Gene editing is a cornerstone of modern biotechnology. It enables the precise manipulation of genetic material, which has implications across various fields, from medicine to agriculture. Recent innovations have pushed the boundaries of this technology, providing tools that enhance precision and expand applicability. The primary challenge in gene editing lies in the complexity of designing… →
Understanding Human and Artificial Intelligence: Human intelligence is complex, encompassing various cognitive abilities such as problem-solving, creativity, emotional intelligence, and social interaction. In contrast, artificial intelligence represents a different paradigm, focusing on specific tasks performed through algorithms, data processing, and machine learning techniques. Fundamental Differences: Human and artificial intelligence differ fundamentally in structure, speed, connectivity,… →
We investigate the capabilities of transformer models on relational reasoning tasks. In these tasks, models are trained on a set of strings encoding abstract relations, and are then tested out-of-distribution on data that contains symbols that did not appear in the training dataset. We prove that for any relational reasoning task in a large family… →
Score-based models have quickly become the de facto choice for generative modeling of images, text and more recently molecules. However, to adapt a score-based generative modeling to these domains the score network needs to be carefully designed, hampering its applicability to arbitrary data domains. In this paper we tackle this problem by taking a textit{functional}… →
Large Language Models (LLMs) have succeeded greatly and are widely used in various fields. LLMs are sensitive to input prompts, and this behavior has led to multiple research studies to understand and exploit this characteristic. This helps to create prompts for learning tasks like zero-shot and in-context. For instance, AutoPrompt recognizes task-specific tokens for zero-shot… →
PyTorch recently introduced ExecuTorch alpha to address the challenge of deploying powerful machine learning models, including extensive language models (LLMs), on edge devices that are limited in resources, such as smartphones and wearables. In the past, such models required a significant amount of computational resources, which rendered their deployment on edge devices impractical. The researchers… →
Artificial intelligence and machine learning are fields focused on creating algorithms to enable machines to understand data, make decisions, and solve problems. Researchers in this domain seek to design models that can process vast amounts of information efficiently and accurately, a crucial aspect in advancing automation and predictive analysis. This focus on the efficiency and… →
Neuro-Symbolic Artificial Intelligence (AI) represents an exciting frontier in the field. It merges the robustness of symbolic reasoning with the adaptive learning capabilities of neural networks. This integration aims to harness the strong points of symbolic and neural approaches to create more versatile and reliable AI systems. Below, Let’s explore key insights and developments from… →
Free LLM Playgrounds and Their Comparative Analysis As the landscape of AI technology advances, the proliferation of free platforms to test large language models (LLMs) online has greatly increased. These ‘playgrounds’ offer a valuable resource for developers, researchers, and enthusiasts to experiment with different models without requiring extensive setup or investment. Let’s explore a comparative… →