That's right. We wrote about it here https://
nytimes.com/2025/02/02/opi
nion/ai-doctors-medicine.html
… bias (automation neglect) and lack of grounding with use of LLMs may contribute
LLMS
-
LLM Bias and Grounding Issues in AI Medicine Applications
By
–
-
Google Releases 3 New Gemini Models
By
–
BREAKING : Google released 3 new models on Gemini – 2.0 Flash Thinking Experimental – previously available on AI Studio
– 2.0 Flash Thinking Experimental with Apps – same model integrated with Google apps – 2.0 Pro Experimental – a long-awaited Pro version -
Mechanistic Interpretability: Understanding How LLMs Think
By
–
Fascinating insight from @tegmark on how LLMs ‘think.’ Because AI is a ‘black box,’ mechanistic interpretability—the emerging study of what goes on under the hood—is vital. One only wonders if our study of AI epistemology can keep up with AI itself. I don’t think it can.
-
Google Releases Gemini 2.0 Pro and Flash Thinking Models
By
–
Today, Google has released Gemini 2.0 Pro (exclusive to Advanced users) and Gemini 2.0 Flash Thinking in the Gemini app. Notably, Gemini 2.0 Flash Thinking integrates with existing Google services, enhancing its reasoning capabilities and making it a highly effective tool for
-
GPT-4 Enhances Physician Performance in Clinical Care Tasks
By
–
A randomized trial of GPT-4 vs 92 physicians with or without this #AI LLM for performance on patient care tasks.
AI improved physician performance, on par with AI alone (based on 5 clinical vignettes) https://
nature.com/articles/s4159
1-024-03456-y
… @NatureMedicine @AdamRodmanMD @jonc101x -
Gemini 2.0 Flash Thinking Introduces App Integration Capabilities
By
–
Gemini 2.0 Flash Thinking with apps?!
-
Reasoning Models Limited by Lack of Multimodal and Function-Calling Abilities
By
–
That's actually a very good and interesting point. I think the challenge is currently that those reasoning models don't have multimodal and function-calling abilities yet, so while they might be useful for EDA from a reasoning perspective, the usage would be quite clumsy there.
-
Distilled Models: 32B vs 3B Trade-offs and Use Cases
By
–
Agree with you. Do you mean the 32B distilled models or the 3B one I hinted at at the end? I think 32B should be just fine but yeah, 3B will be more tricky; I'd say that one is more for educational purposes.
-
Understanding Reasoning LLMs: DeepSeek R1 and Building Methods
By
–
I just finished writing up my take on reasoning models: https://
magazine.sebastianraschka.com/p/understandin
g-reasoning-llms
…
Here, I
1. Discuss the advantages &d disadvantages of reasoning models
2. Of course, describe and discuss DeepSeek R1
3. Describe the 4 main ways to building & improving reasoning models
