Pondhouse Data AI - Edition 2

Subscribe, if this was forwarded to you

Hey there,

We are happy to serve the 2nd edition of our Pondhouse AI newsletter — the go-to resource for learning and experiencing artificial intelligence. Whether you’re looking to understand complex AI concepts, apply AI tools effectively, or explore inspiring use cases, we have you covered.

Let’s dive right in!

Cheers, Andreas & Sascha

In todays edition:

• News: We got a first glimpse inside the “mind” of LLMs

• Tutorial: How to securely run your LLM

• Tip of the week: Human-in-the-loop with AI applications

• Tool of the week: Phidata - a framework for building Autonomous Assistants

Find this Newsletter helpful?
Please forward it to your colleagues and friends - it helps us tremendously.

Top News

“Mapping the Mind of a Large Language Model”

Researchers at Anthropic have made a groundbreaking advancement in understanding AI models by examining the inner workings of Claude Sonnet, a state-of-the-art large language model. Using innovative techniques, they have mapped how millions of concepts are represented within the model, paving the way for improved AI safety and reliability.

The Problem

AI models often operate as black boxes, making it difficult to understand how they process information and why they produce specific responses. This opacity raises concerns about the safety and reliability of these models, as it is challenging to predict and control their behavior, including the potential for biased, harmful, or untruthful outputs.

The Solution

By applying a technique called "dictionary learning," they isolated patterns of neuron activations (features) that represent human-interpretable concepts. This detailed examination revealed how concepts are organized and used within the AI, offering insights into its sophisticated behaviors and capabilities. These features can be manipulated to observe changes in the model's behavior, providing a deeper understanding of how the AI represents and processes information.

What's Next

While this research marks a significant milestone in AI interpretability, it is just the beginning. Future work will focus on identifying a comprehensive set of features and understanding the circuits these features form. Researchers aim to apply these findings to enhance AI safety by monitoring and steering AI behavior towards desirable outcomes and away from dangerous ones. There are also opportunities for further collaboration and innovation in this field, with open positions for those interested in contributing to the advancement of AI interpretability and safety.

Explore all the details of this research and Anthropic's official posting here:

Tutorials & Use Cases

Proven Strategies to Reduce LLM Costs

1. Optimize Prompts: By refining and shortening prompts, you can significantly cut down on token usage, which reduces both cost and response time. For example, instead of verbose questions, use direct and clear queries.

2. Use Smaller, Task-Specific Models: Smaller models, finetuned to specific tasks are not only more cost-efficient but also faster. For instance, a sentiment analysis task may not require the full capability of a large model.

3. Cache Frequent Responses: Implementing a caching mechanism for common queries can greatly reduce redundant processing. This is particularly useful for frequently asked questions or repetitive tasks, ensuring quicker responses and lower computational expenses.

4. Batch Processing: Aggregating multiple requests into a single batch can optimize resource usage and reduce the number of API calls. This is especially beneficial for applications that can process multiple queries simultaneously without waiting for individual responses.

5. Early Stopping: Setting a maximum token limit for responses helps prevent unnecessary generation of tokens. For instance, if a task typically requires short responses, enforcing a token cap can save on processing power and reduce costs.

6. Fine-Tune Models: By fine-tuning models on specific datasets for targeted tasks, you can enhance efficiency and accuracy. This customization allows the model to perform better with fewer resources compared to using a general-purpose model for all tasks.

Find 5 more tips for saving costs when running large language models in our blog post below

OpenAI says it has begun training GPT-5

GPT-3 was a revolution in AI and started the current AI hype. GPT-4 made AI good enough to revolutionize how large portions of the world works and operates. But now what?

OpenAI seems keen on innovating in the AI space and announced, that they are working on a new flagship AI model - assumingly GPT-5.

Highlights

• Ambitious Goals: OpenAI expects the new model to bring “the next level of capabilities”.

• Broader Applications: The new model will enhance products like chatbots, digital assistants, search engines, and image generators.

• Safety and Security Committee: OpenAI has formed a committee to address risks associated with the new model and future technologies.

• Industry Leadership: OpenAI aims to advance AI faster than competitors while addressing concerns about the technology’s risks.

• Voice Controversy: Scarlett Johansson alleged unauthorized use of a voice similar to hers in the new GPT-4o model.

• Leadership Changes: Co-founder Ilya Sutskever's departure raises concerns about OpenAI's commitment to addressing AI dangers.

Octopus v2: Stanford's New On-Device Language Model Outperforms GPT-4

Researchers from Stanford University have introduced Octopus v2, an advanced on-device language model that promises significant improvements in AI-powered applications. Designed for function calling tasks, this model surpasses GPT-4 in both accuracy and speed, offering a reliable solution for various edge devices like smartphones and cars.

Highlights

• Accuracy and Speed: Octopus v2, with its 2 billion parameters, demonstrates superior accuracy and reduces latency by 35-fold compared to existing systems.

• Privacy and Cost Efficiency: Operating on-device, it addresses privacy concerns and eliminates the costs associated with cloud-based models.

• Function Calling Efficiency: By reducing context length by 95%, Octopus v2 enhances the efficiency of function calls, crucial for real-time applications.

• Wide Applicability: Suitable for deployment across a range of devices, including VR headsets and personal computers, this model offers versatility in real-world applications.

This breakthrough in AI technology could redefine the landscape of on-device language processing, making sophisticated AI tools more accessible and efficient.

For more details, check out the full research paper: Octopus v2: On-device language model for super agent

Mistral-finetune: Efficient Fine-Tuning for Mistral Models

The Mistral team has released mistral-finetune, a lightweight and memory-efficient codebase designed for fine-tuning Mistral's AI models. This tool utilizes the LoRA (Low-Rank Adaptation) approach, which significantly reduces the computational load by only training 1-2% of the model's weights.

Key Features

• Memory Efficiency: Optimized for single and multi-GPU setups, making it suitable for both large-scale and smaller model fine-tuning.

• Ease of Use: A guided entry point for fine-tuning, specifically tailored for Mistral models.

• Performance: Best utilized with A100 or H100 GPUs for maximum efficiency.

For more information, visit the Mistral-finetune GitHub repository. ​

Cohere releases Aya: A Multilingual AI Model Covering 101 Languages

Cohere For AI has introduced Aya, a groundbreaking multilingual language model designed to push the boundaries of AI research. Aya covers 101 languages, including over 50 previously underserved languages, making it one of the most comprehensive models available.

Key Features

• Global Collaboration: Aya is the result of a global initiative involving over 3,000 independent researchers from 119 countries.

• Extensive Language Support: The model supports 101 languages, with a focus on enhancing AI capabilities in underserved languages.

• High-Quality Data: Aya boasts a massive dataset with 513 million data points, ensuring robust and reliable performance.

• Community Engagement: With 56 language ambassadors and extensive community support, Aya is set to make a significant impact in the AI research community.

Models Available

• Aya 23 - 8B: A state-of-the-art, accessible research model.

• Aya 23 - 35B: A high-performance model for advanced research needs.

• Aya 101: A massively multilingual model covering 101 languages.

Experience Aya firsthand in the Cohere Playground or download the models directly from Hugging Face.

For more information, visit the Cohere For AI Aya page.

Tip of the week

Human-in-the-loop with AI applications

This week's tip focuses on integrating human approval in language model workflows using LangChain. Human approval helps in refining responses, ensuring quality, and aligning outputs with desired standards. By combining automated processes with human validation, you can improve the accuracy and relevance of your language model applications.

Two examples for human-in-the-loop applications:

1. Customer support: Use LLMs in combination with RAG to create ready-made answers for your clients. However, add a human “gate” for final verification.

2. Running shell commands: AI is perfectly capable of creating shell commands. Run this AI generated commands in your shell, but only after manually approving them.

from langchain.callbacks import HumanApprovalCallbackHandler
from langchain_openai import OpenAI
from langchain.agents import AgentType
from langchain.tools import ShellTool

# Create a tool to be used by our LLM
tools = load_tools(["terminal"], llm=llm)

llm = OpenAI(temperature=0)

# Create an autonomous LLM agent
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)

# Run the agent, but add the human-in-the-loop
agent.run("Delete folder /home", callbacks=[HumanApprovalCallbackHandler()])T

The AI agent is asked to create code to delete folder /home. But instead of running it automatically, the following output is provided:
Do you approve of the following input? Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.
rm -rf /home

Upon entering Yes the created “terminal” - tool will run the code rm -rf /home. When entering anything else, nothing is executed, effectively providing a manual intervention method for us humans.

Tool of the week

Phidata

Phidata is a framework for building Autonomous Assistants (aka Agents) that have long-term memory, contextual knowledge and the ability to take actions using function calling.

Why phidata?

Problem: LLMs have limited context and cannot take actions.

Solution: Add memory, knowledge and tools.

• Memory: Stores chat history in a database and enables LLMs to have long-term conversations.

• Knowledge: Stores information in a vector database and provides LLMs with business context.

• Tools: Enable LLMs to take actions like pulling data from an API, sending emails or querying a database.

How it works

• Step 1: Create an Assistant

• Step 2: Add Tools (functions), Knowledge (vectordb) and Storage (database)

• Step 3: Serve using Streamlit, FastApi or Django to build your AI application

We hope you liked our newsletter and you stay tuned for the next edition. If you need help with your AI tasks and implementations - let us know. We are happy to help