chatbot developed by Hugging Face, great for developers and users with many models to select from.

Broader Context

Well it seems like there is a chatbot for everything. It’s great to explore, especially if there is a way it can help with work. While ChatGPT has won mainstream success and Gemini is its lead competitor, other chatbots like Claude, Grok, and Perplexity have found great success in the developer community. Which have you tried?

Meta
Large Concept Models

Synopsis

Meta's research on Large Concept Models (LCMs) introduces an innovative approach to language modeling by leveraging sentence representation spaces. These advancements enhance natural language understanding (NLU) and semantic comprehension, paving the way for improved AI-driven communication tools and integrations within Meta products like chatbots and recommendation systems.

Core Observations

1. Language Modeling in Sentence Representation Space:
   The reserach explores LCMs' ability to encode semantic relationships within a compact and efficient sentence representation space, enabling more precise language processing and reasoning tasks.

2. Scalability and Flexibility:
   The LCMs are designed to scale effectively for large datasets, making them adaptable to complex applications across diverse linguistic tasks.

3. Enhanced Contextual Understanding:
   By incorporating conceptual relationships, LCMs improve the AI's understanding of nuanced contexts and semantics, which is critical for applications like content moderation, search, and personalized recommendations (e.g. higher order functions).

4. Integration of Multimodal Capabilities:
   LCMs can be adapted to integrate textual, visual, and audio data, broadening their utility across platforms that require multimodal interaction.

Broader Context

Meta’s Large Concept Models represent a significant step in the evolution of language modeling, emphasising efficiency and deeper semantic comprehension. The efforts are aiming at AGI (artificial general intelligence) for a time span of 2-5 years. These efforts have been closely tied to COCONUT paper released earlier this month.

View more
👉️ code
👉️ paper 

Anthropic
MPC: Standardizing AI System Interactions

Synopsis

The Model Context Protocol (MCP) is a collaborative initiative aimed at creating standardized systems for AI integration and communication. It seeks to establish a unified framework for model interaction, enhancing interoperability across AI platforms. Supported by key players like Anthropic and open-source contributors, MCP is poised to streamline AI deployments by focusing on tool discovery and tool invocation.

Core Observations

1. Standardized Interaction Framework:
   MCP defines a universal protocol to facilitate seamless communication between AI models, ensuring consistency across platforms.

2. Support for Popular Programming Languages:
   Python, JavaScript and Kotlin have active developments in github.

3. Open-Source Development:
   MCP is an open-source initiative, hosted on GitHub, encouraging community-driven contributions. View the call for developers and their quick start guide.

4. Enhanced Interoperability:
   The protocol emphasises compatibility, allowing different AI systems to work collaboratively, which is critical for scaling complex applications.

Broader Context

The Model Context Protocol represents a large scale collaborative advancement in AI system design, addressing challenges in integration and communication. By creating a standardized approach, MCP enables smoother interoperability across diverse AI tools and frameworks, reducing deployment complexity. Its open-source nature invites broad participation, ensuring that it evolves to meet the needs of a growing AI landscape.

👉️ Read more or view their code

Trending

• Your Go-To Vocabulary to Navigate the World of AI Agents and Agentic Workflows

• LLM running on Windows 98 PC (with tutorial)

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⚙️ BUILDERS BYTES ⚙️

What will you learn today?

Let’s build a research agent to generate a report using Exa with PhiData that uses OpenAI’s gpt-4o model. The python code will write an article on a topic and save it to a markdown file (.md).

Key Takeaways

1. Agent Configuration:

   • The Agent is configured using the OpenAIChat model (e.g., GPT-4o) and integrates ExaTools for enhanced search and analysis capabilities.

   • The tools include settings like start_published_date for filtering results by date and specifying a type such as "keyword" for contextual search.

2. Task Instructions and Output Design:

   • The agent is tasked to perform multiple searches, analyse results, and generate a well-structured report in markdown format.

   • Instructions emphasise factual accuracy, references, and an engaging writing style, suitable for high-quality content like a New York Times report (well, not quite but that’s what we’re selling 🤔 ).

3. Structured Markdown Output:

   • The expected output follows a structured template with sections including an overview, detailed subsections, takeaways, and references.

   • The template is designed to ensure clarity, engagement, and professionalism.

4. Interactive Features and Debugging:

   • show_tool_calls enables visibility into how tools are utilized during the agent's task execution, aiding in debugging and understanding the workflow.

   • Responses can be streamed and saved automatically to specified file paths, enhancing reusability and documentation.

from textwrap import dedent
from datetime import datetime

from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.tools.exa import ExaTools

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[ExaTools(start_published_date=datetime.now().strftime("%Y-%m-%d"), type="keyword")],
    description="You are an advanced AI researcher writing a report on a topic.",
    instructions=[
        "For the provided topic, run 3 different searches.",
        "Read the results carefully and prepare a NYT worthy report.",
        "Focus on facts and make sure to provide references.",
    ],
    expected_output=dedent("""\
    An engaging, informative, and well-structured report in markdown format:

    ## Engaging Report Title

    ### Overview
    {give a brief introduction of the report and why the user should read this report}
    {make this section engaging and create a hook for the reader}

    ### Section 1
    {break the report into sections}
    {provide details/facts/processes in this section}

    ... more sections as necessary...

    ### Takeaways
    {provide key takeaways from the article}

    ### References
    - [Reference 1](link)
    - [Reference 2](link)
    - [Reference 3](link)

    - published on {date} in dd/mm/yyyy
    """),
    markdown=True,
    show_tool_calls=True,
    add_datetime_to_instructions=True,
    save_response_to_file="tmp/{message}.md",
)
agent.print_response("Simulation theory", stream=True)

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Tools

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