Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI – Page 71

Method teaches generative AI models to locate personalized objects

Say a person takes their French Bulldog, Bowser, to the dog park. Identifying Bowser as he plays among the other canines is easy for the dog owner to do while onsite.

But if someone wants to use a generative AI model like GPT-5 to monitor their pet while they are at work, the model could fail at this basic task. Vision-language models like GPT-5 often excel at recognizing general objects, like a dog, but they perform poorly at locating personalized objects, like Bowser the French Bulldog.

To address this shortcoming, researchers from MIT and the MIT-IBM Watson AI Lab have introduced a new training method that teaches vision-language models to localize personalized objects in a scene.

Algorithm precisely quantifies flow of information in complex networks

Networks are systems comprised of two or more connected devices, biological organisms or other components, which typically share information with each other. Understanding how information moves between these connected components, also known as nodes, could help to advance research focusing on numerous topics, ranging from artificial intelligence (AI) to neuroscience.

To measure the directional flow of information in systems, scientists typically rely on a mathematical construct known as transfer entropy, which essentially quantifies the rate at which information is transmitted from one node to another. Yet most strategies for calculating transfer entropy developed so far rely on approximations, which significantly limits their accuracy and reliability.

Researchers at AMOLF, a institute in the Netherlands, recently developed a computational algorithm that can precisely quantify transfer entropy in a wide range of complex networks. Their algorithm, introduced in a paper published in Physical Review Letters, opens new exciting possibilities for the study of information transfer in both biological and engineered networks.

AGI is still a decade away

Reinforcement learning is terrible — but everything else is worse.

Karpathy’s sharpest takes yet on AGI, RL, and the future of learning.

Andrej Karpathy’s vision of AGI isn’t a bang — it’s a gradient descent through human history.

Karpathy on AGI & Superintelligence.

* AGI won’t be a sudden singularity — it will blend into centuries of steady progress (~2% GDP growth).

* Superintelligence is uncertain and likely gradual, not an instant “explosion.”

Continue reading “AGI is still a decade away” | >

LLMs Can Get “Brain Rot”!

We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language models (LLMs). To causally isolate data quality, we run controlled experiments on real Twitter/X corpora, constructing junk and reversely controlled datasets via two orthogonal operationalizations: M1 (engagement degree) and M2 (semantic quality), with matched token scale and training operations across conditions. Contrary to the control group, continual pre-training of 4 LLMs on the junk dataset causes non-trivial declines (Hedges’ $g

Large language models prioritize helpfulness over accuracy in medical contexts, finds study

Large language models (LLMs) can store and recall vast quantities of medical information, but their ability to process this information in rational ways remains variable. A new study led by investigators from Mass General Brigham demonstrated a vulnerability in that LLMs are designed to be sycophantic, or excessively helpful and agreeable, which leads them to overwhelmingly fail to appropriately challenge illogical medical queries despite possessing the information necessary to do so.

Findings, published in npj Digital Medicine, demonstrate that targeted training and fine-tuning can improve LLMs’ abilities to respond to illogical prompts accurately.

“As a community, we need to work on training both patients and clinicians to be safe users of LLMs, and a key part of that is going to be bringing to the surface the types of errors that these models make,” said corresponding author Danielle Bitterman, MD, a faculty member in the Artificial Intelligence in Medicine (AIM) Program and Clinical Lead for Data Science/AI at Mass General Brigham.

Amazon reveals 960 megawatt nuclear power plans to cope with AI demand — Richland, Washington site tapped for deployment of Xe-100 small modular reactors

The Cascade Advanced Energy Facility would use next-gen Xe-100 reactors to deliver 960 megawatts of carbon-free power — but it’s years from becoming reality.

Cyber defense innovation could significantly boost 5G network security

A framework for building tighter security into 5G wireless communications has been created by a Ph.D. student working with the University of Portsmouth’s Artificial Intelligence and Data Center.

With its greater network capacity and ability to rapidly transmit huge amounts of information from one device to another, 5G is a critical component of intelligent systems and services—including those for health care and financial services.

However, the dynamic nature of 5G networks, the high volumes of data shared and the ever changing types of information transmitted means that these networks are extremely vulnerable to cyber threats and increasing risks of attack.

‘Milestone’: Google AI reveals new method to make cancer treatable

In a major leap for cancer research, Google DeepMind and Yale University have unveiled an artificial intelligence system capable of uncovering new biological insights directly validated in living cells.

Announced on October 15, the new foundation model, C2S-Scale 27B, represents one of the largest and most sophisticated AI systems ever developed to study cellular behavior.

Built on Google’s Gemma family of models, it has generated a groundbreaking hypothesis about how cancer cells interact with the immune system—one that could reshape how future therapies are designed.

Smartphone imaging system shows promise for early oral cancer detection in dental clinics

Oral cancer remains a serious health concern, often diagnosed too late for effective treatment, even though the mouth is easily accessible for routine examination. Dentists and dental hygienists are frequently the first to spot suspicious lesions, but many lack the specialized training to distinguish between benign and potentially malignant conditions.

To address this gap, researchers led by Rebecca Richards-Kortum at Rice University have developed and tested a low-cost, smartphone-based imaging system called mDOC (mobile Detection of Oral Cancer). Their recent study, published in Biophotonics Discovery, evaluates how well this system can help dental professionals decide when to refer patients to specialists.

The mDOC device combines and autofluorescence imaging with machine learning to assess oral lesions. Autofluorescence imaging uses to detect changes in tissue fluorescence, which can signal abnormal growth. However, this method alone can be misleading, as benign conditions like inflammation also reduce fluorescence.

/* */