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A preventative anti-aging therapy seems like wishful thinking.

Yet a new study led by Dr. Corina Amor Vegas at Cold Spring Harbor Laboratory describes a treatment that brings the dream to life—at least for mice. Given a single injection in young adulthood, they aged more slowly compared to their peers.

By the equivalent of roughly 65 years of age in humans, the mice were slimmer, could better regulate blood sugar and insulin levels, and had lower inflammation and a more youthful metabolic profile. They even kept up their love for running, whereas untreated seniors turned into couch potatoes.

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Despite the Harvard 48 logical #qubits paper is perhaps the biggest leap in #quantum technologies, still the final circuit is classically simulable.

Politics makes strange bedfellows, apparently so does quantum benchmarking.

In a surprising development, IBM Quantum and IonQ researchers teamed up to reveal an alternative classical simulation algorithm for an impressive error correction study conducted by a Harvard and QuEra team and published recently in Nature. IBM is a leader in superconducting quantum computers, while IonQ is noted as a pioneer in trapped ion devices.

The IBM-IonQ team reports in ArXiv that their classical algorithm accomplished the same computational task that was performed by the 48-qubit quantum setup in that Nature study, in a mere 0.00257947 seconds.

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Researchers have unveiled a pioneering “bone bandage” that not only regenerates damaged bones in mice but also holds the promise of transforming bone regeneration in humans.

Developed by scientists at the Korea Advanced Institute of Science and Technology (KAIST), this biomimetic scaffold combines piezoelectric materials and the growth-promoting properties of hydroxyapatite (HAp), a naturally occurring mineral found in bones.

The innovative approach KAIST researchers took, although very much sounding like science fiction, is simply a freestanding scaffold that generates electrical signals when pressure is applied.

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It’s possible the OnlyFake owner is exaggerating, and it’s also worth noting that counterfeiting documents is nothing new. The difference here, though, is that the firm’s software is capable of cranking out hundreds of fake, but very real looking, IDs. It feels like it’s a matter of time before both banks and crypto firms alike are swamped by a wave of bots seeking to open accounts that possess convincing fake IDs.

You can add to this an impending wave of AI-based tools that will be used to overcome the anti-fraud measures, such as voice-based authentication, used by banks and others. We are also seeing AI being used to carry out audacious new forms of robbery—including the jaw-dropping story this week of a criminal gang that persuaded some poor employee in Hong Kong to transfer $25 million of company funds during a Zoom meeting. It turned out that all the members on the Zoom call were AI-generated replicas of the employee’s boss and coworkers.

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If you listen to a lot of podcasts, there is a chance you might remember funny tidbits and are wondering… “Wait, who talked about eating fries with sriracha again?” or more serious questions. To look for the answers, you have to first find the podcast and then search through their transcripts. Dexa is trying to make podcast search easier by leveraging AI.

The tool lets you ask questions about a single podcast, like Andrew Humberman’s Huberman Lab podcast in the screenshot below, or query all the podcasts in Dexa’s database — there are currently more than 120 with more being added. The search results will give you an AI-generated summary of the answer along with pointers to podcasts where the participant discussed the topic.

For instance, you can ask questions like “What’s the best way to get more sleep?” and find answers to that from Dexa’s podcast library with timestamped links to those conversations. You can also @mention a specific podcast to narrow down your search results.

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The Allen Institute for AI created the Open Language Model, or OLMo, which is an open-source large language model with the aim of advancing the science of language models through open research.

AI2 has partnered with organizations such as Surge AI and MosaicML for data and training code. These partnerships are crucial for providing the diverse datasets and sophisticated training methodologies that underpin OLMo’s capabilities. The collaboration with the Paul G. Allen School of Computer Science and Engineering at the University of Washington and Databricks Inc. has also been pivotal in realizing the OLMo project.

It is important to note that the current architecture of OLMo is not the same as the models that power chatbots or AI assistants, which use instruction-based models. However, that’s on the roadmap. According to AI2, there will be multiple enhancements made to the model in the future. In the coming months, there are plans to iterate on OLMo by introducing different model sizes, modalities, datasets, and capabilities into the OLMo family. This iterative process is aimed at continuously improving the model’s performance and utility for the research community.

OLMo’s open and transparent approach, along with its advanced capabilities and commitment to continuous improvement, make it a major milestone in the evolution of LLMs.

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