Recent advancements in econometric modeling and hypothesis testing have witnessed a paradigm shift towards integrating machine learning techniques. While strides have been made in estimating econometric models of human behavior, more research still needs to be conducted on effectively generating and rigorously testing these models. Researchers from MIT and Harvard introduce a novel approach to…
In the age of digital transformation, data is the new gold. Businesses are increasingly reliant on data for strategic decision-making, but this dependency brings significant challenges, particularly when it comes to collaborating with external partners. The traditional methods of sharing data often entail transferring sensitive information to third parties, significantly increasing the risk of security…
The development of natural language processing has been significantly propelled by the advancements in large language models (LLMs). These models have showcased remarkable performance in tasks like translation, question answering, and text summarization, proving their efficiency in generating high-quality text. However, despite their effectiveness, one major limitation remains their slow inference speed, which hinders their…
Imagine you’re looking for the perfect gift for your kid – a fun yet safe tricycle that ticks all the boxes. You might search with a query like “Can you help me find a push-along tricycle from Radio Flyer that’s both fun and safe for my kid?” Sounds pretty specific, right? But what if the…
Fine-tuning large language models (LLMs) efficiently and effectively is a common challenge. Imagine you have a massive LLM that needs adjustments or training for specific tasks, but the process is slow and resource-intensive. This can slow down the progress and make it difficult to deploy AI solutions quickly. Currently, some solutions are available for fine-tuning…
Many applications have used large language models (LLMs). However, when deployed to GPU servers, their high memory and computing demands result in substantial energy and financial expenditures. Some acceleration solutions can be used with laptop commodity GPUs, but their precision could be better. Although many LLM acceleration methods aim to decrease the number of non-zero…
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}…