Large Language Models (LLMs) present a potential problem in their inability to accurately represent uncertainty about the reliability of their output. This uncertainty can have serious consequences in areas such as healthcare, where stakeholder confidence in the system's predictions is critical. Variations in freeform language generation can further complicate the issue, as these cannot be…
With their capacity to process and generate human-like text, Large Language Models (LLMs) have become critical tools that empower a variety of applications, from chatbots and data analysis to other advanced AI applications. The success of LLMs relies heavily on the diversity and quality of instructional data used for training.
One of the operative challenges in…
Artificial Intelligence (AI) aims to create systems that can execute tasks normally requiring human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. Such technologies are highly beneficial in various industries such as healthcare, finance, transportation, and entertainment. Consequently, optimizing AI models to efficiently and precisely perform these tasks is a significant challenge…
Large Language Models (LLMs) are crucial for a variety of applications, from machine translation to predictive text completion. They face challenges, including capturing complex, long-term dependencies and enabling efficient large-scale parallelisation. Attention-based models that have dominated LLM architectures struggle with computational complexity and extrapolating to longer sequences. Meanwhile, State Space Models (SSMs) offer linear computation…
Neural networks using gradient descent often perform well even when overparameterized and initialized randomly. They frequently find global optimal solutions, achieving zero training error without overfitting, a phenomenon referred to as "benign overfitting." However, in the case of Rectified Linear Unit (ReLU) networks, solutions can lead to overfitting if they interpolate the data. Particularly in…
Pre-trained Large language models (LLMs), such as transformers, typically have a fixed context window size, most commonly around 4K tokens. Nevertheless, numerous applications require processing significantly longer contexts, going all the way up to 256K tokens. The challenge that arises in elongating the context length of these models lies primarily in the efficient use of…
The proliferation of Large Language Models (LLMs) in the field of Artificial Intelligence (AI) has been a topic of much debate on Reddit. In a post, a user highlighted the existence of over 700,000 LLMs, raising questions about the usefulness and potential of these models. This has sparked a broad debate about the consequences of…
The advent of digital technology has created a need for increased efficiency in software and application development. Automation of repetitive tasks reduces debugging time, freeing up programmers' time for more strategic tasks. This can be particularly beneficial for businesses that are heavily dependent on software development. The newly launched AI-powered Python notebook, Thread, addresses these…
Embedded analytic solutions, which can cost up to six figures, often fail to satisfy users due to their complex interface and lack of advanced analytics. Often, users find themselves extracting the data and doing the analysis themselves, a far from ideal process. However, recent breakthroughs in Artificial Intelligence (AI) have facilitated a natural language interface…
Large language models (LLMs), flexible tools for language generation, have shown promising potential in various areas, including medical education, research, and clinical practice. LLMs enhance the analysis of healthcare data, providing detailed reports, medical differential diagnoses, standardized mental functioning assessments, and delivery of psychological interventions. They extract valuable information from 'clinical data', illustrating their possible…
A growing reliance on AI-generated data has led to concerns about model collapse, a phenomenon where a model's performance significantly deteriorates when trained on synthesized data. This issue has the potential to obstruct the development of methods for efficiently creating high-quality text summaries from large volumes of data.
Currently, the methods used to prevent model…