A new study says what should be obvious: people lose faith in relationships when their friends take shortcuts, AI-assisted or otherwise.
Mapping molecular structure to odor perception is a key challenge in olfaction. Here, we use graph neural networks (GNN) to generate a Principal Odor Map (POM) that preserves perceptual relationships and enables odor quality prediction for novel odorants. The model is as reliable as a human in describing odor quality: on a prospective validation set of 400 novel odorants, the model-generated odor profile more closely matched the trained panel mean (n=15) than did the median panelist. Applying simple, interpretable, theoretically-rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.
One-Sentence Summary An odor map achieves human-level odor description performance and generalizes to diverse odor-prediction tasks.
The authors have declared no competing interest.
The idea that genetic modification can improve humanity isn’t new, but it has taken some interesting turns within the scientific community over the past few years. One of the most notable comes from the mind of He Jiankui, a Chinese scientist whose gene editing of human babies led to infamy and a prison sentence. Now, He, known as JK to friends, thinks that gene-edited humans could be the future of our species.
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In a new report, the federal department charged with analyzing how efficiently US taxpayer dollars are spent, the Government Accountability Office, says NASA lacks transparency on the true costs of its Space Launch System rocket program.
Published on Thursday, the new report (see.pdf) examines the billions of dollars spent by NASA on the development of the massive rocket, which made a successful debut launch in late 2022 with the Artemis I mission. Surprisingly, as part of the reporting process, NASA officials admitted the rocket was too expensive to support its lunar exploration efforts as part of the Artemis program.
“Senior NASA officials told GAO that at current cost levels, the SLS program is unaffordable,” the new report states.
Machina Labs to Unveil The Machina Deployable System at FABTECH Portable System for Metal Forming Combines AI and Robotics for […].
Artificial intelligence (AI) large language models (LLM) like OpenAI’s hit GPT-3, 3.5, and 4, encode a wealth of information about how we live, communicate, and behave, and researchers are constantly finding new ways to put this knowledge to use.
A recent study conducted by Stanford University researchers has demonstrated that, with the right design, LLMs can be harnessed to simulate human behavior in a dynamic and convincingly realistic manner.
The study, titled “Generative Agents: Interactive Simulacra of Human Behavior,” explores the potential of generative models in creating an AI agent architecture that remembers its interactions, reflects on the information it receives, and plans long-and short-term goals based on an ever-expanding memory stream. These AI agents are capable of simulating the behavior of a human in their daily lives, from mundane tasks to complex decision-making processes.
Mixing and matching various strands of DNA can create versatile biological computer circuits that can take the square roots of numbers or solve quadratic equations.
Are large language models sentient? If they are, how would we know?
As a new generation of AI models have rendered the decades-old measure of a machine’s ability to exhibit human-like behavior (the Turing test) obsolete, the question of whether AI is ushering in a generation of machines that are self-conscious is stirring lively discussion.
Former Google software engineer Blake Lemoine suggested the large language model LaMDA was sentient.
Some of the world’s leading physicists believe they have found startling new evidence showing the existence of universes other than our own. See more in Season 3, Episode 2, “Parallel Universes.”
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The “no-hair theorem” of black holes, which greatly simplifies the way we model them, may not be true if an alternative theory of gravity known as the teleparallel formulation is correct, an unpublished paper argues. This could make the study of black holes considerably more complicated, but it would also allow physicists to understand them in ways many have feared they never will.
According to the “no-hair theorem”, a black hole’s mass, electric charge, and spin can tell us everything there is to know about that hole. Anything else we might measure, such as its magnetic moment, can be derived from these three measures.
Crucially, that means that when matter is swallowed by a black hole’s event horizon, all the information contained within it is lost, once the black hole has emitted any gravitational waves or light associated with its meal. It doesn’t matter what elements went into forming a black hole’s predecessor star, or even if it was made of antimatter rather than matter – under the no-hair theorem, it would appear identical to anyone outside its event horizon. The term “hair” is a metaphor for information streaming out of a black hole beyond the point of no return for incoming objects.