Skip to content Skip to sidebar Skip to footer

Machine learning

The AI article released by Google DeepMind presents advanced learning abilities through Multiple-Shot In-Context Learning.

In-context learning (ICL) in large language models utilizes input and output examples to adapt to new tasks. While it has revolutionized how models manage various tasks, few-shot ICL struggles with more complex tasks that require a deep understanding, largely due to its limited input data. This presents an issue for applications that require detailed analysis…

Read More

MIT experts have leveraged artificial intelligence to pinpoint a potential new category of antibiotic candidates.

MIT researchers have leveraged the power of deep learning, a branch of artificial intelligence (AI), to discover a class of compounds that can potentially kill methicillin-resistant Staphylococcus aureus (MRSA). The discovery, described in a paper published in the journal Nature, saw the use of AI to predict the antibiotic potency of various molecules, an insight…

Read More

The language network in the brain is challenged more when dealing with intricate and new sentences.

Neuroscientists at MIT, assisted by an artificial language network, have discovered that complex sentences with unusual grammar or unexpected meaning, stimulate the brain's key language processing centres more effectively. Interestingly, both straightforward sentences and nonsensical sequences of words had minimal engagement in these regions. The findings were part of a study led by MIT graduate…

Read More

Microsoft’s GeckOpt improves large language models: Boosting computational performance through selection of tools based on intent in machine learning systems.

Large Language Models (LLMs) are a critical component of several computational platforms, driving technological innovation across a wide range of applications. While they are key for processing and analyzing a vast amount of data, they often face challenges related to high operational costs and inefficiencies in system tool usage. Traditionally, LLMs operate under systems that activate…

Read More

TD3-BST: An Artificial Intelligence Technique for Dynamic Regularization Strength Adjustment through Uncertainty Modeling

Reinforcement Learning (RL) is a method of learning that engages an agent with its environment to gather experiences and maximize received rewards. Given the policy rollouts necessary in the experience collection and improvement process, this is known as online RL. However, these online interactions required by both on-policy and off-policy RL can be impractical due…

Read More

This AI research proposes FLORA, a unique approach to machine learning that uses federated learning and parameter-efficient adapters for training Visual-Language Models (VLMs).

Training vision-language models (VLMs) traditionally requires centralized aggregation of large datasets, a process that raises issues of privacy and scalability. A recent solution to this issue is federated learning, a methodology allowing models to train across a range of devices while maintaining local data. However, adapting VLMs to this framework presents its challenges. Intel Corporation…

Read More

MIT researchers employ Artificial Intelligence to discover a fresh category of potential antibiotics.

At MIT, a team of researchers is utilizing deep learning—a type of artificial intelligence—to discover new, potentially life-saving antibiotics. Their focus is on combating one of the world's deadliest drug-resistant bacterium: methicillin-resistant Staphylococcus aureus (MRSA), which takes over 10,000 lives in America annually. Published in Nature, MIT's study reveals that a new class of compounds, identified…

Read More

The brain’s language network has to exert more effort when dealing with complex and unfamiliar sentences.

MIT neuroscientists have used an artificial language network to determine the type of sentences that stimulate the brain's key language-processing centers most. The research revealed that complex sentences, whether due to unusual grammar or unexpected meaning, generated stronger responses in these centers. Straightforward sentences barely engaged these regions and nonsensical sequences of words yielded little…

Read More

Improving Time Series Predictions: The Influence of Bi-Mamba4TS’s Bidirectional State Space Modeling on the Accuracy of Long-Term Forecasts

Time series forecasting is a crucial tool leveraged by numerous industries, including meteorology, finance, and energy management. As organizations today strive towards precision in forecasting future trends and patterns, time series forecasting has emerged as a game-changer. It not only refines decision-making processes but also helps optimize resource allocation over extended periods. However, making accurate…

Read More

MIT researchers utilize Artificial Intelligence to discover a new category of potential antibiotics.

Utilizing deep learning, researchers from the Massachusetts Institute of Technology (MIT) have identified a new class of compounds that effectively kill methicillin-resistant Staphylococcus aureus (MRSA), a drug-resistant bacterium responsible for over 10,000 deaths each year in the United States. The compounds have been found to have low toxicity against human cells, a key characteristic for…

Read More

The language network of the brain exerts more effort when dealing with intricate and unfamiliar sentences.

MIT neuroscientists discovered that sentences that are more complex, either because of unusual grammar or unexpected meaning, generate stronger responses in the brain's key language processing centers. This discovery was made possible with the help of an artificial language network. Conversely, straightforward sentences barely engaged these centers, and nonsensical sequences of words produced minimal responses.…

Read More

Scientists at DeepMind have proposed an innovative self-training machine learning technique known as Naturalized Execution Tuning (NExT). It significantly enhances the ability of Language Models (LLMs) to infer about program execution.

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…

Read More