An AI agent is made up of both the environment it operates in (e.g., a game, the internet, or computer system) and the set of actions it can perform through its available tools. This dual definition is fundamental to understanding how agents work.
👨💻 Agent Example
The figure shows an example of an agent built on top of GPT-4. The environment is the computer which has access to a terminal and filesystem. The set of action include navigate, searching files, viewing files, etc.
Google recently published this great whitepaper on Agents.
2025 is going to be a huge year for AI Agents.
Here's what's included:
- Introduction to AI Agents
- The role of tools in Agents
- Enhancing model performance with targeted learning
- Quick start to Agents with LangChain
- Production applications with Vertex AI Agents
- o1 is launching out of preview in the API
- support for function calling, structured output, and developer messages
- reasoning_effort parameter to tell the model how much effort to spend on thinking
- vision inputs in the API is here too
Visual inputs with developer message (this is a new spin to system message for better steering the model) inside of the OpenAI Playground
Cool to see support for function calling and response format for o1
- introduces reinforcement fine-tuning (RFT) of o1
- tune o1 to learn to reason in new ways in custom domains
- RFT is better and more efficient than regular fine-tuning; needs just a few examples
1/n
How it looks in the dev platform. Examples show how to select RFT on o1-mini
What does data look like to use RFT. Uses a grader to grade the answer of the model. Different graders will be provided, with the ability to use custom grading.
JUST IN: Google DeepMind releases Gemma, a series of open models inspired by the same research and tech used for Gemini.
Open models fit various use cases so this is a very smart move from Google.
Great to see that Google recognizes the importance of openness in AI science and technology.
There are 2B (trained on 2T tokens) and 7B (trained on 6T tokens) models including base and instruction tuned versions. Trained on a context length of 8192 tokens.
Commercial use is allowed.
These are not multimodal models but based on the reported experimental results they appear to outperform Llama 2 7B and Mistral 7B.
I am excited about those MATH, HumanEval, GSM8K, and AGIEval results. These are really incredible results for a model this size.
Excited to dive deeper into these models. The model prompting guide is dropping soon. Stay tuned!
When I said outperforms other models, I meant generally outperforms them on all the benchmarks. Llama has a lot of catching up to do but it is interesting to see Mistral 7B trail Gemma very closely. These numbers don't really mean much in the context of real-world applications.
If you follow me here on X, you know how excited I get about unlocking unique value and use cases with small language models (SLMs). It will also be fun to run these locally and other other small devices. As I have been saying, SLMs are underexplored. It's a mistake to just see them as research artifacts.