Abdelkader RHOUATI

Data Scientist & Data Architect & Ph.D. researcher on AI, Deep Learning, NLP, and XAI

[veritas-ai @veritas_ai_labo]

[Today Article]
Exploring the Frontier of AI Video Generation 🎥✨

AI's ability to generate images from text has evolved remarkably, and now, the technology is stepping into the complex world of video. Text-to-video generation is not just about visualizing scenes but understanding motion and time, making it significantly more challenging than its predecessor.

Key insights:

- Neural networks must now comprehend motion, apply physics, and ensure temporal coherence.
- Advances like Video Diffusion Models (VDM) and Meta AI's Make-A-Video are pioneering this space, blending image-text data with unlabelled video to teach AI the dynamics of the real world.

Despite challenges in computational demands and data scarcity, the integration of 3D U-Nets, latent diffusion models, and innovative training approaches are pushing the boundaries of what AI can achieve in video production.

For more details : https://t.co/dTHqI6auFc

#AI #Innovation #VideoTechnology #MachineLearning #TextToVideo

[veritas-ai @veritas_ai_labo]

Discover our last article on Medium,

"Explainable AI: From Black Box to Clarity Using Interactive Dashboards"

Despite the growing integration of AI in critical sectors such as finance, healthcare, and criminal justice, the opacity of these models—often referred to as "black boxes"—remains a significant issue due to their lack of transparency. This opacity can erode trust and raise ethical concerns, particularly where decisions have profound human and societal impacts. Addressing this, Explainable AI (XAI) emerges as a crucial field that seeks to make AI decision-making processes transparent, understandable, and trustworthy for both technical and non-technical users. This article delves into how dashboards are increasingly utilized to demystify AI operations, offering a real-time, interactive visualization of how AI models analyze and reach decisions, thereby bridging the gap between AI functionality and human oversight.

https://t.co/WqfEOnV18L

#ExplainableAI #EthicalAI #AITransparency #AIDashboards #AIethics #DataVisualization #MachineLearning #TechForGood #VeritasAI

[Christopher Manning @chrmanning]

There are 2 mistakes you can make about LLMs:

① Thinking everything LLMs say is correct, they can reason, and with a bit more scale they’ll get us to superintelligence

② Thinking LLMs are good for almost nothing—they are FAR better at all #NLProc tasks than previous methods

[Abdelkader RHOUATI @arhouati]

One of the rare articles that exposes the question of AI's morality.
"Chatbot Morality? Exposing the promise and perils of ChatGPT’s convincing moral rhetoric"

https://t.co/ao4fynvV3c

#AI #chatbot #ChatGPT #LLM

[Abdelkader RHOUATI @arhouati]

This new model "GPT-4o" is mindblowing.
After GPT3 and 4, I thought that the next big set would be in a few more years, However, the speed at which the OpenIA progresses is huge.
Here is the demos :
https://t.co/uIZyL0eVBr
https://t.co/zWyVJHa851

#OpenAI #ChatGPT4o

[Abdelkader RHOUATI @arhouati]

Day 42: A very intuitive question that no one ever had in mind: "How #LLM Know When to Stop Generating?"
The response is in this article:
https://t.co/tmtGseqUGd

#AI #MachineLearning #chatgpt4

[Abdelkader RHOUATI @arhouati]

Day 41 : "5 Things to do When Evaluating ELT/ETL Tools "
https://t.co/BefQEl9CJd

[Abdelkader RHOUATI @arhouati]

Day 40 : "From 512 to 1M+ tokens, the evolution of #LLM context windows is reshaping how #AI understands. Discover how models like #GPT3 and Google’s Gemini push the limits of nuanced AI conversations but at a cost. Are #ExtendedContexts worth it? 🤔"
https://t.co/XyRX2TOwpA

[Ons Jabeur @Ons_Jabeur]

❤️❤️💪💪

[Abdelkader RHOUATI @arhouati]

Day 39 : Top Important LLM Papers for the Week from 15/04 to 21/04
https://t.co/sZ1fJ4v9dq
#LLM #AI #Articles

[Abdelkader RHOUATI @arhouati]

- Phase 3: Solution Deployment - Implement the solution in a real-world business context and establish monitoring.
- Phase 4: Evaluation & Documentation - Review project outcomes and document findings and learnings.

[Abdelkader RHOUATI @arhouati]

Day 38: This article discusses the role of a project manager (PM) in managing data science projects.
https://t.co/Z0G91O8KE6
#DataScience
#ProjectManagement
#MachineLearning
#AI

[Abdelkader RHOUATI @arhouati]

- Phase 1: Data Acquisition, Exploration, & Preparation - Gather and prepare data, crucial for future phases.
- Phase 2: Solution Development - Develop and iterate on the ML models with stakeholder feedback.

[Novelis Innovation @novelis_io]

Check our new research paper "Low-Cost Language Models: Survey and Performance Evaluation on #Python #CodeGeneration" written by our research team, published on @arxiv and submitted to the Journal Engineering Applications of #ArtificialIntelligence 📝

https://t.co/ENq3ZOJNKp

[Abdelkader RHOUATI @arhouati]

Day 37: Exploring the ethical landscape of advanced AI assistants, DeepMind's latest blog emphasizes the crucial role of aligning AI with human values to ensure they contribute positively and safely to society. https://t.co/6OVmPr1zSi #AIethics #DeepMind

[Andrew Ng @AndrewYNg]

New short course with @MistralAI !

Mistral's open-source Mixtral 8x7B model uses a "mixture of experts" (MoE) architecture. Unlike a standard transformer, an MoE model has multiple expert feed-forward networks (8 in this case), with a gating network selecting two experts at inference time. This enables MoE to match the performance of a large model but faster inference. Mixtral 8x7B has 46.7B parameters but activates only 12.9B at inference to predict the next token.

In Getting Started with Mistral, you’ll learn from Mistral’s @sophiamyang to:
- Explore Mistral's open-source models (Mistral 7B, Mixtral 8x7B) and commercial models via API calls and Mistral AI's Le Chat website
- Implement JSON mode to generate structured outputs to integrate directly into larger software systems.
- Use function calling for Tool Use, such as calling custom Python code that queries tabular data
- Ground your LLM's response with external knowledge sources using RAG
- Build a Mistral-powered chat interface that can reference external documents

This course will help deepen your prompt engineering skills. Please sign up here: https://t.co/weYwGmPlLA

[Abdelkader RHOUATI @arhouati]

Day 36 : "Why LLMs are not Good for Coding" by @andvalenzu in @TDataScience
https://t.co/nzZaP0cNeN

[Abdelkader RHOUATI @arhouati]

Day 35: Explainable AI: GradCAM using PyTorch by Shubham Bhandari https://t.co/998p0XKi9U

#AI #XAI #arhouati #CleverApps #OnePlatform