Lifeboat Foundation

UNIVERSITY PARK, Pa.— A person’s distrust in humans predicts they will have more trust in artificial intelligence’s ability to moderate content online, according to a recently published study. The findings, the researchers say, have practical implications for both designers and users of AI tools in social media.

“We found a systematic pattern of individuals who have less trust in other humans showing greater trust in AI’s classification,” said S. Shyam Sundar, the James P. Jimirro Professor of Media Effects at Penn State. “Based on our analysis, this seems to be due to the users invoking the idea that machines are accurate, objective and free from ideological bias.”

The study, published in the journal of New Media & Society also found that “power users” who are experienced users of information technology, had the opposite tendency. They trusted the AI moderators less because they believe that machines lack the ability to detect nuances of human language.

Watch NVIDIA CEO Jensen Huang unveil the new Ada Lovelace GPU architecture, new advances to its computing platforms, and new cloud services to further the era of AI and the metaverse, and transform every industry.

Dive into the announcements and discover more content at https://www.nvidia.com/gtc.

Continue reading “GTC Sept 2022 Keynote with NVIDIA CEO Jensen Huang” »

Inflated expectations around the capabilities of AI technologies may lead people to believe that computers can’t be wrong. The truth is AI failures are not a matter of if but when. AI is a human endeavor that combines information about people and the physical world into mathematical constructs. Such technologies typically rely on statistical methods, with the possibility for errors throughout an AI system’s lifespan. As AI systems become more widely used across domains, especially in high-stakes scenarios where people’s safety and wellbeing can be affected, a critical question must be addressed: how trustworthy are AI systems, and how much and when should people trust AI?

From connectomics to behavioural biology, artificial intelligence is making it faster and easier to extract information from images.

Corporate Venturing For Integrated Digital Healthcare Solutions — Bill Taranto, President, Global Health Innovation Fund, Merck

Bill Taranto is President of the Global Health Innovation Fund at Merck (https://www.merckghifund.com/taranto.html) and founding partner since inception in 2010.

Continue reading “Bill Taranto — President, GHI Fund, Merck — Corporate Venturing For Integrated Healthcare Solutions” »

At the beginning of my research career around 15 years ago, any suggestion that a bee, or any invertebrate, had a mind of its own or that it could experience the world in an intricate and multifaceted way would be met with ridicule. As Lars Chittka points out in the opening chapters of “The Mind of a Bee,” the attribution of human emotions and experiences was seen as naivety and ignorance; anthropomorphism was a dirty word.

Pet owners eagerly ascribe emotions to their animals, but the simple brain of a bee surely could not experience the rich tapestry that is our existence. They are far too simplistic and robotic, right?

Continue reading “What constitutes a mind? Researcher challenges perceptions of sentience with the smallest of creatures” »

The genetic encoding of ncAAs with distinct chemical, biological, and physical properties requires the engineering of bioorthogonal translational machinery, consisting of an evolved aminoacyl-tRNA synthetase/tRNA pair and a “blank” codon. To achieve this, the researchers mimicked the ibis’ ability to synthesize sTyr and incorporate it into proteins.

The Xiao lab employed a mutant amber stop codon to encode the desired sulfotransferase, resulting in a completely autonomous mammalian cell line capable of biosynthesizing sTyr and incorporating it with great precision into proteins.

These engineered cells, the authors wrote, can produce “site-specifically sulfated proteins at a higher yield than cells fed exogenously with the highest level of sTyr reported in the literature.” They used the cells to prepare highly potent thrombin inhibitors with site-specific sulfation.

Language is the most intuitive way for us to express how we feel and what we want. However, despite recent advancements in artificial intelligence, it is still very hard to control a robot using natural language instructions. Free-form commands such as “Robot, please go a little slower when you pass close to my TV” or “Stay far away from the swimming pool!” are hard to parse into actionable robot behaviors, and most human-robot interfaces today still rely on complex strategies such directly programming cost functions which define the desired behavior.

With our latest work, we attempt to change this reality through the introduction of “LaTTe: Language Trajectory Transformer”. LaTTe is a deep machine learning model that lets us send language commands to robots in an intuitive way with ease. When given an input sentence by the user, the model fuses it with camera images of objects that the robot observes in its surroundings, and outputs the desired robot behavior.

Continue reading “Just say the magic word: using language to program robots” »

face_with_colon_three circa 2020.

Watch any kid play with a tape measure and you’ll understand where researchers from the Seoul National University of Science and Technology found the inspiration for their new chameleon-like robot that can snag objects with its artificial tongue over 30 inches away in less than 600 milliseconds.