Yo fam, this ain't just another AI dropโ this is the FUTURE of emotional intelligence! ๐
Introducing HAI-SER, powered by Structured Emotional Reasoning (SER), the next-level AI that doesnโt just understand your wordsโit feels you, analyzes your emotions, and helps you navigate lifeโs toughest moments. ๐ก
๐ฅ What makes HAI-SER a game-changer? ๐น Emotional Vibe Check โ Gets the mood, energy, and whatโs really going on ๐ญ ๐น Mind-State Analysis โ Breaks down your thoughts, beliefs, and patterns ๐คฏ ๐น Root Cause Deep-Dive โ Unpacks the WHY behind your emotions ๐ก ๐น Impact Check โ Sees how itโs affecting your life and mental health ๐ ๐น Safety Check โ Prioritizes your well-being and crisis management ๐จ ๐น Healing Game Plan โ Custom strategies to help you bounce back ๐ช ๐น Growth Potential โ Turns struggles into opportunities for self-improvement ๐ ๐น How to Approach โ Teaches you and others how to communicate and heal ๐ค ๐น Personalized Response โ Not just generic adviceโreal talk, tailored to YOU ๐ฏ
No more robotic AI responses. No more surface-level advice. HAI-SER gets deep, analyzing emotions with precision and giving real, actionable support.
This ainโt just AIโthis is your digital therapist, life coach, and hype squad all in one. Whether itโs mental health, career struggles, relationships, or personal growth, HAI-SER has your back.
๐ The future of emotionally intelligent AI is HERE. Are you ready? ๐ฅ๐ฏ
Evolution and The Knightian Blindspot of Machine Learning
The paper discusses machine learning's limitations in addressing Knightian Uncertainty (KU), highlighting the fragility of models like reinforcement learning (RL) in unpredictable, open-world environments. KU refers to uncertainty that can't be quantified or predicted, a challenge that RL fails to handle due to its reliance on fixed data distributions and limited formalisms.
### Key Approaches:
1. **Artificial Life (ALife):** Simulating diverse, evolving systems to generate adaptability, mimicking biological evolution's robustness to unpredictable environments. 2. **Open-Endedness:** Creating AI systems capable of continuous innovation and adaptation, drawing inspiration from human creativity and scientific discovery.
3. **Revising RL Formalisms:** Modifying reinforcement learning (RL) models to handle dynamic, open-world environments by integrating more flexible assumptions and evolutionary strategies.
These approaches aim to address MLโs limitations in real-world uncertainty and move toward more adaptive, general intelligence.
Artificial Kuramoto Oscillatory Neurons (AKOrN) differ from traditional artificial neurons by oscillating, rather than just turning on or off. Each neuron is represented by a rotating vector on a sphere, influenced by its connections to other neurons. This behavior is based on the Kuramoto model, which describes how oscillators (like neurons) tend to synchronize, similar to pendulums swinging in unison.
Key points:
Oscillating Neurons: Each AKOrNโs rotation is influenced by its connections, and they try to synchronize or oppose each other. Synchronization: When neurons synchronize, they "bind," allowing the network to represent complex concepts (e.g., "a blue square toy") by compressing information. Updating Mechanism: Neurons update their rotations based on connected neurons, input stimuli, and their natural frequency, using a Kuramoto update formula. Network Structure: AKOrNs can be used in various network layers, with iterative blocks combining Kuramoto layers and feature extraction modules. Reasoning: This model can perform reasoning tasks, like solving Sudoku puzzles, by adjusting neuron interactions. Advantages: AKOrNs offer robust feature binding, reasoning capabilities, resistance to adversarial data, and well-calibrated uncertainty estimation. In summary, AKOrN's oscillatory neurons and synchronization mechanisms enable the network to learn, reason, and handle complex tasks like image classification and object discovery with enhanced robustness and flexibility.
Understanding and Benchmarking Artificial Intelligence: OpenAI's o3 Is Not AGI
It's an interesting paper that argues "new approaches are required that can reliably solve a wide variety of problems without existing skills." "It is therefore hoped that the benchmark outlined in this article contributes to further exploration of this direction of research and incentivises the development of new AGI approaches that focus on intelligence rather than skills."
๐ChemQwen-vL is a vision-language model fine-tuned based on the Qwen2VL-2B Instruct model. It has been trained using the International Chemical Identifier (InChI) format for chemical compounds and is optimized for chemical compound identification. The model excels at generating the InChI and providing descriptions of chemical compounds based on their images. Its architecture operates within a multi-modal framework, combining image-text-text capabilities. It has been fine-tuned using datasets from: https://iupac.org/projects/
๐๐ปโโ๏ธHey there folks , Open LLM Europe just released Lucie 7B-Instruct model , a billingual instruct model trained on open data ! You can check out my unofficial demo here while we wait for the official inference api from the group : Tonic/Lucie-7B hope you like it ๐
Published a new blogpost ๐ In this blogpost I have gone through the transformers' architecture emphasizing how shapes propagate throughout each layer. ๐ https://huggingface.co/blog/not-lain/tensor-dims some interesting takeaways :
