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Scientists improve sideline sight in AI prototypes.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 16, 202440Views0Likes0Comments
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…

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Three Inquiries: Essential Information on Audio Deepfakes You Should Know

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Ethics, Interview, Machine learning, MIT Schwarzman College of Computing, Natural language processing, School of Engineering, Technology and society, UncategorizedJune 16, 202438Views0Likes0Comments
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…

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Galileo Unveils Luna: A Comprehensive Evaluation Framework for Detecting Language Model Inconsistencies with Outstanding Precision and Economy

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, New Releases, Staff, Tech News, Technology, UncategorizedJune 15, 202437Views0Likes0Comments
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…

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ObjectiveBot: An AI Structure Aiming to Improve Skills of an LLM-based Agent for Accomplishing Superior Objectives.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJune 15, 202437Views0Likes0Comments
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…

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An AI paper from China suggests a new method based on dReLU sparsification that enhances the model’s sparsity up to 90% without compromising its performance. This innovative approach yields a two to five times acceleration during inference.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJune 15, 202437Views0Likes0Comments
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…

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Scientists from Stanford university and Duolingo have illustrated effective methods for achieving a specified proficiency level using proprietary models like GPT4 and open-source methods.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJune 15, 202437Views0Likes0Comments
A team from Stanford and Duolingo has proposed a new way to manage the proficiency level in texts generated by large language models (LLMs), overcoming limitations in current methods. The Common European Framework of Reference for Languages (CEFR)-aligned language model (CALM) combines techniques of finetuning and proximal policy optimization (PPO) for aligning the proficiency levels…

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Improving software testing by utilizing generative AI

Alumni/ae, Artificial Intelligence, Data, Electrical Engineering & Computer Science (eecs), Faculty, Innovation and Entrepreneurship (I&E), Laboratory for Information and Decision Systems (LIDS), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, Startups, UncategorizedJune 15, 202439Views0Likes0Comments
Generative AI, which can create text and images, also has extensive potential in creating realistic synthetic data for various applications. Being able to produce synthetic data can assist organizations, particularly in situations where real-world data is lacking or sensitive. For instance, it can help in patient care, rerouting of flights due to adverse weather, or…

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Scientists improve sideline viewing capabilities in AI systems.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 15, 202437Views0Likes0Comments
Peripheral vision, the ability to see objects outside of our direct line of sight, has been simulated by researchers at MIT to be used with AI technology. Unlike human vision, AI lacks the capability to perceive peripherally. Enhancing AI with this ability could greatly enhance its proactivity in identifying threats, and could even predict if…

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Three Queries: Essential Information on Audio Deepfakes You Should Understand

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Ethics, Interview, Machine learning, MIT Schwarzman College of Computing, Natural language processing, School of Engineering, Technology and society, UncategorizedJune 15, 202439Views0Likes0Comments
Audio deepfakes, or AI-generated audio, have lately been in the limelight due to harmful deception applied by ill-intentioned individuals. Cases such as robocalls impersonating political figures, spear-phishers tricking individuals into revealing personal information, and actors misusing technology to preserve their voices have surfaced in the media. While these negative instances have been widely publicized, MIT…

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Leading Stanford Courses in Artificial Intelligence (AI)

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJune 15, 202436Views0Likes0Comments
Stanford University is renowned for its contributions to artificial intelligence research and advancements, offering numerous courses equipped with practical knowledge for its students. Various AI aspects are covered, including machine learning, deep learning, natural language processing, and other crucial AI technologies. The courses are revered for their depth, relevance, and rigor making them paramount for…

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Deciphering Protein Language: The Transformation of Protein Sequence Comprehension through Advanced Language Models

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Staff, Tech News, Technology, UncategorizedJune 15, 202435Views0Likes0Comments
In recent years, comparisons have been made between protein sequences and natural language due to their sequential structures, facilitating notable progress in deep learning models in both areas. Large language models (LLMs), for example, have seen significant success in natural language processing (NLP) tasks, prompting attempts to adapt them to interpret protein sequences. However, these efforts…

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