Brian Jalaian @BJalaian
🚀 Associate Professor @UWF | Researching LLMs, Agentic AI, and AI Optimization at the Edge | Building the future of robust, efficient AI. brianjalaian.com Pensacola, Florida Joined April 2019-
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Reasoning models are great for complex 20-step tasks. But for everyday agents? You don't need that extra overhead. When the harness handles orchestration, older models do surprisingly well. Stop asking "is it new enough?" and start asking "does it fit my harness?"
A late 2024 model like Qwen2.5-14B quietly changed how I view agentic AI. Tool calling was solved earlier than most think, and agency lives mostly in the harness (memory, planning, routing). A solid 14B model just needs to follow instructions and emit clean tool calls. 🧵
AI coding agents & LLMs can generate code, but they can’t replace technical intuition.If you don’t understand how models learn, evaluate, and fail, you can’t debug them when it counts.Master the fundamentals in Module 3: courses.brianjalaian.com#MachineLearning #AI #BuildInPublic
What if you could talk to your drones? We’re building an AI-enabled autonomy stack with WebTAK, edge AI, open-source VLMs, and visual reasoning. Full demo 👇 youtu.be/rQce-XPbb2s #PhysicalAI #EdgeAI #Robotics
@danvahdat @elonmusk Thank you @elonmusk
Year 3 of our Machine Learning in Data Science course is here—with a new focus on AI tools like Copilot 🤖⚡ Excited to explore how these tools can boost learning and creativity. Big shoutout to the research team pushing boundaries! #MachineLearning #AI #DataScience
We trained ML models to detect cognitive load using wearable biosignal data. 👨⚕️ Wristbands + EEG → Features + Spectrograms → Transfer Learning Here’s a 3-min clip breaking it all down: ▶️ youtu.be/z8jsEZ9_x2U #DeepLearning #Neurotech #Biosignals #TechForGood
🧠 Can AI detect your mental effort using wearables? We tested ML, DNNs & transfer learning on biosignal data (EEG, PPG, BVP) to decode cognitive states. 📺 Full video: youtube.com/watch?v=z8jsEZ… #AI #DeepLearning #Wearables #CognitiveLoad #MachineLearning
🎥 New 3-min explainer: Visual Reasoning Agent (VRA) = training-free agentic AI for vision systems 💡 Up to +40% accuracy on tough remote sensing tasks — no retraining, just smart reasoning. Think. Critique. Act. 📽️ Watch how VRA boosts robustness👇 #VLM #AgenticAI
@Sama @Sama 🔥 Practicing o3 3+ hrs/day—what learning path works best? Structured tutorials vs OSS deep dives? 📚 vs 🛠️ balance? Tips on feedback loops or AI/human mentors? How do YOU use AI to level up your #AIresearch game? #skillsmax #o3
🌟 Hydra shows that structured, agentic reasoning can dramatically improve VLM reliability efficiently. We’re excited about future work combining Hydra with even larger LLMs and edge deployment! 📄 arxiv.org/abs/2504.14395 #AI #LLMs #RobustAI #VisionLanguageModels
🚀 Excited to share our new research! We present Hydra, boosting adversarial robustness and reducing hallucinations in VLMs. Hydra enhances reliability through iterative reasoning—no extra training needed. 📄 arxiv.org/abs/2504.14395 #AI #VLM #Robustness #ML #Research
🧪 Results: Hydra outperforms existing plug-in methods across benchmarks like ScienceQA, GQA, and TextVQA. It’s particularly effective against unseen adversarial examples — even without explicit defenses.
🏋️♂️ Why is this important? Stronger resilience against adversarial attacks Reduced hallucination rates Better factual accuracy in open-world settings Works without expensive retraining or fine-tuning!
It uses Chain-of-Thought (CoT) and In-Context Learning (ICL) techniques to generate, critique, and refine answers across multiple reasoning passes. Hydra acts like an "agent," adapting based on feedback.
Vision-Language Models are incredibly powerful but vulnerable to adversarial attacks and hallucinations. Hydra tackles both issues by introducing an iterative reasoning framework — no retraining needed.
🚨 Join the AAAI FS 2024 - ATRACC for expert talks on AI Validation, Risk, & Trustworthiness! 🗓️ Nov 7-9, 2024 📍 Arlington, VA 🔑 Hotel Deadline: Oct 17 – Limited rooms! 🏨 More info: buff.ly/4eVwTHY #AAAI #ATRACC2024 #AITrustworthiness #AIConference 🚨
Yes, many of us have always been working on "AGI", whatever you mean by that. Perceptron, General Problem Solver, expert systems, machine learning, backprop, RL, SSL, transformers, LLMs...: For some, these were going to be "The One Weird Trick" that was gonna take us to AGI. 1/
@mezaoptimizer Old time AI people like me have always been working on AGI. We just called it AI. To me AGI is just a marketing term. We old timers think we know how hard the problem is, and we are lolling as the younger generation discovers this
Extraordinary new paper from Google on medicine & AI: When Google tuned a AI chatbot to answer common medical questions, doctors judged 92.6% of its answers right … compared to 92.9% of answers given by other doctors. And look at the pace of improvement! arxiv.org/pdf/2212.13138…
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