
Explore technical challenges in deep learning, including data hunger, limited transfer capacity, open-ended inference, and explainability. Gain insights from Gary Marcus's critical appraisal.

Explore technical challenges in deep learning, including integration with prior knowledge, correlation vs. causation, and data distribution issues. Gain insights into limitations and potential improvements.

Explore Q&A session on differentiable inductive logic programming, discussing key concepts and applications in neuro-symbolic AI with Prof. Paulo Shakarian.

Explore probabilistic circuits in the SLASH framework. Learn their importance and applications in neuro-symbolic AI and machine learning.

Explore experiments in the SLASH framework for neuro-symbolic AI. Gain insights into cutting-edge research combining symbolic methods and deep learning.

Explore spatio-temporal perception logic for verifying perceptual systems in autonomous vehicles and other safety-critical applications.

Explore spatio-temporal perception logic for verifying perceptual systems in autonomous vehicles, building on Part 1's foundations.

Explore spatio-temporal perception logic for verifying perceptual systems in autonomous vehicles, building on previous parts of the series.

Explore Google's challenges with LLM-based "AI Overviews" and the ongoing issue of hallucinations in AI products. Gain insights into the latest developments in neuro-symbolic AI and its potential impact on AGI.

Explore SAT Net's original work for learning MAXSAT constraints, presented by Paulo Shakarian in an ASU seminar on neurosymbolic reasoning.

Explore primal-dual methods for training Logical Neural Networks in neuro-symbolic reasoning, continuing the analysis of Lu et al.'s 2021 IBM research paper.

Explore reasoning shortcuts in neurosymbolic systems, their impact, and mitigation strategies for effective deployment. Gain insights from recent research papers.

Explore a novel PyTorch-native framework for LLM training, combining automatic parallelism and ease of use. Scale your models efficiently with single-device code and transparent nD parallelization.

Learn to generate personalized Flux AI photos quickly using PuLID technique, without fine-tuning or LoRA. Create custom AI images efficiently with this innovative method.

Learn to implement Intersection over Union (IoU) for object recognition using PyTorch. Covers theory, formulas, and practical code for bounding boxes and segmentation masks.

Innovative framework for video-driven portrait animation, balancing efficiency and control. Utilizes implicit keypoints, large-scale training, and advanced modules for superior performance and speed.

Explores how large language models potentially store factual information, focusing on multilayer perceptrons in transformers and the concept of superposition in neural networks.

Learn to boost YOLOv8's object detection using SAHI library. From installation to implementation, discover how to perform sliced predictions, visualize results, and compare performance with standard YOLOv8 for improved computer vision projects.

Implement object detection for bone fractures using YOLOv8 and Python. Learn to load models, process images, and visualize predictions with hands-on coding examples.

Explore foundational concepts of Generative AI, its evolution, and key models like DALL·E and GPT. Learn how these technologies reshape creativity and innovation across various industries.

Learn to leverage AI for efficient code generation using OpenAI's LLMs. Explore practical examples, from simple Fibonacci sequences to complex neural networks, and discover how to integrate AI tools into your programming workflow.