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Artificial Intelligence Nudges Scientist to Try Simpler Approach to Quantum Entanglement

PRESS RELEASE — Physicists have discovered a simpler way to create quantum entanglement between two distant photons — without starting with entanglement, without resorting to Bell-state measurements, and even without detecting all ancillary photons — an advance that challenges long-held assumptions in quantum networking.

And all it took was a friendly nudge from an artificial intelligence tool.

An international team of scientists led by researchers from Nanjing University and the Max Planck Institute for the Science of Light described their method in Physical Review Letters — accessed for this article through arXiv — that demonstrated entanglement can emerge from the indistinguishability of photon paths alone. Instead of relying on standard procedures that start from prepared entangled pairs and complex joint measurements, their technique leverages a basic quantum principle: when multiple photons could have come from several possible sources, erasing the clues to their origins can produce entanglement where none existed before.

OpenAI’s o1 model sure tries to deceive humans a lot

OpenAI finally released the full version of o1, which gives smarter answers than GPT-4o by using additional compute to “think” about questions. However, AI safety testers found that o1’s reasoning abilities also make it try to deceive human users at a higher rate than GPT-4o — or, for that matter, leading AI models from Meta, Anthropic, and Google.

That’s according to red team research published by OpenAI and Apollo Research on Thursday: “While we find it exciting that reasoning can significantly improve the enforcement of our safety policies, we are mindful that these new capabilities could form the basis for dangerous applications,” said OpenAI in the paper.

OpenAI released these results in its system card for o1 on Thursday after giving third party red teamers at Apollo Research early access to o1, which released its own paper as well.

From Dictation to Automation: The Rise of AI Scribes in Healthcare

Despite technological advances like electronic health records (EHRs) and dictation tools, the administrative load on healthcare providers has only grown, often overshadowing the time and energy dedicated to direct patient care. This escalation in clerical tasks is a major contributor to physician burnout and dissatisfaction, affecting not only the well-being of providers but also the quality of care they deliver.

During consultations, the focus on documentation can detract from meaningful patient interactions, resulting in fragmented, rushed, and sometimes impersonal communication. The need for a solution that both streamlines documentation and restores the patient-centred nature of healthcare has never been more pressing. This is where AI-powered medical scribes come into play, offering a promising path from traditional dictation to fully automated, integrated documentation support.

AI medical scribe software utilises advanced artificial intelligence and machine learning to transcribe, in real time, entire patient-physician consultations without the need for traditional audio recordings. Leveraging sophisticated speech recognition and natural-language processing (NLP) algorithms, AI scribes are capable of interpreting and processing complex medical conversations with impressive accuracy. These systems can intelligently filter out non-essential dialogue, such as greetings and small talk, to create a streamlined and detailed clinical note.

Automated Cyborg Cockroach Factory Could Churn Out a Bug a Minute for Search and Rescue

Envisioning armies of electronically controllable insects is probably nightmare fuel for most people. But scientists think they could help rescue workers scour challenging and hazardous terrain. An automated cyborg cockroach factory could help bring the idea to life.

The merger of living creatures with machines is a staple of science fiction, but it’s also a serious line of research for academics. Several groups have implanted electronics into moths, beetles, and cockroaches that allow simple control of the insects.

However, building these cyborgs is tricky as it takes considerable dexterity and patience to surgically implant electrodes in their delicate bodies. This means that creating enough for most practical applications is simply too time-consuming.

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