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…
Generative AI, which can create text and images, is becoming an essential tool in today's data-driven society. It's now being utilized to produce realistic synthetic data, which can effectively solve problems where real data is limited or sensitive. For the past three years, DataCebo, an MIT spinoff, has been offering a Synthetic Data Vault (SDV)…
MIT researchers are replicating peripheral vision—a human's ability to detect objects outside their direct line of sight—in AI systems, which could enable these machines to more effectively identify imminent dangers or predict human behavior. By equipping machine learning models with an extensive image dataset to imitate peripheral vision, the team found these models were better…
Recently, an AI-generated robocall mimicking Joe Biden urged New Hampshire residents not to vote. Meanwhile, "spear-phishers" – phishing campaigns targeting specific people or groups – are using audio deepfakes to extract money. However, less attention has been paid to how audio deepfakes could positively impact society. Postdoctoral fellow Nauman Dawalatabad does just that in a…
The author's return to the exploration of LoRAs, or Long Range Ambiguity, was inspired by a resurgence of interest in the artistic LoRA creations made by Araminta. As part of their testing process, the author built a new workflow in an interface known as ComfyUI. This workflow was designed specifically for the purpose of testing…
The Galileo Luna is a transformative tool in the evaluation of language model processes, specifically addressing the prevalence of hallucinations in large language models (LLMs). Hallucinations refer to situations where models generate information that isn’t specific to a retrieved context, a significant challenge when deploying language models in industry applications. Galileo Luna combats this issue…
Large language models (LLMs), such as those used in AI, can creatively solve complex tasks in ever-changing environments without the need for task-specific training. However, achieving broad, high-level goals with these models remain a challenge due to the objectives' ambiguous nature and delayed rewards. Frequently retraining models to fit new goals and tasks is also…
Large Language Models (LLMs) like Mistral, Gemma, and Llama have significantly contributed to advancements in Natural Language Processing (NLP), but their dense models make them computationally heavy and expensive. As they utilize every parameter during inference, this intensity makes creating affordable, widespread AI challenging.
Conditional computation is seen as an efficiency-enhancing solution, activating specific model parameters…