Neural language models (LMs), particularly those based on transformer architecture, have gained prominence due to their theoretical basis and their impact on various Natural Language Processing (NLP) tasks. These models are often evaluated within the context of binary language recognition, but this approach may create a disconnect between a language model as a distribution over…
Throughout 2023, MIT experienced a number of breakthroughs, key advancements, and noteworthy events, all featuring in the headlines.
At the beginning of the year, President Sally Kornbluth’s inauguration and Mark Rober’s commencement address marked significant moments. Moreover, Professor Moungi Bawendi was awarded the Nobel Prize in Chemistry. Throughout the year, researchers at MIT made significant…
The practice of biomedical research extensively depends on the accurate identification and classification of specialized terms from a vast array of textual data. This process, termed Named Entity Recognition (NER), is crucial for organizing and utilizing information found within medical literature. The proficient extraction of these entities from texts assists researchers and healthcare professionals in…
Coding execution is a crucial skill for developers and is often a struggle for existing large language models in AI software development. A team from Google DeepMind, Yale University, and the University of Illinois has proposed a novel approach to enhancing the ability of these models to reason about code execution. The method, called "Naturalized…
The dilemma of establishing causal relationships in areas such as medicine, economics, and social sciences is characterized as the "Fundamental Problem of Causal Inference". When observing an outcome, it is often unclear what the result might have been under a different intervention. Various indirect methods have been developed to estimate causal effects from observational data…
Web automation technologies play a pivotal role in enhancing efficiency and scalability across various digital operations by automating complex tasks that usually require human attention. However, the effectiveness of traditional web automation tools, largely based on static rules or wrapper software, is compromised in today's rapidly evolving and unpredictable web environments, resulting in inefficient web…
Graphs play a critical role in providing a visual representation of complex relationships in various arenas like social networks, knowledge graphs, and molecular discovery. They have rich topological structures and nodes often have textual features that offer vital context. Graph Machine Learning (Graph ML), particularly Graph Neural Networks (GNNs), have become increasingly influential in effectively…
Artificial intelligence has targeted the capability of models to process and interpret a range of data types; an attempt to mimic human sensory and cognitive processes. However, the challenge is developing systems that not only excel in single-mode tasks such as image recognition or text analysis but can also effectively integrate these different data types…
Neuromorphic computing attempts to mimic the human brain's neural structures and processing methods with advancements in efficiency and performance.
The algorithms that drive it include Spiking Neural Networks (SNNs) which manage binary events or 'spikes' and are efficient for processing temporal and spatial data. Spike-Timing-Dependent Plasticity (STDP) incorporates learning rules that modify the intensity of connections…
The field of vision-language representation seeks to create systems capable of comprehending the complex relationship between images and text. This is crucial as it helps machines to process and understand the vast amounts of visual and textual content available digitally. However, the challenge to conquer this still remains, mainly because the internet provides noisy data…
MIT had an eventful year in 2023, with President Sally Kornbluth's inauguration, Mark Rober's compelling commencement address, and Moungi Bawendi's Nobel Prize in Chemistry win. MIT researchers made significant progress in areas such as exploring the realms of artificial intelligence, creating eco-friendly energy solutions, inventing tools for early cancer detection, and examining the science of…
Machine learning (ML) is a rapidly growing field which has led to the emergence of a variety of training platforms, each tailored to cater to different requirements and restrictions. These platforms comprise Cloud, Centralized Learning, Federated Learning, On-Device ML, and numerous other emerging models.
Cloud and Centralized learning uses remote servers for heavy computations, making…